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Due to recent advances in synthetic biology and artificial life, the origin
of life is currently a hot topic of research. We review the literature and
argue that the two traditionally competing "replicator-first" and
"metabolism-first" approaches are merging into one integrated theory of
individuation and evolution. We contribute to the maturation of this more
inclusive approach by highlighting some problematic assumptions that still lead
to an impoverished conception of the phenomenon of life. In particular, we
argue that the new consensus has so far failed to consider the relevance of
intermediate timescales. We propose that an adequate theory of life must
account for the fact that all living beings are situated in at least four
distinct timescales, which are typically associated with metabolism, motility,
development, and evolution. On this view, self-movement, adaptive behavior and
morphological changes could have already been present at the origin of life. In
order to illustrate this possibility we analyze a minimal model of life-like
phenomena, namely of precarious, individuated, dissipative structures that can
be found in simple reaction-diffusion systems. Based on our analysis we suggest
that processes in intermediate timescales could have already been operative in
prebiotic systems. They may have facilitated and constrained changes occurring
in the faster- and slower-paced timescales of chemical self-individuation and
evolution by natural selection, respectively. | [
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] |
We investigated the possibility of using a machine-learning scheme in
conjunction with commercial wearable EEG-devices for translating listener's
subjective experience of music into scores that can be used for the automated
annotation of music in popular on-demand streaming services. Based on the
established -neuroscientifically sound- concepts of brainwave frequency bands,
activation asymmetry index and cross-frequency-coupling (CFC), we introduce a
Brain Computer Interface (BCI) system that automatically assigns a rating score
to the listened song. Our research operated in two distinct stages: i) a
generic feature engineering stage, in which features from signal-analytics were
ranked and selected based on their ability to associate music induced
perturbations in brainwaves with listener's appraisal of music. ii) a
personalization stage, during which the efficiency of ex- treme learning
machines (ELMs) is exploited so as to translate the derived pat- terns into a
listener's score. Encouraging experimental results, from a pragmatic use of the
system, are presented. | [
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] |
In this meta-ethnography, we explore three different angles of ethical
artificial intelligence (AI) design implementation including the philosophical
ethical viewpoint, the technical perspective, and framing through a political
lens. Our qualitative research includes a literature review that highlights the
cross-referencing of these angles by discussing the value and drawbacks of
contrastive top-down, bottom-up, and hybrid approaches previously published.
The novel contribution to this framework is the political angle, which
constitutes ethics in AI either being determined by corporations and
governments and imposed through policies or law (coming from the top), or
ethics being called for by the people (coming from the bottom), as well as
top-down, bottom-up, and hybrid technicalities of how AI is developed within a
moral construct and in consideration of its users, with expected and unexpected
consequences and long-term impact in the world. There is a focus on
reinforcement learning as an example of a bottom-up applied technical approach
and AI ethics principles as a practical top-down approach. This investigation
includes real-world case studies to impart a global perspective, as well as
philosophical debate on the ethics of AI and theoretical future thought
experimentation based on historical facts, current world circumstances, and
possible ensuing realities. | [
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] |
The technologies and algorithms are growing at an exponential rate. The
technologies are capable enough to solve technically challenging and complex
problems which seemed impossible task. However, the trending methods and
approaches are facing multiple challenges on various fronts of data,
algorithms, software, computational complexities, and energy efficiencies.
Nature also faces similar challenges. Nature has solved those challenges and
formulation of those are available as Nature Inspired Algorithms (NIA), which
are derived based on the study of nature. A novel method based on TRIZ to map
the real-world problems to nature problems is explained here.TRIZ is a Theory
of inventive problem solving. Using the proposed framework, best NIA can be
identified to solve the real-world problems. For this framework to work, a
novel classification of NIA based on the end goal that nature is trying to
achieve is devised. The application of the this framework along with examples
is also discussed. | [
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] |
Artificial Intelligence has looked into biological systems as a source of
inspiration. Although there are many aspects of the brain yet to be discovered,
neuroscience has found evidence that the connections between neurons
continuously grow and reshape as a part of the learning process. This differs
from the design of Artificial Neural Networks, that achieve learning by
evolving the weights in the synapses between them and their topology stays
unaltered through time.
This project has explored rules for growing the connections between the
neurons in Spiking Neural Networks as a learning mechanism. These rules have
been implemented on a multi-agent system for creating simple logic functions,
that establish a base for building up more complex systems and architectures.
Results in a simulation environment showed that for a given set of parameters
it is possible to reach topologies that reproduce the tested functions.
This project also opens the door to the usage of techniques like genetic
algorithms for obtaining the best suited values for the model parameters, and
hence creating neural networks that can adapt to different functions. | [
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Traditionally, Deep Artificial Neural Networks (DNN's) are trained through
gradient descent. Recent research shows that Deep Neuroevolution (DNE) is also
capable of evolving multi-million-parameter DNN's, which proved to be
particularly useful in the field of Reinforcement Learning (RL). This is mainly
due to its excellent scalability and simplicity compared to the traditional
MDP-based RL methods. So far, DNE has only been applied to complex single-agent
problems. As evolutionary methods are a natural choice for multi-agent
problems, the question arises whether DNE can also be applied in a complex
multi-agent setting. In this paper, we describe and validate a new approach
based on Coevolution. To validate our approach, we benchmark two Deep
Coevolutionary Algorithms on a range of multi-agent Atari games and compare our
results against the results of Ape-X DQN. Our results show that these Deep
Coevolutionary algorithms (1) can be successfully trained to play various
games, (2) outperform Ape-X DQN in some of them, and therefore (3) show that
Coevolution can be a viable approach to solving complex multi-agent
decision-making problems. | [
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] |
We examine an evolutionary naming-game model where communicating agents are
equipped with an evolutionarily selected learning ability. Such a coupling of
biological and linguistic ingredients results in an abrupt transition: upon a
small change of a model control parameter a poorly communicating group of
linguistically unskilled agents transforms into almost perfectly communicating
group with large learning abilities. When learning ability is kept fixed, the
transition appears to be continuous. Genetic imprinting of the learning
abilities proceeds via Baldwin effect: initially unskilled communicating agents
learn a language and that creates a niche in which there is an evolutionary
pressure for the increase of learning ability.Our model suggests that when
linguistic (or cultural) processes became intensive enough, a transition took
place where both linguistic performance and biological endowment of our species
experienced an abrupt change that perhaps triggered the rapid expansion of
human civilization. | [
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] |
Interpersonal relations are fickle, with close friendships often dissolving
into enmity. In this work, we explore linguistic cues that presage such
transitions by studying dyadic interactions in an online strategy game where
players form alliances and break those alliances through betrayal. We
characterize friendships that are unlikely to last and examine temporal
patterns that foretell betrayal.
We reveal that subtle signs of imminent betrayal are encoded in the
conversational patterns of the dyad, even if the victim is not aware of the
relationship's fate. In particular, we find that lasting friendships exhibit a
form of balance that manifests itself through language. In contrast, sudden
changes in the balance of certain conversational attributes---such as positive
sentiment, politeness, or focus on future planning---signal impending betrayal. | [
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] |
Public debates are a common platform for presenting and juxtaposing diverging
views on important issues. In this work we propose a methodology for tracking
how ideas flow between participants throughout a debate. We use this approach
in a case study of Oxford-style debates---a competitive format where the winner
is determined by audience votes---and show how the outcome of a debate depends
on aspects of conversational flow. In particular, we find that winners tend to
make better use of a debate's interactive component than losers, by actively
pursuing their opponents' points rather than promoting their own ideas over the
course of the conversation. | [
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] |
Existing neural conversational models process natural language primarily on a
lexico-syntactic level, thereby ignoring one of the most crucial components of
human-to-human dialogue: its affective content. We take a step in this
direction by proposing three novel ways to incorporate affective/emotional
aspects into long short term memory (LSTM) encoder-decoder neural conversation
models: (1) affective word embeddings, which are cognitively engineered, (2)
affect-based objective functions that augment the standard cross-entropy loss,
and (3) affectively diverse beam search for decoding. Experiments show that
these techniques improve the open-domain conversational prowess of
encoder-decoder networks by enabling them to produce emotionally rich responses
that are more interesting and natural. | [
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] |
Questions play a prominent role in social interactions, performing rhetorical
functions that go beyond that of simple informational exchange. The surface
form of a question can signal the intention and background of the person asking
it, as well as the nature of their relation with the interlocutor. While the
informational nature of questions has been extensively examined in the context
of question-answering applications, their rhetorical aspects have been largely
understudied.
In this work we introduce an unsupervised methodology for extracting surface
motifs that recur in questions, and for grouping them according to their latent
rhetorical role. By applying this framework to the setting of question sessions
in the UK parliament, we show that the resulting typology encodes key aspects
of the political discourse---such as the bifurcation in questioning behavior
between government and opposition parties---and reveals new insights into the
effects of a legislator's tenure and political career ambitions. | [
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] |
The ever-growing datasets published on Linked Open Data mainly contain
encyclopedic information. However, there is a lack of quality structured and
semantically annotated datasets extracted from unstructured real-time sources.
In this paper, we present principles for developing a knowledge graph of
interlinked events using the case study of news headlines published on Twitter
which is a real-time and eventful source of fresh information. We represent the
essential pipeline containing the required tasks ranging from choosing
background data model, event annotation (i.e., event recognition and
classification), entity annotation and eventually interlinking events. The
state-of-the-art is limited to domain-specific scenarios for recognizing and
classifying events, whereas this paper plays the role of a domain-agnostic
road-map for developing a knowledge graph of interlinked events. | [
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] |
Multi-channel speech enhancement with ad-hoc sensors has been a challenging
task. Speech model guided beamforming algorithms are able to recover natural
sounding speech, but the speech models tend to be oversimplified or the
inference would otherwise be too complicated. On the other hand, deep learning
based enhancement approaches are able to learn complicated speech distributions
and perform efficient inference, but they are unable to deal with variable
number of input channels. Also, deep learning approaches introduce a lot of
errors, particularly in the presence of unseen noise types and settings. We
have therefore proposed an enhancement framework called DEEPBEAM, which
combines the two complementary classes of algorithms. DEEPBEAM introduces a
beamforming filter to produce natural sounding speech, but the filter
coefficients are determined with the help of a monaural speech enhancement
neural network. Experiments on synthetic and real-world data show that DEEPBEAM
is able to produce clean, dry and natural sounding speech, and is robust
against unseen noise. | [
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] |
One of the main challenges online social systems face is the prevalence of
antisocial behavior, such as harassment and personal attacks. In this work, we
introduce the task of predicting from the very start of a conversation whether
it will get out of hand. As opposed to detecting undesirable behavior after the
fact, this task aims to enable early, actionable prediction at a time when the
conversation might still be salvaged.
To this end, we develop a framework for capturing pragmatic devices---such as
politeness strategies and rhetorical prompts---used to start a conversation,
and analyze their relation to its future trajectory. Applying this framework in
a controlled setting, we demonstrate the feasibility of detecting early warning
signs of antisocial behavior in online discussions. | [
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Social media platforms may provide potential space for discourses that
contain hate speech, and even worse, can act as a propagation mechanism for
hate crimes. The FBI's Uniform Crime Reporting (UCR) Program collects hate
crime data and releases statistic report yearly. These statistics provide
information in determining national hate crime trends. The statistics can also
provide valuable holistic and strategic insight for law enforcement agencies or
justify lawmakers for specific legislation. However, the reports are mostly
released next year and lag behind many immediate needs. Recent research mainly
focuses on hate speech detection in social media text or empirical studies on
the impact of a confirmed crime. This paper proposes a framework that first
utilizes text mining techniques to extract hate crime events from New York
Times news, then uses the results to facilitate predicting American
national-level and state-level hate crime trends. Experimental results show
that our method can significantly enhance the prediction performance compared
with time series or regression methods without event-related factors. Our
framework broadens the methods of national-level and state-level hate crime
trends prediction. | [
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] |
Fighting the ongoing COVID-19 infodemic has been declared as one of the most
important focus areas by the World Health Organization since the onset of the
COVID-19 pandemic. While the information that is consumed and disseminated
consists of promoting fake cures, rumors, and conspiracy theories to spreading
xenophobia and panic, at the same time there is information (e.g., containing
advice, promoting cure) that can help different stakeholders such as
policy-makers. Social media platforms enable the infodemic and there has been
an effort to curate the content on such platforms, analyze and debunk them.
While a majority of the research efforts consider one or two aspects (e.g.,
detecting factuality) of such information, in this study we focus on a
multifaceted approach, including an
API,\url{https://app.swaggerhub.com/apis/yifan2019/Tanbih/0.8.0/} and a demo
system,\url{https://covid19.tanbih.org}, which we made freely and publicly
available. We believe that this will facilitate researchers and different
stakeholders. A screencast of the API services and demo is
available.\url{https://youtu.be/zhbcSvxEKMk} | [
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] |
The last decade has witnessed a surge in the interaction of people through
social networking platforms. While there are several positive aspects of these
social platforms, the proliferation has led them to become the breeding ground
for cyber-bullying and hate speech. Recent advances in NLP have often been used
to mitigate the spread of such hateful content. Since the task of hate speech
detection is usually applicable in the context of social networks, we introduce
CRUSH, a framework for hate speech detection using user-anchored
self-supervision and contextual regularization. Our proposed approach secures ~
1-12% improvement in test set metrics over best performing previous approaches
on two types of tasks and multiple popular english social media datasets. | [
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] |
Affective responses to music are highly personal. Despite consensus that
idiosyncratic factors play a key role in regulating how listeners emotionally
respond to music, precisely measuring the marginal effects of these variables
has proved challenging. To address this gap, we develop computational methods
to measure affective responses to music from over 403M listener comments on a
Chinese social music platform. Building on studies from music psychology in
systematic and quasi-causal analyses, we test for musical, lyrical, contextual,
demographic, and mental health effects that drive listener affective responses.
Finally, motivated by the social phenomenon known as w\v{a}ng-y\`i-y\'un, we
identify influencing factors of platform user self-disclosures, the social
support they receive, and notable differences in discloser user activity. | [
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] |
Various recent experimental results show that large language models (LLM)
exhibit emergent abilities that are not present in small models. System
performance is greatly improved after passing a certain critical threshold of
scale. In this letter, we provide a simple explanation for such a phase
transition phenomenon. For this, we model an LLM as a sequence-to-sequence
random function. Instead of using instant generation at each step, we use a
list decoder that keeps a list of candidate sequences at each step and defers
the generation of the output sequence at the end. We show that there is a
critical threshold such that the expected number of erroneous candidate
sequences remains bounded when an LLM is below the threshold, and it grows
exponentially when an LLM is above the threshold. Such a threshold is related
to the basic reproduction number in a contagious disease. | [
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We present Text-to-OverpassQL, a task designed to facilitate a natural
language interface for querying geodata from OpenStreetMap (OSM). The Overpass
Query Language (OverpassQL) allows users to formulate complex database queries
and is widely adopted in the OSM ecosystem. Generating Overpass queries from
natural language input serves multiple use-cases. It enables novice users to
utilize OverpassQL without prior knowledge, assists experienced users with
crafting advanced queries, and enables tool-augmented large language models to
access information stored in the OSM database. In order to assess the
performance of current sequence generation models on this task, we propose
OverpassNL, a dataset of 8,352 queries with corresponding natural language
inputs. We further introduce task specific evaluation metrics and ground the
evaluation of the Text-to-OverpassQL task by executing the queries against the
OSM database. We establish strong baselines by finetuning sequence-to-sequence
models and adapting large language models with in-context examples. The
detailed evaluation reveals strengths and weaknesses of the considered learning
strategies, laying the foundations for further research into the
Text-to-OverpassQL task. | [
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] |
We assess the ability of GPT -- a large language model -- to serve as a
financial robo-advisor for the masses, by using a financial literacy test.
Davinci and ChatGPT based on GPT-3.5 score 66% and 65% on the financial
literacy test, respectively, compared to a baseline of 33%. However, ChatGPT
based on GPT-4 achieves a near-perfect 99% score, pointing to financial
literacy becoming an emergent ability of state-of-the-art models. We use the
Judge-Advisor System and a savings dilemma to illustrate how researchers might
assess advice-utilization from large language models. We also present a number
of directions for future research. | [
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] |
Historical emphasis on writing mastery has shifted with advances in
generative AI, especially in scientific writing. This study analysed six AI
chatbots for scholarly writing in humanities and archaeology. Using methods
that assessed factual correctness and scientific contribution, ChatGPT-4 showed
the highest quantitative accuracy, closely followed by ChatGPT-3.5, Bing, and
Bard. However, Claude 2 and Aria scored considerably lower. Qualitatively, all
AIs exhibited proficiency in merging existing knowledge, but none produced
original scientific content. Inter-estingly, our findings suggest ChatGPT-4
might represent a plateau in large language model size. This research
emphasizes the unique, intricate nature of human research, suggesting that AI's
emulation of human originality in scientific writing is challenging. As of
2023, while AI has transformed content generation, it struggles with original
contributions in humanities. This may change as AI chatbots continue to evolve
into LLM-powered software. | [
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] |
We introduce a new cross-modal fusion technique designed for generative error
correction in automatic speech recognition (ASR). Our methodology leverages
both acoustic information and external linguistic representations to generate
accurate speech transcription contexts. This marks a step towards a fresh
paradigm in generative error correction within the realm of n-best hypotheses.
Unlike the existing ranking-based rescoring methods, our approach adeptly uses
distinct initialization techniques and parameter-efficient algorithms to boost
ASR performance derived from pre-trained speech and text models. Through
evaluation across diverse ASR datasets, we evaluate the stability and
reproducibility of our fusion technique, demonstrating its improved word error
rate relative (WERR) performance in comparison to n-best hypotheses by
relatively 37.66%. To encourage future research, we have made our code and
pre-trained models open source at
https://github.com/Srijith-rkr/Whispering-LLaMA. | [
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] |
The Minimum Description Length (MDL) principle is solidly based on a provably
ideal method of inference using Kolmogorov complexity. We test how the theory
behaves in practice on a general problem in model selection: that of learning
the best model granularity. The performance of a model depends critically on
the granularity, for example the choice of precision of the parameters. Too
high precision generally involves modeling of accidental noise and too low
precision may lead to confusion of models that should be distinguished. This
precision is often determined ad hoc. In MDL the best model is the one that
most compresses a two-part code of the data set: this embodies ``Occam's
Razor.'' In two quite different experimental settings the theoretical value
determined using MDL coincides with the best value found experimentally. In the
first experiment the task is to recognize isolated handwritten characters in
one subject's handwriting, irrespective of size and orientation. Based on a new
modification of elastic matching, using multiple prototypes per character, the
optimal prediction rate is predicted for the learned parameter (length of
sampling interval) considered most likely by MDL, which is shown to coincide
with the best value found experimentally. In the second experiment the task is
to model a robot arm with two degrees of freedom using a three layer
feed-forward neural network where we need to determine the number of nodes in
the hidden layer giving best modeling performance. The optimal model (the one
that extrapolizes best on unseen examples) is predicted for the number of nodes
in the hidden layer considered most likely by MDL, which again is found to
coincide with the best value found experimentally. | [
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] |
Gesture recognition is mainly apprehensive on analyzing the functionality of
human wits. The main goal of gesture recognition is to create a system which
can recognize specific human gestures and use them to convey information or for
device control. Hand gestures provide a separate complementary modality to
speech for expressing ones ideas. Information associated with hand gestures in
a conversation is degree,discourse structure, spatial and temporal structure.
The approaches present can be mainly divided into Data-Glove Based and Vision
Based approaches. An important face feature point is the nose tip. Since nose
is the highest protruding point from the face. Besides that, it is not affected
by facial expressions.Another important function of the nose is that it is able
to indicate the head pose. Knowledge of the nose location will enable us to
align an unknown 3D face with those in a face database. Eye detection is
divided into eye position detection and eye contour detection. Existing works
in eye detection can be classified into two major categories: traditional
image-based passive approaches and the active IR based approaches. The former
uses intensity and shape of eyes for detection and the latter works on the
assumption that eyes have a reflection under near IR illumination and produce
bright/dark pupil effect. The traditional methods can be broadly classified
into three categories: template based methods,appearance based methods and
feature based methods. The purpose of this paper is to compare various human
Gesture recognition systems for interfacing machines directly to human wits
without any corporeal media in an ambient environment. | [
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] |
Quantum information technologies, and intelligent learning systems, are both
emergent technologies that will likely have a transforming impact on our
society. The respective underlying fields of research -- quantum information
(QI) versus machine learning (ML) and artificial intelligence (AI) -- have
their own specific challenges, which have hitherto been investigated largely
independently. However, in a growing body of recent work, researchers have been
probing the question to what extent these fields can learn and benefit from
each other. QML explores the interaction between quantum computing and ML,
investigating how results and techniques from one field can be used to solve
the problems of the other. Recently, we have witnessed breakthroughs in both
directions of influence. For instance, quantum computing is finding a vital
application in providing speed-ups in ML, critical in our "big data" world.
Conversely, ML already permeates cutting-edge technologies, and may become
instrumental in advanced quantum technologies. Aside from quantum speed-up in
data analysis, or classical ML optimization used in quantum experiments,
quantum enhancements have also been demonstrated for interactive learning,
highlighting the potential of quantum-enhanced learning agents. Finally, works
exploring the use of AI for the very design of quantum experiments, and for
performing parts of genuine research autonomously, have reported their first
successes. Beyond the topics of mutual enhancement, researchers have also
broached the fundamental issue of quantum generalizations of ML/AI concepts.
This deals with questions of the very meaning of learning and intelligence in a
world that is described by quantum mechanics. In this review, we describe the
main ideas, recent developments, and progress in a broad spectrum of research
investigating machine learning and artificial intelligence in the quantum
domain. | [
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] |
We consider the case in which a robot has to navigate in an unknown
environment but does not have enough on-board power or payload to carry a
traditional depth sensor (e.g., a 3D lidar) and thus can only acquire a few
(point-wise) depth measurements. We address the following question: is it
possible to reconstruct the geometry of an unknown environment using sparse and
incomplete depth measurements? Reconstruction from incomplete data is not
possible in general, but when the robot operates in man-made environments, the
depth exhibits some regularity (e.g., many planar surfaces with only a few
edges); we leverage this regularity to infer depth from a small number of
measurements. Our first contribution is a formulation of the depth
reconstruction problem that bridges robot perception with the compressive
sensing literature in signal processing. The second contribution includes a set
of formal results that ascertain the exactness and stability of the depth
reconstruction in 2D and 3D problems, and completely characterize the geometry
of the profiles that we can reconstruct. Our third contribution is a set of
practical algorithms for depth reconstruction: our formulation directly
translates into algorithms for depth estimation based on convex programming. In
real-world problems, these convex programs are very large and general-purpose
solvers are relatively slow. For this reason, we discuss ad-hoc solvers that
enable fast depth reconstruction in real problems. The last contribution is an
extensive experimental evaluation in 2D and 3D problems, including Monte Carlo
runs on simulated instances and testing on multiple real datasets. Empirical
results confirm that the proposed approach ensures accurate depth
reconstruction, outperforms interpolation-based strategies, and performs well
even when the assumption of structured environment is violated. | [
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] |
The 11th Summer Workshop on Multimodal Interfaces eNTERFACE 2015 was hosted
by the Numediart Institute of Creative Technologies of the University of Mons
from August 10th to September 2015. During the four weeks, students and
researchers from all over the world came together in the Numediart Institute of
the University of Mons to work on eight selected projects structured around
intelligent interfaces. Eight projects were selected and their reports are
shown here. | [
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] |
Currently, many critical care indices are repetitively assessed and recorded
by overburdened nurses, e.g. physical function or facial pain expressions of
nonverbal patients. In addition, many essential information on patients and
their environment are not captured at all, or are captured in a non-granular
manner, e.g. sleep disturbance factors such as bright light, loud background
noise, or excessive visitations. In this pilot study, we examined the
feasibility of using pervasive sensing technology and artificial intelligence
for autonomous and granular monitoring of critically ill patients and their
environment in the Intensive Care Unit (ICU). As an exemplar prevalent
condition, we also characterized delirious and non-delirious patients and their
environment. We used wearable sensors, light and sound sensors, and a
high-resolution camera to collected data on patients and their environment. We
analyzed collected data using deep learning and statistical analysis. Our
system performed face detection, face recognition, facial action unit
detection, head pose detection, facial expression recognition, posture
recognition, actigraphy analysis, sound pressure and light level detection, and
visitation frequency detection. We were able to detect patient's face (Mean
average precision (mAP)=0.94), recognize patient's face (mAP=0.80), and their
postures (F1=0.94). We also found that all facial expressions, 11 activity
features, visitation frequency during the day, visitation frequency during the
night, light levels, and sound pressure levels during the night were
significantly different between delirious and non-delirious patients
(p-value<0.05). In summary, we showed that granular and autonomous monitoring
of critically ill patients and their environment is feasible and can be used
for characterizing critical care conditions and related environment factors. | [
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] |
Tumor segmentation in oncological PET is challenging, a major reason being
the partial-volume effects that arise due to low system resolution and finite
voxel size. The latter results in tissue-fraction effects, i.e. voxels contain
a mixture of tissue classes. Conventional segmentation methods are typically
designed to assign each voxel in the image as belonging to a certain tissue
class. Thus, these methods are inherently limited in modeling tissue-fraction
effects. To address the challenge of accounting for partial-volume effects, and
in particular, tissue-fraction effects, we propose a Bayesian approach to
tissue-fraction estimation for oncological PET segmentation. Specifically, this
Bayesian approach estimates the posterior mean of fractional volume that the
tumor occupies within each voxel of the image. The proposed method, implemented
using a deep-learning-based technique, was first evaluated using clinically
realistic 2-D simulation studies with known ground truth, in the context of
segmenting the primary tumor in PET images of patients with lung cancer. The
evaluation studies demonstrated that the method accurately estimated the
tumor-fraction areas and significantly outperformed widely used conventional
PET segmentation methods, including a U-net-based method, on the task of
segmenting the tumor. In addition, the proposed method was relatively
insensitive to partial-volume effects and yielded reliable tumor segmentation
for different clinical-scanner configurations. The method was then evaluated
using clinical images of patients with stage IIB/III non-small cell lung cancer
from ACRIN 6668/RTOG 0235 multi-center clinical trial. Here, the results showed
that the proposed method significantly outperformed all other considered
methods and yielded accurate tumor segmentation on patient images with Dice
similarity coefficient (DSC) of 0.82 (95 % CI: [0.78, 0.86]). | [
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] |
This study evaluated generative methods to potentially mitigate AI bias when
diagnosing diabetic retinopathy (DR) resulting from training data imbalance, or
domain generalization which occurs when deep learning systems (DLS) face
concepts at test/inference time they were not initially trained on. The public
domain Kaggle-EyePACS dataset (88,692 fundi and 44,346 individuals, originally
diverse for ethnicity) was modified by adding clinician-annotated labels and
constructing an artificial scenario of data imbalance and domain generalization
by disallowing training (but not testing) exemplars for images of retinas with
DR warranting referral (DR-referable) and from darker-skin individuals, who
presumably have greater concentration of melanin within uveal melanocytes, on
average, contributing to retinal image pigmentation. A traditional/baseline
diagnostic DLS was compared against new DLSs that would use training data
augmented via generative models for debiasing. Accuracy (95% confidence
intervals [CI]) of the baseline diagnostics DLS for fundus images of
lighter-skin individuals was 73.0% (66.9%, 79.2%) vs. darker-skin of 60.5%
(53.5%, 67.3%), demonstrating bias/disparity (delta=12.5%) (Welch t-test
t=2.670, P=.008) in AI performance across protected subpopulations. Using novel
generative methods for addressing missing subpopulation training data
(DR-referable darker-skin) achieved instead accuracy, for lighter-skin, of
72.0% (65.8%, 78.2%), and for darker-skin, of 71.5% (65.2%,77.8%),
demonstrating closer parity (delta=0.5%) in accuracy across subpopulations
(Welch t-test t=0.111, P=.912). Findings illustrate how data imbalance and
domain generalization can lead to disparity of accuracy across subpopulations,
and show that novel generative methods of synthetic fundus images may play a
role for debiasing AI. | [
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] |
A core problem in cognitive science and machine learning is to understand how
humans derive semantic representations from perceptual objects, such as color
from an apple, pleasantness from a musical chord, or seriousness from a face.
Markov Chain Monte Carlo with People (MCMCP) is a prominent method for studying
such representations, in which participants are presented with binary choice
trials constructed such that the decisions follow a Markov Chain Monte Carlo
acceptance rule. However, while MCMCP has strong asymptotic properties, its
binary choice paradigm generates relatively little information per trial, and
its local proposal function makes it slow to explore the parameter space and
find the modes of the distribution. Here we therefore generalize MCMCP to a
continuous-sampling paradigm, where in each iteration the participant uses a
slider to continuously manipulate a single stimulus dimension to optimize a
given criterion such as 'pleasantness'. We formulate both methods from a
utility-theory perspective, and show that the new method can be interpreted as
'Gibbs Sampling with People' (GSP). Further, we introduce an aggregation
parameter to the transition step, and show that this parameter can be
manipulated to flexibly shift between Gibbs sampling and deterministic
optimization. In an initial study, we show GSP clearly outperforming MCMCP; we
then show that GSP provides novel and interpretable results in three other
domains, namely musical chords, vocal emotions, and faces. We validate these
results through large-scale perceptual rating experiments. The final
experiments use GSP to navigate the latent space of a state-of-the-art image
synthesis network (StyleGAN), a promising approach for applying GSP to
high-dimensional perceptual spaces. We conclude by discussing future cognitive
applications and ethical implications. | [
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] |
Our objective is to show the feasibility of using simulated mammograms to
detect mammographically-occult (MO) cancer in women with dense breasts and a
normal screening mammogram who could be triaged for additional screening with
magnetic resonance imaging (MRI) or ultrasound. We developed a Conditional
Generative Adversarial Network (CGAN) to simulate a mammogram with normal
appearance using the opposite mammogram as the condition. We used a
Convolutional Neural Network (CNN) trained on Radon Cumulative Distribution
Transform (RCDT) processed mammograms to detect MO cancer. For training CGAN,
we used screening mammograms of 1366 women. For MO cancer detection, we used
screening mammograms of 333 women (97 MO cancer) with dense breasts. We
simulated the right mammogram for normal controls and the cancer side for MO
cancer cases. We created two RCDT images, one from a real mammogram pair and
another from a real-simulated mammogram pair. We finetuned a VGG16 on resulting
RCDT images to classify the women with MO cancer. We compared the
classification performance of the CNN trained on fused RCDT images, CNN_{Fused}
to that of trained only on real RCDT images, CNN_{Real}, and to that of trained
only on simulated RCDT images, CNN_{Simulated}. The test AUC for CNN_{Fused}
was 0.77 with a 95% confidence interval (95CI) of [0.71, 0.83], which was
statistically better (p-value < 0.02) than the CNN_{Real} AUC of 0.70 with a
95CI of [0.64, 0.77] and CNN_{Simulated} AUC of 0.68 with a 95CI of [0.62,
0.75]. It showed that CGAN simulated mammograms can help MO cancer detection. | [
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] |
Advanced attack campaigns span across multiple stages and stay stealthy for
long time periods. There is a growing trend of attackers using off-the-shelf
tools and pre-installed system applications (such as \emph{powershell} and
\emph{wmic}) to evade the detection because the same tools are also used by
system administrators and security analysts for legitimate purposes for their
routine tasks. To start investigations, event logs can be collected from
operational systems; however, these logs are generic enough and it often
becomes impossible to attribute a potential attack to a specific attack group.
Recent approaches in the literature have used anomaly detection techniques,
which aim at distinguishing between malicious and normal behavior of computers
or network systems. Unfortunately, anomaly detection systems based on point
anomalies are too rigid in a sense that they could miss the malicious activity
and classify the attack, not an outlier. Therefore, there is a research
challenge to make better detection of malicious activities. To address this
challenge, in this paper, we leverage Group Anomaly Detection (GAD), which
detects anomalous collections of individual data points.
Our approach is to build a neural network model utilizing Adversarial
Autoencoder (AAE-$\alpha$) in order to detect the activity of an attacker who
leverages off-the-shelf tools and system applications. In addition, we also
build \textit{Behavior2Vec} and \textit{Command2Vec} sentence embedding deep
learning models specific for feature extraction tasks. We conduct extensive
experiments to evaluate our models on real-world datasets collected for a
period of two months. The empirical results demonstrate that our approach is
effective and robust in discovering targeted attacks, pen-tests, and attack
campaigns leveraging custom tools. | [
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] |
Convolutional Neural Networks (CNNs) achieve excellent computer-assisted
diagnosis with sufficient annotated training data. However, most medical
imaging datasets are small and fragmented. In this context, Generative
Adversarial Networks (GANs) can synthesize realistic/diverse additional
training images to fill the data lack in the real image distribution;
researchers have improved classification by augmenting data with noise-to-image
(e.g., random noise samples to diverse pathological images) or image-to-image
GANs (e.g., a benign image to a malignant one). Yet, no research has reported
results combining noise-to-image and image-to-image GANs for further
performance boost. Therefore, to maximize the DA effect with the GAN
combinations, we propose a two-step GAN-based DA that generates and refines
brain Magnetic Resonance (MR) images with/without tumors separately: (i)
Progressive Growing of GANs (PGGANs), multi-stage noise-to-image GAN for
high-resolution MR image generation, first generates realistic/diverse 256 X
256 images; (ii) Multimodal UNsupervised Image-to-image Translation (MUNIT)
that combines GANs/Variational AutoEncoders or SimGAN that uses a DA-focused
GAN loss, further refines the texture/shape of the PGGAN-generated images
similarly to the real ones. We thoroughly investigate CNN-based tumor
classification results, also considering the influence of pre-training on
ImageNet and discarding weird-looking GAN-generated images. The results show
that, when combined with classic DA, our two-step GAN-based DA can
significantly outperform the classic DA alone, in tumor detection (i.e.,
boosting sensitivity 93.67% to 97.48%) and also in other medical imaging tasks. | [
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] |
As artificial intelligence and machine learning algorithms make further
inroads into society, calls are increasing from multiple stakeholders for these
algorithms to explain their outputs. At the same time, these stakeholders,
whether they be affected citizens, government regulators, domain experts, or
system developers, present different requirements for explanations. Toward
addressing these needs, we introduce AI Explainability 360
(http://aix360.mybluemix.net/), an open-source software toolkit featuring eight
diverse and state-of-the-art explainability methods and two evaluation metrics.
Equally important, we provide a taxonomy to help entities requiring
explanations to navigate the space of explanation methods, not only those in
the toolkit but also in the broader literature on explainability. For data
scientists and other users of the toolkit, we have implemented an extensible
software architecture that organizes methods according to their place in the AI
modeling pipeline. We also discuss enhancements to bring research innovations
closer to consumers of explanations, ranging from simplified, more accessible
versions of algorithms, to tutorials and an interactive web demo to introduce
AI explainability to different audiences and application domains. Together, our
toolkit and taxonomy can help identify gaps where more explainability methods
are needed and provide a platform to incorporate them as they are developed. | [
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] |
To interact with humans in collaborative environments, machines need to be
able to predict (i.e., anticipate) future events, and execute actions in a
timely manner. However, the observation of the human limb movements may not be
sufficient to anticipate their actions unambiguously. In this work, we consider
two additional sources of information (i.e., context) over time, gaze, movement
and object information, and study how these additional contextual cues improve
the action anticipation performance. We address action anticipation as a
classification task, where the model takes the available information as the
input and predicts the most likely action. We propose to use the uncertainty
about each prediction as an online decision-making criterion for action
anticipation. Uncertainty is modeled as a stochastic process applied to a
time-based neural network architecture, which improves the conventional
class-likelihood (i.e., deterministic) criterion. The main contributions of
this paper are four-fold: (i) We propose a novel and effective decision-making
criterion that can be used to anticipate actions even in situations of high
ambiguity; (ii) we propose a deep architecture that outperforms previous
results in the action anticipation task when using the Acticipate collaborative
dataset; (iii) we show that contextual information is important to disambiguate
the interpretation of similar actions; and (iv) we also provide a formal
description of three existing performance metrics that can be easily used to
evaluate action anticipation models.Our results on the Acticipate dataset
showed the importance of contextual information and the uncertainty criterion
for action anticipation. We achieve an average accuracy of 98.75% in the
anticipation task using only an average of 25% of observations. | [
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] |
Sequential pulling policies to flatten and smooth fabrics have applications
from surgery to manufacturing to home tasks such as bed making and folding
clothes. Due to the complexity of fabric states and dynamics, we apply deep
imitation learning to learn policies that, given color (RGB), depth (D), or
combined color-depth (RGBD) images of a rectangular fabric sample, estimate
pick points and pull vectors to spread the fabric to maximize coverage. To
generate data, we develop a fabric simulator and an algorithmic supervisor that
has access to complete state information. We train policies in simulation using
domain randomization and dataset aggregation (DAgger) on three tiers of
difficulty in the initial randomized configuration. We present results
comparing five baseline policies to learned policies and report systematic
comparisons of RGB vs D vs RGBD images as inputs. In simulation, learned
policies achieve comparable or superior performance to analytic baselines. In
180 physical experiments with the da Vinci Research Kit (dVRK) surgical robot,
RGBD policies trained in simulation attain coverage of 83% to 95% depending on
difficulty tier, suggesting that effective fabric smoothing policies can be
learned from an algorithmic supervisor and that depth sensing is a valuable
addition to color alone. Supplementary material is available at
https://sites.google.com/view/fabric-smoothing. | [
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] |
Deep neural networks (DNN) have shown great success in many computer vision
applications. However, they are also known to be susceptible to backdoor
attacks. When conducting backdoor attacks, most of the existing approaches
assume that the targeted DNN is always available, and an attacker can always
inject a specific pattern to the training data to further fine-tune the DNN
model. However, in practice, such attack may not be feasible as the DNN model
is encrypted and only available to the secure enclave.
In this paper, we propose a novel black-box backdoor attack technique on face
recognition systems, which can be conducted without the knowledge of the
targeted DNN model. To be specific, we propose a backdoor attack with a novel
color stripe pattern trigger, which can be generated by modulating LED in a
specialized waveform. We also use an evolutionary computing strategy to
optimize the waveform for backdoor attack. Our backdoor attack can be conducted
in a very mild condition: 1) the adversary cannot manipulate the input in an
unnatural way (e.g., injecting adversarial noise); 2) the adversary cannot
access the training database; 3) the adversary has no knowledge of the training
model as well as the training set used by the victim party.
We show that the backdoor trigger can be quite effective, where the attack
success rate can be up to $88\%$ based on our simulation study and up to $40\%$
based on our physical-domain study by considering the task of face recognition
and verification based on at most three-time attempts during authentication.
Finally, we evaluate several state-of-the-art potential defenses towards
backdoor attacks, and find that our attack can still be effective. We highlight
that our study revealed a new physical backdoor attack, which calls for the
attention of the security issue of the existing face recognition/verification
techniques. | [
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] |
Autonomous navigation consists in an agent being able to navigate without
human intervention or supervision, it affects both high level planning and low
level control. Navigation is at the crossroad of multiple disciplines, it
combines notions of computer vision, robotics and control. This work aimed at
creating, in a simulation, a navigation pipeline whose transfer to the real
world could be done with as few efforts as possible. Given the limited time and
the wide range of problematic to be tackled, absolute navigation performances
while important was not the main objective. The emphasis was rather put on
studying the sim2real gap which is one the major bottlenecks of modern robotics
and autonomous navigation. To design the navigation pipeline four main
challenges arise; environment, localization, navigation and planning. The
iGibson simulator is picked for its photo-realistic textures and physics
engine. A topological approach to tackle space representation was picked over
metric approaches because they generalize better to new environments and are
less sensitive to change of conditions. The navigation pipeline is decomposed
as a localization module, a planning module and a local navigation module.
These modules utilize three different networks, an image representation
extractor, a passage detector and a local policy. The laters are trained on
specifically tailored tasks with some associated datasets created for those
specific tasks. Localization is the ability for the agent to localize itself
against a specific space representation. It must be reliable, repeatable and
robust to a wide variety of transformations. Localization is tackled as an
image retrieval task using a deep neural network trained on an auxiliary task
as a feature descriptor extractor. The local policy is trained with behavioral
cloning from expert trajectories gathered with ROS navigation stack. | [
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] |
We present iGibson 1.0, a novel simulation environment to develop robotic
solutions for interactive tasks in large-scale realistic scenes. Our
environment contains 15 fully interactive home-sized scenes with 108 rooms
populated with rigid and articulated objects. The scenes are replicas of
real-world homes, with distribution and the layout of objects aligned to those
of the real world. iGibson 1.0 integrates several key features to facilitate
the study of interactive tasks: i) generation of high-quality virtual sensor
signals (RGB, depth, segmentation, LiDAR, flow and so on), ii) domain
randomization to change the materials of the objects (both visual and physical)
and/or their shapes, iii) integrated sampling-based motion planners to generate
collision-free trajectories for robot bases and arms, and iv) intuitive
human-iGibson interface that enables efficient collection of human
demonstrations. Through experiments, we show that the full interactivity of the
scenes enables agents to learn useful visual representations that accelerate
the training of downstream manipulation tasks. We also show that iGibson 1.0
features enable the generalization of navigation agents, and that the
human-iGibson interface and integrated motion planners facilitate efficient
imitation learning of human demonstrated (mobile) manipulation behaviors.
iGibson 1.0 is open-source, equipped with comprehensive examples and
documentation. For more information, visit our project website:
http://svl.stanford.edu/igibson/ | [
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] |
Artificial intelligence (AI) systems struggle to generalize beyond their
training data and abstract general properties from the specifics of the
training examples. We propose a model that reproduces the apparent human
ability to come up with a number sense through unsupervised everyday
experience. The ability to understand and manipulate numbers and quantities
emerges during childhood, but the mechanism through which humans acquire and
develop this ability is still poorly understood. In particular, it is not known
whether acquiring such a number sense is possible without supervision from a
teacher. We explore this question through a model, assuming that the learner is
able to pick and place small objects and will spontaneously engage in
undirected manipulation. We assume that the learner's visual system will
monitor the changing arrangements of objects in the scene and will learn to
predict the effects of each action by comparing perception with the efferent
signal of the motor system. We model perception using standard deep networks
for feature extraction and classification. We find that, from learning the
unrelated task of action prediction, an unexpected image representation emerges
exhibiting regularities that foreshadow the perception and representation of
numbers. These include distinct categories for the first few natural numbers, a
strict ordering of the numbers, and a one-dimensional signal that correlates
with numerical quantity. As a result, our model acquires the ability to
estimate numerosity and subitize. Remarkably, subitization and numerosity
estimation extrapolate to scenes containing many objects, far beyond the three
objects used during training. We conclude that important aspects of a facility
with numbers and quantities may be learned without teacher supervision. | [
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] |
In deformable registration, the geometric framework - large deformation
diffeomorphic metric mapping or LDDMM, in short - has inspired numerous
techniques for comparing, deforming, averaging and analyzing shapes or images.
Grounded in flows, which are akin to the equations of motion used in fluid
dynamics, LDDMM algorithms solve the flow equation in the space of plausible
deformations, i.e. diffeomorphisms. In this work, we make use of deep residual
neural networks to solve the non-stationary ODE (flow equation) based on a
Euler's discretization scheme. The central idea is to represent time-dependent
velocity fields as fully connected ReLU neural networks (building blocks) and
derive optimal weights by minimizing a regularized loss function. Computing
minimizing paths between deformations, thus between shapes, turns to find
optimal network parameters by back-propagating over the intermediate building
blocks. Geometrically, at each time step, ResNet-LDDMM searches for an optimal
partition of the space into multiple polytopes, and then computes optimal
velocity vectors as affine transformations on each of these polytopes. As a
result, different parts of the shape, even if they are close (such as two
fingers of a hand), can be made to belong to different polytopes, and therefore
be moved in different directions without costing too much energy. Importantly,
we show how diffeomorphic transformations, or more precisely bilipshitz
transformations, are predicted by our algorithm. We illustrate these ideas on
diverse registration problems of 3D shapes under complex topology-preserving
transformations. We thus provide essential foundations for more advanced shape
variability analysis under a novel joint geometric-neural networks
Riemannian-like framework, i.e. ResNet-LDDMM. | [
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With the advance in user-friendly and powerful video editing tools, anyone
can easily manipulate videos without leaving prominent visual traces.
Frame-rate up-conversion (FRUC), a representative temporal-domain operation,
increases the motion continuity of videos with a lower frame-rate and is used
by malicious counterfeiters in video tampering such as generating fake
frame-rate video without improving the quality or mixing temporally spliced
videos. FRUC is based on frame interpolation schemes and subtle artifacts that
remain in interpolated frames are often difficult to distinguish. Hence,
detecting such forgery traces is a critical issue in video forensics. This
paper proposes a frame-rate conversion detection network (FCDNet) that learns
forensic features caused by FRUC in an end-to-end fashion. The proposed network
uses a stack of consecutive frames as the input and effectively learns
interpolation artifacts using network blocks to learn spatiotemporal features.
This study is the first attempt to apply a neural network to the detection of
FRUC. Moreover, it can cover the following three types of frame interpolation
schemes: nearest neighbor interpolation, bilinear interpolation, and
motion-compensated interpolation. In contrast to existing methods that exploit
all frames to verify integrity, the proposed approach achieves a high detection
speed because it observes only six frames to test its authenticity. Extensive
experiments were conducted with conventional forensic methods and neural
networks for video forensic tasks to validate our research. The proposed
network achieved state-of-the-art performance in terms of detecting the
interpolated artifacts of FRUC. The experimental results also demonstrate that
our trained model is robust for an unseen dataset, unlearned frame-rate, and
unlearned quality factor. | [
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] |
Plot-based Graphic API recommendation (Plot2API) is an unstudied but
meaningful issue, which has several important applications in the context of
software engineering and data visualization, such as the plotting guidance of
the beginner, graphic API correlation analysis, and code conversion for
plotting. Plot2API is a very challenging task, since each plot is often
associated with multiple APIs and the appearances of the graphics drawn by the
same API can be extremely varied due to the different settings of the
parameters. Additionally, the samples of different APIs also suffer from
extremely imbalanced. Considering the lack of technologies in Plot2API, we
present a novel deep multi-task learning approach named Semantic Parsing Guided
Neural Network (SPGNN) which translates the Plot2API issue as a multi-label
image classification and an image semantic parsing tasks for the solution. In
SPGNN, the recently advanced Convolutional Neural Network (CNN) named
EfficientNet is employed as the backbone network for API recommendation.
Meanwhile, a semantic parsing module is complemented to exploit the semantic
relevant visual information in feature learning and eliminate the
appearance-relevant visual information which may confuse the
visual-information-based API recommendation. Moreover, the recent data
augmentation technique named random erasing is also applied for alleviating the
imbalance of API categories. We collect plots with the graphic APIs used to
drawn them from Stack Overflow, and release three new Plot2API datasets
corresponding to the graphic APIs of R and Python programming languages for
evaluating the effectiveness of Plot2API techniques. Extensive experimental
results not only demonstrate the superiority of our method over the recent deep
learning baselines but also show the practicability of our method in the
recommendation of graphic APIs. | [
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] |
Understanding the reasons behind the predictions made by deep neural networks
is critical for gaining human trust in many important applications, which is
reflected in the increasing demand for explainability in AI (XAI) in recent
years. Saliency-based feature attribution methods, which highlight important
parts of images that contribute to decisions by classifiers, are often used as
XAI methods, especially in the field of computer vision. In order to compare
various saliency-based XAI methods quantitatively, several approaches for
automated evaluation schemes have been proposed; however, there is no guarantee
that such automated evaluation metrics correctly evaluate explainability, and a
high rating by an automated evaluation scheme does not necessarily mean a high
explainability for humans. In this study, instead of the automated evaluation,
we propose a new human-based evaluation scheme using crowdsourcing to evaluate
XAI methods. Our method is inspired by a human computation game, "Peek-a-boom",
and can efficiently compare different XAI methods by exploiting the power of
crowds. We evaluate the saliency maps of various XAI methods on two datasets
with automated and crowd-based evaluation schemes. Our experiments show that
the result of our crowd-based evaluation scheme is different from those of
automated evaluation schemes. In addition, we regard the crowd-based evaluation
results as ground truths and provide a quantitative performance measure to
compare different automated evaluation schemes. We also discuss the impact of
crowd workers on the results and show that the varying ability of crowd workers
does not significantly impact the results. | [
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] |
Positron Emission Tomography (PET) is an important tool for studying
Alzheimer's disease (AD). PET scans can be used as diagnostics tools, and to
provide molecular characterization of patients with cognitive disorders.
However, multiple tracers are needed to measure glucose metabolism (18F-FDG),
synaptic vesicle protein (11C-UCB-J), and $\beta$-amyloid (11C-PiB).
Administering multiple tracers to patient will lead to high radiation dose and
cost. In addition, access to PET scans using new or less-available tracers with
sophisticated production methods and short half-life isotopes may be very
limited. Thus, it is desirable to develop an efficient multi-tracer PET
synthesis model that can generate multi-tracer PET from single-tracer PET.
Previous works on medical image synthesis focus on one-to-one fixed domain
translations, and cannot simultaneously learn the feature from multi-tracer
domains. Given 3 or more tracers, relying on previous methods will also create
a heavy burden on the number of models to be trained. To tackle these issues,
we propose a 3D unified anatomy-aware cyclic adversarial network (UCAN) for
translating multi-tracer PET volumes with one unified generative model, where
MR with anatomical information is incorporated. Evaluations on a multi-tracer
PET dataset demonstrate the feasibility that our UCAN can generate high-quality
multi-tracer PET volumes, with NMSE less than 15% for all PET tracers. | [
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] |
For people who ardently love painting but unfortunately have visual
impairments, holding a paintbrush to create a work is a very difficult task.
People in this special group are eager to pick up the paintbrush, like Leonardo
da Vinci, to create and make full use of their own talents. Therefore, to
maximally bridge this gap, we propose a painting navigation system to assist
blind people in painting and artistic creation. The proposed system is composed
of cognitive system and guidance system. The system adopts drawing board
positioning based on QR code, brush navigation based on target detection and
bush real-time positioning. Meanwhile, this paper uses human-computer
interaction on the basis of voice and a simple but efficient position
information coding rule. In addition, we design a criterion to efficiently
judge whether the brush reaches the target or not. According to the
experimental results, the thermal curves extracted from the faces of testers
show that it is relatively well accepted by blindfolded and even blind testers.
With the prompt frequency of 1s, the painting navigation system performs best
with the completion degree of 89% with SD of 8.37% and overflow degree of 347%
with SD of 162.14%. Meanwhile, the excellent and good types of brush tip
trajectory account for 74%, and the relative movement distance is 4.21 with SD
of 2.51. This work demonstrates that it is practicable for the blind people to
feel the world through the brush in their hands. In the future, we plan to
deploy Angle's Eyes on the phone to make it more portable. The demo video of
the proposed painting navigation system is available at:
https://doi.org/10.6084/m9.figshare.9760004.v1. | [
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] |
The capability of a mobile robot to efficiently and safely perform complex
missions is limited by its knowledge of the environment, namely the situation.
Advanced reasoning, decision-making, and execution skills enable an intelligent
agent to act autonomously in unknown environments. Situational Awareness (SA)
is a fundamental capability of humans that has been deeply studied in various
fields, such as psychology, military, aerospace, and education. Nevertheless,
it has yet to be considered in robotics, which has focused on single
compartmentalized concepts such as sensing, spatial perception, sensor fusion,
state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the
present research aims to connect the broad multidisciplinary existing knowledge
to pave the way for a complete SA system for mobile robotics that we deem
paramount for autonomy. To this aim, we define the principal components to
structure a robotic SA and their area of competence. Accordingly, this paper
investigates each aspect of SA, surveying the state-of-the-art robotics
algorithms that cover them, and discusses their current limitations.
Remarkably, essential aspects of SA are still immature since the current
algorithmic development restricts their performance to only specific
environments. Nevertheless, Artificial Intelligence (AI), particularly Deep
Learning (DL), has brought new methods to bridge the gap that maintains these
fields apart from the deployment to real-world scenarios. Furthermore, an
opportunity has been discovered to interconnect the vastly fragmented space of
robotic comprehension algorithms through the mechanism of Situational Graph
(S-Graph), a generalization of the well-known scene graph. Therefore, we
finally shape our vision for the future of robotic Situational Awareness by
discussing interesting recent research directions. | [
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] |
Depth estimation-based obstacle avoidance has been widely adopted by
autonomous systems (drones and vehicles) for safety purpose. It normally relies
on a stereo camera to automatically detect obstacles and make flying/driving
decisions, e.g., stopping several meters ahead of the obstacle in the path or
moving away from the detected obstacle. In this paper, we explore new security
risks associated with the stereo vision-based depth estimation algorithms used
for obstacle avoidance. By exploiting the weaknesses of the stereo matching in
depth estimation algorithms and the lens flare effect in optical imaging, we
propose DoubleStar, a long-range attack that injects fake obstacle depth by
projecting pure light from two complementary light sources.
DoubleStar includes two distinctive attack formats: beams attack and orbs
attack, which leverage projected light beams and lens flare orbs respectively
to cause false depth perception. We successfully attack two commercial stereo
cameras designed for autonomous systems (ZED and Intel RealSense). The
visualization of fake depth perceived by the stereo cameras illustrates the
false stereo matching induced by DoubleStar. We further use Ardupilot to
simulate the attack and demonstrate its impact on drones. To validate the
attack on real systems, we perform a real-world attack towards a commercial
drone equipped with state-of-the-art obstacle avoidance algorithms. Our attack
can continuously bring a flying drone to a sudden stop or drift it away across
a long distance under various lighting conditions, even bypassing sensor fusion
mechanisms. Specifically, our experimental results show that DoubleStar creates
fake depth up to 15 meters in distance at night and up to 8 meters during the
daytime. To mitigate this newly discovered threat, we provide discussions on
potential countermeasures to defend against DoubleStar. | [
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] |
The prevalence of diabetic retinopathy (DR) has reached 34.6% worldwide and
is a major cause of blindness among middle-aged diabetic patients. Regular DR
screening using fundus photography helps detect its complications and prevent
its progression to advanced levels. As manual screening is time-consuming and
subjective, machine learning (ML) and deep learning (DL) have been employed to
aid graders. However, the existing CNN-based methods use either pre-trained CNN
models or a brute force approach to design new CNN models, which are not
customized to the complexity of fundus images. To overcome this issue, we
introduce an approach for custom-design of CNN models, whose architectures are
adapted to the structural patterns of fundus images and better represent the
DR-relevant features. It takes the leverage of k-medoid clustering, principal
component analysis (PCA), and inter-class and intra-class variations to
automatically determine the depth and width of a CNN model. The designed models
are lightweight, adapted to the internal structures of fundus images, and
encode the discriminative patterns of DR lesions. The technique is validated on
a local dataset from King Saud University Medical City, Saudi Arabia, and two
challenging benchmark datasets from Kaggle: EyePACS and APTOS2019. The
custom-designed models outperform the famous pre-trained CNN models like
ResNet152, Densnet121, and ResNeSt50 with a significant decrease in the number
of parameters and compete well with the state-of-the-art CNN-based DR screening
methods. The proposed approach is helpful for DR screening under diverse
clinical settings and referring the patients who may need further assessment
and treatment to expert ophthalmologists. | [
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] |
Background & Aims: Hepatic steatosis is a major cause of chronic liver
disease. 2D ultrasound is the most widely used non-invasive tool for screening
and monitoring, but associated diagnoses are highly subjective. We developed a
scalable deep learning (DL) algorithm for quantitative scoring of liver
steatosis from 2D ultrasound images.
Approach & Results: Using retrospectively collected multi-view ultrasound
data from 3,310 patients, 19,513 studies, and 228,075 images, we trained a DL
algorithm to diagnose steatosis stages (healthy, mild, moderate, or severe)
from ultrasound diagnoses. Performance was validated on two multi-scanner
unblinded and blinded (initially to DL developer) histology-proven cohorts (147
and 112 patients) with histopathology fatty cell percentage diagnoses, and a
subset with FibroScan diagnoses. We also quantified reliability across scanners
and viewpoints. Results were evaluated using Bland-Altman and receiver
operating characteristic (ROC) analysis. The DL algorithm demonstrates
repeatable measurements with a moderate number of images (3 for each viewpoint)
and high agreement across 3 premium ultrasound scanners. High diagnostic
performance was observed across all viewpoints: area under the curves of the
ROC to classify >=mild, >=moderate, =severe steatosis grades were 0.85, 0.90,
and 0.93, respectively. The DL algorithm outperformed or performed at least
comparably to FibroScan with statistically significant improvements for all
levels on the unblinded histology-proven cohort, and for =severe steatosis on
the blinded histology-proven cohort.
Conclusions: The DL algorithm provides a reliable quantitative steatosis
assessment across view and scanners on two multi-scanner cohorts. Diagnostic
performance was high with comparable or better performance than FibroScan. | [
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] |
Background. With the rise of highly portable, wireless, and low-cost
ultrasound devices and automatic ultrasound acquisition techniques, an
automated interpretation method requiring only a limited set of views as input
could make preliminary cardiovascular disease diagnoses more accessible. In
this study, we developed a deep learning (DL) method for automated detection of
impaired left ventricular (LV) function and aortic valve (AV) regurgitation
from apical four-chamber (A4C) ultrasound cineloops and investigated which
anatomical structures or temporal frames provided the most relevant information
for the DL model to enable disease classification.
Methods and Results. A4C ultrasounds were extracted from 3,554
echocardiograms of patients with either impaired LV function (n=928), AV
regurgitation (n=738), or no significant abnormalities (n=1,888). Two
convolutional neural networks (CNNs) were trained separately to classify the
respective disease cases against normal cases. The overall classification
accuracy of the impaired LV function detection model was 86%, and that of the
AV regurgitation detection model was 83%. Feature importance analyses
demonstrated that the LV myocardium and mitral valve were important for
detecting impaired LV function, while the tip of the mitral valve anterior
leaflet, during opening, was considered important for detecting AV
regurgitation.
Conclusion. The proposed method demonstrated the feasibility of a 3D CNN
approach in detection of impaired LV function and AV regurgitation using A4C
ultrasound cineloops. The current research shows that DL methods can exploit
large training data to detect diseases in a different way than conventionally
agreed upon methods, and potentially reveal unforeseen diagnostic image
features. | [
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] |
Particle image velocimetry (PIV) is essential in experimental fluid dynamics.
In the current work, we propose a new velocity field estimation paradigm, which
achieves a synergetic combination of the deep learning method and the
traditional cross-correlation method. Specifically, the deep learning method is
used to optimize and correct a coarse velocity guess to achieve a
super-resolution calculation. And the cross-correlation method provides the
initial velocity field based on a coarse correlation with a large interrogation
window. As a reference, the coarse velocity guess helps with improving the
robustness of the proposed algorithm. This fully convolutional network with
embedded cross-correlation is named as CC-FCN. CC-FCN has two types of input
layers, one is for the particle images, and the other is for the initial
velocity field calculated using cross-correlation with a coarse resolution.
Firstly, two pyramidal modules extract features of particle images and initial
velocity field respectively. Then the fusion module appropriately fuses these
features. Finally, CC-FCN achieves the super-resolution calculation through a
series of deconvolution layers to obtain the single-pixel velocity field. As
the supervised learning strategy is considered, synthetic data sets including
ground-truth fluid motions are generated to train the network parameters.
Synthetic and real experimental PIV data sets are used to test the trained
neural network in terms of accuracy, precision, spatial resolution and
robustness. The test results show that these attributes of CC-FCN are further
improved compared with those of other tested PIV algorithms. The proposed model
could therefore provide competitive and robust estimations for PIV experiments. | [
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] |
Federated learning, which shares the weights of the neural network across
clients, is gaining attention in the healthcare sector as it enables training
on a large corpus of decentralized data while maintaining data privacy. For
example, this enables neural network training for COVID-19 diagnosis on chest
X-ray (CXR) images without collecting patient CXR data across multiple
hospitals. Unfortunately, the exchange of the weights quickly consumes the
network bandwidth if highly expressive network architecture is employed.
So-called split learning partially solves this problem by dividing a neural
network into a client and a server part, so that the client part of the network
takes up less extensive computation resources and bandwidth. However, it is not
clear how to find the optimal split without sacrificing the overall network
performance. To amalgamate these methods and thereby maximize their distinct
strengths, here we show that the Vision Transformer, a recently developed deep
learning architecture with straightforward decomposable configuration, is
ideally suitable for split learning without sacrificing performance. Even under
the non-independent and identically distributed data distribution which
emulates a real collaboration between hospitals using CXR datasets from
multiple sources, the proposed framework was able to attain performance
comparable to data-centralized training. In addition, the proposed framework
along with heterogeneous multi-task clients also improves individual task
performances including the diagnosis of COVID-19, eliminating the need for
sharing large weights with innumerable parameters. Our results affirm the
suitability of Transformer for collaborative learning in medical imaging and
pave the way forward for future real-world implementations. | [
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] |
Purpose: To assess whether the three-dimensional (3D) structural
configuration of the central retinal vessel trunk and its branches (CRVT&B)
could be used as a diagnostic marker for glaucoma. Method: We trained a deep
learning network to automatically segment the CRVT&B from the B-scans of the
optical coherence tomography (OCT) volume of the optic nerve head (ONH).
Subsequently, two different approaches were used for glaucoma diagnosis using
the structural configuration of the CRVT&B as extracted from the OCT volumes.
In the first approach, we aimed to provide a diagnosis using only 3D CNN and
the 3D structure of the CRVT&B. For the second approach, we projected the 3D
structure of the CRVT&B orthographically onto three planes to obtain 2D images,
and then a 2D CNN was used for diagnosis. The segmentation accuracy was
evaluated using the Dice coefficient, whereas the diagnostic accuracy was
assessed using the area under the receiver operating characteristic curves
(AUC). The diagnostic performance of the CRVT&B was also compared with that of
retinal nerve fiber layer (RNFL) thickness. Results: Our segmentation network
was able to efficiently segment retinal blood vessels from OCT scans. On a test
set, we achieved a Dice coefficient of 0.81\pm0.07. The 3D and 2D diagnostic
networks were able to differentiate glaucoma from non-glaucoma subjects with
accuracies of 82.7% and 83.3%, respectively. The corresponding AUCs for CRVT&B
were 0.89 and 0.90, higher than those obtained with RNFL thickness alone.
Conclusions: Our work demonstrated that the diagnostic power of the CRVT&B is
superior to that of a gold-standard glaucoma parameter, i.e., RNFL thickness.
Our work also suggested that the major retinal blood vessels form a skeleton --
the configuration of which may be representative of major ONH structural
changes as typically observed with the development and progression of glaucoma. | [
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] |
Breast cancer is the most commonly diagnosed cancer and registers the highest
number of deaths for women with cancer. Recent advancements in diagnostic
activities combined with large-scale screening policies have significantly
lowered the mortality rates for breast cancer patients. However, the manual
inspection of tissue slides by the pathologists is cumbersome, time-consuming,
and is subject to significant inter- and intra-observer variability. Recently,
the advent of whole-slide scanning systems have empowered the rapid
digitization of pathology slides, and enabled to develop digital workflows.
These advances further enable to leverage Artificial Intelligence (AI) to
assist, automate, and augment pathological diagnosis. But the AI techniques,
especially Deep Learning (DL), require a large amount of high-quality annotated
data to learn from. Constructing such task-specific datasets poses several
challenges, such as, data-acquisition level constrains, time-consuming and
expensive annotations, and anonymization of private information. In this paper,
we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of
annotated Hematoxylin & Eosin (H&E)-stained images to facilitate the
characterization of breast lesions. BRACS contains 547 Whole-Slide Images
(WSIs), and 4539 Regions of Interest (ROIs) extracted from the WSIs. Each WSI,
and respective ROIs, are annotated by the consensus of three board-certified
pathologists into different lesion categories. Specifically, BRACS includes
three lesion types, i.e., benign, malignant and atypical, which are further
subtyped into seven categories. It is, to the best of our knowledge, the
largest annotated dataset for breast cancer subtyping both at WSI- and
ROI-level. Further, by including the understudied atypical lesions, BRACS
offers an unique opportunity for leveraging AI to better understand their
characteristics. | [
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] |
In the last decade, convolutional neural networks (ConvNets) have been a
major focus of research in medical image analysis. However, the performances of
ConvNets may be limited by a lack of explicit consideration of the long-range
spatial relationships in an image. Recently Vision Transformer architectures
have been proposed to address the shortcomings of ConvNets and have produced
state-of-the-art performances in many medical imaging applications.
Transformers may be a strong candidate for image registration because their
substantially larger receptive field enables a more precise comprehension of
the spatial correspondence between moving and fixed images. Here, we present
TransMorph, a hybrid Transformer-ConvNet model for volumetric medical image
registration. This paper also presents diffeomorphic and Bayesian variants of
TransMorph: the diffeomorphic variants ensure the topology-preserving
deformations, and the Bayesian variant produces a well-calibrated registration
uncertainty estimate. We extensively validated the proposed models using 3D
medical images from three applications: inter-patient and atlas-to-patient
brain MRI registration and phantom-to-CT registration. The proposed models are
evaluated in comparison to a variety of existing registration methods and
Transformer architectures. Qualitative and quantitative results demonstrate
that the proposed Transformer-based model leads to a substantial performance
improvement over the baseline methods, confirming the effectiveness of
Transformers for medical image registration. | [
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] |
Spectral super-resolution (SSR) refers to the hyperspectral image (HSI)
recovery from an RGB counterpart. Due to the one-to-many nature of the SSR
problem, a single RGB image can be reprojected to many HSIs. The key to tackle
this ill-posed problem is to plug into multi-source prior information such as
the natural spatial context-prior of RGB images, deep feature-prior or inherent
statistical-prior of HSIs, etc., so as to effectively alleviate the degree of
ill-posedness. However, most current approaches only consider the general and
limited priors in their customized convolutional neural networks (CNNs), which
leads to the inability to guarantee the confidence and fidelity of
reconstructed spectra. In this paper, we propose a novel holistic
prior-embedded relation network (HPRN) to integrate comprehensive priors to
regularize and optimize the solution space of SSR. Basically, the core
framework is delicately assembled by several multi-residual relation blocks
(MRBs) that fully facilitate the transmission and utilization of the
low-frequency content prior of RGBs. Innovatively, the semantic prior of RGB
inputs is introduced to mark category attributes, and a semantic-driven spatial
relation module (SSRM) is invented to perform the feature aggregation of
clustered similar range for refining recovered characteristics. Additionally,
we develop a transformer-based channel relation module (TCRM), which breaks the
habit of employing scalars as the descriptors of channel-wise relations in the
previous deep feature-prior, and replaces them with certain vectors to make the
mapping function more robust and smoother. In order to maintain the
mathematical correlation and spectral consistency between hyperspectral bands,
the second-order prior constraints (SOPC) are incorporated into the loss
function to guide the HSI reconstruction. | [
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] |
Objective: Multiple Sclerosis (MS) is an autoimmune, and demyelinating
disease that leads to lesions in the central nervous system. This disease can
be tracked and diagnosed using Magnetic Resonance Imaging (MRI). Up to now a
multitude of multimodality automatic biomedical approaches is used to segment
lesions which are not beneficial for patients in terms of cost, time, and
usability. The authors of the present paper propose a method employing just one
modality (FLAIR image) to segment MS lesions accurately. Methods: A patch-based
Convolutional Neural Network (CNN) is designed, inspired by 3D-ResNet and
spatial-channel attention module, to segment MS lesions. The proposed method
consists of three stages: (1) the contrast-limited adaptive histogram
equalization (CLAHE) is applied to the original images and concatenated to the
extracted edges in order to create 4D images; (2) the patches of size 80 * 80 *
80 * 2 are randomly selected from the 4D images; and (3) the extracted patches
are passed into an attention-based CNN which is used to segment the lesions.
Finally, the proposed method was compared to previous studies of the same
dataset. Results: The current study evaluates the model, with a test set of
ISIB challenge data. Experimental results illustrate that the proposed approach
significantly surpasses existing methods in terms of Dice similarity and
Absolute Volume Difference while the proposed method use just one modality
(FLAIR) to segment the lesions. Conclusions: The authors have introduced an
automated approach to segment the lesions which is based on, at most, two
modalities as an input. The proposed architecture is composed of convolution,
deconvolution, and an SCA-VoxRes module as an attention module. The results
show, the proposed method outperforms well compare to other methods. | [
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] |
Today, deep convolutional neural networks (CNNs) have demonstrated
state-of-the-art performance for medical image segmentation, on various imaging
modalities and tasks. Despite early success, segmentation networks may still
generate anatomically aberrant segmentations, with holes or inaccuracies near
the object boundaries. To enforce anatomical plausibility, recent research
studies have focused on incorporating prior knowledge such as object shape or
boundary, as constraints in the loss function. Prior integrated could be
low-level referring to reformulated representations extracted from the
ground-truth segmentations, or high-level representing external medical
information such as the organ's shape or size. Over the past few years,
prior-based losses exhibited a rising interest in the research field since they
allow integration of expert knowledge while still being architecture-agnostic.
However, given the diversity of prior-based losses on different medical imaging
challenges and tasks, it has become hard to identify what loss works best for
which dataset. In this paper, we establish a benchmark of recent prior-based
losses for medical image segmentation. The main objective is to provide
intuition onto which losses to choose given a particular task or dataset. To
this end, four low-level and high-level prior-based losses are selected. The
considered losses are validated on 8 different datasets from a variety of
medical image segmentation challenges including the Decathlon, the ISLES and
the WMH challenge. Results show that whereas low-level prior-based losses can
guarantee an increase in performance over the Dice loss baseline regardless of
the dataset characteristics, high-level prior-based losses can increase
anatomical plausibility as per data characteristics. | [
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] |
Ultrasound is the second most used modality in medical imaging. It is cost
effective, hazardless, portable and implemented routinely in numerous clinical
procedures. Nonetheless, image quality is characterized by granulated
appearance, poor SNR and speckle noise. Specific for malignant tumors, the
margins are blurred and indistinct. Thus, there is a great need for improving
ultrasound image quality. We hypothesize that this can be achieved, using
neural networks, by translation into a more realistic display which mimics an
anatomical cut through the tissue. In order to achieve this goal, the
preferable approach would be to use a set of paired images. However, this is
practically impossible in our case. Therefore, Cycle Generative Adversarial
Network (CycleGAN) was used, in order to learn each domain properties
separately and enforce cross domain cycle consistency. The two datasets which
were used for training the model were "Breast Ultrasound Images" (BUSI) and a
set of optic images of poultry breast tissue samples acquired at our lab. The
generated pseudo anatomical images provide improved visual discrimination of
the lesions with clearer border definition and pronounced contrast. In order to
evaluate the preservation of the anatomical features, the lesions in the
ultrasonic images and the generated pseudo anatomical images were both
automatically segmented and compared. This comparison yielded median dice score
of 0.91 for the benign tumors and 0.70 for the malignant ones. The median
lesion center error was 0.58% and 3.27% for the benign and malignancies
respectively and the median area error index was 0.40% and 4.34% for the benign
and malignancies respectively. In conclusion, these generated pseudo anatomical
images, which are presented in a more intuitive way, enhance tissue anatomy and
can potentially simplify the diagnosis and improve the clinical outcome. | [
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] |
Synthetic digital twins based on medical data accelerate the acquisition,
labelling and decision making procedure in digital healthcare. A core part of
digital healthcare twins is model-based data synthesis, which permits the
generation of realistic medical signals without requiring to cope with the
modelling complexity of anatomical and biochemical phenomena producing them in
reality. Unfortunately, algorithms for cardiac data synthesis have been so far
scarcely studied in the literature. An important imaging modality in the
cardiac examination is three-directional CINE multi-slice myocardial velocity
mapping (3Dir MVM), which provides a quantitative assessment of cardiac motion
in three orthogonal directions of the left ventricle. The long acquisition time
and complex acquisition produce make it more urgent to produce synthetic
digital twins of this imaging modality. In this study, we propose a hybrid deep
learning (HDL) network, especially for synthetic 3Dir MVM data. Our algorithm
is featured by a hybrid UNet and a Generative Adversarial Network with a
foreground-background generation scheme. The experimental results show that
from temporally down-sampled magnitude CINE images (six times), our proposed
algorithm can still successfully synthesise high temporal resolution 3Dir MVM
CMR data (PSNR=42.32) with precise left ventricle segmentation (DICE=0.92).
These performance scores indicate that our proposed HDL algorithm can be
implemented in real-world digital twins for myocardial velocity mapping data
simulation. To the best of our knowledge, this work is the first one in the
literature investigating digital twins of the 3Dir MVM CMR, which has shown
great potential for improving the efficiency of clinical studies via
synthesised cardiac data. | [
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] |
A critical step in virtual dental treatment planning is to accurately
delineate all tooth-bone structures from CBCT with high fidelity and accurate
anatomical information. Previous studies have established several methods for
CBCT segmentation using deep learning. However, the inherent resolution
discrepancy of CBCT and the loss of occlusal and dentition information largely
limited its clinical applicability. Here, we present a Deep Dental Multimodal
Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral
scan (IOS) segmentation model (the most accurate digital dental model), and a
fusion model to generate 3D fused crown-root-bone structures with high fidelity
and accurate occlusal and dentition information. Our model was trained with a
large-scale dataset with 503 CBCT and 28,559 IOS meshes manually annotated by
experienced human experts. For CBCT segmentation, we use a five-fold cross
validation test, each with 50 CBCT, and our model achieves an average Dice
coefficient and IoU of 93.99% and 88.68%, respectively, significantly
outperforming the baselines. For IOS segmentations, our model achieves an mIoU
of 93.07% and 95.70% on the maxillary and mandible on a test set of 200 IOS
meshes, which are 1.77% and 3.52% higher than the state-of-art method. Our DDMA
framework takes about 20 to 25 minutes to generate the fused 3D mesh model
following the sequential processing order, compared to over 5 hours by human
experts. Notably, our framework has been incorporated into a software by a
clear aligner manufacturer, and real-world clinical cases demonstrate that our
model can visualize crown-root-bone structures during the entire orthodontic
treatment and can predict risks like dehiscence and fenestration. These
findings demonstrate the potential of multi-modal deep learning to improve the
quality of digital dental models and help dentists make better clinical
decisions. | [
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] |
The high prevalence of work-related musculoskeletal disorders (WMSDs) could
be addressed by optimizing Human-Robot Collaboration (HRC) frameworks for
manufacturing applications. In this context, this paper proposes two hypotheses
for ergonomically effective task delegation and HRC. The first hypothesis
states that it is possible to quantify ergonomically professional tasks using
motion data from a reduced set of sensors. Then, the most dangerous tasks can
be delegated to a collaborative robot. The second hypothesis is that by
including gesture recognition and spatial adaptation, the ergonomics of an HRC
scenario can be improved by avoiding needless motions that could expose
operators to ergonomic risks and by lowering the physical effort required of
operators. An HRC scenario for a television manufacturing process is optimized
to test both hypotheses. For the ergonomic evaluation, motion primitives with
known ergonomic risks were modeled for their detection in professional tasks
and to estimate a risk score based on the European Assembly Worksheet (EAWS). A
Deep Learning gesture recognition module trained with egocentric television
assembly data was used to complement the collaboration between the human
operator and the robot. Additionally, a skeleton-tracking algorithm provided
the robot with information about the operator's pose, allowing it to spatially
adapt its motion to the operator's anthropometrics. Three experiments were
conducted to determine the effect of gesture recognition and spatial adaptation
on the operator's range of motion. The rate of spatial adaptation was used as a
key performance indicator (KPI), and a new KPI for measuring the reduction in
the operator's motion is presented in this paper. | [
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] |
Purpose - To develop and validate a deep learning (DL) framework for the
detection and quantification of drusen and reticular pseudodrusen (RPD) on
optical coherence tomography scans.
Design - Development and validation of deep learning models for
classification and feature segmentation.
Methods - A DL framework was developed consisting of a classification model
and an out-of-distribution (OOD) detection model for the identification of
ungradable scans; a classification model to identify scans with drusen or RPD;
and an image segmentation model to independently segment lesions as RPD or
drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with
a self-reported diagnosis of age-related macular degeneration (AMD) and 250
UKBB controls. Drusen and RPD were manually delineated by five retina
specialists. The main outcome measures were sensitivity, specificity, area
under the ROC curve (AUC), kappa, accuracy and intraclass correlation
coefficient (ICC).
Results - The classification models performed strongly at their respective
tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans
classifier, the OOD model, and the drusen and RPD classification model). The
mean ICC for drusen and RPD area vs. graders was 0.74 and 0.61, respectively,
compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that
the model's sensitivity was close to human performance.
Conclusions - The models achieved high classification and segmentation
performance, similar to human performance. Application of this robust framework
will further our understanding of RPD as a separate entity from drusen in both
research and clinical settings. | [
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] |
We present a large-scale study of imitating human demonstrations on tasks
that require a virtual robot to search for objects in new environments -- (1)
ObjectGoal Navigation (e.g. 'find & go to a chair') and (2) Pick&Place (e.g.
'find mug, pick mug, find counter, place mug on counter'). First, we develop a
virtual teleoperation data-collection infrastructure -- connecting Habitat
simulator running in a web browser to Amazon Mechanical Turk, allowing remote
users to teleoperate virtual robots, safely and at scale. We collect 80k
demonstrations for ObjectNav and 12k demonstrations for Pick&Place, which is an
order of magnitude larger than existing human demonstration datasets in
simulation or on real robots.
Second, we attempt to answer the question -- how does large-scale imitation
learning (IL) (which hasn't been hitherto possible) compare to reinforcement
learning (RL) (which is the status quo)? On ObjectNav, we find that IL (with no
bells or whistles) using 70k human demonstrations outperforms RL using 240k
agent-gathered trajectories. The IL-trained agent demonstrates efficient
object-search behavior -- it peeks into rooms, checks corners for small
objects, turns in place to get a panoramic view -- none of these are exhibited
as prominently by the RL agent, and to induce these behaviors via RL would
require tedious reward engineering. Finally, accuracy vs. training data size
plots show promising scaling behavior, suggesting that simply collecting more
demonstrations is likely to advance the state of art further. On Pick&Place,
the comparison is starker -- IL agents achieve ${\sim}$18% success on episodes
with new object-receptacle locations when trained with 9.5k human
demonstrations, while RL agents fail to get beyond 0%. Overall, our work
provides compelling evidence for investing in large-scale imitation learning.
Project page: https://ram81.github.io/projects/habitat-web. | [
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] |
This study established a feature-enhanced adversarial semi-supervised
semantic segmentation model to automatically annotate pulmonary embolism lesion
areas in computed tomography pulmonary angiogram (CTPA) images. In current
studies, all of the PE CTPA image segmentation methods are trained by
supervised learning. However, the supervised learning models need to be
retrained and the images need to be relabeled when the CTPA images come from
different hospitals. This study proposed a semi-supervised learning method to
make the model applicable to different datasets by adding a small amount of
unlabeled images. By training the model with both labeled and unlabeled images,
the accuracy of unlabeled images can be improved and the labeling cost can be
reduced. Our semi-supervised segmentation model includes a segmentation network
and a discriminator network. We added feature information generated from the
encoder of segmentation network to the discriminator so that it can learn the
similarity between predicted mask and ground truth mask. This HRNet-based
architecture can maintain a higher resolution for convolutional operations so
the prediction of small PE lesion areas can be improved. We used the labeled
open-source dataset and the unlabeled National Cheng Kung University Hospital
(NCKUH) (IRB number: B-ER-108-380) dataset to train the semi-supervised
learning model, and the resulting mean intersection over union (mIOU), dice
score, and sensitivity achieved 0.3510, 0.4854, and 0.4253, respectively on the
NCKUH dataset. Then, we fine-tuned and tested the model with a small amount of
unlabeled PE CTPA images from China Medical University Hospital (CMUH) (IRB
number: CMUH110-REC3-173) dataset. Comparing the results of our semi-supervised
model with the supervised model, the mIOU, dice score, and sensitivity improved
from 0.2344, 0.3325, and 0.3151 to 0.3721, 0.5113, and 0.4967, respectively. | [
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] |
Ischemic strokes are often caused by large vessel occlusions (LVOs), which
can be visualized and diagnosed with Computed Tomography Angiography scans. As
time is brain, a fast, accurate and automated diagnosis of these scans is
desirable. Human readers compare the left and right hemispheres in their
assessment of strokes. A large training data set is required for a standard
deep learning-based model to learn this strategy from data. As labeled medical
data in this field is rare, other approaches need to be developed. To both
include the prior knowledge of side comparison and increase the amount of
training data, we propose an augmentation method that generates artificial
training samples by recombining vessel tree segmentations of the hemispheres or
hemisphere subregions from different patients. The subregions cover vessels
commonly affected by LVOs, namely the internal carotid artery (ICA) and middle
cerebral artery (MCA). In line with the augmentation scheme, we use a
3D-DenseNet fed with task-specific input, fostering a side-by-side comparison
between the hemispheres. Furthermore, we propose an extension of that
architecture to process the individual hemisphere subregions. All
configurations predict the presence of an LVO, its side, and the affected
subregion. We show the effect of recombination as an augmentation strategy in a
5-fold cross validated ablation study. We enhanced the AUC for patient-wise
classification regarding the presence of an LVO of all investigated
architectures. For one variant, the proposed method improved the AUC from 0.73
without augmentation to 0.89. The best configuration detects LVOs with an AUC
of 0.91, LVOs in the ICA with an AUC of 0.96, and in the MCA with 0.91 while
accurately predicting the affected side. | [
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] |
[Purpose] The pathology is decisive for disease diagnosis, but relies heavily
on the experienced pathologists. Recently, pathological artificial intelligence
(PAI) is thought to improve diagnostic accuracy and efficiency. However, the
high performance of PAI based on deep learning in the laboratory generally
cannot be reproduced in the clinic. [Methods] Because the data preparation is
important for PAI, the paper has reviewed PAI-related studies in the PubMed
database published from January 2017 to February 2022, and 118 studies were
included. The in-depth analysis of methods for preparing data is performed,
including obtaining slides of pathological tissue, cleaning, screening, and
then digitizing. Expert review, image annotation, dataset division for model
training and validation are also discussed. We further discuss the reasons why
the high performance of PAI is not reproducible in the clinical practices and
show some effective ways to improve clinical performances of PAI. [Results] The
robustness of PAI depend on randomized collection of representative disease
slides, including rigorous quality control and screening, correction of digital
discrepancies, reasonable annotation, and the amount of data. The digital
pathology is fundamental of clinical-grade PAI, and the techniques of data
standardization and weakly supervised learning methods based on whole slide
image (WSI) are effective ways to overcome obstacles of performance
reproduction. [Conclusion] The representative data, the amount of labeling and
consistency from multi-centers is the key to performance reproduction. The
digital pathology for clinical diagnosis, data standardization and technique of
WSI-based weakly supervised learning hopefully build clinical-grade PAI.
Keywords: pathological artificial intelligence; data preparation;
clinical-grade; deep learning | [
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] |
Eosinophilic esophagitis (EoE) is a chronic allergic inflammatory condition
of the esophagus associated with elevated esophageal eosinophils. Second only
to gastroesophageal reflux disease, EoE is one of the leading causes of chronic
refractory dysphagia in adults and children. EoE diagnosis requires enumerating
the density of esophageal eosinophils in esophageal biopsies, a somewhat
subjective task that is time-consuming, thus reducing the ability to process
the complex tissue structure. Previous artificial intelligence (AI) approaches
that aimed to improve histology-based diagnosis focused on recapitulating
identification and quantification of the area of maximal eosinophil density.
However, this metric does not account for the distribution of eosinophils or
other histological features, over the whole slide image. Here, we developed an
artificial intelligence platform that infers local and spatial biomarkers based
on semantic segmentation of intact eosinophils and basal zone distributions.
Besides the maximal density of eosinophils (referred to as Peak Eosinophil
Count [PEC]) and a maximal basal zone fraction, we identify two additional
metrics that reflect the distribution of eosinophils and basal zone fractions.
This approach enables a decision support system that predicts EoE activity and
classifies the histological severity of EoE patients. We utilized a cohort that
includes 1066 biopsy slides from 400 subjects to validate the system's
performance and achieved a histological severity classification accuracy of
86.70%, sensitivity of 84.50%, and specificity of 90.09%. Our approach
highlights the importance of systematically analyzing the distribution of
biopsy features over the entire slide and paves the way towards a personalized
decision support system that will assist not only in counting cells but can
also potentially improve diagnosis and provide treatment prediction. | [
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] |
With the increasing popularity and accessibility of high dynamic range (HDR)
photography, tone mapping operators (TMOs) for dynamic range compression are
practically demanding. In this paper, we develop a two-stage neural
network-based TMO that is self-calibrated and perceptually optimized. In Stage
one, motivated by the physiology of the early stages of the human visual
system, we first decompose an HDR image into a normalized Laplacian pyramid. We
then use two lightweight deep neural networks (DNNs), taking the normalized
representation as input and estimating the Laplacian pyramid of the
corresponding LDR image. We optimize the tone mapping network by minimizing the
normalized Laplacian pyramid distance (NLPD), a perceptual metric aligning with
human judgments of tone-mapped image quality. In Stage two, the input HDR image
is self-calibrated to compute the final LDR image. We feed the same HDR image
but rescaled with different maximum luminances to the learned tone mapping
network, and generate a pseudo-multi-exposure image stack with different detail
visibility and color saturation. We then train another lightweight DNN to fuse
the LDR image stack into a desired LDR image by maximizing a variant of the
structural similarity index for multi-exposure image fusion (MEF-SSIM), which
has been proven perceptually relevant to fused image quality. The proposed
self-calibration mechanism through MEF enables our TMO to accept uncalibrated
HDR images, while being physiology-driven. Extensive experiments show that our
method produces images with consistently better visual quality. Additionally,
since our method builds upon three lightweight DNNs, it is among the fastest
local TMOs. | [
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] |
Precipitation governs Earth's hydroclimate, and its daily spatiotemporal
fluctuations have major socioeconomic effects. Advances in Numerical weather
prediction (NWP) have been measured by the improvement of forecasts for various
physical fields such as temperature and pressure; however, large biases exist
in precipitation prediction. We augment the output of the well-known NWP model
CFSv2 with deep learning to create a hybrid model that improves short-range
global precipitation at 1-, 2-, and 3-day lead times. To hybridise, we address
the sphericity of the global data by using modified DLWP-CS architecture which
transforms all the fields to cubed-sphere projection. Dynamical model
precipitation and surface temperature outputs are fed into a modified DLWP-CS
(UNET) to forecast ground truth precipitation. While CFSv2's average bias is +5
to +7 mm/day over land, the multivariate deep learning model decreases it to
within -1 to +1 mm/day. Hurricane Katrina in 2005, Hurricane Ivan in 2004,
China floods in 2010, India floods in 2005, and Myanmar storm Nargis in 2008
are used to confirm the substantial enhancement in the skill for the hybrid
dynamical-deep learning model. CFSv2 typically shows a moderate to large bias
in the spatial pattern and overestimates the precipitation at short-range time
scales. The proposed deep learning augmented NWP model can address these biases
and vastly improve the spatial pattern and magnitude of predicted
precipitation. Deep learning enhanced CFSv2 reduces mean bias by 8x over
important land regions for 1 day lead compared to CFSv2. The spatio-temporal
deep learning system opens pathways to further the precision and accuracy in
global short-range precipitation forecasts. | [
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] |
Automated segmentation and volumetry of brain magnetic resonance imaging
(MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and
Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we
adopt deep learning (DL) models in brain segmentation and compared their
performance with the gold-standard non-DL method. We collected brain MRI scans
of healthy controls (n=105) and patients with PD (n=105), multiple systemic
atrophy (n=132), and progressive supranuclear palsy (n=69) at Samsung Medical
Center from January 2017 to December 2020. Using the gold-standard non-DL
model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons,
caudate, putamen, pallidum, and third ventricle, and considered them as
annotating data for DL models, the representative V-Net and UNETR. The Dice
scores and area under the curve (AUC) for differentiating normal, PD, and
P-plus cases were calculated. The segmentation times of V-Net and UNETR for the
six brain structures per patient were 3.48 +- 0.17 and 48.14 +- 0.97 s,
respectively, being at least 300 times faster than FS (15,735 +- 1.07 s). Dice
scores of both DL models were sufficiently high (>0.85), and their AUCs for
disease classification were superior to that of FS. For classification of
normal vs. P-plus and PD vs. multiple systemic atrophy (cerebellar type), the
DL models and FS showed AUCs above 0.8. DL significantly reduces the analysis
time without compromising the performance of brain segmentation and
differential diagnosis. Our findings may contribute to the adoption of DL brain
MRI segmentation in clinical settings and advance brain research. | [
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] |
The growth of unruptured intracranial aneurysms (UIAs) is a predictor of
rupture. Therefore, for further imaging surveillance and treatment planning, it
is important to be able to predict if an UIA is likely to grow based on an
initial baseline Time-of-Flight MRA (TOF-MRA). It is known that the size and
shape of UIAs are predictors of aneurysm growth and/or rupture. We perform a
feasibility study of using a mesh convolutional neural network for future UIA
growth prediction from baseline TOF-MRAs. We include 151 TOF-MRAs, with 169
UIAs where 49 UIAs were classified as growing and 120 as stable, based on the
clinical definition of growth (>1 mm increase in size in follow-up scan). UIAs
were segmented from TOF-MRAs and meshes were automatically generated. We
investigate the input of both UIA mesh only and region-of-interest (ROI) meshes
including UIA and surrounding parent vessels. We develop a classification model
to predict UIAs that will grow or remain stable. The model consisted of a mesh
convolutional neural network including additional novel input edge features of
shape index and curvedness which describe the surface topology. It was
investigated if input edge mid-point co-ordinates influenced the model
performance. The model with highest AUC (63.8%) for growth prediction was using
UIA meshes with input edge mid-point co-ordinate features (average F1 score =
62.3%, accuracy = 66.9%, sensitivity = 57.3%, specificity = 70.8%). We present
a future UIA growth prediction model based on a mesh convolutional neural
network with promising results. | [
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] |
Social engineering (SE) is a form of deception that aims to trick people into
giving access to data, information, networks and even money. For decades SE has
been a key method for attackers to gain access to an organization, virtually
skipping all lines of defense. Attackers also regularly use SE to scam innocent
people by making threatening phone calls which impersonate an authority or by
sending infected emails which look like they have been sent from a loved one.
SE attacks will likely remain a top attack vector for criminals because humans
are the weakest link in cyber security.
Unfortunately, the threat will only get worse now that a new technology
called deepfakes as arrived. A deepfake is believable media (e.g., videos)
created by an AI. Although the technology has mostly been used to swap the
faces of celebrities, it can also be used to `puppet' different personas.
Recently, researchers have shown how this technology can be deployed in
real-time to clone someone's voice in a phone call or reenact a face in a video
call. Given that any novice user can download this technology to use it, it is
no surprise that criminals have already begun to monetize it to perpetrate
their SE attacks.
In this paper, we propose a lightweight application which can protect
organizations and individuals from deepfake SE attacks. Through a challenge and
response approach, we leverage the technical and theoretical limitations of
deepfake technologies to expose the attacker. Existing defence solutions are
too heavy as an end-point solution and can be evaded by a dynamic attacker. In
contrast, our approach is lightweight and breaks the reactive arms race,
putting the attacker at a disadvantage. | [
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] |
Device Model Generalization (DMG) is a practical yet under-investigated
research topic for on-device machine learning applications. It aims to improve
the generalization ability of pre-trained models when deployed on
resource-constrained devices, such as improving the performance of pre-trained
cloud models on smart mobiles. While quite a lot of works have investigated the
data distribution shift across clouds and devices, most of them focus on model
fine-tuning on personalized data for individual devices to facilitate DMG.
Despite their promising, these approaches require on-device re-training, which
is practically infeasible due to the overfitting problem and high time delay
when performing gradient calculation on real-time data. In this paper, we argue
that the computational cost brought by fine-tuning can be rather unnecessary.
We consequently present a novel perspective to improving DMG without increasing
computational cost, i.e., device-specific parameter generation which directly
maps data distribution to parameters. Specifically, we propose an efficient
Device-cloUd collaborative parametErs generaTion framework DUET. DUET is
deployed on a powerful cloud server that only requires the low cost of
forwarding propagation and low time delay of data transmission between the
device and the cloud. By doing so, DUET can rehearse the device-specific model
weight realizations conditioned on the personalized real-time data for an
individual device. Importantly, our DUET elegantly connects the cloud and
device as a 'duet' collaboration, frees the DMG from fine-tuning, and enables a
faster and more accurate DMG paradigm. We conduct an extensive experimental
study of DUET on three public datasets, and the experimental results confirm
our framework's effectiveness and generalisability for different DMG tasks. | [
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Autonomous vehicles have limited computational resources; hence, their
control systems must be efficient. The cost and size of sensors have limited
the development of self-driving cars. To overcome these restrictions, this
study proposes an efficient framework for the operation of vision-based
automatic vehicles; the framework requires only a monocular camera and a few
inexpensive radars. The proposed algorithm comprises a multi-task UNet (MTUNet)
network for extracting image features and constrained iterative linear
quadratic regulator (CILQR) and vision predictive control (VPC) modules for
rapid motion planning and control. MTUNet is designed to simultaneously solve
lane line segmentation, the ego vehicle's heading angle regression, road type
classification, and traffic object detection tasks at approximately 40 FPS
(frames per second) for 228 x 228 pixel RGB input images. The CILQR controllers
then use the MTUNet outputs and radar data as inputs to produce driving
commands for lateral and longitudinal vehicle guidance within only 1 ms. In
particular, the VPC algorithm is included to reduce steering command latency to
below actuator latency to prevent vehicle understeer during tight turns. The
VPC algorithm uses road curvature data from MTUNet to estimate the correction
of the current steering angle at a look-ahead point to adjust the turning
amount. Including the VPC algorithm in a VPC-CILQR controller leads to higher
performance than CILQR alone; this controller can minimize the influence of
command lag, maintaining the ego car's speed and lateral offset at 76 km/h and
within 0.52 m, respectively, on a simulated road with a curvature of 0.03 1/m.
Our experiments demonstrate that the proposed autonomous driving system, which
does not require high-definition maps, could be applied in current autonomous
vehicles. | [
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] |
Purpose: (1) To develop a deep learning algorithm to automatically segment
structures of the optic nerve head (ONH) and macula in 3D wide-field optical
coherence tomography (OCT) scans; (2) To assess whether 3D macula or ONH
structures (or the combination of both) provide the best diagnostic power for
glaucoma. Methods: A cross-sectional comparative study was performed which
included wide-field swept-source OCT scans from 319 glaucoma subjects and 298
non-glaucoma subjects. All scans were compensated to improve deep-tissue
visibility. We developed a deep learning algorithm to automatically label all
major ONH tissue structures by using 270 manually annotated B-scans for
training. The performance of our algorithm was assessed using the Dice
coefficient (DC). A glaucoma classification algorithm (3D CNN) was then
designed using a combination of 500 OCT volumes and their corresponding
automatically segmented masks. This algorithm was trained and tested on 3
datasets: OCT scans cropped to contain the macular tissues only, those to
contain the ONH tissues only, and the full wide-field OCT scans. The
classification performance for each dataset was reported using the AUC.
Results: Our segmentation algorithm was able to segment ONH and macular tissues
with a DC of 0.94 $\pm$ 0.003. The classification algorithm was best able to
diagnose glaucoma using wide-field 3D-OCT volumes with an AUC of 0.99 $\pm$
0.01, followed by ONH volumes with an AUC of 0.93 $\pm$ 0.06, and finally
macular volumes with an AUC of 0.91 $\pm$ 0.11. Conclusions: this study showed
that using wide-field OCT as compared to the typical OCT images containing just
the ONH or macular may allow for a significantly improved glaucoma diagnosis.
This may encourage the mainstream adoption of 3D wide-field OCT scans. For
clinical AI studies that use traditional machines, we would recommend the use
of ONH scans as opposed to macula scans. | [
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] |
While vision transformers (ViTs) have continuously achieved new milestones in
the field of computer vision, their sophisticated network architectures with
high computation and memory costs have impeded their deployment on
resource-limited edge devices. In this paper, we propose a hardware-efficient
image-adaptive token pruning framework called HeatViT for efficient yet
accurate ViT acceleration on embedded FPGAs. By analyzing the inherent
computational patterns in ViTs, we first design an effective attention-based
multi-head token selector, which can be progressively inserted before
transformer blocks to dynamically identify and consolidate the non-informative
tokens from input images. Moreover, we implement the token selector on hardware
by adding miniature control logic to heavily reuse existing hardware components
built for the backbone ViT. To improve the hardware efficiency, we further
employ 8-bit fixed-point quantization, and propose polynomial approximations
with regularization effect on quantization error for the frequently used
nonlinear functions in ViTs. Finally, we propose a latency-aware multi-stage
training strategy to determine the transformer blocks for inserting token
selectors and optimize the desired (average) pruning rates for inserted token
selectors, in order to improve both the model accuracy and inference latency on
hardware. Compared to existing ViT pruning studies, under the similar
computation cost, HeatViT can achieve 0.7%$\sim$8.9% higher accuracy; while
under the similar model accuracy, HeatViT can achieve more than
28.4%$\sim$65.3% computation reduction, for various widely used ViTs, including
DeiT-T, DeiT-S, DeiT-B, LV-ViT-S, and LV-ViT-M, on the ImageNet dataset.
Compared to the baseline hardware accelerator, our implementations of HeatViT
on the Xilinx ZCU102 FPGA achieve 3.46$\times$$\sim$4.89$\times$ speedup. | [
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] |
Purpose: Surgery scene understanding with tool-tissue interaction recognition
and automatic report generation can play an important role in intra-operative
guidance, decision-making and postoperative analysis in robotic surgery.
However, domain shifts between different surgeries with inter and intra-patient
variation and novel instruments' appearance degrade the performance of model
prediction. Moreover, it requires output from multiple models, which can be
computationally expensive and affect real-time performance.
Methodology: A multi-task learning (MTL) model is proposed for surgical
report generation and tool-tissue interaction prediction that deals with domain
shift problems. The model forms of shared feature extractor, mesh-transformer
branch for captioning and graph attention branch for tool-tissue interaction
prediction. The shared feature extractor employs class incremental contrastive
learning (CICL) to tackle intensity shift and novel class appearance in the
target domain. We design Laplacian of Gaussian (LoG) based curriculum learning
into both shared and task-specific branches to enhance model learning. We
incorporate a task-aware asynchronous MTL optimization technique to fine-tune
the shared weights and converge both tasks optimally.
Results: The proposed MTL model trained using task-aware optimization and
fine-tuning techniques reported a balanced performance (BLEU score of 0.4049
for scene captioning and accuracy of 0.3508 for interaction detection) for both
tasks on the target domain and performed on-par with single-task models in
domain adaptation.
Conclusion: The proposed multi-task model was able to adapt to domain shifts,
incorporate novel instruments in the target domain, and perform tool-tissue
interaction detection and report generation on par with single-task models. | [
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] |
The Artemis program requires robotic and crewed lunar rovers for resource
prospecting and exploitation, construction and maintenance of facilities, and
human exploration. These rovers must support navigation for 10s of kilometers
(km) from base camps. A lunar science rover mission concept - Endurance-A, has
been recommended by the new Decadal Survey as the highest priority medium-class
mission of the Lunar Discovery and Exploration Program, and would be required
to traverse approximately 2000 km in the South Pole-Aitkin (SPA) Basin, with
individual drives of several kilometers between stops for downlink. These rover
mission scenarios require functionality that provides onboard, autonomous,
global position knowledge ( aka absolute localization). However, planetary
rovers have no onboard global localization capability to date; they have only
used relative localization, by integrating combinations of wheel odometry,
visual odometry, and inertial measurements during each drive to track position
relative to the start of each drive. In this work, we summarize recent
developments from the LunarNav project, where we have developed algorithms and
software to enable lunar rovers to estimate their global position and heading
on the Moon with a goal performance of position error less than 5 meters (m)
and heading error less than 3-degree, 3-sigma, in sunlit areas. This will be
achieved autonomously onboard by detecting craters in the vicinity of the rover
and matching them to a database of known craters mapped from orbit. The overall
technical framework consists of three main elements: 1) crater detection, 2)
crater matching, and 3) state estimation. In previous work, we developed crater
detection algorithms for three different sensing modalities. Our results
suggest that rover localization with an error less than 5 m is highly probable
during daytime operations. | [
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] |
Challenges in strawberry picking made selective harvesting robotic technology
demanding. However, selective harvesting of strawberries is complicated forming
a few scientific research questions. Most available solutions only deal with a
specific picking scenario, e.g., picking only a single variety of fruit in
isolation. Nonetheless, most economically viable (e.g. high-yielding and/or
disease-resistant) varieties of strawberry are grown in dense clusters. The
current perception technology in such use cases is inefficient. In this work,
we developed a novel system capable of harvesting strawberries with several
unique features. The features allow the system to deal with very complex
picking scenarios, e.g. dense clusters. Our concept of a modular system makes
our system reconfigurable to adapt to different picking scenarios. We designed,
manufactured, and tested a picking head with 2.5 DOF (2 independent mechanisms
and 1 dependent cutting system) capable of removing possible occlusions and
harvesting targeted strawberries without contacting fruit flesh to avoid damage
and bruising. In addition, we developed a novel perception system to localise
strawberries and detect their key points, picking points, and determine their
ripeness. For this purpose, we introduced two new datasets. Finally, we tested
the system in a commercial strawberry growing field and our research farm with
three different strawberry varieties. The results show the effectiveness and
reliability of the proposed system. The designed picking head was able to
remove occlusions and harvest strawberries effectively. The perception system
was able to detect and determine the ripeness of strawberries with 95%
accuracy. In total, the system was able to harvest 87% of all detected
strawberries with a success rate of 83% for all pluckable fruits. We also
discuss a series of open research questions in the discussion section. | [
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] |
There is growing concern that male reproduction is affected by environmental
chemicals. One way to determine the adverse effect of environmental pollutants
is to use wild animals as monitors and evaluate testicular toxicity using
histopathology. Automated methods are necessary tools in the quantitative
assessment of histopathology to overcome the subjectivity of manual evaluation
and accelerate the process. We propose an automated method to process histology
images of testicular tissue. Segmenting the epithelial layer of the
seminiferous tubule is a prerequisite for developing automated methods to
detect abnormalities in tissue. We suggest an encoder-decoder fully connected
convolutional neural network (F-CNN) model to segment the epithelial layer of
the seminiferous tubules in histological images. Using ResNet-34 modules in the
encoder adds a shortcut mechanism to avoid the gradient vanishing and
accelerate the network convergence. The squeeze & excitation (SE) attention
block is integrated into the encoding module improving the segmentation and
localization of epithelium. We applied the proposed method for the 2-class
problem where the epithelial layer of the tubule is the target class. The
f-score and IoU of the proposed method are 0.85 and 0.92. Although the proposed
method is trained on a limited training set, it performs well on an independent
dataset and outperforms other state-of-the-art methods. The pretrained
ResNet-34 in the encoder and attention block suggested in the decoder result in
better segmentation and generalization. The proposed method can be applied to
testicular tissue images from any mammalian species and can be used as the
first part of a fully automated testicular tissue processing pipeline. The
dataset and codes are publicly available on GitHub. | [
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] |
Artificial intelligence methods show great promise in increasing the quality
and speed of work with large astronomical datasets, but the high complexity of
these methods leads to the extraction of dataset-specific, non-robust features.
Therefore, such methods do not generalize well across multiple datasets. We
present a universal domain adaptation method, \textit{DeepAstroUDA}, as an
approach to overcome this challenge. This algorithm performs semi-supervised
domain adaptation and can be applied to datasets with different data
distributions and class overlaps. Non-overlapping classes can be present in any
of the two datasets (the labeled source domain, or the unlabeled target
domain), and the method can even be used in the presence of unknown classes. We
apply our method to three examples of galaxy morphology classification tasks of
different complexities ($3$-class and $10$-class problems), with anomaly
detection: 1) datasets created after different numbers of observing years from
a single survey (LSST mock data of $1$ and $10$ years of observations); 2) data
from different surveys (SDSS and DECaLS); and 3) data from observing fields
with different depths within one survey (wide field and Stripe 82 deep field of
SDSS). For the first time, we demonstrate the successful use of domain
adaptation between very discrepant observational datasets.
\textit{DeepAstroUDA} is capable of bridging the gap between two astronomical
surveys, increasing classification accuracy in both domains (up to $40\%$ on
the unlabeled data), and making model performance consistent across datasets.
Furthermore, our method also performs well as an anomaly detection algorithm
and successfully clusters unknown class samples even in the unlabeled target
dataset. | [
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] |
Patient motion during PET is inevitable. Its long acquisition time not only
increases the motion and the associated artifacts but also the patient's
discomfort, thus PET acceleration is desirable. However, accelerating PET
acquisition will result in reconstructed images with low SNR, and the image
quality will still be degraded by motion-induced artifacts. Most of the
previous PET motion correction methods are motion type specific that require
motion modeling, thus may fail when multiple types of motion present together.
Also, those methods are customized for standard long acquisition and could not
be directly applied to accelerated PET. To this end, modeling-free universal
motion correction reconstruction for accelerated PET is still highly
under-explored. In this work, we propose a novel deep learning-aided motion
correction and reconstruction framework for accelerated PET, called
Fast-MC-PET. Our framework consists of a universal motion correction (UMC) and
a short-to-long acquisition reconstruction (SL-Reon) module. The UMC enables
modeling-free motion correction by estimating quasi-continuous motion from
ultra-short frame reconstructions and using this information for
motion-compensated reconstruction. Then, the SL-Recon converts the accelerated
UMC image with low counts to a high-quality image with high counts for our
final reconstruction output. Our experimental results on human studies show
that our Fast-MC-PET can enable 7-fold acceleration and use only 2 minutes
acquisition to generate high-quality reconstruction images that
outperform/match previous motion correction reconstruction methods using
standard 15 minutes long acquisition data. | [
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] |
Large-scale embedding-based retrieval (EBR) is the cornerstone of
search-related industrial applications. Given a user query, the system of EBR
aims to identify relevant information from a large corpus of documents that may
be tens or hundreds of billions in size. The storage and computation turn out
to be expensive and inefficient with massive documents and high concurrent
queries, making it difficult to further scale up. To tackle the challenge, we
propose a binary embedding-based retrieval (BEBR) engine equipped with a
recurrent binarization algorithm that enables customized bits per dimension.
Specifically, we compress the full-precision query and document embeddings,
formulated as float vectors in general, into a composition of multiple binary
vectors using a lightweight transformation model with residual multilayer
perception (MLP) blocks. We can therefore tailor the number of bits for
different applications to trade off accuracy loss and cost savings.
Importantly, we enable task-agnostic efficient training of the binarization
model using a new embedding-to-embedding strategy. We also exploit the
compatible training of binary embeddings so that the BEBR engine can support
indexing among multiple embedding versions within a unified system. To further
realize efficient search, we propose Symmetric Distance Calculation (SDC) to
achieve lower response time than Hamming codes. We successfully employed the
introduced BEBR to Tencent products, including Sogou, Tencent Video, QQ World,
etc. The binarization algorithm can be seamlessly generalized to various tasks
with multiple modalities. Extensive experiments on offline benchmarks and
online A/B tests demonstrate the efficiency and effectiveness of our method,
significantly saving 30%~50% index costs with almost no loss of accuracy at the
system level. | [
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] |
The promising research on Artificial Intelligence usages in suicide
prevention has principal gaps, including black box methodologies, inadequate
outcome measures, and scarce research on non-verbal inputs, such as social
media images (despite their popularity today, in our digital era). This study
addresses these gaps and combines theory-driven and bottom-up strategies to
construct a hybrid and interpretable prediction model of valid suicide risk
from images. The lead hypothesis was that images contain valuable information
about emotions and interpersonal relationships, two central concepts in
suicide-related treatments and theories. The dataset included 177,220 images by
841 Facebook users who completed a gold-standard suicide scale. The images were
represented with CLIP, a state-of-the-art algorithm, which was utilized,
unconventionally, to extract predefined features that served as inputs to a
simple logistic-regression prediction model (in contrast to complex neural
networks). The features addressed basic and theory-driven visual elements using
everyday language (e.g., bright photo, photo of sad people). The results of the
hybrid model (that integrated theory-driven and bottom-up methods) indicated
high prediction performance that surpassed common bottom-up algorithms, thus
providing a first proof that images (alone) can be leveraged to predict
validated suicide risk. Corresponding with the lead hypothesis, at-risk users
had images with increased negative emotions and decreased belonginess. The
results are discussed in the context of non-verbal warning signs of suicide.
Notably, the study illustrates the advantages of hybrid models in such
complicated tasks and provides simple and flexible prediction strategies that
could be utilized to develop real-life monitoring tools of suicide. | [
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] |
Adversarial attacks are valuable for evaluating the robustness of deep
learning models. Existing attacks are primarily conducted on the visible light
spectrum (e.g., pixel-wise texture perturbation). However, attacks targeting
texture-free X-ray images remain underexplored, despite the widespread
application of X-ray imaging in safety-critical scenarios such as the X-ray
detection of prohibited items. In this paper, we take the first step toward the
study of adversarial attacks targeted at X-ray prohibited item detection, and
reveal the serious threats posed by such attacks in this safety-critical
scenario. Specifically, we posit that successful physical adversarial attacks
in this scenario should be specially designed to circumvent the challenges
posed by color/texture fading and complex overlapping. To this end, we propose
X-adv to generate physically printable metals that act as an adversarial agent
capable of deceiving X-ray detectors when placed in luggage. To resolve the
issues associated with color/texture fading, we develop a differentiable
converter that facilitates the generation of 3D-printable objects with
adversarial shapes, using the gradients of a surrogate model rather than
directly generating adversarial textures. To place the printed 3D adversarial
objects in luggage with complex overlapped instances, we design a policy-based
reinforcement learning strategy to find locations eliciting strong attack
performance in worst-case scenarios whereby the prohibited items are heavily
occluded by other items. To verify the effectiveness of the proposed X-Adv, we
conduct extensive experiments in both the digital and the physical world
(employing a commercial X-ray security inspection system for the latter case).
Furthermore, we present the physical-world X-ray adversarial attack dataset
XAD. | [
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] |
U-10Zr-based nuclear fuel is pursued as a primary candidate for
next-generation sodium-cooled fast reactors. However, more advanced
characterization and analysis are needed to form a fundamental understating of
the fuel performance, and make U-10Zr fuel qualify for commercial use. The
movement of lanthanides across the fuel section from the hot fuel center to the
cool cladding surface is one of the key factors to affect fuel performance. In
the advanced annular U-10Zr fuel, the lanthanides present as fission gas
bubbles. Due to a lack of annotated data, existing literature utilized a
multiple-threshold method to separate the bubbles and calculate bubble
statistics on an annular fuel. However, the multiple-threshold method cannot
achieve robust performance on images with different qualities and contrasts,
and cannot distinguish different bubbles. This paper proposes a hybrid
framework for efficient bubble segmentation. We develop a bubble annotation
tool and generate the first fission gas bubble dataset with more than 3000
bubbles from 24 images. A multi-task deep learning network integrating U-Net
and ResNet is designed to accomplish instance-level bubble segmentation.
Combining the segmentation results and image processing step achieves the best
recall ratio of more than 90% with very limited annotated data. Our model shows
outstanding improvement by comparing the previously proposed thresholding
method. The proposed method has promising to generate a more accurate
quantitative analysis of fission gas bubbles on U-10Zr annular fuels. The
results will contribute to identifying the bubbles with lanthanides and finally
build the relationship between the thermal gradation and lanthanides movements
of U-10Zr annular fuels. Mover, the deep learning model is applicable to other
similar material micro-structure segmentation tasks. | [
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] |
For robots to follow instructions from people, they must be able to connect
the rich semantic information in human vocabulary, e.g. "can you get me the
pink stuffed whale?" to their sensory observations and actions. This brings up
a notably difficult challenge for robots: while robot learning approaches allow
robots to learn many different behaviors from first-hand experience, it is
impractical for robots to have first-hand experiences that span all of this
semantic information. We would like a robot's policy to be able to perceive and
pick up the pink stuffed whale, even if it has never seen any data interacting
with a stuffed whale before. Fortunately, static data on the internet has vast
semantic information, and this information is captured in pre-trained
vision-language models. In this paper, we study whether we can interface robot
policies with these pre-trained models, with the aim of allowing robots to
complete instructions involving object categories that the robot has never seen
first-hand. We develop a simple approach, which we call Manipulation of
Open-World Objects (MOO), which leverages a pre-trained vision-language model
to extract object-identifying information from the language command and image,
and conditions the robot policy on the current image, the instruction, and the
extracted object information. In a variety of experiments on a real mobile
manipulator, we find that MOO generalizes zero-shot to a wide range of novel
object categories and environments. In addition, we show how MOO generalizes to
other, non-language-based input modalities to specify the object of interest
such as finger pointing, and how it can be further extended to enable
open-world navigation and manipulation. The project's website and evaluation
videos can be found at https://robot-moo.github.io/ | [
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] |
Multi-modal fusion of sensors is a commonly used approach to enhance the
performance of odometry estimation, which is also a fundamental module for
mobile robots. However, the question of \textit{how to perform fusion among
different modalities in a supervised sensor fusion odometry estimation task?}
is still one of challenging issues remains. Some simple operations, such as
element-wise summation and concatenation, are not capable of assigning adaptive
attentional weights to incorporate different modalities efficiently, which make
it difficult to achieve competitive odometry results. Recently, the Transformer
architecture has shown potential for multi-modal fusion tasks, particularly in
the domains of vision with language. In this work, we propose an end-to-end
supervised Transformer-based LiDAR-Inertial fusion framework (namely
TransFusionOdom) for odometry estimation. The multi-attention fusion module
demonstrates different fusion approaches for homogeneous and heterogeneous
modalities to address the overfitting problem that can arise from blindly
increasing the complexity of the model. Additionally, to interpret the learning
process of the Transformer-based multi-modal interactions, a general
visualization approach is introduced to illustrate the interactions between
modalities. Moreover, exhaustive ablation studies evaluate different
multi-modal fusion strategies to verify the performance of the proposed fusion
strategy. A synthetic multi-modal dataset is made public to validate the
generalization ability of the proposed fusion strategy, which also works for
other combinations of different modalities. The quantitative and qualitative
odometry evaluations on the KITTI dataset verify the proposed TransFusionOdom
could achieve superior performance compared with other related works. | [
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The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution. | [
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] |
Multimodal emotion recognition identifies human emotions from various data
modalities like video, text, and audio. However, we found that this task can be
easily affected by noisy information that does not contain useful semantics. To
this end, we present a novel paradigm that attempts to extract noise-resistant
features in its pipeline and introduces a noise-aware learning scheme to
effectively improve the robustness of multimodal emotion understanding. Our new
pipeline, namely Noise-Resistant Multimodal Transformer (NORM-TR), mainly
introduces a Noise-Resistant Generic Feature (NRGF) extractor and a Transformer
for the multimodal emotion recognition task. In particular, we make the NRGF
extractor learn a generic and disturbance-insensitive representation so that
consistent and meaningful semantics can be obtained. Furthermore, we apply a
Transformer to incorporate Multimodal Features (MFs) of multimodal inputs based
on their relations to the NRGF. Therefore, the possible insensitive but useful
information of NRGF could be complemented by MFs that contain more details. To
train the NORM-TR properly, our proposed noise-aware learning scheme
complements normal emotion recognition losses by enhancing the learning against
noises. Our learning scheme explicitly adds noises to either all the modalities
or a specific modality at random locations of a multimodal input sequence. We
correspondingly introduce two adversarial losses to encourage the NRGF
extractor to learn to extract the NRGFs invariant to the added noises, thus
facilitating the NORM-TR to achieve more favorable multimodal emotion
recognition performance. In practice, on several popular multimodal datasets,
our NORM-TR achieves state-of-the-art performance and outperforms existing
methods by a large margin, which demonstrates that the ability to resist noisy
information is important for effective emotion recognition. | [
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] |
Ocular Toxoplasmosis (OT), is a common eye infection caused by T. gondii that
can cause vision problems. Diagnosis is typically done through a clinical
examination and imaging, but these methods can be complicated and costly,
requiring trained personnel. To address this issue, we have created a benchmark
study that evaluates the effectiveness of existing pre-trained networks using
transfer learning techniques to detect OT from fundus images. Furthermore, we
have also analysed the performance of transfer-learning based segmentation
networks to segment lesions in the images. This research seeks to provide a
guide for future researchers looking to utilise DL techniques and develop a
cheap, automated, easy-to-use, and accurate diagnostic method. We have
performed in-depth analysis of different feature extraction techniques in order
to find the most optimal one for OT classification and segmentation of lesions.
For classification tasks, we have evaluated pre-trained models such as VGG16,
MobileNetV2, InceptionV3, ResNet50, and DenseNet121 models. Among them,
MobileNetV2 outperformed all other models in terms of Accuracy (Acc), Recall,
and F1 Score outperforming the second-best model, InceptionV3 by 0.7% higher
Acc. However, DenseNet121 achieved the best result in terms of Precision, which
was 0.1% higher than MobileNetv2. For the segmentation task, this work has
exploited U-Net architecture. In order to utilize transfer learning the encoder
block of the traditional U-Net was replaced by MobileNetV2, InceptionV3,
ResNet34, and VGG16 to evaluate different architectures moreover two different
two different loss functions (Dice loss and Jaccard loss) were exploited in
order to find the most optimal one. The MobileNetV2/U-Net outperformed ResNet34
by 0.5% and 2.1% in terms of Acc and Dice Score, respectively when Jaccard loss
function is employed during the training. | [
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] |
Humans and animals have a rich and flexible understanding of the physical
world, which enables them to infer the underlying dynamical trajectories of
objects and events, plausible future states, and use that to plan and
anticipate the consequences of actions. However, the neural mechanisms
underlying these computations are unclear. We combine a goal-driven modeling
approach with dense neurophysiological data and high-throughput human
behavioral readouts to directly impinge on this question. Specifically, we
construct and evaluate several classes of sensory-cognitive networks to predict
the future state of rich, ethologically-relevant environments, ranging from
self-supervised end-to-end models with pixel-wise or object-centric objectives,
to models that future predict in the latent space of purely static image-based
or dynamic video-based pretrained foundation models. We find strong
differentiation across these model classes in their ability to predict neural
and behavioral data both within and across diverse environments. In particular,
we find that neural responses are currently best predicted by models trained to
predict the future state of their environment in the latent space of pretrained
foundation models optimized for dynamic scenes in a self-supervised manner.
Notably, models that future predict in the latent space of video foundation
models that are optimized to support a diverse range of sensorimotor tasks,
reasonably match both human behavioral error patterns and neural dynamics
across all environmental scenarios that we were able to test. Overall, these
findings suggest that the neural mechanisms and behaviors of primate mental
simulation are thus far most consistent with being optimized to future predict
on dynamic, reusable visual representations that are useful for Embodied AI
more generally. | [
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] |
HomeRobot (noun): An affordable compliant robot that navigates homes and
manipulates a wide range of objects in order to complete everyday tasks.
Open-Vocabulary Mobile Manipulation (OVMM) is the problem of picking any object
in any unseen environment, and placing it in a commanded location. This is a
foundational challenge for robots to be useful assistants in human
environments, because it involves tackling sub-problems from across robotics:
perception, language understanding, navigation, and manipulation are all
essential to OVMM. In addition, integration of the solutions to these
sub-problems poses its own substantial challenges. To drive research in this
area, we introduce the HomeRobot OVMM benchmark, where an agent navigates
household environments to grasp novel objects and place them on target
receptacles. HomeRobot has two components: a simulation component, which uses a
large and diverse curated object set in new, high-quality multi-room home
environments; and a real-world component, providing a software stack for the
low-cost Hello Robot Stretch to encourage replication of real-world experiments
across labs. We implement both reinforcement learning and heuristic
(model-based) baselines and show evidence of sim-to-real transfer. Our
baselines achieve a 20% success rate in the real world; our experiments
identify ways future research work improve performance. See videos on our
website: https://ovmm.github.io/. | [
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] |
Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in
the brain tissues, which have explicit relation to various cerebrovascular
diseases depending on their anatomical location, including cognitive decline,
intracerebral hemorrhage, and cerebral infarction. However, manual detection of
CMBs is a time-consuming and error-prone process because of their sparse and
tiny structural properties. The detection of CMBs is commonly affected by the
presence of many CMB mimics that cause a high false-positive rate (FPR), such
as calcification and pial vessels. This paper proposes a novel 3D deep learning
framework that does not only detect CMBs but also inform their anatomical
location in the brain (i.e., lobar, deep, and infratentorial regions). For the
CMB detection task, we propose a single end-to-end model by leveraging the
U-Net as a backbone with Region Proposal Network (RPN). To significantly reduce
the FPs within the same single model, we develop a new scheme, containing
Feature Fusion Module (FFM) that detects small candidates utilizing contextual
information and Hard Sample Prototype Learning (HSPL) that mines CMB mimics and
generates additional loss term called concentration loss using Convolutional
Prototype Learning (CPL). The anatomical localization task does not only tell
to which region the CMBs belong but also eliminate some FPs from the detection
task by utilizing anatomical information. The results show that the proposed
RPN that utilizes the FFM and HSPL outperforms the vanilla RPN and achieves a
sensitivity of 94.66% vs. 93.33% and an average number of false positives per
subject (FPavg) of 0.86 vs. 14.73. Also, the anatomical localization task
further improves the detection performance by reducing the FPavg to 0.56 while
maintaining the sensitivity of 94.66%. | [
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] |
Recent developments and the beginning market introduction of high-resolution
imaging 4D (3+1D) radar sensors have initialized deep learning-based radar
perception research. We investigate deep learning-based models operating on
radar point clouds for 3D object detection. 3D object detection on lidar point
cloud data is a mature area of 3D vision. Many different architectures have
been proposed, each with strengths and weaknesses. Due to similarities between
3D lidar point clouds and 3+1D radar point clouds, those existing 3D object
detectors are a natural basis to start deep learning-based 3D object detection
on radar data. Thus, the first step is to analyze the detection performance of
the existing models on the new data modality and evaluate them in depth. In
order to apply existing 3D point cloud object detectors developed for lidar
point clouds to the radar domain, they need to be adapted first. While some
detectors, such as PointPillars, have already been adapted to be applicable to
radar data, we have adapted others, e.g., Voxel R-CNN, SECOND, PointRCNN, and
PV-RCNN. To this end, we conduct a cross-model validation (evaluating a set of
models on one particular data set) as well as a cross-data set validation
(evaluating all models in the model set on several data sets). The
high-resolution radar data used are the View-of-Delft and Astyx data sets.
Finally, we evaluate several adaptations of the models and their training
procedures. We also discuss major factors influencing the detection performance
on radar data and propose possible solutions indicating potential future
research avenues. | [
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] |
Quantitative organ assessment is an essential step in automated abdominal
disease diagnosis and treatment planning. Artificial intelligence (AI) has
shown great potential to automatize this process. However, most existing AI
algorithms rely on many expert annotations and lack a comprehensive evaluation
of accuracy and efficiency in real-world multinational settings. To overcome
these limitations, we organized the FLARE 2022 Challenge, the largest abdominal
organ analysis challenge to date, to benchmark fast, low-resource, accurate,
annotation-efficient, and generalized AI algorithms. We constructed an
intercontinental and multinational dataset from more than 50 medical groups,
including Computed Tomography (CT) scans with different races, diseases,
phases, and manufacturers. We independently validated that a set of AI
algorithms achieved a median Dice Similarity Coefficient (DSC) of 90.0\% by
using 50 labeled scans and 2000 unlabeled scans, which can significantly reduce
annotation requirements. The best-performing algorithms successfully
generalized to holdout external validation sets, achieving a median DSC of
89.5\%, 90.9\%, and 88.3\% on North American, European, and Asian cohorts,
respectively. They also enabled automatic extraction of key organ biology
features, which was labor-intensive with traditional manual measurements. This
opens the potential to use unlabeled data to boost performance and alleviate
annotation shortages for modern AI models. | [
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