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The proliferation of zero-day threats (ZDTs) to companies' networks has been
immensely costly and requires novel methods to scan traffic for malicious
behavior at massive scale. The diverse nature of normal behavior along with the
huge landscape of attack types makes deep learning methods an attractive option
for their ability to capture highly-nonlinear behavior patterns. In this paper,
the authors demonstrate an improvement upon a previously introduced
methodology, which used a dual-autoencoder approach to identify ZDTs in network
flow telemetry. In addition to the previously-introduced asset-level graph
features, which help abstractly represent the role of a host in its network,
this new model uses metric learning to train the second autoencoder on labeled
attack data. This not only produces stronger performance, but it has the added
advantage of improving the interpretability of the model by allowing for
multiclass classification in the latent space. This can potentially save human
threat hunters time when they investigate predicted ZDTs by showing them which
known attack classes were nearby in the latent space. The models presented here
are also trained and evaluated with two more datasets, and continue to show
promising results even when generalizing to new network topologies. | [
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] |
Counterfactual reasoning -- envisioning hypothetical scenarios, or possible
worlds, where some circumstances are different from what (f)actually occurred
(counter-to-fact) -- is ubiquitous in human cognition. Conventionally,
counterfactually-altered circumstances have been treated as "small miracles"
that locally violate the laws of nature while sharing the same initial
conditions. In Pearl's structural causal model (SCM) framework this is made
mathematically rigorous via interventions that modify the causal laws while the
values of exogenous variables are shared. In recent years, however, this purely
interventionist account of counterfactuals has increasingly come under scrutiny
from both philosophers and psychologists. Instead, they suggest a backtracking
account of counterfactuals, according to which the causal laws remain unchanged
in the counterfactual world; differences to the factual world are instead
"backtracked" to altered initial conditions (exogenous variables). In the
present work, we explore and formalise this alternative mode of counterfactual
reasoning within the SCM framework. Despite ample evidence that humans
backtrack, the present work constitutes, to the best of our knowledge, the
first general account and algorithmisation of backtracking counterfactuals. We
discuss our backtracking semantics in the context of related literature and
draw connections to recent developments in explainable artificial intelligence
(XAI). | [
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] |
Explainable Artificial Intelligence (XAI) is transforming the field of
Artificial Intelligence (AI) by enhancing the trust of end-users in machines.
As the number of connected devices keeps on growing, the Internet of Things
(IoT) market needs to be trustworthy for the end-users. However, existing
literature still lacks a systematic and comprehensive survey work on the use of
XAI for IoT. To bridge this lacking, in this paper, we address the XAI
frameworks with a focus on their characteristics and support for IoT. We
illustrate the widely-used XAI services for IoT applications, such as security
enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and
Internet of City Things (IoCT). We also suggest the implementation choice of
XAI models over IoT systems in these applications with appropriate examples and
summarize the key inferences for future works. Moreover, we present the
cutting-edge development in edge XAI structures and the support of
sixth-generation (6G) communication services for IoT applications, along with
key inferences. In a nutshell, this paper constitutes the first holistic
compilation on the development of XAI-based frameworks tailored for the demands
of future IoT use cases. | [
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] |
To meet the practical requirements of low latency, low cost, and good privacy
in online intelligent services, more and more deep learning models are
offloaded from the cloud to mobile devices. To further deal with cross-device
data heterogeneity, the offloaded models normally need to be fine-tuned with
each individual user's local samples before being put into real-time inference.
In this work, we focus on the fundamental click-through rate (CTR) prediction
task in recommender systems and study how to effectively and efficiently
perform on-device fine-tuning. We first identify the bottleneck issue that each
individual user's local CTR (i.e., the ratio of positive samples in the local
dataset for fine-tuning) tends to deviate from the global CTR (i.e., the ratio
of positive samples in all the users' mixed datasets on the cloud for training
out the initial model). We further demonstrate that such a CTR drift problem
makes on-device fine-tuning even harmful to item ranking. We thus propose a
novel label correction method, which requires each user only to change the
labels of the local samples ahead of on-device fine-tuning and can well align
the locally prior CTR with the global CTR. The offline evaluation results over
three datasets and five CTR prediction models as well as the online A/B testing
results in Mobile Taobao demonstrate the necessity of label correction in
on-device fine-tuning and also reveal the improvement over cloud-based learning
without fine-tuning. | [
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] |
New findings in natural language processing (NLP) demonstrate that the strong
memorization capability contributes a lot to the success of Large Language
Models (LLM). This inspires us to explicitly bring an independent memory
mechanism into CTR ranking model to learn and memorize cross features'
representations. In this paper, we propose multi-Hash Codebook NETwork (HCNet)
as the memory mechanism for efficiently learning and memorizing representations
of cross features in CTR tasks. HCNet uses a multi-hash codebook as the main
memory place and the whole memory procedure consists of three phases:
multi-hash addressing, memory restoring, and feature shrinking. We also propose
a new CTR model named MemoNet which combines HCNet with a DNN backbone.
Extensive experimental results on three public datasets and online test show
that MemoNet reaches superior performance over state-of-the-art approaches.
Besides, MemoNet shows scaling law of large language model in NLP, which means
we can enlarge the size of the codebook in HCNet to sustainably obtain
performance gains. Our work demonstrates the importance and feasibility of
learning and memorizing representations of cross features, which sheds light on
a new promising research direction. | [
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] |
Prior knowledge about the imaging physics provides a mechanistic forward
operator that plays an important role in image reconstruction, although myriad
sources of possible errors in the operator could negatively impact the
reconstruction solutions. In this work, we propose to embed the traditional
mechanistic forward operator inside a neural function, and focus on modeling
and correcting its unknown errors in an interpretable manner. This is achieved
by a conditional generative model that transforms a given mechanistic operator
with unknown errors, arising from a latent space of self-organizing clusters of
potential sources of error generation. Once learned, the generative model can
be used in place of a fixed forward operator in any traditional
optimization-based reconstruction process where, together with the inverse
solution, the error in prior mechanistic forward operator can be minimized and
the potential source of error uncovered. We apply the presented method to the
reconstruction of heart electrical potential from body surface potential. In
controlled simulation experiments and in-vivo real data experiments, we
demonstrate that the presented method allowed reduction of errors in the
physics-based forward operator and thereby delivered inverse reconstruction of
heart-surface potential with increased accuracy. | [
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] |
A simple gripper can solve more complex manipulation tasks if it can utilize
the external environment such as pushing the object against the table or a
vertical wall, known as "Extrinsic Dexterity." Previous work in extrinsic
dexterity usually has careful assumptions about contacts which impose
restrictions on robot design, robot motions, and the variations of the physical
parameters. In this work, we develop a system based on reinforcement learning
(RL) to address these limitations. We study the task of "Occluded Grasping"
which aims to grasp the object in configurations that are initially occluded;
the robot needs to move the object into a configuration from which these grasps
can be achieved. We present a system with model-free RL that successfully
achieves this task using a simple gripper with extrinsic dexterity. The policy
learns emergent behaviors of pushing the object against the wall to rotate and
then grasp it without additional reward terms on extrinsic dexterity. We
discuss important components of the system including the design of the RL
problem, multi-grasp training and selection, and policy generalization with
automatic curriculum. Most importantly, the policy trained in simulation is
zero-shot transferred to a physical robot. It demonstrates dynamic and
contact-rich motions with a simple gripper that generalizes across objects with
various size, density, surface friction, and shape with a 78% success rate.
Videos can be found at https://sites.google.com/view/grasp-ungraspable/. | [
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] |
Increasingly, malwares are becoming complex and they are spreading on
networks targeting different infrastructures and personal-end devices to
collect, modify, and destroy victim information. Malware behaviors are
polymorphic, metamorphic, persistent, able to hide to bypass detectors and
adapt to new environments, and even leverage machine learning techniques to
better damage targets. Thus, it makes them difficult to analyze and detect with
traditional endpoint detection and response, intrusion detection and prevention
systems. To defend against malwares, recent work has proposed different
techniques based on signatures and machine learning. In this paper, we propose
to use an algebraic topological approach called topological-based data analysis
(TDA) to efficiently analyze and detect complex malware patterns. Next, we
compare the different TDA techniques (i.e., persistence homology, tomato, TDA
Mapper) and existing techniques (i.e., PCA, UMAP, t-SNE) using different
classifiers including random forest, decision tree, xgboost, and lightgbm. We
also propose some recommendations to deploy the best-identified models for
malware detection at scale. Results show that TDA Mapper (combined with PCA) is
better for clustering and for identifying hidden relationships between malware
clusters compared to PCA. Persistent diagrams are better to identify
overlapping malware clusters with low execution time compared to UMAP and
t-SNE. For malware detection, malware analysts can use Random Forest and
Decision Tree with t-SNE and Persistent Diagram to achieve better performance
and robustness on noised data. | [
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] |
We present a learning-enabled Task and Motion Planning (TAMP) algorithm for
solving mobile manipulation problems in environments with many articulated and
movable obstacles. Our idea is to bias the search procedure of a traditional
TAMP planner with a learned plan feasibility predictor. The core of our
algorithm is PIGINet, a novel Transformer-based learning method that takes in a
task plan, the goal, and the initial state, and predicts the probability of
finding motion trajectories associated with the task plan. We integrate PIGINet
within a TAMP planner that generates a diverse set of high-level task plans,
sorts them by their predicted likelihood of feasibility, and refines them in
that order. We evaluate the runtime of our TAMP algorithm on seven families of
kitchen rearrangement problems, comparing its performance to that of
non-learning baselines. Our experiments show that PIGINet substantially
improves planning efficiency, cutting down runtime by 80% on problems with
small state spaces and 10%-50% on larger ones, after being trained on only
150-600 problems. Finally, it also achieves zero-shot generalization to
problems with unseen object categories thanks to its visual encoding of
objects. Project page https://piginet.github.io/. | [
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] |
Location based services, already popular with end users, are now inevitably
becoming part of new wireless infrastructures and emerging business processes.
The increasingly popular Deep Learning (DL) artificial intelligence methods
perform very well in wireless fingerprinting localization based on extensive
indoor radio measurement data. However, with the increasing complexity these
methods become computationally very intensive and energy hungry, both for their
training and subsequent operation. Considering only mobile users, estimated to
exceed 7.4 billion by the end of 2025, and assuming that the networks serving
these users will need to perform only one localization per user per hour on
average, the machine learning models used for the calculation would need to
perform $65 \times 10^{12}$ predictions per year. Add to this equation tens of
billions of other connected devices and applications that rely heavily on more
frequent location updates, and it becomes apparent that localization will
contribute significantly to carbon emissions unless more energy-efficient
models are developed and used. In this Chapter, we discuss the latest results
and trends in wireless localization and look at paths towards achieving more
sustainable AI. We then elaborate on a methodology for computing DL model
complexity, energy consumption and carbon footprint and show on a concrete
example how to develop a more resource-aware model for fingerprinting. We
finally compare relevant works in terms of complexity and training CO$_2$
footprint. | [
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] |
New emerging technologies powered by Artificial Intelligence (AI) have the
potential to disruptively transform our societies for the better. In
particular, data-driven learning approaches (i.e., Machine Learning (ML)) have
been a true revolution in the advancement of multiple technologies in various
application domains. But at the same time there is growing concern about
certain intrinsic characteristics of these methodologies that carry potential
risks to both safety and fundamental rights. Although there are mechanisms in
the adoption process to minimize these risks (e.g., safety regulations), these
do not exclude the possibility of harm occurring, and if this happens, victims
should be able to seek compensation. Liability regimes will therefore play a
key role in ensuring basic protection for victims using or interacting with
these systems. However, the same characteristics that make AI systems
inherently risky, such as lack of causality, opacity, unpredictability or their
self and continuous learning capabilities, may lead to considerable
difficulties when it comes to proving causation. This paper presents three case
studies, as well as the methodology to reach them, that illustrate these
difficulties. Specifically, we address the cases of cleaning robots, delivery
drones and robots in education. The outcome of the proposed analysis suggests
the need to revise liability regimes to alleviate the burden of proof on
victims in cases involving AI technologies. | [
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] |
Molecular property prediction is essential for drug discovery. In recent
years, deep learning methods have been introduced to this area and achieved
state-of-the-art performances. However, most of existing methods ignore the
intrinsic relations between molecular properties which can be utilized to
improve the performances of corresponding prediction tasks. In this paper, we
propose a new approach, namely Physics properties Enhanced Molecular Property
prediction (PEMP), to utilize relations between molecular properties revealed
by previous physics theory and physical chemistry studies. Specifically, we
enhance the training of the chemical and physiological property predictors with
related physics property prediction tasks. We design two different methods for
PEMP, respectively based on multi-task learning and transfer learning. Both
methods include a model-agnostic molecule representation module and a property
prediction module. In our implementation, we adopt both the state-of-the-art
molecule embedding models under the supervised learning paradigm and the
pretraining paradigm as the molecule representation module of PEMP,
respectively. Experimental results on public benchmark MoleculeNet show that
the proposed methods have the ability to outperform corresponding
state-of-the-art models. | [
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In peer review systems, reviewers are often asked to evaluate various
features of submissions, such as technical quality or novelty. A score is given
to each of the predefined features and based on these the reviewer has to
provide an overall quantitative recommendation. It may be assumed that each
reviewer has her own mapping from the set of features to a recommendation, and
that different reviewers have different mappings in mind. This introduces an
element of arbitrariness known as commensuration bias. In this paper we discuss
a framework, introduced by Noothigattu, Shah and Procaccia, and then applied by
the organizers of the AAAI 2022 conference. Noothigattu, Shah and Procaccia
proposed to aggregate reviewer's mapping by minimizing certain loss functions,
and studied axiomatic properties of this approach, in the sense of social
choice theory. We challenge several of the results and assumptions used in
their work and report a number of negative results. On the one hand, we study a
trade-off between some of the axioms proposed and the ability of the method to
properly capture agreements of the majority of reviewers. On the other hand, we
show that dropping a certain unrealistic assumption has dramatic effects,
including causing the method to be discontinuous. | [
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] |
Skill-based reinforcement learning (RL) has emerged as a promising strategy
to leverage prior knowledge for accelerated robot learning. Skills are
typically extracted from expert demonstrations and are embedded into a latent
space from which they can be sampled as actions by a high-level RL agent.
However, this skill space is expansive, and not all skills are relevant for a
given robot state, making exploration difficult. Furthermore, the downstream RL
agent is limited to learning structurally similar tasks to those used to
construct the skill space. We firstly propose accelerating exploration in the
skill space using state-conditioned generative models to directly bias the
high-level agent towards only sampling skills relevant to a given state based
on prior experience. Next, we propose a low-level residual policy for
fine-grained skill adaptation enabling downstream RL agents to adapt to unseen
task variations. Finally, we validate our approach across four challenging
manipulation tasks that differ from those used to build the skill space,
demonstrating our ability to learn across task variations while significantly
accelerating exploration, outperforming prior works. Code and videos are
available on our project website: https://krishanrana.github.io/reskill. | [
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] |
Resource-disaggregated data centre architectures promise a means of pooling
resources remotely within data centres, allowing for both more flexibility and
resource efficiency underlying the increasingly important
infrastructure-as-a-service business. This can be accomplished by means of
using an optically circuit switched backbone in the data centre network (DCN);
providing the required bandwidth and latency guarantees to ensure reliable
performance when applications are run across non-local resource pools. However,
resource allocation in this scenario requires both server-level \emph{and}
network-level resource to be co-allocated to requests. The online nature and
underlying combinatorial complexity of this problem, alongside the typical
scale of DCN topologies, makes exact solutions impossible and heuristic based
solutions sub-optimal or non-intuitive to design. We demonstrate that
\emph{deep reinforcement learning}, where the policy is modelled by a
\emph{graph neural network} can be used to learn effective \emph{network-aware}
and \emph{topologically-scalable} allocation policies end-to-end. Compared to
state-of-the-art heuristics for network-aware resource allocation, the method
achieves up to $20\%$ higher acceptance ratio; can achieve the same acceptance
ratio as the best performing heuristic with $3\times$ less networking resources
available and can maintain all-around performance when directly applied (with
no further training) to DCN topologies with $10^2\times$ more servers than the
topologies seen during training. | [
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] |
Emotion recognition using EEG signals is an emerging area of research due to
its broad applicability in BCI. Emotional feelings are hard to stimulate in the
lab. Emotions do not last long, yet they need enough context to be perceived
and felt. However, most EEG-related emotion databases either suffer from
emotionally irrelevant details (due to prolonged duration stimulus) or have
minimal context doubting the feeling of any emotion using the stimulus. We
tried to reduce the impact of this trade-off by designing an experiment in
which participants are free to report their emotional feelings simultaneously
watching the emotional stimulus. We called these reported emotional feelings
"Emotional Events" in our Dataset on Emotion with Naturalistic Stimuli (DENS).
We used EEG signals to classify emotional events on different combinations of
Valence(V) and Arousal(A) dimensions and compared the results with benchmark
datasets of DEAP and SEED. STFT is used for feature extraction and used in the
classification model consisting of CNN-LSTM hybrid layers. We achieved
significantly higher accuracy with our data compared to DEEP and SEED data. We
conclude that having precise information about emotional feelings improves the
classification accuracy compared to long-duration EEG signals which might be
contaminated by mind-wandering. | [
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] |
The motor imagery (MI) classification has been a prominent research topic in
brain-computer interfaces based on electroencephalography (EEG). Over the past
few decades, the performance of MI-EEG classifiers has seen gradual
enhancement. In this study, we amplify the geometric deep learning-based MI-EEG
classifiers from the perspective of time-frequency analysis, introducing a new
architecture called Graph-CSPNet. We refer to this category of classifiers as
Geometric Classifiers, highlighting their foundation in differential geometry
stemming from EEG spatial covariance matrices. Graph-CSPNet utilizes novel
manifold-valued graph convolutional techniques to capture the EEG features in
the time-frequency domain, offering heightened flexibility in signal
segmentation for capturing localized fluctuations. To evaluate the
effectiveness of Graph-CSPNet, we employ five commonly-used publicly available
MI-EEG datasets, achieving near-optimal classification accuracies in nine out
of eleven scenarios. The Python repository can be found at
https://github.com/GeometricBCI/Tensor-CSPNet-and-Graph-CSPNet. | [
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Recent advances in deep learning have led to the development of models
approaching the human level of accuracy. However, healthcare remains an area
lacking in widespread adoption. The safety-critical nature of healthcare
results in a natural reticence to put these black-box deep learning models into
practice. This paper explores interpretable methods for a clinical decision
support system called sleep staging, an essential step in diagnosing sleep
disorders. Clinical sleep staging is an arduous process requiring manual
annotation for each 30s of sleep using physiological signals such as
electroencephalogram (EEG). Recent work has shown that sleep staging using
simple models and an exhaustive set of features can perform nearly as well as
deep learning approaches but only for some specific datasets. Moreover, the
utility of those features from a clinical standpoint is ambiguous. On the other
hand, the proposed framework, NormIntSleep demonstrates exceptional performance
across different datasets by representing deep learning embeddings using
normalized features. NormIntSleep performs 4.5% better than the exhaustive
feature-based approach and 1.5% better than other representation learning
approaches. An empirical comparison between the utility of the interpretations
of these models highlights the improved alignment with clinical expectations
when performance is traded-off slightly. NormIntSleep paired with a clinically
meaningful set of features can best balance this trade-off by providing
reliable, clinically relevant interpretation with robust performance. | [
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] |
This paper studies reinforcement learning (RL) in doubly inhomogeneous
environments under temporal non-stationarity and subject heterogeneity. In a
number of applications, it is commonplace to encounter datasets generated by
system dynamics that may change over time and population, challenging
high-quality sequential decision making. Nonetheless, most existing RL
solutions require either temporal stationarity or subject homogeneity, which
would result in sub-optimal policies if both assumptions were violated. To
address both challenges simultaneously, we propose an original algorithm to
determine the ``best data chunks" that display similar dynamics over time and
across individuals for policy learning, which alternates between most recent
change point detection and cluster identification. Our method is general, and
works with a wide range of clustering and change point detection algorithms. It
is multiply robust in the sense that it takes multiple initial estimators as
input and only requires one of them to be consistent. Moreover, by borrowing
information over time and population, it allows us to detect weaker signals and
has better convergence properties when compared to applying the clustering
algorithm per time or the change point detection algorithm per subject.
Empirically, we demonstrate the usefulness of our method through extensive
simulations and a real data application. | [
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] |
Recently, many neural network-based image compression methods have shown
promising results superior to the existing tool-based conventional codecs.
However, most of them are often trained as separate models for different target
bit rates, thus increasing the model complexity. Therefore, several studies
have been conducted for learned compression that supports variable rates with
single models, but they require additional network modules, layers, or inputs
that often lead to complexity overhead, or do not provide sufficient coding
efficiency. In this paper, we firstly propose a selective compression method
that partially encodes the latent representations in a fully generalized manner
for deep learning-based variable-rate image compression. The proposed method
adaptively determines essential representation elements for compression of
different target quality levels. For this, we first generate a 3D importance
map as the nature of input content to represent the underlying importance of
the representation elements. The 3D importance map is then adjusted for
different target quality levels using importance adjustment curves. The
adjusted 3D importance map is finally converted into a 3D binary mask to
determine the essential representation elements for compression. The proposed
method can be easily integrated with the existing compression models with a
negligible amount of overhead increase. Our method can also enable continuously
variable-rate compression via simple interpolation of the importance adjustment
curves among different quality levels. The extensive experimental results show
that the proposed method can achieve comparable compression efficiency as those
of the separately trained reference compression models and can reduce decoding
time owing to the selective compression. The sample codes are publicly
available at https://github.com/JooyoungLeeETRI/SCR. | [
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] |
Health sensing for chronic disease management creates immense benefits for
social welfare. Existing health sensing studies primarily focus on the
prediction of physical chronic diseases. Depression, a widespread complication
of chronic diseases, is however understudied. We draw on the medical literature
to support depression prediction using motion sensor data. To connect human
expertise in the decision-making, safeguard trust for this high-stake
prediction, and ensure algorithm transparency, we develop an interpretable deep
learning model: Temporal Prototype Network (TempPNet). TempPNet is built upon
the emergent prototype learning models. To accommodate the temporal
characteristic of sensor data and the progressive property of depression,
TempPNet differs from existing prototype learning models in its capability of
capturing the temporal progression of depression. Extensive empirical analyses
using real-world motion sensor data show that TempPNet outperforms
state-of-the-art benchmarks in depression prediction. Moreover, TempPNet
interprets its predictions by visualizing the temporal progression of
depression and its corresponding symptoms detected from sensor data. We further
conduct a user study to demonstrate its superiority over the benchmarks in
interpretability. This study offers an algorithmic solution for impactful
social good - collaborative care of chronic diseases and depression in health
sensing. Methodologically, it contributes to extant literature with a novel
interpretable deep learning model for depression prediction from sensor data.
Patients, doctors, and caregivers can deploy our model on mobile devices to
monitor patients' depression risks in real-time. Our model's interpretability
also allows human experts to participate in the decision-making by reviewing
the interpretation of prediction outcomes and making informed interventions. | [
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Biomedical knowledge graphs (KG) are heterogenous networks consisting of
biological entities as nodes and relations between them as edges. These
entities and relations are extracted from millions of research papers and
unified in a single resource. The goal of biomedical multi-hop
question-answering over knowledge graph (KGQA) is to help biologist and
scientist to get valuable insights by asking questions in natural language.
Relevant answers can be found by first understanding the question and then
querying the KG for right set of nodes and relationships to arrive at an
answer. To model the question, language models such as RoBERTa and BioBERT are
used to understand context from natural language question. One of the
challenges in KGQA is missing links in the KG. Knowledge graph embeddings (KGE)
help to overcome this problem by encoding nodes and edges in a dense and more
efficient way. In this paper, we use a publicly available KG called Hetionet
which is an integrative network of biomedical knowledge assembled from 29
different databases of genes, compounds, diseases, and more. We have enriched
this KG dataset by creating a multi-hop biomedical question-answering dataset
in natural language for testing the biomedical multi-hop question-answering
system and this dataset will be made available to the research community. The
major contribution of this research is an integrated system that combines
language models with KG embeddings to give highly relevant answers to free-form
questions asked by biologists in an intuitive interface. Biomedical multi-hop
question-answering system is tested on this data and results are highly
encouraging. | [
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] |
In September 2016, Stanford's "One Hundred Year Study on Artificial
Intelligence" project (AI100) issued the first report of its planned long-term
periodic assessment of artificial intelligence (AI) and its impact on society.
It was written by a panel of 17 study authors, each of whom is deeply rooted in
AI research, chaired by Peter Stone of the University of Texas at Austin. The
report, entitled "Artificial Intelligence and Life in 2030," examines eight
domains of typical urban settings on which AI is likely to have impact over the
coming years: transportation, home and service robots, healthcare, education,
public safety and security, low-resource communities, employment and workplace,
and entertainment. It aims to provide the general public with a scientifically
and technologically accurate portrayal of the current state of AI and its
potential and to help guide decisions in industry and governments, as well as
to inform research and development in the field. The charge for this report was
given to the panel by the AI100 Standing Committee, chaired by Barbara Grosz of
Harvard University. | [
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] |
Given a natural language instruction and an input scene, our goal is to train
a model to output a manipulation program that can be executed by the robot.
Prior approaches for this task possess one of the following limitations: (i)
rely on hand-coded symbols for concepts limiting generalization beyond those
seen during training [1] (ii) infer action sequences from instructions but
require dense sub-goal supervision [2] or (iii) lack semantics required for
deeper object-centric reasoning inherent in interpreting complex instructions
[3]. In contrast, our approach can handle linguistic as well as perceptual
variations, end-to-end trainable and requires no intermediate supervision. The
proposed model uses symbolic reasoning constructs that operate on a latent
neural object-centric representation, allowing for deeper reasoning over the
input scene. Central to our approach is a modular structure consisting of a
hierarchical instruction parser and an action simulator to learn disentangled
action representations. Our experiments on a simulated environment with a 7-DOF
manipulator, consisting of instructions with varying number of steps and scenes
with different number of objects, demonstrate that our model is robust to such
variations and significantly outperforms baselines, particularly in the
generalization settings. The code, dataset and experiment videos are available
at https://nsrmp.github.io | [
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] |
Federated learning (FL) allows multiple participants to collaboratively build
deep learning (DL) models without directly sharing data. Consequently, the
issue of copyright protection in FL becomes important since unreliable
participants may gain access to the jointly trained model. Application of
homomorphic encryption (HE) in secure FL framework prevents the central server
from accessing plaintext models. Thus, it is no longer feasible to embed the
watermark at the central server using existing watermarking schemes. In this
paper, we propose a novel client-side FL watermarking scheme to tackle the
copyright protection issue in secure FL with HE. To our best knowledge, it is
the first scheme to embed the watermark to models under the Secure FL
environment. We design a black-box watermarking scheme based on client-side
backdooring to embed a pre-designed trigger set into an FL model by a
gradient-enhanced embedding method. Additionally, we propose a trigger set
construction mechanism to ensure the watermark cannot be forged. Experimental
results demonstrate that our proposed scheme delivers outstanding protection
performance and robustness against various watermark removal attacks and
ambiguity attack. | [
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] |
Federated learning (FL) is a distributed machine learning paradigm allowing
multiple clients to collaboratively train a global model without sharing their
local data. However, FL entails exposing the model to various participants.
This poses a risk of unauthorized model distribution or resale by the malicious
client, compromising the intellectual property rights of the FL group. To deter
such misbehavior, it is essential to establish a mechanism for verifying the
ownership of the model and as well tracing its origin to the leaker among the
FL participants. In this paper, we present FedTracker, the first FL model
protection framework that provides both ownership verification and
traceability. FedTracker adopts a bi-level protection scheme consisting of
global watermark mechanism and local fingerprint mechanism. The former
authenticates the ownership of the global model, while the latter identifies
which client the model is derived from. FedTracker leverages Continual Learning
(CL) principles to embedding the watermark in a way that preserves the utility
of the FL model on both primitive task and watermark task. FedTracker also
devises a novel metric to better discriminate different fingerprints.
Experimental results show FedTracker is effective in ownership verification,
traceability, and maintains good fidelity and robustness against various
watermark removal attacks. | [
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The white-matter (micro-)structural architecture of the brain promotes
synchrony among neuronal populations, giving rise to richly patterned
functional connections. A fundamental problem for systems neuroscience is
determining the best way to relate structural and functional networks
quantified by diffusion tensor imaging and resting-state functional MRI. As one
of the state-of-the-art approaches for network analysis, graph convolutional
networks (GCN) have been separately used to analyze functional and structural
networks, but have not been applied to explore inter-network relationships. In
this work, we propose to couple the two networks of an individual by adding
inter-network edges between corresponding brain regions, so that the joint
structure-function graph can be directly analyzed by a single GCN. The weights
of inter-network edges are learnable, reflecting non-uniform structure-function
coupling strength across the brain. We apply our Joint-GCN to predict age and
sex of 662 participants from the public dataset of the National Consortium on
Alcohol and Neurodevelopment in Adolescence (NCANDA) based on their functional
and micro-structural white-matter networks. Our results support that the
proposed Joint-GCN outperforms existing multi-modal graph learning approaches
for analyzing structural and functional networks. | [
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] |
Advances in deep neural network (DNN) architectures have enabled new
prediction techniques for stock market data. Unlike other multivariate
time-series data, stock markets show two unique characteristics: (i)
\emph{multi-order dynamics}, as stock prices are affected by strong
non-pairwise correlations (e.g., within the same industry); and (ii)
\emph{internal dynamics}, as each individual stock shows some particular
behaviour. Recent DNN-based methods capture multi-order dynamics using
hypergraphs, but rely on the Fourier basis in the convolution, which is both
inefficient and ineffective. In addition, they largely ignore internal dynamics
by adopting the same model for each stock, which implies a severe information
loss.
In this paper, we propose a framework for stock movement prediction to
overcome the above issues. Specifically, the framework includes temporal
generative filters that implement a memory-based mechanism onto an LSTM network
in an attempt to learn individual patterns per stock. Moreover, we employ
hypergraph attentions to capture the non-pairwise correlations. Here, using the
wavelet basis instead of the Fourier basis, enables us to simplify the message
passing and focus on the localized convolution. Experiments with US market data
over six years show that our framework outperforms state-of-the-art methods in
terms of profit and stability. Our source code and data are available at
\url{https://github.com/thanhtrunghuynh93/estimate}. | [
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] |
Pseudo-label (PL) filtering forms a crucial part of Self-Training (ST)
methods for unsupervised domain adaptation. Dropout-based Uncertainty-driven
Self-Training (DUST) proceeds by first training a teacher model on source
domain labeled data. Then, the teacher model is used to provide PLs for the
unlabeled target domain data. Finally, we train a student on augmented labeled
and pseudo-labeled data. The process is iterative, where the student becomes
the teacher for the next DUST iteration. A crucial step that precedes the
student model training in each DUST iteration is filtering out noisy PLs that
could lead the student model astray. In DUST, we proposed a simple, effective,
and theoretically sound PL filtering strategy based on the teacher model's
uncertainty about its predictions on unlabeled speech utterances. We estimate
the model's uncertainty by computing disagreement amongst multiple samples
drawn from the teacher model during inference by injecting noise via dropout.
In this work, we show that DUST's PL filtering, as initially used, may fail
under severe source and target domain mismatch. We suggest several approaches
to eliminate or alleviate this issue. Further, we bring insights from the
research in neural network model calibration to DUST and show that a
well-calibrated model correlates strongly with a positive outcome of the DUST
PL filtering step. | [
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Training novice users to operate an excavator for learning different skills
requires the presence of expert teachers. Considering the complexity of the
problem, it is comparatively expensive to find skilled experts as the process
is time-consuming and requires precise focus. Moreover, since humans tend to be
biased, the evaluation process is noisy and will lead to high variance in the
final score of different operators with similar skills. In this work, we
address these issues and propose a novel strategy for the automatic evaluation
of excavator operators. We take into account the internal dynamics of the
excavator and the safety criterion at every time step to evaluate the
performance. To further validate our approach, we use this score prediction
model as a source of reward for a reinforcement learning agent to learn the
task of maneuvering an excavator in a simulated environment that closely
replicates the real-world dynamics. For a policy learned using these external
reward prediction models, our results demonstrate safer solutions following the
required dynamic constraints when compared to policy trained with task-based
reward functions only, making it one step closer to real-life adoption. For
future research, we release our codebase at
https://github.com/pranavAL/InvRL_Auto-Evaluate and video results
https://drive.google.com/file/d/1jR1otOAu8zrY8mkhUOUZW9jkBOAKK71Z/view?usp=share_link . | [
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] |
Sampling-based methods are widely adopted solutions for robot motion
planning. The methods are straightforward to implement, effective in practice
for many robotic systems. It is often possible to prove that they have
desirable properties, such as probabilistic completeness and asymptotic
optimality. Nevertheless, they still face challenges as the complexity of the
underlying planning problem increases, especially under tight computation time
constraints, which impact the quality of returned solutions or given inaccurate
models. This has motivated machine learning to improve the computational
efficiency and applicability of Sampling-Based Motion Planners (SBMPs). This
survey reviews such integrative efforts and aims to provide a classification of
the alternative directions that have been explored in the literature. It first
discusses how learning has been used to enhance key components of SBMPs, such
as node sampling, collision detection, distance or nearest neighbor
computation, local planning, and termination conditions. Then, it highlights
planners that use learning to adaptively select between different
implementations of such primitives in response to the underlying problem's
features. It also covers emerging methods, which build complete machine
learning pipelines that reflect the traditional structure of SBMPs. It also
discusses how machine learning has been used to provide data-driven models of
robots, which can then be used by a SBMP. Finally, it provides a comparative
discussion of the advantages and disadvantages of the approaches covered, and
insights on possible future directions of research. An online version of this
survey can be found at: https://prx-kinodynamic.github.io/ | [
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] |
With the rapid growth of computing powers and recent advances in deep
learning, we have witnessed impressive demonstrations of novel robot
capabilities in research settings. Nonetheless, these learning systems exhibit
brittle generalization and require excessive training data for practical tasks.
To harness the capabilities of state-of-the-art robot learning models while
embracing their imperfections, we present Sirius, a principled framework for
humans and robots to collaborate through a division of work. In this framework,
partially autonomous robots are tasked with handling a major portion of
decision-making where they work reliably; meanwhile, human operators monitor
the process and intervene in challenging situations. Such a human-robot team
ensures safe deployments in complex tasks. Further, we introduce a new learning
algorithm to improve the policy's performance on the data collected from the
task executions. The core idea is re-weighing training samples with
approximated human trust and optimizing the policies with weighted behavioral
cloning. We evaluate Sirius in simulation and on real hardware, showing that
Sirius consistently outperforms baselines over a collection of contact-rich
manipulation tasks, achieving an 8% boost in simulation and 27% on real
hardware than the state-of-the-art methods in policy success rate, with twice
faster convergence and 85% memory size reduction. Videos and more details are
available at https://ut-austin-rpl.github.io/sirius/ | [
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An understanding of how Social Overhead Capital (SOC) is associated with the
land value of the local community is important for effective urban planning.
However, even within a district, there are multiple sections used for different
purposes; the term for this is spatial heterogeneity. The spatial heterogeneity
issue has to be considered when attempting to comprehend land prices. If there
is spatial heterogeneity within a district, land prices can be managed by
adopting the spatial clustering method. In this study, spatial attributes
including SOC, socio-demographic features, and spatial information in a
specific district are analyzed with Finite Mixture Modeling (FMM) in order to
find (a) the optimal number of clusters and (b) the association among SOCs,
socio-demographic features, and land prices. FMM is a tool used to find
clusters and the attributes' coefficients simultaneously. Using the FMM method,
the results show that four clusters exist in one district and the four clusters
have different associations among SOCs, demographic features, and land prices.
Policymakers and managerial administration need to look for information to make
policy about land prices. The current study finds the consideration of
closeness to SOC to be a significant factor on land prices and suggests the
potential policy direction related to SOC. | [
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] |
Building on the recent trend of new deep generative models known as
Normalizing Flows (NF), simulation-based inference (SBI) algorithms can now
efficiently accommodate arbitrary complex and high-dimensional data
distributions. The development of appropriate validation methods however has
fallen behind. Indeed, most of the existing metrics either require access to
the true posterior distribution, or fail to provide theoretical guarantees on
the consistency of the inferred approximation beyond the one-dimensional
setting. This work proposes easy to interpret validation diagnostics for
multi-dimensional conditional (posterior) density estimators based on NF. It
also offers theoretical guarantees based on results of local consistency. The
proposed workflow can be used to check, analyse and guarantee consistent
behavior of the estimator. The method is illustrated with a challenging example
that involves tightly coupled parameters in the context of computational
neuroscience. This work should help the design of better specified models or
drive the development of novel SBI-algorithms, hence allowing to build up trust
on their ability to address important questions in experimental science. | [
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] |
Multiple-input multiple-output and orthogonal frequency-division multiplexing
(MIMO-OFDM) are the key technologies in 4G and subsequent wireless
communication systems. Conventionally, the MIMO-OFDM receiver is performed by
multiple cascaded blocks with different functions and the algorithm in each
block is designed based on ideal assumptions of wireless channel distributions.
However, these assumptions may fail in practical complex wireless environments.
The deep learning (DL) method has the ability to capture key features from
complex and huge data. In this paper, a novel end-to-end MIMO-OFDM receiver
framework based on \textit{transformer}, named SigT, is proposed. By regarding
the signal received from each antenna as a token of the transformer, the
spatial correlation of different antennas can be learned and the critical
zero-shot problem can be mitigated. Furthermore, the proposed SigT framework
can work well without the inserted pilots, which improves the useful data
transmission efficiency. Experiment results show that SigT achieves much higher
performance in terms of signal recovery accuracy than benchmark methods, even
in a low SNR environment or with a small number of training samples. Code is
available at https://github.com/SigTransformer/SigT. | [
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Traditional recommender systems are typically passive in that they try to
adapt their recommendations to the user's historical interests. However, it is
highly desirable for commercial applications, such as e-commerce, advertisement
placement, and news portals, to be able to expand the users' interests so that
they would accept items that they were not originally aware of or interested in
to increase customer interactions. In this paper, we present Influential
Recommender System (IRS), a new recommendation paradigm that aims to
proactively lead a user to like a given objective item by progressively
recommending to the user a sequence of carefully selected items (called an
influence path). We propose the Influential Recommender Network (IRN), which is
a Transformer-based sequential model to encode the items' sequential
dependencies. Since different people react to external influences differently,
we introduce the Personalized Impressionability Mask (PIM) to model how
receptive a user is to external influence to generate the most effective
influence path for the user. To evaluate IRN, we design several performance
metrics to measure whether or not the influence path can smoothly expand the
user interest to include the objective item while maintaining the user's
satisfaction with the recommendation. Experimental results show that IRN
significantly outperforms the baseline recommenders and demonstrates its
capability of influencing users' interests. | [
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] |
Malwares are the key means leveraged by threat actors in the cyber space for
their attacks. There is a large array of commercial solutions in the market and
significant scientific research to tackle the challenge of the detection and
defense against malwares. At the same time, attackers also advance their
capabilities in creating polymorphic and metamorphic malwares to make it
increasingly challenging for existing solutions. To tackle this issue, we
propose a methodology to perform malware detection and family attribution. The
proposed methodology first performs the extraction of opcodes from malwares in
each family and constructs their respective opcode graphs. We explore the use
of clustering algorithms on the opcode graphs to detect clusters of malwares
within the same malware family. Such clusters can be seen as belonging to
different sub-family groups. Opcode graph signatures are built from each
detected cluster. Hence, for each malware family, a group of signatures is
generated to represent the family. These signatures are used to classify an
unknown sample as benign or belonging to one the malware families. We evaluate
our methodology by performing experiments on a dataset consisting of both
benign files and malware samples belonging to a number of different malware
families and comparing the results to existing approach. | [
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] |
Internet of Things (IoT) has become a popular paradigm to fulfil needs of the
industry such as asset tracking, resource monitoring and automation. As
security mechanisms are often neglected during the deployment of IoT devices,
they are more easily attacked by complicated and large volume intrusion attacks
using advanced techniques. Artificial Intelligence (AI) has been used by the
cyber security community in the past decade to automatically identify such
attacks. However, deep learning methods have yet to be extensively explored for
Intrusion Detection Systems (IDS) specifically for IoT. Most recent works are
based on time sequential models like LSTM and there is short of research in
CNNs as they are not naturally suited for this problem. In this article, we
propose a novel solution to the intrusion attacks against IoT devices using
CNNs. The data is encoded as the convolutional operations to capture the
patterns from the sensors data along time that are useful for attacks detection
by CNNs. The proposed method is integrated with two classical CNNs: ResNet and
EfficientNet, where the detection performance is evaluated. The experimental
results show significant improvement in both true positive rate and false
positive rate compared to the baseline using LSTM. | [
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] |
Despite the rapid improvement of autonomous driving technology in recent
years, automotive manufacturers must resolve liability issues to commercialize
autonomous passenger car of SAE J3016 Level 3 or higher. To cope with the
product liability law, manufacturers develop autonomous driving systems in
compliance with international standards for safety such as ISO 26262 and ISO
21448. Concerning the safety of the intended functionality (SOTIF) requirement
in ISO 26262, the driving policy recommends providing an explicit rational
basis for maneuver decisions. In this case, mathematical models such as Safety
Force Field (SFF) and Responsibility-Sensitive Safety (RSS) which have
interpretability on decision, may be suitable. In this work, we implement SFF
from scratch to substitute the undisclosed NVIDIA's source code and integrate
it with CARLA open-source simulator. Using SFF and CARLA, we present a
predictor for claimed sets of vehicles, and based on the predictor, propose an
integrated driving policy that consistently operates regardless of safety
conditions it encounters while passing through dynamic traffic. The policy does
not have a separate plan for each condition, but using safety potential, it
aims human-like driving blended in with traffic flow. | [
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] |
The active inference framework (AIF) is a promising new computational
framework grounded in contemporary neuroscience that can produce human-like
behavior through reward-based learning. In this study, we test the ability for
the AIF to capture the role of anticipation in the visual guidance of action in
humans through the systematic investigation of a visual-motor task that has
been well-explored -- that of intercepting a target moving over a ground plane.
Previous research demonstrated that humans performing this task resorted to
anticipatory changes in speed intended to compensate for semi-predictable
changes in target speed later in the approach. To capture this behavior, our
proposed "neural" AIF agent uses artificial neural networks to select actions
on the basis of a very short term prediction of the information about the task
environment that these actions would reveal along with a long-term estimate of
the resulting cumulative expected free energy. Systematic variation revealed
that anticipatory behavior emerged only when required by limitations on the
agent's movement capabilities, and only when the agent was able to estimate
accumulated free energy over sufficiently long durations into the future. In
addition, we present a novel formulation of the prior function that maps a
multi-dimensional world-state to a uni-dimensional distribution of free-energy.
Together, these results demonstrate the use of AIF as a plausible model of
anticipatory visually guided behavior in humans. | [
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] |
Optimal Interpolation (OI) is a widely used, highly trusted algorithm for
interpolation and reconstruction problems in geosciences. With the influx of
more satellite missions, we have access to more and more observations and it is
becoming more pertinent to take advantage of these observations in applications
such as forecasting and reanalysis. With the increase in the volume of
available data, scalability remains an issue for standard OI and it prevents
many practitioners from effectively and efficiently taking advantage of these
large sums of data to learn the model hyperparameters. In this work, we
leverage recent advances in Neural Fields (NerFs) as an alternative to the OI
framework where we show how they can be easily applied to standard
reconstruction problems in physical oceanography. We illustrate the relevance
of NerFs for gap-filling of sparse measurements of sea surface height (SSH) via
satellite altimetry and demonstrate how NerFs are scalable with comparable
results to the standard OI. We find that NerFs are a practical set of methods
that can be readily applied to geoscience interpolation problems and we
anticipate a wider adoption in the future. | [
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] |
Knowledge graph embedding (KGE) aims at learning powerful representations to
benefit various artificial intelligence applications. Meanwhile, contrastive
learning has been widely leveraged in graph learning as an effective mechanism
to enhance the discriminative capacity of the learned representations. However,
the complex structures of KG make it hard to construct appropriate contrastive
pairs. Only a few attempts have integrated contrastive learning strategies with
KGE. But, most of them rely on language models ( e.g., Bert) for contrastive
pair construction instead of fully mining information underlying the graph
structure, hindering expressive ability. Surprisingly, we find that the
entities within a relational symmetrical structure are usually similar and
correlated. To this end, we propose a knowledge graph contrastive learning
framework based on relation-symmetrical structure, KGE-SymCL, which mines
symmetrical structure information in KGs to enhance the discriminative ability
of KGE models. Concretely, a plug-and-play approach is proposed by taking
entities in the relation-symmetrical positions as positive pairs. Besides, a
self-supervised alignment loss is designed to pull together positive pairs.
Experimental results on link prediction and entity classification datasets
demonstrate that our KGE-SymCL can be easily adopted to various KGE models for
performance improvements. Moreover, extensive experiments show that our model
could outperform other state-of-the-art baselines. | [
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] |
Analogical reasoning is the process of discovering and mapping
correspondences from a target subject to a base subject. As the most well-known
computational method of analogical reasoning, Structure-Mapping Theory (SMT)
abstracts both target and base subjects into relational graphs and forms the
cognitive process of analogical reasoning by finding a corresponding subgraph
(i.e., correspondence) in the target graph that is aligned with the base graph.
However, incorporating deep learning for SMT is still under-explored due to
several obstacles: 1) the combinatorial complexity of searching for the
correspondence in the target graph; 2) the correspondence mining is restricted
by various cognitive theory-driven constraints. To address both challenges, we
propose a novel framework for Analogical Reasoning (DeepGAR) that identifies
the correspondence between source and target domains by assuring cognitive
theory-driven constraints. Specifically, we design a geometric constraint
embedding space to induce subgraph relation from node embeddings for efficient
subgraph search. Furthermore, we develop novel learning and optimization
strategies that could end-to-end identify correspondences that are strictly
consistent with constraints driven by the cognitive theory. Extensive
experiments are conducted on synthetic and real-world datasets to demonstrate
the effectiveness of the proposed DeepGAR over existing methods. | [
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] |
The energy transition potentially poses an existential risk for major
international oil companies (IOCs) if they fail to adapt to low-carbon business
models. Projections of energy futures, however, are met with diverging
assumptions on its scale and pace, causing disagreement among IOC
decision-makers and their stakeholders over what the business model of an
incumbent fossil fuel company should be. In this work, we used deep multi-agent
reinforcement learning to solve an energy systems wargame wherein players
simulate IOC decision-making, including hydrocarbon and low-carbon investments
decisions, dividend policies, and capital structure measures, through an
uncertain energy transition to explore critical and non-linear governance
questions, from leveraged transitions to reserve replacements. Adversarial play
facilitated by state-of-the-art algorithms revealed decision-making strategies
robust to energy transition uncertainty and against multiple IOCs. In all
games, robust strategies emerged in the form of low-carbon business models as a
result of early transition-oriented movement. IOCs adopting such strategies
outperformed business-as-usual and delayed transition strategies regardless of
hydrocarbon demand projections. In addition to maximizing value, these
strategies benefit greater society by contributing substantial amounts of
capital necessary to accelerate the global low-carbon energy transition. Our
findings point towards the need for lenders and investors to effectively
mobilize transition-oriented finance and engage with IOCs to ensure responsible
reallocation of capital towards low-carbon business models that would enable
the emergence of fossil fuel incumbents as future low-carbon leaders. | [
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] |
In recent years, much progress has been made in learning robotic manipulation
policies that follow natural language instructions. Such methods typically
learn from corpora of robot-language data that was either collected with
specific tasks in mind or expensively re-labelled by humans with rich language
descriptions in hindsight. Recently, large-scale pretrained vision-language
models (VLMs) like CLIP or ViLD have been applied to robotics for learning
representations and scene descriptors. Can these pretrained models serve as
automatic labelers for robot data, effectively importing Internet-scale
knowledge into existing datasets to make them useful even for tasks that are
not reflected in their ground truth annotations? To accomplish this, we
introduce Data-driven Instruction Augmentation for Language-conditioned control
(DIAL): we utilize semi-supervised language labels leveraging the semantic
understanding of CLIP to propagate knowledge onto large datasets of unlabelled
demonstration data and then train language-conditioned policies on the
augmented datasets. This method enables cheaper acquisition of useful language
descriptions compared to expensive human labels, allowing for more efficient
label coverage of large-scale datasets. We apply DIAL to a challenging
real-world robotic manipulation domain where 96.5% of the 80,000 demonstrations
do not contain crowd-sourced language annotations. DIAL enables imitation
learning policies to acquire new capabilities and generalize to 60 novel
instructions unseen in the original dataset. | [
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] |
Mining genuine mechanisms underlying the complex data generation process in
real-world systems is a fundamental step in promoting interpretability of, and
thus trust in, data-driven models. Therefore, we propose a variation-based
cause effect identification (VCEI) framework for causal discovery in bivariate
systems from a single observational setting. Our framework relies on the
principle of independence of cause and mechanism (ICM) under the assumption of
an existing acyclic causal link, and offers a practical realization of this
principle. Principally, we artificially construct two settings in which the
marginal distributions of one covariate, claimed to be the cause, are
guaranteed to have non-negligible variations. This is achieved by re-weighting
samples of the marginal so that the resultant distribution is notably distinct
from this marginal according to some discrepancy measure. In the causal
direction, such variations are expected to have no impact on the effect
generation mechanism. Therefore, quantifying the impact of these variations on
the conditionals reveals the genuine causal direction. Moreover, we formulate
our approach in the kernel-based maximum mean discrepancy, lifting all
constraints on the data types of cause-and-effect covariates, and rendering
such artificial interventions a convex optimization problem. We provide a
series of experiments on real and synthetic data showing that VCEI is, in
principle, competitive to other cause effect identification frameworks. | [
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Today, machine learning (ML) is widely used in industry to provide the core
functionality of production systems. However, it is practically always used in
production systems as part of a larger end-to-end software system that is made
up of several other components in addition to the ML model. Due to production
demand and time constraints, automated software engineering practices are
highly applicable. The increased use of automated ML software engineering
practices in industries such as manufacturing and utilities requires an
automated Quality Assurance (QA) approach as an integral part of ML software.
Here, QA helps reduce risk by offering an objective perspective on the software
task. Although conventional software engineering has automated tools for QA
data analysis for data-driven ML, the use of QA practices for ML in operation
(MLOps) is lacking. This paper examines the QA challenges that arise in
industrial MLOps and conceptualizes modular strategies to deal with data
integrity and Data Quality (DQ). The paper is accompanied by real industrial
use-cases from industrial partners. The paper also presents several challenges
that may serve as a basis for future studies. | [
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] |
An electroencephalogram (EEG) signal is currently accepted as a standard for
automatic sleep staging. Lately, Near-human accuracy in automated sleep staging
has been achievable by Deep Learning (DL) based approaches, enabling multi-fold
progress in this area. However, An extensive and expensive clinical setup is
required for EEG based sleep staging. Additionally, the EEG setup being
obtrusive in nature and requiring an expert for setup adds to the inconvenience
of the subject under study, making it adverse in the point of care setting. An
unobtrusive and more suitable alternative to EEG is Electrocardiogram (ECG).
Unsurprisingly, compared to EEG in sleep staging, its performance remains
sub-par. In order to take advantage of both the modalities, transferring
knowledge from EEG to ECG is a reasonable approach, ultimately boosting the
performance of ECG based sleep staging. Knowledge Distillation (KD) is a
promising notion in DL that shares knowledge from a superior performing but
usually more complex teacher model to an inferior but compact student model.
Building upon this concept, a cross-modality KD framework assisting features
learned through models trained on EEG to improve ECG-based sleep staging
performance is proposed. Additionally, to better understand the distillation
approach, extensive experimentation on the independent modules of the proposed
model was conducted. Montreal Archive of Sleep Studies (MASS) dataset
consisting of 200 subjects was utilized for this study. The results from the
proposed model for weighted-F1-score in 3-class and 4-class sleep staging
showed a 13.40 \% and 14.30 \% improvement, respectively. This study
demonstrates the feasibility of KD for single-channel ECG based sleep staging's
performance enhancement in 3-class (W-R-N) and 4-class (W-R-L-D)
classification. | [
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] |
Recent years have witnessed a large amount of decentralized data in various
(edge) devices of end-users, while the decentralized data aggregation remains
complicated for machine learning jobs because of regulations and laws. As a
practical approach to handling decentralized data, Federated Learning (FL)
enables collaborative global machine learning model training without sharing
sensitive raw data. The servers schedule devices to jobs within the training
process of FL. In contrast, device scheduling with multiple jobs in FL remains
a critical and open problem. In this paper, we propose a novel multi-job FL
framework, which enables the training process of multiple jobs in parallel. The
multi-job FL framework is composed of a system model and a scheduling method.
The system model enables a parallel training process of multiple jobs, with a
cost model based on the data fairness and the training time of diverse devices
during the parallel training process. We propose a novel intelligent scheduling
approach based on multiple scheduling methods, including an original
reinforcement learning-based scheduling method and an original Bayesian
optimization-based scheduling method, which corresponds to a small cost while
scheduling devices to multiple jobs. We conduct extensive experimentation with
diverse jobs and datasets. The experimental results reveal that our proposed
approaches significantly outperform baseline approaches in terms of training
time (up to 12.73 times faster) and accuracy (up to 46.4% higher). | [
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] |
We study the computational complexity of counterfactual reasoning in relation
to the complexity of associational and interventional reasoning on structural
causal models (SCMs). We show that counterfactual reasoning is no harder than
associational or interventional reasoning on fully specified SCMs in the
context of two computational frameworks. The first framework is based on the
notion of treewidth and includes the classical variable elimination and
jointree algorithms. The second framework is based on the more recent and
refined notion of causal treewidth which is directed towards models with
functional dependencies such as SCMs. Our results are constructive and based on
bounding the (causal) treewidth of twin networks -- used in standard
counterfactual reasoning that contemplates two worlds, real and imaginary -- to
the (causal) treewidth of the underlying SCM structure. In particular, we show
that the latter (causal) treewidth is no more than twice the former plus one.
Hence, if associational or interventional reasoning is tractable on a fully
specified SCM then counterfactual reasoning is tractable too. We extend our
results to general counterfactual reasoning that requires contemplating more
than two worlds and discuss applications of our results to counterfactual
reasoning with a partially specified SCM that is coupled with data. We finally
present empirical results that measure the gap between the complexities of
counterfactual reasoning and associational/interventional reasoning on random
SCMs. | [
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] |
Recent works have shown that joint source-channel coding (JSCC) schemes using
deep neural networks (DNNs), called DeepJSCC, provide promising results in
wireless image transmission. However, these methods mostly focus on the
distortion of the reconstructed signals with respect to the input image, rather
than their perception by humans. However, focusing on traditional distortion
metrics alone does not necessarily result in high perceptual quality,
especially in extreme physical conditions, such as very low bandwidth
compression ratio (BCR) and low signal-to-noise ratio (SNR) regimes. In this
work, we propose two novel JSCC schemes that leverage the perceptual quality of
deep generative models (DGMs) for wireless image transmission, namely
InverseJSCC and GenerativeJSCC. While the former is an inverse problem approach
to DeepJSCC, the latter is an end-to-end optimized JSCC scheme. In both, we
optimize a weighted sum of mean squared error (MSE) and learned perceptual
image patch similarity (LPIPS) losses, which capture more semantic similarities
than other distortion metrics. InverseJSCC performs denoising on the distorted
reconstructions of a DeepJSCC model by solving an inverse optimization problem
using style-based generative adversarial network (StyleGAN). Our simulation
results show that InverseJSCC significantly improves the state-of-the-art
(SotA) DeepJSCC in terms of perceptual quality in edge cases. In
GenerativeJSCC, we carry out end-to-end training of an encoder and a
StyleGAN-based decoder, and show that GenerativeJSCC significantly outperforms
DeepJSCC both in terms of distortion and perceptual quality. | [
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] |
Every automaton can be decomposed into a cascade of basic prime automata.
This is the Prime Decomposition Theorem by Krohn and Rhodes. Guided by this
theory, we propose automata cascades as a structured, modular, way to describe
automata as complex systems made of many components, each implementing a
specific functionality. Any automaton can serve as a component; using specific
components allows for a fine-grained control of the expressivity of the
resulting class of automata; using prime automata as components implies
specific expressivity guarantees. Moreover, specifying automata as cascades
allows for describing the sample complexity of automata in terms of their
components. We show that the sample complexity is linear in the number of
components and the maximum complexity of a single component, modulo logarithmic
factors. This opens to the possibility of learning automata representing large
dynamical systems consisting of many parts interacting with each other. It is
in sharp contrast with the established understanding of the sample complexity
of automata, described in terms of the overall number of states and input
letters, which implies that it is only possible to learn automata where the
number of states is linear in the amount of data available. Instead our results
show that one can learn automata with a number of states that is exponential in
the amount of data available. | [
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Deep reinforcement learning (DRL) is one of the most popular algorithms to
realize an autonomous driving (AD) system. The key success factor of DRL is
that it embraces the perception capability of deep neural networks which,
however, have been proven vulnerable to Trojan attacks. Trojan attacks have
been widely explored in supervised learning (SL) tasks (e.g., image
classification), but rarely in sequential decision-making tasks solved by DRL.
Hence, in this paper, we explore Trojan attacks on DRL for AD tasks. First, we
propose a spatio-temporal DRL algorithm based on the recurrent neural network
and attention mechanism to prove that capturing spatio-temporal traffic
features is the key factor to the effectiveness and safety of a DRL-augment AD
system. We then design a spatial-temporal Trojan attack on DRL policies, where
the trigger is hidden in a sequence of spatial and temporal traffic features,
rather than a single instant state used in existing Trojan on SL and DRL tasks.
With our Trojan, the adversary acts as a surrounding normal vehicle and can
trigger attacks via specific spatial-temporal driving behaviors, rather than
physical or wireless access. Through extensive experiments, we show that while
capturing spatio-temporal traffic features can improve the performance of DRL
for different AD tasks, they suffer from Trojan attacks since our designed
Trojan shows high stealthy (various spatio-temporal trigger patterns),
effective (less than 3.1\% performance variance rate and more than 98.5\%
attack success rate), and sustainable to existing advanced defenses. | [
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] |
Knowledge graph data are prevalent in real-world applications, and knowledge
graph neural networks (KGNNs) are essential techniques for knowledge graph
representation learning. Although KGNN effectively models the structural
information from knowledge graphs, these frameworks amplify the underlying data
bias that leads to discrimination towards certain groups or individuals in
resulting applications. Additionally, as existing debiasing approaches mainly
focus on the entity-wise bias, eliminating the multi-hop relational bias that
pervasively exists in knowledge graphs remains an open question. However, it is
very challenging to eliminate relational bias due to the sparsity of the paths
that generate the bias and the non-linear proximity structure of knowledge
graphs. To tackle the challenges, we propose Fair-KGNN, a KGNN framework that
simultaneously alleviates multi-hop bias and preserves the proximity
information of entity-to-relation in knowledge graphs. The proposed framework
is generalizable to mitigate the relational bias for all types of KGNN. We
develop two instances of Fair-KGNN incorporating with two state-of-the-art KGNN
models, RGCN and CompGCN, to mitigate gender-occupation and nationality-salary
bias. The experiments carried out on three benchmark knowledge graph datasets
demonstrate that the Fair-KGNN can effectively mitigate unfair situations
during representation learning while preserving the predictive performance of
KGNN models. | [
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] |
Recommender systems influence many of our interactions in the digital world
-- impacting how we shop for clothes, sorting what we see when browsing YouTube
or TikTok, and determining which restaurants and hotels we are shown when using
hospitality platforms. Modern recommender systems are large, opaque models
trained on a mixture of proprietary and open-source datasets. Naturally, issues
of trust arise on both the developer and user side: is the system working
correctly, and why did a user receive (or not receive) a particular
recommendation? Providing an explanation alongside a recommendation alleviates
some of these concerns. The status quo for auxiliary recommender system
feedback is either user-specific explanations (e.g., "users who bought item B
also bought item A") or item-specific explanations (e.g., "we are recommending
item A because you watched/bought item B"). However, users bring personalized
context into their search experience, valuing an item as a function of that
item's attributes and their own personal preferences. In this work, we propose
RecXplainer, a novel method for generating fine-grained explanations based on a
user's preferences over the attributes of recommended items. We evaluate
RecXplainer on five real-world and large-scale recommendation datasets using
five different kinds of recommender systems to demonstrate the efficacy of
RecXplainer in capturing users' preferences over item attributes and using them
to explain recommendations. We also compare RecXplainer to five baselines and
show RecXplainer's exceptional performance on ten metrics. | [
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] |
We investigate the sample complexity of learning the optimal arm for
multi-task bandit problems. Arms consist of two components: one that is shared
across tasks (that we call representation) and one that is task-specific (that
we call predictor). The objective is to learn the optimal (representation,
predictor)-pair for each task, under the assumption that the optimal
representation is common to all tasks. Within this framework, efficient
learning algorithms should transfer knowledge across tasks. We consider the
best-arm identification problem for a fixed confidence, where, in each round,
the learner actively selects both a task, and an arm, and observes the
corresponding reward. We derive instance-specific sample complexity lower
bounds satisfied by any $(\delta_G,\delta_H)$-PAC algorithm (such an algorithm
identifies the best representation with probability at least $1-\delta_G$, and
the best predictor for a task with probability at least $1-\delta_H$). We
devise an algorithm OSRL-SC whose sample complexity approaches the lower bound,
and scales at most as $H(G\log(1/\delta_G)+ X\log(1/\delta_H))$, with $X,G,H$
being, respectively, the number of tasks, representations and predictors. By
comparison, this scaling is significantly better than the classical best-arm
identification algorithm that scales as $HGX\log(1/\delta)$. | [
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] |
Identification of Alzheimer's Disease (AD)-related transcriptomic signatures
from blood is important for early diagnosis of the disease. Deep learning
techniques are potent classifiers for AD diagnosis, but most have been unable
to identify biomarkers because of their lack of interpretability. To address
these challenges, we propose a pathway information-based neural network
(PINNet) to predict AD patients and analyze blood and brain transcriptomic
signatures using an interpretable deep learning model. PINNet is a deep neural
network (DNN) model with pathway prior knowledge from either the Gene Ontology
or Kyoto Encyclopedia of Genes and Genomes databases. Then, a
backpropagation-based model interpretation method was applied to reveal
essential pathways and genes for predicting AD. We compared the performance of
PINNet with a DNN model without a pathway. Performances of PINNet outperformed
or were similar to those of DNN without a pathway using blood and brain gene
expressions, respectively. Moreover, PINNet considers more AD-related genes as
essential features than DNN without a pathway in the learning process. Pathway
analysis of protein-protein interaction modules of highly contributed genes
showed that AD-related genes in blood were enriched with cell migration,
PI3K-Akt, MAPK signaling, and apoptosis in blood. The pathways enriched in the
brain module included cell migration, PI3K-Akt, MAPK signaling, apoptosis,
protein ubiquitination, and t-cell activation. Collectively, with prior
knowledge about pathways, PINNet reveals essential pathways related to AD. | [
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] |
This work demonstrates the ability to produce readily interpretable
statistical metrics for model fit, fixed effects covariance coefficients, and
prediction confidence. Importantly, this work compares 4 suitable and commonly
applied epistemic UQ approaches, BNN, SWAG, MC dropout, and ensemble approaches
in their ability to calculate these statistical metrics for the ARMED MEDL
models. In our experiment for AD prognosis, not only do the UQ methods provide
these benefits, but several UQ methods maintain the high performance of the
original ARMED method, some even provide a modest (but not statistically
significant) performance improvement. The ensemble models, especially the
ensemble method with a 90% subsampling, performed well across all metrics we
tested with (1) high performance that was comparable to the non-UQ ARMED model,
(2) properly deweights the confounds probes and assigns them statistically
insignificant p-values, (3) attains relatively high calibration of the output
prediction confidence. Based on the results, the ensemble approaches,
especially with a subsampling of 90%, provided the best all-round performance
for prediction and uncertainty estimation, and achieved our goals to provide
statistical significance for model fit, statistical significance covariate
coefficients, and confidence in prediction, while maintaining the baseline
performance of MEDL using ARMED | [
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] |
In this work, we propose a communication-efficient hierarchical federated
learning algorithm for distributed setups including core servers and multiple
edge servers with clusters of devices. Assuming different learning tasks,
clusters with a same task collaborate. To implement the algorithm over wireless
links, we propose a scalable clustered over-the-air aggregation scheme for the
uplink with a bandwidth-limited broadcast scheme for the downlink that requires
only a single resource block for each algorithm iteration, independent of the
number of edge servers and devices. This setup is faced with interference of
devices in the uplink and interference of edge servers in the downlink that are
to be modeled rigorously. We first develop a spatial model for the setup by
modeling devices as a Poisson cluster process over the edge servers and
quantify uplink and downlink error terms due to the interference. Accordingly,
we present a comprehensive mathematical approach to derive the convergence
bound for the proposed algorithm including any number of collaborating clusters
and provide special cases and design remarks. Finally, we show that despite the
interference and data heterogeneity, the proposed algorithm not only achieves
high learning accuracy for a variety of parameters but also significantly
outperforms the conventional hierarchical learning algorithm. | [
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] |
Causal effect estimation from observational data is a fundamental task in
empirical sciences. It becomes particularly challenging when unobserved
confounders are involved in a system. This paper focuses on front-door
adjustment -- a classic technique which, using observed mediators allows to
identify causal effects even in the presence of unobserved confounding. While
the statistical properties of the front-door estimation are quite well
understood, its algorithmic aspects remained unexplored for a long time.
Recently, Jeong, Tian, and Barenboim [NeurIPS 2022] have presented the first
polynomial-time algorithm for finding sets satisfying the front-door criterion
in a given directed acyclic graph (DAG), with an $O(n^3(n+m))$ run time, where
$n$ denotes the number of variables and $m$ the number of edges of the causal
graph. In our work, we give the first linear-time, i.e., $O(n+m)$, algorithm
for this task, which thus reaches the asymptotically optimal time complexity.
This result implies an $O(n(n+m))$ delay enumeration algorithm of all
front-door adjustment sets, again improving previous work by Jeong et al. by a
factor of $n^3$. Moreover, we provide the first linear-time algorithm for
finding a minimal front-door adjustment set. We offer implementations of our
algorithms in multiple programming languages to facilitate practical usage and
empirically validate their feasibility, even for large graphs. | [
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] |
The prediction of protein structures from sequences is an important task for
function prediction, drug design, and related biological processes
understanding. Recent advances have proved the power of language models (LMs)
in processing the protein sequence databases, which inherit the advantages of
attention networks and capture useful information in learning representations
for proteins. The past two years have witnessed remarkable success in tertiary
protein structure prediction (PSP), including evolution-based and
single-sequence-based PSP. It seems that instead of using energy-based models
and sampling procedures, protein language model (pLM)-based pipelines have
emerged as mainstream paradigms in PSP. Despite the fruitful progress, the PSP
community needs a systematic and up-to-date survey to help bridge the gap
between LMs in the natural language processing (NLP) and PSP domains and
introduce their methodologies, advancements and practical applications. To this
end, in this paper, we first introduce the similarities between protein and
human languages that allow LMs extended to pLMs, and applied to protein
databases. Then, we systematically review recent advances in LMs and pLMs from
the perspectives of network architectures, pre-training strategies,
applications, and commonly-used protein databases. Next, different types of
methods for PSP are discussed, particularly how the pLM-based architectures
function in the process of protein folding. Finally, we identify challenges
faced by the PSP community and foresee promising research directions along with
the advances of pLMs. This survey aims to be a hands-on guide for researchers
to understand PSP methods, develop pLMs and tackle challenging problems in this
field for practical purposes. | [
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Due to the noises in crowdsourced labels, label aggregation (LA) has emerged
as a standard procedure to post-process crowdsourced labels. LA methods
estimate true labels from crowdsourced labels by modeling worker qualities.
Most existing LA methods are iterative in nature. They need to traverse all the
crowdsourced labels multiple times in order to jointly and iteratively update
true labels and worker qualities until convergence. Consequently, these methods
have high space and time complexities. In this paper, we treat LA as a dynamic
system and model it as a Dynamic Bayesian network. From the dynamic model we
derive two light-weight algorithms, LA\textsuperscript{onepass} and
LA\textsuperscript{twopass}, which can effectively and efficiently estimate
worker qualities and true labels by traversing all the labels at most twice.
Due to the dynamic nature, the proposed algorithms can also estimate true
labels online without re-visiting historical data. We theoretically prove the
convergence property of the proposed algorithms, and bound the error of
estimated worker qualities. We also analyze the space and time complexities of
the proposed algorithms and show that they are equivalent to those of majority
voting. Experiments conducted on 20 real-world datasets demonstrate that the
proposed algorithms can effectively and efficiently aggregate labels in both
offline and online settings even if they traverse all the labels at most twice. | [
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] |
Cyber intrusion attacks that compromise the users' critical and sensitive
data are escalating in volume and intensity, especially with the growing
connections between our daily life and the Internet. The large volume and high
complexity of such intrusion attacks have impeded the effectiveness of most
traditional defence techniques. While at the same time, the remarkable
performance of the machine learning methods, especially deep learning, in
computer vision, had garnered research interests from the cyber security
community to further enhance and automate intrusion detections. However, the
expensive data labeling and limitation of anomalous data make it challenging to
train an intrusion detector in a fully supervised manner. Therefore, intrusion
detection based on unsupervised anomaly detection is an important feature too.
In this paper, we propose a three-stage deep learning anomaly detection based
network intrusion attack detection framework. The framework comprises an
integration of unsupervised (K-means clustering), semi-supervised (GANomaly)
and supervised learning (CNN) algorithms. We then evaluated and showed the
performance of our implemented framework on three benchmark datasets: NSL-KDD,
CIC-IDS2018, and TON_IoT. | [
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] |
Crypto-coins (also known as cryptocurrencies) are tradable digital assets.
Notable examples include Bitcoin, Ether and Litecoin. Ownerships of cryptocoins
are registered on distributed ledgers (i.e., blockchains). Secure encryption
techniques guarantee the security of the transactions (transfers of coins
across owners), registered into the ledger. Cryptocoins are exchanged for
specific trading prices. While history has shown the extreme volatility of such
trading prices across all different sets of crypto-assets, it remains unclear
what and if there are tight relations between the trading prices of different
cryptocoins. Major coin exchanges (i.e., Coinbase) provide trend correlation
indicators to coin owners, suggesting possible acquisitions or sells. However,
these correlations remain largely unvalidated. In this paper, we shed lights on
the trend correlations across a large variety of cryptocoins, by investigating
their coin-price correlation trends over a period of two years. Our
experimental results suggest strong correlation patterns between main coins
(Ethereum, Bitcoin) and alt-coins. We believe our study can support forecasting
techniques for time-series modeling in the context of crypto-coins. We release
our dataset and code to reproduce our analysis to the research community. | [
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] |
ML-based motion planning is a promising approach to produce agents that
exhibit complex behaviors, and automatically adapt to novel environments. In
the context of autonomous driving, it is common to treat all available training
data equally. However, this approach produces agents that do not perform
robustly in safety-critical settings, an issue that cannot be addressed by
simply adding more data to the training set - we show that an agent trained
using only a 10% subset of the data performs just as well as an agent trained
on the entire dataset. We present a method to predict the inherent difficulty
of a driving situation given data collected from a fleet of autonomous vehicles
deployed on public roads. We then demonstrate that this difficulty score can be
used in a zero-shot transfer to generate curricula for an imitation-learning
based planning agent. Compared to training on the entire unbiased training
dataset, we show that prioritizing difficult driving scenarios both reduces
collisions by 15% and increases route adherence by 14% in closed-loop
evaluation, all while using only 10% of the training data. | [
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] |
We provide a brief, and inevitably incomplete overview of the use of Machine
Learning (ML) and other AI methods in astronomy, astrophysics, and cosmology.
Astronomy entered the big data era with the first digital sky surveys in the
early 1990s and the resulting Terascale data sets, which required automating of
many data processing and analysis tasks, for example the star-galaxy
separation, with billions of feature vectors in hundreds of dimensions. The
exponential data growth continued, with the rise of synoptic sky surveys and
the Time Domain Astronomy, with the resulting Petascale data streams and the
need for a real-time processing, classification, and decision making. A broad
variety of classification and clustering methods have been applied for these
tasks, and this remains a very active area of research. Over the past decade we
have seen an exponential growth of the astronomical literature involving a
variety of ML/AI applications of an ever increasing complexity and
sophistication. ML and AI are now a standard part of the astronomical toolkit.
As the data complexity continues to increase, we anticipate further advances
leading towards a collaborative human-AI discovery. | [
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] |
We consider an offline reinforcement learning (RL) setting where the agent
need to learn from a dataset collected by rolling out multiple behavior
policies. There are two challenges for this setting: 1) The optimal trade-off
between optimizing the RL signal and the behavior cloning (BC) signal changes
on different states due to the variation of the action coverage induced by
different behavior policies. Previous methods fail to handle this by only
controlling the global trade-off. 2) For a given state, the action distribution
generated by different behavior policies may have multiple modes. The BC
regularizers in many previous methods are mean-seeking, resulting in policies
that select out-of-distribution (OOD) actions in the middle of the modes. In
this paper, we address both challenges by using adaptively weighted reverse
Kullback-Leibler (KL) divergence as the BC regularizer based on the TD3
algorithm. Our method not only trades off the RL and BC signals with per-state
weights (i.e., strong BC regularization on the states with narrow action
coverage, and vice versa) but also avoids selecting OOD actions thanks to the
mode-seeking property of reverse KL. Empirically, our algorithm can outperform
existing offline RL algorithms in the MuJoCo locomotion tasks with the standard
D4RL datasets as well as the mixed datasets that combine the standard datasets. | [
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] |
We explore how observational and interventional causal discovery methods can
be combined. A state-of-the-art observational causal discovery algorithm for
time series capable of handling latent confounders and contemporaneous effects,
called LPCMCI, is extended to profit from casual constraints found through
randomized control trials. Numerical results show that, given perfect
interventional constraints, the reconstructed structural causal models (SCMs)
of the extended LPCMCI allow 84.6% of the time for the optimal prediction of
the target variable. The implementation of interventional and observational
causal discovery is modular, allowing causal constraints from other sources.
The second part of this thesis investigates the question of regret minimizing
control by simultaneously learning a causal model and planning actions through
the causal model. The idea is that an agent to optimize a measured variable
first learns the system's mechanics through observational causal discovery. The
agent then intervenes on the most promising variable with randomized values
allowing for the exploitation and generation of new interventional data. The
agent then uses the interventional data to enhance the causal model further,
allowing improved actions the next time.
The extended LPCMCI can be favorable compared to the original LPCMCI
algorithm. The numerical results show that detecting and using interventional
constraints leads to reconstructed SCMs that allow 60.9% of the time for the
optimal prediction of the target variable in contrast to the baseline of 53.6%
when using the original LPCMCI algorithm. Furthermore, the induced average
regret decreases from 1.2 when using the original LPCMCI algorithm to 1.0 when
using the extended LPCMCI algorithm with interventional discovery. | [
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] |
The interplay between quantum physics and machine learning gives rise to the
emergent frontier of quantum machine learning, where advanced quantum learning
models may outperform their classical counterparts in solving certain
challenging problems. However, quantum learning systems are vulnerable to
adversarial attacks: adding tiny carefully-crafted perturbations on legitimate
input samples can cause misclassifications. To address this issue, we propose a
general scheme to protect quantum learning systems from adversarial attacks by
randomly encoding the legitimate data samples through unitary or quantum error
correction encoders. In particular, we rigorously prove that both global and
local random unitary encoders lead to exponentially vanishing gradients (i.e.
barren plateaus) for any variational quantum circuits that aim to add
adversarial perturbations, independent of the input data and the inner
structures of adversarial circuits and quantum classifiers. In addition, we
prove a rigorous bound on the vulnerability of quantum classifiers under local
unitary adversarial attacks. We show that random black-box quantum error
correction encoders can protect quantum classifiers against local adversarial
noises and their robustness increases as we concatenate error correction codes.
To quantify the robustness enhancement, we adapt quantum differential privacy
as a measure of the prediction stability for quantum classifiers. Our results
establish versatile defense strategies for quantum classifiers against
adversarial perturbations, which provide valuable guidance to enhance the
reliability and security for both near-term and future quantum learning
technologies. | [
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] |
Learning from Demonstration (LfD) is a powerful method for enabling robots to
perform novel tasks as it is often more tractable for a non-roboticist end-user
to demonstrate the desired skill and for the robot to efficiently learn from
the associated data than for a human to engineer a reward function for the
robot to learn the skill via reinforcement learning (RL). Safety issues arise
in modern LfD techniques, e.g., Inverse Reinforcement Learning (IRL), just as
they do for RL; yet, safe learning in LfD has received little attention. In the
context of agile robots, safety is especially vital due to the possibility of
robot-environment collision, robot-human collision, and damage to the robot. In
this paper, we propose a safe IRL framework, CBFIRL, that leverages the Control
Barrier Function (CBF) to enhance the safety of the IRL policy. The core idea
of CBFIRL is to combine a loss function inspired by CBF requirements with the
objective in an IRL method, both of which are jointly optimized via gradient
descent. In the experiments, we show our framework performs safer compared to
IRL methods without CBF, that is $\sim15\%$ and $\sim20\%$ improvement for two
levels of difficulty of a 2D racecar domain and $\sim 50\%$ improvement for a
3D drone domain. | [
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Current advances in recommender systems have been remarkably successful in
optimizing immediate engagement. However, long-term user engagement, a more
desirable performance metric, remains difficult to improve. Meanwhile, recent
reinforcement learning (RL) algorithms have shown their effectiveness in a
variety of long-term goal optimization tasks. For this reason, RL is widely
considered as a promising framework for optimizing long-term user engagement in
recommendation. Though promising, the application of RL heavily relies on
well-designed rewards, but designing rewards related to long-term user
engagement is quite difficult. To mitigate the problem, we propose a novel
paradigm, recommender systems with human preferences (or Preference-based
Recommender systems), which allows RL recommender systems to learn from
preferences about users historical behaviors rather than explicitly defined
rewards. Such preferences are easily accessible through techniques such as
crowdsourcing, as they do not require any expert knowledge. With PrefRec, we
can fully exploit the advantages of RL in optimizing long-term goals, while
avoiding complex reward engineering. PrefRec uses the preferences to
automatically train a reward function in an end-to-end manner. The reward
function is then used to generate learning signals to train the recommendation
policy. Furthermore, we design an effective optimization method for PrefRec,
which uses an additional value function, expectile regression and reward model
pre-training to improve the performance. We conduct experiments on a variety of
long-term user engagement optimization tasks. The results show that PrefRec
significantly outperforms previous state-of-the-art methods in all the tasks. | [
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] |
Clustering analysis of sequence data continues to address many applications
in engineering design, aided with the rapid growth of machine learning in
applied science. This paper presents an unsupervised machine learning algorithm
to extract defining characteristics of earthquake ground-motion spectra, also
called latent features, to aid in ground-motion selection (GMS). In this
context, a latent feature is a low-dimensional machine-discovered spectral
characteristic learned through nonlinear relationships of a neural network
autoencoder. Machine discovered latent features can be combined with
traditionally defined intensity measures and clustering can be performed to
select a representative subgroup from a large ground-motion suite. The
objective of efficient GMS is to choose characteristic records representative
of what the structure will probabilistically experience in its lifetime. Three
examples are presented to validate this approach, including the use of
synthetic and field recorded ground-motion datasets. The presented deep
embedding clustering of ground-motion spectra has three main advantages: 1.
defining characteristics the represent the sparse spectral content of
ground-motions are discovered efficiently through training of the autoencoder,
2. domain knowledge is incorporated into the machine learning framework with
conditional variables in the deep embedding scheme, and 3. method exhibits
excellent performance when compared to a benchmark seismic hazard analysis. | [
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] |
While reinforcement learning (RL) has become a more popular approach for
robotics, designing sufficiently informative reward functions for complex tasks
has proven to be extremely difficult due their inability to capture human
intent and policy exploitation. Preference based RL algorithms seek to overcome
these challenges by directly learning reward functions from human feedback.
Unfortunately, prior work either requires an unreasonable number of queries
implausible for any human to answer or overly restricts the class of reward
functions to guarantee the elicitation of the most informative queries,
resulting in models that are insufficiently expressive for realistic robotics
tasks. Contrary to most works that focus on query selection to \emph{minimize}
the amount of data required for learning reward functions, we take an opposite
approach: \emph{expanding} the pool of available data by viewing
human-in-the-loop RL through the more flexible lens of multi-task learning.
Motivated by the success of meta-learning, we pre-train preference models on
prior task data and quickly adapt them for new tasks using only a handful of
queries. Empirically, we reduce the amount of online feedback needed to train
manipulation policies in Meta-World by 20$\times$, and demonstrate the
effectiveness of our method on a real Franka Panda Robot. Moreover, this
reduction in query-complexity allows us to train robot policies from actual
human users. Videos of our results and code can be found at
https://sites.google.com/view/few-shot-preference-rl/home. | [
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In recent years, unmanned aerial vehicle (UAV) related technology has
expanded knowledge in the area, bringing to light new problems and challenges
that require solutions. Furthermore, because the technology allows processes
usually carried out by people to be automated, it is in great demand in
industrial sectors. The automation of these vehicles has been addressed in the
literature, applying different machine learning strategies. Reinforcement
learning (RL) is an automation framework that is frequently used to train
autonomous agents. RL is a machine learning paradigm wherein an agent interacts
with an environment to solve a given task. However, learning autonomously can
be time consuming, computationally expensive, and may not be practical in
highly-complex scenarios. Interactive reinforcement learning allows an external
trainer to provide advice to an agent while it is learning a task. In this
study, we set out to teach an RL agent to control a drone using reward-shaping
and policy-shaping techniques simultaneously. Two simulated scenarios were
proposed for the training; one without obstacles and one with obstacles. We
also studied the influence of each technique. The results show that an agent
trained simultaneously with both techniques obtains a lower reward than an
agent trained using only a policy-based approach. Nevertheless, the agent
achieves lower execution times and less dispersion during training. | [
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] |
With the progress of sensor technology in wearables, the collection and
analysis of PPG signals are gaining more interest. Using Machine Learning, the
cardiac rhythm corresponding to PPG signals can be used to predict different
tasks such as activity recognition, sleep stage detection, or more general
health status. However, supervised learning is often limited by the amount of
available labeled data, which is typically expensive to obtain. To address this
problem, we propose a Self-Supervised Learning (SSL) method with a pretext task
of signal reconstruction to learn an informative generalized PPG
representation. The performance of the proposed SSL framework is compared with
two fully supervised baselines. The results show that in a very limited label
data setting (10 samples per class or less), using SSL is beneficial, and a
simple classifier trained on SSL-learned representations outperforms fully
supervised deep neural networks. However, the results reveal that the
SSL-learned representations are too focused on encoding the subjects.
Unfortunately, there is high inter-subject variability in the SSL-learned
representations, which makes working with this data more challenging when
labeled data is scarce. The high inter-subject variability suggests that there
is still room for improvements in learning representations. In general, the
results suggest that SSL may pave the way for the broader use of machine
learning models on PPG data in label-scarce regimes. | [
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] |
In recent years, large language models have achieved great success due to
their unprecedented size. However, training these models poses a challenge for
most researchers as it requires a substantial number of GPUs. To reduce GPU
memory usage, memory partitioning, and memory offloading have been proposed.
These approaches eliminate memory redundancies and offload memory usage to the
CPU and NVMe memory, respectively, enabling training on small GPU clusters.
However, directly deploying these solutions often leads to suboptimal
efficiency. Only experienced experts can unleash the full potential of hardware
by carefully tuning the distributed configuration. Thus, we present a novel
solution, Elixir, which automates efficient large-model training based on
pre-runtime model profiling. Elixir aims to identify the optimal combination of
partitioning and offloading techniques to maximize training throughput. In our
experiments, Elixir significantly outperforms the current state-of-the-art
baseline. Our optimal configuration achieves up to a 3.4$\times$ speedup on
GPT-2 models compared with SOTA solutions. We hope that our work will benefit
individuals who lack computing resources and expertise, granting them access to
large models. The beta version of Elixir is now available at
https://github.com/hpcaitech/ColossalAI/tree/feature/elixir. | [
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Large-scale training have propelled significant progress in various
sub-fields of AI such as computer vision and natural language processing.
However, building robot learning systems at a comparable scale remains
challenging. To develop robots that can perform a wide range of skills and
adapt to new scenarios, efficient methods for collecting vast and diverse
amounts of data on physical robot systems are required, as well as the
capability to train high-capacity policies using such datasets. In this work,
we propose a framework for scaling robot learning, with specific focus on
multi-task and multi-scene manipulation in kitchen environments, both in
simulation and in the real world. Our proposed framework, CACTI, comprises four
stages that separately handle: data collection, data augmentation, visual
representation learning, and imitation policy training, to enable scalability
in robot learning . We make use of state-of-the-art generative models as part
of the data augmentation stage, and use pre-trained out-of-domain visual
representations to improve training efficiency. Experimental results
demonstrate the effectiveness of our approach. On a real robot setup, CACTI
enables efficient training of a single policy that can perform 10 manipulation
tasks involving kitchen objects, and is robust to varying layouts of
distractors. In a simulated kitchen environment, CACTI trains a single policy
to perform 18 semantic tasks across 100 layout variations for each individual
task. We will release the simulation task benchmark and augmented datasets in
both real and simulated environments to facilitate future research. | [
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] |
Driven by the global decarbonization effort, the rapid integration of
renewable energy into the conventional electricity grid presents new challenges
and opportunities for the battery energy storage system (BESS) participating in
the energy market. Energy arbitrage can be a significant source of revenue for
the BESS due to the increasing price volatility in the spot market caused by
the mismatch between renewable generation and electricity demand. In addition,
the Frequency Control Ancillary Services (FCAS) markets established to
stabilize the grid can offer higher returns for the BESS due to their
capability to respond within milliseconds. Therefore, it is crucial for the
BESS to carefully decide how much capacity to assign to each market to maximize
the total profit under uncertain market conditions. This paper formulates the
bidding problem of the BESS as a Markov Decision Process, which enables the
BESS to participate in both the spot market and the FCAS market to maximize
profit. Then, Proximal Policy Optimization, a model-free deep reinforcement
learning algorithm, is employed to learn the optimal bidding strategy from the
dynamic environment of the energy market under a continuous bidding scale. The
proposed model is trained and validated using real-world historical data of the
Australian National Electricity Market. The results demonstrate that our
developed joint bidding strategy in both markets is significantly profitable
compared to individual markets. | [
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] |
Blockchain has empowered computer systems to be more secure using a
distributed network. However, the current blockchain design suffers from
fairness issues in transaction ordering. Miners are able to reorder
transactions to generate profits, the so-called miner extractable value (MEV).
Existing research recognizes MEV as a severe security issue and proposes
potential solutions, including prominent Flashbots. However, previous studies
have mostly analyzed blockchain data, which might not capture the impacts of
MEV in a much broader AI society. Thus, in this research, we applied natural
language processing (NLP) methods to comprehensively analyze topics in tweets
on MEV. We collected more than 20000 tweets with #MEV and #Flashbots hashtags
and analyzed their topics. Our results show that the tweets discussed profound
topics of ethical concern, including security, equity, emotional sentiments,
and the desire for solutions to MEV. We also identify the co-movements of MEV
activities on blockchain and social media platforms. Our study contributes to
the literature at the interface of blockchain security, MEV solutions, and AI
ethics. | [
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As modern data pipelines continue to collect, produce, and store a variety of
data formats, extracting and combining value from traditional and context-rich
sources such as strings, text, video, audio, and logs becomes a manual process
where such formats are unsuitable for RDBMS. To tap into the dark data, domain
experts analyze and extract insights and integrate them into the data
repositories. This process can involve out-of-DBMS, ad-hoc analysis, and
processing resulting in ETL, engineering effort, and suboptimal performance.
While AI systems based on ML models can automate the analysis process, they
often further generate context-rich answers. Using multiple sources of truth,
for either training the models or in the form of knowledge bases, further
exacerbates the problem of consolidating the data of interest.
We envision an analytical engine co-optimized with components that enable
context-rich analysis. Firstly, as the data from different sources or resulting
from model answering cannot be cleaned ahead of time, we propose using online
data integration via model-assisted similarity operations. Secondly, we aim for
a holistic pipeline cost- and rule-based optimization across relational and
model-based operators. Thirdly, with increasingly heterogeneous hardware and
equally heterogeneous workloads ranging from traditional relational analytics
to generative model inference, we envision a system that just-in-time adapts to
the complex analytical query requirements. To solve increasingly complex
analytical problems, ML offers attractive solutions that must be combined with
traditional analytical processing and benefit from decades of database
community research to achieve scalability and performance effortless for the
end user. | [
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] |
Structural Health Monitoring (SHM) describes a process for inferring
quantifiable metrics of structural condition, which can serve as input to
support decisions on the operation and maintenance of infrastructure assets.
Given the long lifespan of critical structures, this problem can be cast as a
sequential decision making problem over prescribed horizons. Partially
Observable Markov Decision Processes (POMDPs) offer a formal framework to solve
the underlying optimal planning task. However, two issues can undermine the
POMDP solutions. Firstly, the need for a model that can adequately describe the
evolution of the structural condition under deterioration or corrective actions
and, secondly, the non-trivial task of recovery of the observation process
parameters from available monitoring data. Despite these potential challenges,
the adopted POMDP models do not typically account for uncertainty on model
parameters, leading to solutions which can be unrealistically confident. In
this work, we address both key issues. We present a framework to estimate POMDP
transition and observation model parameters directly from available data, via
Markov Chain Monte Carlo (MCMC) sampling of a Hidden Markov Model (HMM)
conditioned on actions. The MCMC inference estimates distributions of the
involved model parameters. We then form and solve the POMDP problem by
exploiting the inferred distributions, to derive solutions that are robust to
model uncertainty. We successfully apply our approach on maintenance planning
for railway track assets on the basis of a "fractal value" indicator, which is
computed from actual railway monitoring data. | [
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] |
Reliably planning fingertip grasps for multi-fingered hands lies as a key
challenge for many tasks including tool use, insertion, and dexterous in-hand
manipulation. This task becomes even more difficult when the robot lacks an
accurate model of the object to be grasped. Tactile sensing offers a promising
approach to account for uncertainties in object shape. However, current robotic
hands tend to lack full tactile coverage. As such, a problem arises of how to
plan and execute grasps for multi-fingered hands such that contact is made with
the area covered by the tactile sensors. To address this issue, we propose an
approach to grasp planning that explicitly reasons about where the fingertips
should contact the estimated object surface while maximizing the probability of
grasp success. Key to our method's success is the use of visual surface
estimation for initial planning to encode the contact constraint. The robot
then executes this plan using a tactile-feedback controller that enables the
robot to adapt to online estimates of the object's surface to correct for
errors in the initial plan. Importantly, the robot never explicitly integrates
object pose or surface estimates between visual and tactile sensing, instead it
uses the two modalities in complementary ways. Vision guides the robots motion
prior to contact; touch updates the plan when contact occurs differently than
predicted from vision. We show that our method successfully synthesises and
executes precision grasps for previously unseen objects using surface estimates
from a single camera view. Further, our approach outperforms a state of the art
multi-fingered grasp planner, while also beating several baselines we propose. | [
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We propose a layered hierarchical architecture called UCLA (Universal
Causality Layered Architecture), which combines multiple levels of categorical
abstraction for causal inference. At the top-most level, causal interventions
are modeled combinatorially using a simplicial category of ordinal numbers. At
the second layer, causal models are defined by a graph-type category. The
non-random ``surgical" operations on causal structures, such as edge deletion,
are captured using degeneracy and face operators from the simplicial layer
above. The third categorical abstraction layer corresponds to the data layer in
causal inference. The fourth homotopy layer comprises of additional structure
imposed on the instance layer above, such as a topological space, which enables
evaluating causal models on datasets. Functors map between every pair of layers
in UCLA. Each functor between layers is characterized by a universal arrow,
which defines an isomorphism between every pair of categorical layers. These
universal arrows define universal elements and representations through the
Yoneda Lemma, and in turn lead to a new category of elements based on a
construction introduced by Grothendieck. Causal inference between each pair of
layers is defined as a lifting problem, a commutative diagram whose objects are
categories, and whose morphisms are functors that are characterized as
different types of fibrations. We illustrate the UCLA architecture using a
range of examples, including integer-valued multisets that represent a
non-graphical framework for conditional independence, and causal models based
on graphs and string diagrams using symmetric monoidal categories. We define
causal effect in terms of the homotopy colimit of the nerve of the category of
elements. | [
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] |
Medical systematic reviews typically require assessing all the documents
retrieved by a search. The reason is two-fold: the task aims for ``total
recall''; and documents retrieved using Boolean search are an unordered set,
and thus it is unclear how an assessor could examine only a subset. Screening
prioritisation is the process of ranking the (unordered) set of retrieved
documents, allowing assessors to begin the downstream processes of the
systematic review creation earlier, leading to earlier completion of the
review, or even avoiding screening documents ranked least relevant.
Screening prioritisation requires highly effective ranking methods.
Pre-trained language models are state-of-the-art on many IR tasks but have yet
to be applied to systematic review screening prioritisation. In this paper, we
apply several pre-trained language models to the systematic review document
ranking task, both directly and fine-tuned. An empirical analysis compares how
effective neural methods compare to traditional methods for this task. We also
investigate different types of document representations for neural methods and
their impact on ranking performance.
Our results show that BERT-based rankers outperform the current
state-of-the-art screening prioritisation methods. However, BERT rankers and
existing methods can actually be complementary, and thus, further improvements
may be achieved if used in conjunction. | [
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] |
Boolean query construction is often critical for medical systematic review
literature search. To create an effective Boolean query, systematic review
researchers typically spend weeks coming up with effective query terms and
combinations. One challenge to creating an effective systematic review Boolean
query is the selection of effective MeSH Terms to include in the query. In our
previous work, we created neural MeSH term suggestion methods and compared them
to state-of-the-art MeSH term suggestion methods. We found neural MeSH term
suggestion methods to be highly effective.
In this demonstration, we build upon our previous work by creating (1) a
Web-based MeSH term suggestion prototype system that allows users to obtain
suggestions from a number of underlying methods and (2) a Python library that
implements ours and others' MeSH term suggestion methods and that is aimed at
researchers who want to further investigate, create or deploy such type of
methods. We describe the architecture of the web-based system and how to use it
for the MeSH term suggestion task. For the Python library, we describe how the
library can be used for advancing further research and experimentation, and we
validate the results of the methods contained in the library on standard
datasets. Our web-based prototype system is available at
http://ielab-mesh-suggest.uqcloud.net, while our Python library is at
https://github.com/ielab/meshsuggestlib. | [
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Algorithms and technologies are essential tools that pervade all aspects of
our daily lives. In the last decades, health care research benefited from new
computer-based recruiting methods, the use of federated architectures for data
storage, the introduction of innovative analyses of datasets, and so on.
Nevertheless, health care datasets can still be affected by data bias. Due to
data bias, they provide a distorted view of reality, leading to wrong analysis
results and, consequently, decisions. For example, in a clinical trial that
studied the risk of cardiovascular diseases, predictions were wrong due to the
lack of data on ethnic minorities. It is, therefore, of paramount importance
for researchers to acknowledge data bias that may be present in the datasets
they use, eventually adopt techniques to mitigate them and control if and how
analyses results are impacted. This paper proposes a method to address bias in
datasets that: (i) defines the types of data bias that may be present in the
dataset, (ii) characterizes and quantifies data bias with adequate metrics,
(iii) provides guidelines to identify, measure, and mitigate data bias for
different data sources. The method we propose is applicable both for
prospective and retrospective clinical trials. We evaluate our proposal both
through theoretical considerations and through interviews with researchers in
the health care environment. | [
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] |
In recent years, indoor human presence detection based on supervised learning
(SL) and channel state information (CSI) has attracted much attention. However,
existing studies that rely on spatial information of CSI are susceptible to
environmental changes which degrade prediction accuracy. Moreover, SL-based
methods require time-consuming data labeling for retraining models. Therefore,
it is imperative to design a continuously monitored model using a
semi-supervised learning (SSL) based scheme. In this paper, we conceive a
bifold teacher-student (BTS) learning approach for indoor human presence
detection in an adjoining two-room scenario. The proposed SSL-based primal-dual
teacher-student network intelligently learns spatial and temporal features from
labeled and unlabeled CSI datasets. Additionally, the enhanced penalized loss
function leverages entropy and distance measures to distinguish drifted data,
i.e., features of new datasets affected by time-varying effects and altered
from the original distribution. Experimental results demonstrate that the
proposed BTS system sustains asymptotic accuracy after retraining the model
with unlabeled data. Furthermore, BTS outperforms existing SSL-based models in
terms of the highest detection accuracy while achieving the asymptotic
performance of SL-based methods. | [
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] |
Candidate axiom scoring is the task of assessing the acceptability of a
candidate axiom against the evidence provided by known facts or data. The
ability to score candidate axioms reliably is required for automated schema or
ontology induction, but it can also be valuable for ontology and/or knowledge
graph validation. Accurate axiom scoring heuristics are often computationally
expensive, which is an issue if you wish to use them in iterative search
techniques like level-wise generate-and-test or evolutionary algorithms, which
require scoring a large number of candidate axioms. We address the problem of
developing a predictive model as a substitute for reasoning that predicts the
possibility score of candidate class axioms and is quick enough to be employed
in such situations. We use a semantic similarity measure taken from an
ontology's subsumption structure for this purpose. We show that the approach
provided in this work can accurately learn the possibility scores of candidate
OWL class axioms and that it can do so for a variety of OWL class axioms. | [
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] |
Problem statement: Standardisation of AI fairness rules and benchmarks is
challenging because AI fairness and other ethical requirements depend on
multiple factors such as context, use case, type of the AI system, and so on.
In this paper, we elaborate that the AI system is prone to biases at every
stage of its lifecycle, from inception to its usage, and that all stages
require due attention for mitigating AI bias. We need a standardised approach
to handle AI fairness at every stage. Gap analysis: While AI fairness is a hot
research topic, a holistic strategy for AI fairness is generally missing. Most
researchers focus only on a few facets of AI model-building. Peer review shows
excessive focus on biases in the datasets, fairness metrics, and algorithmic
bias. In the process, other aspects affecting AI fairness get ignored. The
solution proposed: We propose a comprehensive approach in the form of a novel
seven-layer model, inspired by the Open System Interconnection (OSI) model, to
standardise AI fairness handling. Despite the differences in the various
aspects, most AI systems have similar model-building stages. The proposed model
splits the AI system lifecycle into seven abstraction layers, each
corresponding to a well-defined AI model-building or usage stage. We also
provide checklists for each layer and deliberate on potential sources of bias
in each layer and their mitigation methodologies. This work will facilitate
layer-wise standardisation of AI fairness rules and benchmarking parameters. | [
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] |
We introduce an end-to-end computational framework that allows for
hyperparameter optimization using the DeepHyper library, accelerated model
training, and interpretable AI inference. The framework is based on
state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN,
MPNN-transformer, and TorchMD-NET. We employ these AI models along with the
benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict
user-specified material properties within modern computing environments. We
demonstrate transferable applications in the modeling of small molecules,
inorganic crystals and nanoporous metal organic frameworks with a unified,
standalone framework. We have deployed and tested this framework in the
ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the
Delta supercomputer at the National Center for Supercomputing Applications to
provide researchers with modern tools to conduct accelerated AI-driven
discovery in leadership-class computing environments. We release these digital
assets as open source scientific software in GitLab, and ready-to-use Jupyter
notebooks in Google Colab. | [
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] |
Automated Program Repair (APR) is defined as the process of fixing a
bug/defect in the source code, by an automated tool. APR tools have recently
experienced promising results by leveraging state-of-the-art Neural Language
Processing (NLP) techniques. APR tools such as TFix and CodeXGLUE combine
text-to-text transformers with software-specific techniques are outperforming
alternatives, these days. However, in most APR studies the train and test sets
are chosen from the same set of projects. In reality, however, APR models are
meant to be generalizable to new and different projects. Therefore, there is a
potential threat that reported APR models with high effectiveness perform
poorly when the characteristics of the new project or its bugs are different
than the training set's(Domain Shift).
In this study, we first define and measure the domain shift problem in
automated program repair. Then, we then propose a domain adaptation framework
that can adapt an APR model for a given target project. We conduct an empirical
study with three domain adaptation methods FullFineTuning,
TuningWithLightWeightAdapterLayers, and CurriculumLearning using two
state-of-the-art domain adaptation tools (TFix and CodeXGLUE) and two APR
models on 611 bugs from 19 projects. The results show that our proposed
framework can improve the effectiveness of TFix by 13.05% and CodeXGLUE by
23.4%. Another contribution of this study is the proposal of a data synthesis
method to address the lack of labelled data in APR. We leverage transformers to
create a bug generator model. We use the generated synthetic data to domain
adapt TFix and CodeXGLUE on the projects with no data (Zero-shot learning),
which results in an average improvement of 5.76% and 24.42% for TFix and
CodeXGLUE, respectively. | [
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] |
Retrosynthesis is the task of breaking down a chemical compound recursively
step-by-step into molecular precursors until a set of commercially available
molecules is found. Consequently, the goal is to provide a valid synthesis
route for a molecule. As more single-step models develop, we see increasing
accuracy in the prediction of molecular disconnections, potentially improving
the creation of synthetic paths. Multi-step approaches repeatedly apply the
chemical information stored in single-step retrosynthesis models. However, this
connection is not reflected in contemporary research, fixing either the
single-step model or the multi-step algorithm in the process. In this work, we
establish a bridge between both tasks by benchmarking the performance and
transfer of different single-step retrosynthesis models to the multi-step
domain by leveraging two common search algorithms, Monte Carlo Tree Search and
Retro*. We show that models designed for single-step retrosynthesis, when
extended to multi-step, can have a tremendous impact on the route finding
capabilities of current multi-step methods, improving performance by up to +30%
compared to the most widely used model. Furthermore, we observe no clear link
between contemporary single-step and multi-step evaluation metrics, showing
that single-step models need to be developed and tested for the multi-step
domain and not as an isolated task to find synthesis routes for molecules of
interest. | [
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] |
Real-time bidding (RTB) has become a major paradigm of display advertising.
Each ad impression generated from a user visit is auctioned in real time, where
demand-side platform (DSP) automatically provides bid price usually relying on
the ad impression value estimation and the optimal bid price determination.
However, the current bid strategy overlooks large randomness of the user
behaviors (e.g., click) and the cost uncertainty caused by the auction
competition. In this work, we explicitly factor in the uncertainty of estimated
ad impression values and model the risk preference of a DSP under a specific
state and market environment via a sequential decision process. Specifically,
we propose a novel adaptive risk-aware bidding algorithm with budget constraint
via reinforcement learning, which is the first to simultaneously consider
estimation uncertainty and the dynamic risk tendency of a DSP. We theoretically
unveil the intrinsic relation between the uncertainty and the risk tendency
based on value at risk (VaR). Consequently, we propose two instantiations to
model risk tendency, including an expert knowledge-based formulation embracing
three essential properties and an adaptive learning method based on
self-supervised reinforcement learning. We conduct extensive experiments on
public datasets and show that the proposed framework outperforms
state-of-the-art methods in practical settings. | [
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] |
In recent years, reinforcement learning (RL) has become increasingly
successful in its application to science and the process of scientific
discovery in general. However, while RL algorithms learn to solve increasingly
complex problems, interpreting the solutions they provide becomes ever more
challenging. In this work, we gain insights into an RL agent's learned behavior
through a post-hoc analysis based on sequence mining and clustering.
Specifically, frequent and compact subroutines, used by the agent to solve a
given task, are distilled as gadgets and then grouped by various metrics. This
process of gadget discovery develops in three stages: First, we use an RL agent
to generate data, then, we employ a mining algorithm to extract gadgets and
finally, the obtained gadgets are grouped by a density-based clustering
algorithm. We demonstrate our method by applying it to two quantum-inspired RL
environments. First, we consider simulated quantum optics experiments for the
design of high-dimensional multipartite entangled states where the algorithm
finds gadgets that correspond to modern interferometer setups. Second, we
consider a circuit-based quantum computing environment where the algorithm
discovers various gadgets for quantum information processing, such as quantum
teleportation. This approach for analyzing the policy of a learned agent is
agent and environment agnostic and can yield interesting insights into any
agent's policy. | [
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] |
Methods based on ordinary differential equations (ODEs) are widely used to
build generative models of time-series. In addition to high computational
overhead due to explicitly computing hidden states recurrence, existing
ODE-based models fall short in learning sequence data with sharp transitions -
common in many real-world systems - due to numerical challenges during
optimization. In this work, we propose LS4, a generative model for sequences
with latent variables evolving according to a state space ODE to increase
modeling capacity. Inspired by recent deep state space models (S4), we achieve
speedups by leveraging a convolutional representation of LS4 which bypasses the
explicit evaluation of hidden states. We show that LS4 significantly
outperforms previous continuous-time generative models in terms of marginal
distribution, classification, and prediction scores on real-world datasets in
the Monash Forecasting Repository, and is capable of modeling highly stochastic
data with sharp temporal transitions. LS4 sets state-of-the-art for
continuous-time latent generative models, with significant improvement of mean
squared error and tighter variational lower bounds on irregularly-sampled
datasets, while also being x100 faster than other baselines on long sequences. | [
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] |
Configurable software systems are employed in many important application
domains. Understanding the performance of the systems under all configurations
is critical to prevent potential performance issues caused by misconfiguration.
However, as the number of configurations can be prohibitively large, it is not
possible to measure the system performance under all configurations. Thus, a
common approach is to build a prediction model from a limited measurement data
to predict the performance of all configurations as scalar values. However, it
has been pointed out that there are different sources of uncertainty coming
from the data collection or the modeling process, which can make the scalar
predictions not certainly accurate. To address this problem, we propose a
Bayesian deep learning based method, namely BDLPerf, that can incorporate
uncertainty into the prediction model. BDLPerf can provide both scalar
predictions for configurations' performance and the corresponding confidence
intervals of these scalar predictions. We also develop a novel uncertainty
calibration technique to ensure the reliability of the confidence intervals
generated by a Bayesian prediction model. Finally, we suggest an efficient
hyperparameter tuning technique so as to train the prediction model within a
reasonable amount of time whilst achieving high accuracy. Our experimental
results on 10 real-world systems show that BDLPerf achieves higher accuracy
than existing approaches, in both scalar performance prediction and confidence
interval estimation. | [
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] |
In addition to its public health crisis, COVID-19 pandemic has led to the
shutdown and closure of workplaces with an estimated total cost of more than
$16 trillion. Given the long hours an average person spends in buildings and
indoor environments, this research article proposes data-driven control
strategies to design optimal indoor airflow to minimize the exposure of
occupants to viral pathogens in built environments. A general control framework
is put forward for designing an optimal velocity field and proximal policy
optimization, a reinforcement learning algorithm is employed to solve the
control problem in a data-driven fashion. The same framework is used for
optimal placement of disinfectants to neutralize the viral pathogens as an
alternative to the airflow design when the latter is practically infeasible or
hard to implement. We show, via simulation experiments, that the control agent
learns the optimal policy in both scenarios within a reasonable time. The
proposed data-driven control framework in this study will have significant
societal and economic benefits by setting the foundation for an improved
methodology in designing case-specific infection control guidelines that can be
realized by affordable ventilation devices and disinfectants. | [
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] |
Tour itinerary planning and recommendation are challenging problems for
tourists visiting unfamiliar cities. Many tour recommendation algorithms only
consider factors such as the location and popularity of Points of Interest
(POIs) but their solutions may not align well with the user's own preferences
and other location constraints. Additionally, these solutions do not take into
consideration of the users' preference based on their past POIs selection. In
this paper, we propose POIBERT, an algorithm for recommending personalized
itineraries using the BERT language model on POIs. POIBERT builds upon the
highly successful BERT language model with the novel adaptation of a language
model to our itinerary recommendation task, alongside an iterative approach to
generate consecutive POIs.
Our recommendation algorithm is able to generate a sequence of POIs that
optimizes time and users' preference in POI categories based on past
trajectories from similar tourists. Our tour recommendation algorithm is
modeled by adapting the itinerary recommendation problem to the sentence
completion problem in natural language processing (NLP). We also innovate an
iterative algorithm to generate travel itineraries that satisfies the time
constraints which is most likely from past trajectories. Using a Flickr dataset
of seven cities, experimental results show that our algorithm out-performs many
sequence prediction algorithms based on measures in recall, precision and
F1-scores. | [
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] |
In this paper, we propose a Siamese sleep transformer (SST) that effectively
extracts features from single-channel raw electroencephalogram signals for
robust sleep stage scoring. Despite the significant advances in sleep stage
scoring in the last few years, most of them mainly focused on the increment of
model performance. However, other problems still exist: the bias of labels in
datasets and the instability of model performance by repetitive training. To
alleviate these problems, we propose the SST, a novel sleep stage scoring model
with a selective batch sampling strategy and self-knowledge distillation. To
evaluate how robust the model was to the bias of labels, we used different
datasets for training and testing: the sleep heart health study and the
Sleep-EDF datasets. In this condition, the SST showed competitive performance
in sleep stage scoring. In addition, we demonstrated the effectiveness of the
selective batch sampling strategy with a reduction of the standard deviation of
performance by repetitive training. These results could show that SST extracted
effective learning features against the bias of labels in datasets, and the
selective batch sampling strategy worked for the model robustness in training. | [
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With growing sophistication and volume of cyber attacks combined with complex
network structures, it is becoming extremely difficult for security analysts to
corroborate evidences to identify multistage campaigns on their network. This
work develops HeAT (Heated Alert Triage): given a critical indicator of
compromise (IoC), e.g., a severe IDS alert, HeAT produces a HeATed Attack
Campaign (HAC) depicting the multistage activities that led up to the critical
event. We define the concept of "Alert Episode Heat" to represent the analysts
opinion of how much an event contributes to the attack campaign of the critical
IoC given their knowledge of the network and security expertise. Leveraging a
network-agnostic feature set, HeAT learns the essence of analyst's assessment
of "HeAT" for a small set of IoC's, and applies the learned model to extract
insightful attack campaigns for IoC's not seen before, even across networks by
transferring what have been learned. We demonstrate the capabilities of HeAT
with data collected in Collegiate Penetration Testing Competition (CPTC) and
through collaboration with a real-world SOC. We developed HeAT-Gain metrics to
demonstrate how analysts may assess and benefit from the extracted attack
campaigns in comparison to common practices where IP addresses are used to
corroborate evidences. Our results demonstrates the practical uses of HeAT by
finding campaigns that span across diverse attack stages, remove a significant
volume of irrelevant alerts, and achieve coherency to the analyst's original
assessments. | [
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