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The exploration of transition state (TS) geometries is crucial for
elucidating chemical reaction mechanisms and modeling their kinetics. Recently,
machine learning (ML) models have shown remarkable performance for prediction
of TS geometries. However, they require 3D conformations of reactants and
products often with their appropriate orientations as input, which demands
substantial efforts and computational cost. Here, we propose a generative
approach based on the stochastic diffusion method, namely TSDiff, for
prediction of TS geometries just from 2D molecular graphs. TSDiff outperformed
the existing ML models with 3D geometries in terms of both accuracy and
efficiency. Moreover, it enables to sample various TS conformations, because it
learned the distribution of TS geometries for diverse reactions in training.
Thus, TSDiff was able to find more favorable reaction pathways with lower
barrier heights than those in the reference database. These results demonstrate
that TSDiff shows promising potential for an efficient and reliable TS
exploration. | [
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] |
Human activity recognition (HAR) is a rapidly growing field that utilizes
smart devices, sensors, and algorithms to automatically classify and identify
the actions of individuals within a given environment. These systems have a
wide range of applications, including assisting with caring tasks, increasing
security, and improving energy efficiency. However, there are several
challenges that must be addressed in order to effectively utilize HAR systems
in multi-resident environments. One of the key challenges is accurately
associating sensor observations with the identities of the individuals
involved, which can be particularly difficult when residents are engaging in
complex and collaborative activities. This paper provides a brief overview of
the design and implementation of HAR systems, including a summary of the
various data collection devices and approaches used for human activity
identification. It also reviews previous research on the use of these systems
in multi-resident environments and offers conclusions on the current state of
the art in the field. | [
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] |
Designing molecules that bind to specific target proteins is a fundamental
task in drug discovery. Recent models leverage geometric constraints to
generate ligand molecules that bind cohesively with specific protein pockets.
However, these models cannot effectively generate 3D molecules with 2D skeletal
curtailments and property constraints, which are pivotal to drug potency and
development. To tackle this challenge, we propose GraphVF, a variational
flow-based framework that combines 2D topology and 3D geometry, for
controllable generation of binding 3D molecules. Empirically, our method
achieves state-of-the-art binding affinity and realistic sub-structural layouts
for protein-specific generation. In particular, GraphVF represents the first
controllable geometry-aware, protein-specific molecule generation method, which
can generate binding 3D molecules with tailored sub-structures and
physio-chemical properties. Our code is available at
https://github.com/Franco-Solis/GraphVF-code. | [
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] |
This paper proposes a non-intrusive, declarative, dynamic and transparent
system called `HiQ` to track Python program runtime information without
compromising on the run-time system performance and losing insight. HiQ can be
used for monolithic and distributed systems, offline and online applications.
HiQ is developed when we optimize our large deep neural network (DNN) models
which are written in Python, but it can be generalized to any Python program or
distributed system, or even other languages like Java. We have implemented the
system and adopted it in our deep learning model life cycle management system
to catch the bottleneck while keeping our production code clean and highly
performant. The implementation is open-sourced at:
[https://github.com/oracle/hiq](https://github.com/oracle/hiq). | [
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] |
Explainable AI (XAI) aims to address the human need for safe and reliable AI
systems. However, numerous surveys emphasize the absence of a sound
mathematical formalization of key XAI notions -- remarkably including the term
"explanation" which still lacks a precise definition. To bridge this gap, this
paper presents the first mathematically rigorous definitions of key XAI notions
and processes, using the well-funded formalism of Category theory. We show that
our categorical framework allows to: (i) model existing learning schemes and
architectures, (ii) formally define the term "explanation", (iii) establish a
theoretical basis for XAI taxonomies, and (iv) analyze commonly overlooked
aspects of explaining methods. As a consequence, our categorical framework
promotes the ethical and secure deployment of AI technologies as it represents
a significant step towards a sound theoretical foundation of explainable AI. | [
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] |
Detection and mitigation of critical web vulnerabilities and attacks like
cross-site scripting (XSS), and cross-site request forgery (CSRF) have been a
great concern in the field of web security. Such web attacks are evolving and
becoming more challenging to detect. Several ideas from different perspectives
have been put forth that can be used to improve the performance of detecting
these web vulnerabilities and preventing the attacks from happening. Machine
learning techniques have lately been used by researchers to defend against XSS
and CSRF, and given the positive findings, it can be concluded that it is a
promising research direction. The objective of this paper is to briefly report
on the research works that have been published in this direction of applying
classical and advanced machine learning to identify and prevent XSS and CSRF.
The purpose of providing this survey is to address different machine learning
approaches that have been implemented, understand the key takeaway of every
research, discuss their positive impact and the downsides that persists, so
that it can help the researchers to determine the best direction to develop new
approaches for their own research and to encourage researchers to focus towards
the intersection between web security and machine learning. | [
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] |
Automated and accurate human activity recognition (HAR) using body-worn
sensors enables practical and cost efficient remote monitoring of Activity of
DailyLiving (ADL), which are shown to provide clinical insights across multiple
therapeutic areas. Development of accurate algorithms for human activity
recognition(HAR) is hindered by the lack of large real-world labeled datasets.
Furthermore, algorithms seldom work beyond the specific sensor on which they
are prototyped, prompting debate about whether accelerometer-based HAR is even
possible [Tong et al., 2020]. Here we develop a 6-class HAR model with strong
performance when evaluated on real-world datasets not seen during training. Our
model is based on a frozen self-supervised representation learned on a large
unlabeled dataset, combined with a shallow multi-layer perceptron with temporal
smoothing. The model obtains in-dataset state-of-the art performance on the
Capture24 dataset ($\kappa= 0.86$). Out-of-distribution (OOD) performance is
$\kappa = 0.7$, with both the representation and the perceptron models being
trained on data from a different sensor. This work represents a key step
towards device-agnostic HAR models, which can help contribute to increased
standardization of model evaluation in the HAR field. | [
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] |
Epilepsy affects more than 50 million people worldwide, making it one of the
world's most prevalent neurological diseases. The main symptom of epilepsy is
seizures, which occur abruptly and can cause serious injury or death. The
ability to predict the occurrence of an epileptic seizure could alleviate many
risks and stresses people with epilepsy face. Most of the previous work is
focused at seizure detection, we pivot our focus to seizure prediction problem.
We formulate the problem of detecting preictal (or pre-seizure) with reference
to normal EEG as a precursor to incoming seizure. To this end, we developed
several supervised deep learning approaches model to identify preictal EEG from
normal EEG. We further develop novel unsupervised deep learning approaches to
train the models on only normal EEG, and detecting pre-seizure EEG as an
anomalous event. These deep learning models were trained and evaluated on two
large EEG seizure datasets in a person-specific manner. We found that both
supervised and unsupervised approaches are feasible; however, their performance
varies depending on the patient, approach and architecture. This new line of
research has the potential to develop therapeutic interventions and save human
lives. | [
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] |
Accurate lane change prediction can reduce potential accidents and contribute
to higher road safety. Adaptive cruise control (ACC), lane departure avoidance
(LDA), and lane keeping assistance (LKA) are some conventional modules in
advanced driver assistance systems (ADAS). Thanks to vehicle-to-vehicle
communication (V2V), vehicles can share traffic information with surrounding
vehicles, enabling cooperative adaptive cruise control (CACC). While ACC relies
on the vehicle's sensors to obtain the position and velocity of the leading
vehicle, CACC also has access to the acceleration of multiple vehicles through
V2V communication. This paper compares the type of information (position,
velocity, acceleration) and the number of surrounding vehicles for driver lane
change prediction. We trained an LSTM (Long Short-Term Memory) on the HighD
dataset to predict lane change intention. Results indicate a significant
improvement in accuracy with an increase in the number of surrounding vehicles
and the information received from them. Specifically, the proposed model can
predict the ego vehicle lane change with 59.15% and 92.43% accuracy in ACC and
CACC scenarios, respectively. | [
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] |
Using knowledge graphs to assist deep learning models in making
recommendation decisions has recently been proven to effectively improve the
model's interpretability and accuracy. This paper introduces an end-to-end deep
learning model, named RKGCN, which dynamically analyses each user's preferences
and makes a recommendation of suitable items. It combines knowledge graphs on
both the item side and user side to enrich their representations to maximize
the utilization of the abundant information in knowledge graphs. RKGCN is able
to offer more personalized and relevant recommendations in three different
scenarios. The experimental results show the superior effectiveness of our
model over 5 baseline models on three real-world datasets including movies,
books, and music. | [
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] |
Based on the traditional VAE, a novel neural network model is presented, with
the latest molecular representation, SELFIES, to improve the effect of
generating new molecules. In this model, multi-layer convolutional network and
Fisher information are added to the original encoding layer to learn the data
characteristics and guide the encoding process, which makes the features of the
data hiding layer more aggregated, and integrates the Long Short Term Memory
neural network (LSTM) into the decoding layer for better data generation, which
effectively solves the degradation phenomenon generated by the encoding layer
and decoding layer of the original VAE model. Through experiments on zinc
molecular data sets, it is found that the similarity in the new VAE is 8.47%
higher than that of the original ones. SELFIES are better at generating a
variety of molecules than the traditional molecular representation, SELFIES.
Experiments have shown that using SELFIES and the new VAE model presented in
this paper can improve the effectiveness of generating new molecules. | [
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] |
eXplainable artificial intelligence (XAI) methods have emerged to convert the
black box of machine learning models into a more digestible form. These methods
help to communicate how the model works with the aim of making machine learning
models more transparent and increasing the trust of end-users into their
output. SHapley Additive exPlanations (SHAP) and Local Interpretable Model
Agnostic Explanation (LIME) are two widely used XAI methods particularly with
tabular data. In this commentary piece, we discuss the way the explainability
metrics of these two methods are generated and propose a framework for
interpretation of their outputs, highlighting their weaknesses and strengths. | [
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] |
This is a speculative essay on interface design and artificial intelligence.
Recently there has been a surge of attention to chatbots based on large
language models, including widely reported unsavory interactions. We contend
that part of the problem is that text is not all you need: sophisticated AI
systems should have dashboards, just like all other complicated devices.
Assuming the hypothesis that AI systems based on neural networks will contain
interpretable models of aspects of the world around them, we discuss what data
such dashboards might display. We conjecture that, for many systems, the two
most important models will be of the user and of the system itself. We call
these the System Model and User Model. We argue that, for usability and safety,
interfaces to dialogue-based AI systems should have a parallel display based on
the state of the System Model and the User Model. Finding ways to identify,
interpret, and display these two models should be a core part of interface
research for AI. | [
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] |
Graph neural networks (GNNs) based methods have achieved impressive
performance on node clustering task. However, they are designed on the
homophilic assumption of graph and clustering on heterophilic graph is
overlooked. Due to the lack of labels, it is impossible to first identify a
graph as homophilic or heterophilic before a suitable GNN model can be found.
Hence, clustering on real-world graph with various levels of homophily poses a
new challenge to the graph research community. To fill this gap, we propose a
novel graph clustering method, which contains three key components: graph
reconstruction, a mixed filter, and dual graph clustering network. To be
graph-agnostic, we empirically construct two graphs which are high homophily
and heterophily from each data. The mixed filter based on the new graphs
extracts both low-frequency and high-frequency information. To reduce the
adverse coupling between node attribute and topological structure, we
separately map them into two subspaces in dual graph clustering network.
Extensive experiments on 11 benchmark graphs demonstrate our promising
performance. In particular, our method dominates others on heterophilic graphs. | [
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Logic is the main formal language to perform automated reasoning, and it is
further a human-interpretable language, at least for small formulae. Learning
and optimising logic requirements and rules has always been an important
problem in Artificial Intelligence. State of the art Machine Learning (ML)
approaches are mostly based on gradient descent optimisation in continuous
spaces, while learning logic is framed in the discrete syntactic space of
formulae. Using continuous optimisation to learn logic properties is a
challenging problem, requiring to embed formulae in a continuous space in a
meaningful way, i.e. preserving the semantics. Current methods are able to
construct effective semantic-preserving embeddings via kernel methods (for
linear temporal logic), but the map they define is not invertible. In this work
we address this problem, learning how to invert such an embedding leveraging
deep architectures based on the Graph Variational Autoencoder framework. We
propose a novel model specifically designed for this setting, justifying our
design choices through an extensive experimental evaluation. Reported results
in the context of propositional logic are promising, and several challenges
regarding learning invertible embeddings of formulae are highlighted and
addressed. | [
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] |
Answering complex queries on knowledge graphs is important but particularly
challenging because of the data incompleteness. Query embedding methods address
this issue by learning-based models and simulating logical reasoning with set
operators. Previous works focus on specific forms of embeddings, but scoring
functions between embeddings are underexplored. In contrast to existing scoring
functions motivated by local comparison or global transport, this work
investigates the local and global trade-off with unbalanced optimal transport
theory. Specifically, we embed sets as bounded measures in $\real$ endowed with
a scoring function motivated by the Wasserstein-Fisher-Rao metric. Such a
design also facilitates closed-form set operators in the embedding space.
Moreover, we introduce a convolution-based algorithm for linear time
computation and a block-diagonal kernel to enforce the trade-off. Results show
that WFRE can outperform existing query embedding methods on standard datasets,
evaluation sets with combinatorially complex queries, and hierarchical
knowledge graphs. Ablation study shows that finding a better local and global
trade-off is essential for performance improvement. | [
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] |
Proteins are complex biomolecules that perform a variety of crucial functions
within living organisms. Designing and generating novel proteins can pave the
way for many future synthetic biology applications, including drug discovery.
However, it remains a challenging computational task due to the large modeling
space of protein structures. In this study, we propose a latent diffusion model
that can reduce the complexity of protein modeling while flexibly capturing the
distribution of natural protein structures in a condensed latent space.
Specifically, we propose an equivariant protein autoencoder that embeds
proteins into a latent space and then uses an equivariant diffusion model to
learn the distribution of the latent protein representations. Experimental
results demonstrate that our method can effectively generate novel protein
backbone structures with high designability and efficiency. | [
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We consider a service robot in a household environment given a sequence of
high-level tasks one at a time. Most existing task planners, lacking knowledge
of what they may be asked to do next, solve each task in isolation and so may
unwittingly introduce side effects that make subsequent tasks more costly. In
order to reduce the overall cost of completing all tasks, we consider that the
robot must anticipate the impact its actions could have on future tasks. Thus,
we propose anticipatory planning: an approach in which estimates of the
expected future cost, from a graph neural network, augment model-based task
planning. Our approach guides the robot towards behaviors that encourage
preparation and organization, reducing overall costs in long-lived planning
scenarios. We evaluate our method on blockworld environments and show that our
approach reduces the overall planning costs by 5% as compared to planning
without anticipatory planning. Additionally, if given an opportunity to prepare
the environment in advance (a special case of anticipatory planning), our
planner improves overall cost by 11%. | [
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] |
Model-based reinforcement learning (MBRL) techniques have recently yielded
promising results for real-world autonomous racing using high-dimensional
observations. MBRL agents, such as Dreamer, solve long-horizon tasks by
building a world model and planning actions by latent imagination. This
approach involves explicitly learning a model of the system dynamics and using
it to learn the optimal policy for continuous control over multiple timesteps.
As a result, MBRL agents may converge to sub-optimal policies if the world
model is inaccurate. To improve state estimation for autonomous racing, this
paper proposes a self-supervised sensor fusion technique that combines
egocentric LiDAR and RGB camera observations collected from the F1TENTH Gym.
The zero-shot performance of MBRL agents is empirically evaluated on unseen
tracks and against a dynamic obstacle. This paper illustrates that multimodal
perception improves robustness of the world model without requiring additional
training data. The resulting multimodal Dreamer agent safely avoided collisions
and won the most races compared to other tested baselines in zero-shot
head-to-head autonomous racing. | [
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A central quest in explainable AI relates to understanding the decisions made
by (learned) classifiers. There are three dimensions of this understanding that
have been receiving significant attention in recent years. The first dimension
relates to characterizing conditions on instances that are necessary and
sufficient for decisions, therefore providing abstractions of instances that
can be viewed as the "reasons behind decisions." The next dimension relates to
characterizing minimal conditions that are sufficient for a decision, therefore
identifying maximal aspects of the instance that are irrelevant to the
decision. The last dimension relates to characterizing minimal conditions that
are necessary for a decision, therefore identifying minimal perturbations to
the instance that yield alternate decisions. We discuss in this tutorial a
comprehensive, semantical and computational theory of explainability along
these dimensions which is based on some recent developments in symbolic logic.
The tutorial will also discuss how this theory is particularly applicable to
non-symbolic classifiers such as those based on Bayesian networks, decision
trees, random forests and some types of neural networks. | [
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] |
In the field of Explainable Artificial Intelligence (XAI), counterfactual
examples explain to a user the predictions of a trained decision model by
indicating the modifications to be made to the instance so as to change its
associated prediction. These counterfactual examples are generally defined as
solutions to an optimization problem whose cost function combines several
criteria that quantify desiderata for a good explanation meeting user needs. A
large variety of such appropriate properties can be considered, as the user
needs are generally unknown and differ from one user to another; their
selection and formalization is difficult. To circumvent this issue, several
approaches propose to generate, rather than a single one, a set of diverse
counterfactual examples to explain a prediction. This paper proposes a review
of the numerous, sometimes conflicting, definitions that have been proposed for
this notion of diversity. It discusses their underlying principles as well as
the hypotheses on the user needs they rely on and proposes to categorize them
along several dimensions (explicit vs implicit, universe in which they are
defined, level at which they apply), leading to the identification of further
research challenges on this topic. | [
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] |
We present a new encoder-decoder generative network dubbed EdgeNet, which
introduces a novel encoder-decoder framework for data-driven auction design in
online e-commerce advertising. We break the neural auction paradigm of
Generalized-Second-Price(GSP), and improve the utilization efficiency of data
while ensuring the economic characteristics of the auction mechanism.
Specifically, EdgeNet introduces a transformer-based encoder to better capture
the mutual influence among different candidate advertisements. In contrast to
GSP based neural auction model, we design an autoregressive decoder to better
utilize the rich context information in online advertising auctions. EdgeNet is
conceptually simple and easy to extend to the existing end-to-end neural
auction framework. We validate the efficiency of EdgeNet on a wide range of
e-commercial advertising auction, demonstrating its potential in improving user
experience and platform revenue. | [
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] |
The Multi-Object Navigation (MultiON) task requires a robot to localize an
instance (each) of multiple object classes. It is a fundamental task for an
assistive robot in a home or a factory. Existing methods for MultiON have
viewed this as a direct extension of Object Navigation (ON), the task of
localising an instance of one object class, and are pre-sequenced, i.e., the
sequence in which the object classes are to be explored is provided in advance.
This is a strong limitation in practical applications characterized by dynamic
changes. This paper describes a deep reinforcement learning framework for
sequence-agnostic MultiON based on an actor-critic architecture and a suitable
reward specification. Our framework leverages past experiences and seeks to
reward progress toward individual as well as multiple target object classes. We
use photo-realistic scenes from the Gibson benchmark dataset in the AI Habitat
3D simulation environment to experimentally show that our method performs
better than a pre-sequenced approach and a state of the art ON method extended
to MultiON. | [
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] |
The use of mini-batches of data in training artificial neural networks is
nowadays very common. Despite its broad usage, theories explaining
quantitatively how large or small the optimal mini-batch size should be are
missing. This work presents a systematic attempt at understanding the role of
the mini-batch size in training two-layer neural networks. Working in the
teacher-student scenario, with a sparse teacher, and focusing on tasks of
different complexity, we quantify the effects of changing the mini-batch size
$m$. We find that often the generalization performances of the student strongly
depend on $m$ and may undergo sharp phase transitions at a critical value
$m_c$, such that for $m<m_c$ the training process fails, while for $m>m_c$ the
student learns perfectly or generalizes very well the teacher. Phase
transitions are induced by collective phenomena firstly discovered in
statistical mechanics and later observed in many fields of science. Finding a
phase transition varying the mini-batch size raises several important questions
on the role of a hyperparameter which have been somehow overlooked until now. | [
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] |
Reinforcement learning (RL) is inspired by the way human infants and animals
learn from the environment. The setting is somewhat idealized because, in
actual tasks, other agents in the environment have their own goals and behave
adaptively to the ego agent. To thrive in those environments, the agent needs
to influence other agents so their actions become more helpful and less
harmful. Research in computational economics distills two ways to influence
others directly: by providing tangible goods (mechanism design) and by
providing information (information design). This work investigates information
design problems for a group of RL agents. The main challenges are two-fold. One
is the information provided will immediately affect the transition of the agent
trajectories, which introduces additional non-stationarity. The other is the
information can be ignored, so the sender must provide information that the
receiver is willing to respect. We formulate the Markov signaling game, and
develop the notions of signaling gradient and the extended obedience
constraints that address these challenges. Our algorithm is efficient on
various mixed-motive tasks and provides further insights into computational
economics. Our code is publicly available at
https://github.com/YueLin301/InformationDesignMARL. | [
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] |
In the ongoing quest for hybridizing discrete reasoning with neural nets,
there is an increasing interest in neural architectures that can learn how to
solve discrete reasoning or optimization problems from natural inputs. In this
paper, we introduce a scalable neural architecture and loss function dedicated
to learning the constraints and criteria of NP-hard reasoning problems
expressed as discrete Graphical Models. Our loss function solves one of the
main limitations of Besag's pseudo-loglikelihood, enabling learning of high
energies. We empirically show it is able to efficiently learn how to solve
NP-hard reasoning problems from natural inputs as the symbolic, visual or
many-solutions Sudoku problems as well as the energy optimization formulation
of the protein design problem, providing data efficiency, interpretability, and
\textit{a posteriori} control over predictions. | [
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] |
Existing recommender systems face difficulties with zero-shot items, i.e.
items that have no historical interactions with users during the training
stage. Though recent works extract universal item representation via
pre-trained language models (PLMs), they ignore the crucial item relationships.
This paper presents a novel paradigm for the Zero-Shot Item-based
Recommendation (ZSIR) task, which pre-trains a model on product knowledge graph
(PKG) to refine the item features from PLMs. We identify three challenges for
pre-training PKG, which are multi-type relations in PKG, semantic divergence
between item generic information and relations and domain discrepancy from PKG
to downstream ZSIR task. We address the challenges by proposing four
pre-training tasks and novel task-oriented adaptation (ToA) layers. Moreover,
this paper discusses how to fine-tune the model on new recommendation task such
that the ToA layers are adapted to ZSIR task. Comprehensive experiments on 18
markets dataset are conducted to verify the effectiveness of the proposed model
in both knowledge prediction and ZSIR task. | [
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] |
We study off-policy evaluation (OPE) of contextual bandit policies for large
discrete action spaces where conventional importance-weighting approaches
suffer from excessive variance. To circumvent this variance issue, we propose a
new estimator, called OffCEM, that is based on the conjunct effect model (CEM),
a novel decomposition of the causal effect into a cluster effect and a residual
effect. OffCEM applies importance weighting only to action clusters and
addresses the residual causal effect through model-based reward estimation. We
show that the proposed estimator is unbiased under a new condition, called
local correctness, which only requires that the residual-effect model preserves
the relative expected reward differences of the actions within each cluster. To
best leverage the CEM and local correctness, we also propose a new two-step
procedure for performing model-based estimation that minimizes bias in the
first step and variance in the second step. We find that the resulting OffCEM
estimator substantially improves bias and variance compared to a range of
conventional estimators. Experiments demonstrate that OffCEM provides
substantial improvements in OPE especially in the presence of many actions. | [
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] |
We address an important yet challenging problem - modeling high-dimensional
dependencies across multivariates such as financial indicators in heterogeneous
markets. In reality, a market couples and influences others over time, and the
financial variables of a market are also coupled. We make the first attempt to
integrate variational sequential neural learning with copula-based dependence
modeling to characterize both temporal observable and latent variable-based
dependence degrees and structures across non-normal multivariates. Our
variational neural network WPVC-VLSTM models variational sequential dependence
degrees and structures across multivariate time series by variational long
short-term memory networks and regular vine copula. The regular vine copula
models nonnormal and long-range distributional couplings across multiple
dynamic variables. WPVC-VLSTM is verified in terms of both technical
significance and portfolio forecasting performance. It outperforms benchmarks
including linear models, stochastic volatility models, deep neural networks,
and variational recurrent networks in cross-market portfolio forecasting. | [
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] |
Deep learning-based approaches, such as AlphaFold2 (AF2), have significantly
advanced protein tertiary structure prediction, achieving results comparable to
real biological experimental methods. While AF2 has shown limitations in
predicting the effects of mutations, its robustness against sequence mutations
remains to be determined. Starting with the wild-type (WT) sequence, we
investigate adversarial sequences generated via an evolutionary approach, which
AF2 predicts to be substantially different from WT. Our experiments on CASP14
reveal that by modifying merely three residues in the protein sequence using a
combination of replacement, deletion, and insertion strategies, the alteration
in AF2's predictions, as measured by the Local Distance Difference Test (lDDT),
reaches 46.61. Moreover, when applied to a specific protein, SPNS2, our
proposed algorithm successfully identifies biologically meaningful residues
critical to protein structure determination and potentially indicates
alternative conformations, thus significantly expediting the experimental
process. | [
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] |
Sleep staging is critical for assessing sleep quality and diagnosing sleep
disorders. However, capturing both the spatial and temporal relationships
within electroencephalogram (EEG) signals during different sleep stages remains
challenging. In this paper, we propose a novel framework called the Hybrid
Attention EEG Sleep Staging (HASS) Framework. Specifically, we propose a
well-designed spatio-temporal attention mechanism to adaptively assign weights
to inter-channels and intra-channel EEG segments based on the spatio-temporal
relationship of the brain during different sleep stages. Experiment results on
the MASS and ISRUC datasets demonstrate that HASS can significantly improve
typical sleep staging networks. Our proposed framework alleviates the
difficulties of capturing the spatial-temporal relationship of EEG signals
during sleep staging and holds promise for improving the accuracy and
reliability of sleep assessment in both clinical and research settings. | [
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] |
Social Networks represent one of the most important online sources to share
content across a world-scale audience. In this context, predicting whether a
post will have any impact in terms of engagement is of crucial importance to
drive the profitable exploitation of these media. In the literature, several
studies address this issue by leveraging direct features of the posts,
typically related to the textual content and the user publishing it. In this
paper, we argue that the rise of engagement is also related to another key
component, which is the semantic connection among posts published by users in
social media. Hence, we propose TweetGage, a Graph Neural Network solution to
predict the user engagement based on a novel graph-based model that represents
the relationships among posts. To validate our proposal, we focus on the
Twitter platform and perform a thorough experimental campaign providing
evidence of its quality. | [
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] |
Recently Transformer and Convolution neural network (CNN) based models have
shown promising results in EEG signal processing. Transformer models can
capture the global dependencies in EEG signals through a self-attention
mechanism, while CNN models can capture local features such as sawtooth waves.
In this work, we propose an end-to-end neural epilepsy detection model, EENED,
that combines CNN and Transformer. Specifically, by introducing the convolution
module into the Transformer encoder, EENED can learn the time-dependent
relationship of the patient's EEG signal features and notice local EEG abnormal
mutations closely related to epilepsy, such as the appearance of spikes and the
sprinkling of sharp and slow waves. Our proposed framework combines the ability
of Transformer and CNN to capture different scale features of EEG signals and
holds promise for improving the accuracy and reliability of epilepsy detection.
Our source code will be released soon on GitHub. | [
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] |
A key challenge in dysarthric speech recognition is the speaker-level
diversity attributed to both speaker-identity associated factors such as
gender, and speech impairment severity. Most prior researches on addressing
this issue focused on using speaker-identity only. To this end, this paper
proposes a novel set of techniques to use both severity and speaker-identity in
dysarthric speech recognition: a) multitask training incorporating severity
prediction error; b) speaker-severity aware auxiliary feature adaptation; and
c) structured LHUC transforms separately conditioned on speaker-identity and
severity. Experiments conducted on UASpeech suggest incorporating additional
speech impairment severity into state-of-the-art hybrid DNN, E2E Conformer and
pre-trained Wav2vec 2.0 ASR systems produced statistically significant WER
reductions up to 4.78% (14.03% relative). Using the best system the lowest
published WER of 17.82% (51.25% on very low intelligibility) was obtained on
UASpeech. | [
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The quantum perceptron, the variational circuit, and the Grover algorithm
have been proposed as promising components for quantum machine learning. This
paper presents a new quantum perceptron that combines the quantum variational
circuit and the Grover algorithm. However, this does not guarantee that this
quantum variational perceptron with Grover's algorithm (QVPG) will have any
advantage over its quantum variational (QVP) and classical counterparts. Here,
we examine the performance of QVP and QVP-G by computing their loss function
and analyzing their accuracy on the classification task, then comparing these
two quantum models to the classical perceptron (CP). The results show that our
two quantum models are more efficient than CP, and our novel suggested model
QVP-G outperforms the QVP, demonstrating that the Grover can be applied to the
classification task and even makes the model more accurate, besides the
unstructured search problems. | [
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] |
Parkinson's disease (PD) is a neurological disorder impacting a person's
speech. Among automatic PD assessment methods, deep learning models have gained
particular interest. Recently, the community has explored cross-pathology and
cross-language models which can improve diagnostic accuracy even further.
However, strict patient data privacy regulations largely prevent institutions
from sharing patient speech data with each other. In this paper, we employ
federated learning (FL) for PD detection using speech signals from 3 real-world
language corpora of German, Spanish, and Czech, each from a separate
institution. Our results indicate that the FL model outperforms all the local
models in terms of diagnostic accuracy, while not performing very differently
from the model based on centrally combined training sets, with the advantage of
not requiring any data sharing among collaborators. This will simplify
inter-institutional collaborations, resulting in enhancement of patient
outcomes. | [
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] |
This article proposes a meta-learning method for estimating the conditional
average treatment effect (CATE) from a few observational data. The proposed
method learns how to estimate CATEs from multiple tasks and uses the knowledge
for unseen tasks. In the proposed method, based on the meta-learner framework,
we decompose the CATE estimation problem into sub-problems. For each
sub-problem, we formulate our estimation models using neural networks with
task-shared and task-specific parameters. With our formulation, we can obtain
optimal task-specific parameters in a closed form that are differentiable with
respect to task-shared parameters, making it possible to perform effective
meta-learning. The task-shared parameters are trained such that the expected
CATE estimation performance in few-shot settings is improved by minimizing the
difference between a CATE estimated with a large amount of data and one
estimated with just a few data. Our experimental results demonstrate that our
method outperforms the existing meta-learning approaches and CATE estimation
methods. | [
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] |
We study the complexity of the problem of training neural networks defined
via various activation functions. The training problem is known to be
existsR-complete with respect to linear activation functions and the ReLU
activation function. We consider the complexity of the problem with respect to
the sigmoid activation function and other effectively continuous functions. We
show that these training problems are polynomial-time many-one bireducible to
the existential theory of the reals extended with the corresponding activation
functions. In particular, we establish that the sigmoid activation function
leads to the existential theory of the reals with the exponential function. It
is thus open, and equivalent with the decidability of the existential theory of
the reals with the exponential function, whether training neural networks using
the sigmoid activation function is algorithmically solvable. In contrast, we
obtain that the training problem is undecidable if sinusoidal activation
functions are considered. Finally, we obtain general upper bounds for the
complexity of the training problem in the form of low levels of the
arithmetical hierarchy. | [
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] |
This paper presents a community-centered study of cultural limitations of
text-to-image (T2I) models in the South Asian context. We theorize these
failures using scholarship on dominant media regimes of representations and
locate them within participants' reporting of their existing social
marginalizations. We thus show how generative AI can reproduce an outsiders
gaze for viewing South Asian cultures, shaped by global and regional power
inequities. By centering communities as experts and soliciting their
perspectives on T2I limitations, our study adds rich nuance into existing
evaluative frameworks and deepens our understanding of the culturally-specific
ways AI technologies can fail in non-Western and Global South settings. We
distill lessons for responsible development of T2I models, recommending
concrete pathways forward that can allow for recognition of structural
inequalities. | [
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] |
Stagnant weather condition is one of the major contributors to air pollution
as it is favorable for the formation and accumulation of pollutants. To measure
the atmosphere's ability to dilute air pollutants, Air Stagnation Index (ASI)
has been introduced as an important meteorological index. Therefore, making
long-lead ASI forecasts is vital to make plans in advance for air quality
management. In this study, we found that autumn Ni\~no indices derived from sea
surface temperature (SST) anomalies show a negative correlation with wintertime
ASI in southern China, offering prospects for a prewinter forecast. We
developed an LSTM-based model to predict the future wintertime ASI. Results
demonstrated that multivariate inputs (past ASI and Ni\~no indices) achieve
better forecast performance than univariate input (only past ASI). The model
achieves a correlation coefficient of 0.778 between the actual and predicted
ASI, exhibiting a high degree of consistency. | [
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] |
The measurement of progress using benchmarks evaluations is ubiquitous in
computer science and machine learning. However, common approaches to analyzing
and presenting the results of benchmark comparisons of multiple algorithms over
multiple datasets, such as the critical difference diagram introduced by
Dem\v{s}ar (2006), have important shortcomings and, we show, are open to both
inadvertent and intentional manipulation. To address these issues, we propose a
new approach to presenting the results of benchmark comparisons, the Multiple
Comparison Matrix (MCM), that prioritizes pairwise comparisons and precludes
the means of manipulating experimental results in existing approaches. MCM can
be used to show the results of an all-pairs comparison, or to show the results
of a comparison between one or more selected algorithms and the state of the
art. MCM is implemented in Python and is publicly available. | [
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] |
In this paper, we navigate the intricate domain of reviewer rewards in
open-access academic publishing, leveraging the precision of mathematics and
the strategic acumen of game theory. We conceptualize the prevailing
voucher-based reviewer reward system as a two-player game, subsequently
identifying potential shortcomings that may incline reviewers towards binary
decisions. To address this issue, we propose and mathematically formalize an
alternative reward system with the objective of mitigating this bias and
promoting more comprehensive reviews. We engage in a detailed investigation of
the properties and outcomes of both systems, employing rigorous
game-theoretical analysis and deep reinforcement learning simulations. Our
results underscore a noteworthy divergence between the two systems, with our
proposed system demonstrating a more balanced decision distribution and
enhanced stability. This research not only augments the mathematical
understanding of reviewer reward systems, but it also provides valuable
insights for the formulation of policies within journal review system. Our
contribution to the mathematical community lies in providing a game-theoretical
perspective to a real-world problem and in the application of deep
reinforcement learning to simulate and understand this complex system. | [
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] |
Because implicit user feedback for the collaborative filtering (CF) models is
biased toward popular items, CF models tend to yield recommendation lists with
popularity bias. Previous studies have utilized inverse propensity weighting
(IPW) or causal inference to mitigate this problem. However, they solely employ
pointwise or pairwise loss functions and neglect to adopt a contrastive loss
function for learning meaningful user and item representations. In this paper,
we propose Unbiased ConTrastive Representation Learning (uCTRL), optimizing
alignment and uniformity functions derived from the InfoNCE loss function for
CF models. Specifically, we formulate an unbiased alignment function used in
uCTRL. We also devise a novel IPW estimation method that removes the bias of
both users and items. Despite its simplicity, uCTRL equipped with existing CF
models consistently outperforms state-of-the-art unbiased recommender models,
up to 12.22% for Recall@20 and 16.33% for NDCG@20 gains, on four benchmark
datasets. | [
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] |
State-of-the-art sensorimotor learning algorithms offer policies that can
often produce unstable behaviors, damaging the robot and/or the environment.
Traditional robot learning, on the contrary, relies on dynamical system-based
policies that can be analyzed for stability/safety. Such policies, however, are
neither flexible nor generic and usually work only with proprioceptive sensor
states. In this work, we bridge the gap between generic neural network policies
and dynamical system-based policies, and we introduce Autonomous Neural Dynamic
Policies (ANDPs) that: (a) are based on autonomous dynamical systems, (b)
always produce asymptotically stable behaviors, and (c) are more flexible than
traditional stable dynamical system-based policies. ANDPs are fully
differentiable, flexible generic-policies that can be used in imitation
learning setups while ensuring asymptotic stability. In this paper, we explore
the flexibility and capacity of ANDPs in several imitation learning tasks
including experiments with image observations. The results show that ANDPs
combine the benefits of both neural network-based and dynamical system-based
methods. | [
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] |
In this study, we address the importance of modeling behavior style in
virtual agents for personalized human-agent interaction. We propose a machine
learning approach to synthesize gestures, driven by prosodic features and text,
in the style of different speakers, even those unseen during training. Our
model incorporates zero-shot multimodal style transfer using multimodal data
from the PATS database, which contains videos of diverse speakers. We recognize
style as a pervasive element during speech, influencing the expressivity of
communicative behaviors, while content is conveyed through multimodal signals
and text. By disentangling content and style, we directly infer the style
embedding, even for speakers not included in the training phase, without the
need for additional training or fine-tuning. Objective and subjective
evaluations are conducted to validate our approach and compare it against two
baseline methods. | [
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] |
Ground-state 3D geometries of molecules are essential for many molecular
analysis tasks. Modern quantum mechanical methods can compute accurate 3D
geometries but are computationally prohibitive. Currently, an efficient
alternative to computing ground-state 3D molecular geometries from 2D graphs is
lacking. Here, we propose a novel deep learning framework to predict 3D
geometries from molecular graphs. To this end, we develop an equilibrium
message passing neural network (EMPNN) to better capture ground-state
geometries from molecular graphs. To provide a testbed for 3D molecular
geometry analysis, we develop a benchmark that includes a large-scale molecular
geometry dataset, data splits, and evaluation protocols. Experimental results
show that EMPNN can efficiently predict more accurate ground-state 3D
geometries than RDKit and other deep learning methods. Results also show that
the proposed framework outperforms self-supervised learning methods on property
prediction tasks. | [
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] |
Recent studies have shown competitive performance in protein design that aims
to find the amino acid sequence folding into the desired structure. However,
most of them disregard the importance of predictive confidence, fail to cover
the vast protein space, and do not incorporate common protein knowledge. After
witnessing the great success of pretrained models on diverse protein-related
tasks and the fact that recovery is highly correlated with confidence, we
wonder whether this knowledge can push the limits of protein design further. As
a solution, we propose a knowledge-aware module that refines low-quality
residues. We also introduce a memory-retrieval mechanism to save more than 50\%
of the training time. We extensively evaluate our proposed method on the CATH,
TS50, and TS500 datasets and our results show that our Knowledge-Design method
outperforms the previous PiFold method by approximately 9\% on the CATH
dataset. Specifically, Knowledge-Design is the first method that achieves 60+\%
recovery on CATH, TS50 and TS500 benchmarks. We also provide additional
analysis to demonstrate the effectiveness of our proposed method. The code will
be publicly available. | [
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] |
Is there a unified framework for graph-based retrosynthesis prediction?
Through analysis of full-, semi-, and non-template retrosynthesis methods, we
discovered that they strive to strike an optimal balance between combinability
and consistency: \textit{Should atoms be combined as motifs to simplify the
molecular editing process, or should motifs be broken down into atoms to reduce
the vocabulary and improve predictive consistency?}
Recent works have studied several specific cases, while none of them explores
different combinability-consistency trade-offs. Therefore, we propose
MotifRetro, a dynamic motif editing framework for retrosynthesis prediction
that can explore the entire trade-off space and unify graph-based models.
MotifRetro comprises two components: RetroBPE, which controls the
combinability-consistency trade-off, and a motif editing model, where we
introduce a novel LG-EGAT module to dynamiclly add motifs to the molecule. We
conduct extensive experiments on USPTO-50K to explore how the trade-off affects
the model performance and finally achieve state-of-the-art performance. | [
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] |
This article appears as chapter 21 of Prince (2023, Understanding Deep
Learning); a complete draft of the textbook is available here:
http://udlbook.com. This chapter considers potential harms arising from the
design and use of AI systems. These include algorithmic bias, lack of
explainability, data privacy violations, militarization, fraud, and
environmental concerns. The aim is not to provide advice on being more ethical.
Instead, the goal is to express ideas and start conversations in key areas that
have received attention in philosophy, political science, and the broader
social sciences. | [
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] |
Structured variational autoencoders (SVAEs) combine probabilistic graphical
model priors on latent variables, deep neural networks to link latent variables
to observed data, and structure-exploiting algorithms for approximate posterior
inference. These models are particularly appealing for sequential data, where
the prior can capture temporal dependencies. However, despite their conceptual
elegance, SVAEs have proven difficult to implement, and more general approaches
have been favored in practice. Here, we revisit SVAEs using modern machine
learning tools and demonstrate their advantages over more general alternatives
in terms of both accuracy and efficiency. First, we develop a modern
implementation for hardware acceleration, parallelization, and automatic
differentiation of the message passing algorithms at the core of the SVAE.
Second, we show that by exploiting structure in the prior, the SVAE learns more
accurate models and posterior distributions, which translate into improved
performance on prediction tasks. Third, we show how the SVAE can naturally
handle missing data, and we leverage this ability to develop a novel,
self-supervised training approach. Altogether, these results show that the time
is ripe to revisit structured variational autoencoders. | [
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] |
Recently, zero-shot TTS and VC methods have gained attention due to their
practicality of being able to generate voices even unseen during training.
Among these methods, zero-shot modifications of the VITS model have shown
superior performance, while having useful properties inherited from VITS.
However, the performance of VITS and VITS-based zero-shot models vary
dramatically depending on how the losses are balanced. This can be problematic,
as it requires a burdensome procedure of tuning loss balance hyper-parameters
to find the optimal balance. In this work, we propose a novel framework that
finds this optimum without search, by inducing the decoder of VITS-based models
to its full reconstruction ability. With our framework, we show superior
performance compared to baselines in zero-shot TTS and VC, achieving
state-of-the-art performance. Furthermore, we show the robustness of our
framework in various settings. We provide an explanation for the results in the
discussion. | [
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ChatGPT (Chat Generative Pre-trained Transformer) is a chatbot launched by
OpenAI on November 30, 2022. OpenAI's GPT-3 family of large language models
serve as the foundation for ChatGPT. ChatGPT is fine-tuned with both supervised
and reinforcement learning techniques and has received widespread attention for
its articulate responses across diverse domains of knowledge. In this study, we
explore how ChatGPT can be used to help with common software engineering tasks.
Many of the ubiquitous tasks covering the breadth of software engineering such
as ambiguity resolution in software requirements, method name suggestion, test
case prioritization, code review, log summarization can potentially be
performed using ChatGPT. In this study, we explore fifteen common software
engineering tasks using ChatGPT. We juxtapose and analyze ChatGPT's answers
with the respective state of the art outputs (where available) and/or human
expert ground truth. Our experiments suggest that for many tasks, ChatGPT does
perform credibly and the response from it is detailed and often better than the
human expert output or the state of the art output. However, for a few other
tasks, ChatGPT in its present form provides incorrect answers and hence is not
suited for such tasks. | [
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] |
Owing to their inherently interpretable structure, decision trees are
commonly used in applications where interpretability is essential. Recent work
has focused on improving various aspects of decision trees, including their
predictive power and robustness; however, their instability, albeit
well-documented, has been addressed to a lesser extent. In this paper, we take
a step towards the stabilization of decision tree models through the lens of
real-world health care applications due to the relevance of stability and
interpretability in this space. We introduce a new distance metric for decision
trees and use it to determine a tree's level of stability. We propose a novel
methodology to train stable decision trees and investigate the existence of
trade-offs that are inherent to decision tree models - including between
stability, predictive power, and interpretability. We demonstrate the value of
the proposed methodology through an extensive quantitative and qualitative
analysis of six case studies from real-world health care applications, and we
show that, on average, with a small 4.6% decrease in predictive power, we gain
a significant 38% improvement in the model's stability. | [
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] |
Received waveforms contain rich information for both range information and
environment semantics. However, its full potential is hard to exploit under
multipath and non-line-of-sight conditions. This paper proposes a deep
generative model (DGM) for simultaneous range error mitigation and environment
identification. In particular, we present a Bayesian model for the generative
process of the received waveform composed by latent variables for both
range-related features and environment semantics. The simultaneous range error
mitigation and environment identification is interpreted as an inference
problem based on the DGM, and implemented in a unique end-to-end learning
scheme. Comprehensive experiments on a general Ultra-wideband dataset
demonstrate the superior performance on range error mitigation, scalability to
different environments, and novel capability on simultaneous environment
identification. | [
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] |
Localization systems based on ultra-wide band (UWB) measurements can have
unsatisfactory performance in harsh environments due to the presence of
non-line-of-sight (NLOS) errors. Learning-based methods for error mitigation
have shown great performance improvement via directly exploiting the wideband
waveform instead of handcrafted features. However, these methods require data
samples fully labeled with actual measurement errors for training, which leads
to time-consuming data collection. In this paper, we propose a semi-supervised
learning method based on variational Bayes for UWB ranging error mitigation.
Combining deep learning techniques and statistic tools, our method can
efficiently accumulate knowledge from both labeled and unlabeled data samples.
Extensive experiments illustrate the effectiveness of the proposed method under
different supervision rates, and the superiority compared to other fully
supervised methods even at a low supervision rate. | [
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] |
Emotion recognition plays a crucial role in human-computer interaction, and
electroencephalography (EEG) is advantageous for reflecting human emotional
states. In this study, we propose MACTN, a hierarchical hybrid model for
jointly modeling local and global temporal information. The model is inspired
by neuroscience research on the temporal dynamics of emotions. MACTN extracts
local emotional features through a convolutional neural network (CNN) and
integrates sparse global emotional features through a transformer. Moreover, we
employ channel attention mechanisms to identify the most task-relevant
channels. Through extensive experimentation on two publicly available datasets,
namely THU-EP and DEAP, our proposed method, MACTN, consistently achieves
superior classification accuracy and F1 scores compared to other existing
methods in most experimental settings. Furthermore, ablation studies have shown
that the integration of both self-attention mechanisms and channel attention
mechanisms leads to improved classification performance. Finally, an earlier
version of this method, which shares the same ideas, won the Emotional BCI
Competition's final championship in the 2022 World Robot Contest. | [
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] |
In many scenarios, recommender system user interaction data such as clicks or
ratings is sparse, and item turnover rates (e.g., new articles, job postings)
high. Given this, the integration of contextual "side" information in addition
to user-item ratings is highly desirable. Whilst there are algorithms that can
handle both rating and contextual data simultaneously, these algorithms are
typically limited to making only in-sample recommendations, suffer from the
curse of dimensionality, and do not incorporate multi-armed bandit (MAB)
policies for long-term cumulative reward optimization. We propose multi-view
interactive topic regression (MV-ICTR) a novel partially online latent factor
recommender algorithm that incorporates both rating and contextual information
to model item-specific feature dependencies and users' personal preferences
simultaneously, with multi-armed bandit policies for continued online
personalization. The result is significantly increased performance on datasets
with high percentages of cold-start users and items. | [
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] |
Computerized adaptive testing (CAT) is a form of personalized testing that
accurately measures students' knowledge levels while reducing test length.
Bilevel optimization-based CAT (BOBCAT) is a recent framework that learns a
data-driven question selection algorithm to effectively reduce test length and
improve test accuracy. However, it suffers from high question exposure and test
overlap rates, which potentially affects test security. This paper introduces a
constrained version of BOBCAT to address these problems by changing its
optimization setup and enabling us to trade off test accuracy for question
exposure and test overlap rates. We show that C-BOBCAT is effective through
extensive experiments on two real-world adult testing datasets. | [
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] |
Large amounts of incremental learning algorithms have been proposed to
alleviate the catastrophic forgetting issue arises while dealing with
sequential data on a time series. However, the adversarial robustness of
incremental learners has not been widely verified, leaving potential security
risks. Specifically, for poisoning-based backdoor attacks, we argue that the
nature of streaming data in IL provides great convenience to the adversary by
creating the possibility of distributed and cross-task attacks -- an adversary
can affect \textbf{any unknown} previous or subsequent task by data poisoning
\textbf{at any time or time series} with extremely small amount of backdoor
samples injected (e.g., $0.1\%$ based on our observations). To attract the
attention of the research community, in this paper, we empirically reveal the
high vulnerability of 11 typical incremental learners against poisoning-based
backdoor attack on 3 learning scenarios, especially the cross-task
generalization effect of backdoor knowledge, while the poison ratios range from
$5\%$ to as low as $0.1\%$. Finally, the defense mechanism based on activation
clustering is found to be effective in detecting our trigger pattern to
mitigate potential security risks. | [
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] |
Multirotors flying in close proximity induce aerodynamic wake effects on each
other through propeller downwash. Conventional methods have fallen short of
providing adequate 3D force-based models that can be incorporated into robust
control paradigms for deploying dense formations. Thus, learning a model for
these downwash patterns presents an attractive solution. In this paper, we
present a novel learning-based approach for modelling the downwash forces that
exploits the latent geometries (i.e. symmetries) present in the problem. We
demonstrate that when trained with only 5 minutes of real-world flight data,
our geometry-aware model outperforms state-of-the-art baseline models trained
with more than 15 minutes of data. In dense real-world flights with two
vehicles, deploying our model online improves 3D trajectory tracking by nearly
36% on average (and vertical tracking by 56%). | [
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] |
Autonomous driving has become one of the most popular research topics within
Artificial Intelligence. An autonomous vehicle is understood as a system that
combines perception, decision-making, planning, and control. All of those tasks
require that the vehicle collects surrounding data in order to make a good
decision and action. In particular, the overtaking maneuver is one of the most
critical actions of driving. The process involves lane changes, acceleration
and deceleration actions, and estimation of the speed and distance of the
vehicle in front or in the lane in which it is moving. Despite the amount of
work available in the literature, just a few handle overtaking maneuvers and,
because overtaking can be risky, no real-world dataset is available. This work
contributes in this area by presenting a new synthetic dataset whose focus is
the overtaking maneuver. We start by performing a thorough review of the state
of the art in autonomous driving and then explore the main datasets found in
the literature (public and private, synthetic and real), highlighting their
limitations, and suggesting a new set of features whose focus is the overtaking
maneuver. | [
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] |
Medication recommendation is a fundamental yet crucial branch of healthcare,
which provides opportunities to support clinical physicians with more accurate
medication prescriptions for patients with complex health conditions. Learning
from electronic health records (EHR) to recommend medications is the most
common way in previous studies. However, most of them neglect incorporating
domain knowledge according to the clinical manifestations in the EHR of the
patient. To address these issues, we propose a novel \textbf{D}omain
\textbf{K}nowledge \textbf{I}nformed \textbf{Net}work (DKINet) to integrate
domain knowledge with observable clinical manifestations of the patient, which
is the first dynamic domain knowledge informed framework toward medication
recommendation. In particular, we first design a knowledge-driven encoder to
capture the domain information and then develop a data-driven encoder to
integrate domain knowledge into the observable EHR. To endow the model with the
capability of temporal decision, we design an explicit medication encoder for
learning the longitudinal dependence of the patient. Extensive experiments on
three publicly available datasets verify the superiority of our method. The
code will be public upon acceptance. | [
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] |
The accurate prediction of changes in protein stability under multiple amino
acid substitutions is essential for realising true in-silico protein re-design.
To this purpose, we propose improvements to state-of-the-art Deep learning (DL)
protein stability prediction models, enabling first-of-a-kind predictions for
variable numbers of amino acid substitutions, on structural representations, by
decoupling the atomic and residue scales of protein representations. This was
achieved using E(3)-equivariant graph neural networks (EGNNs) for both atomic
environment (AE) embedding and residue-level scoring tasks. Our AE embedder was
used to featurise a residue-level graph, then trained to score mutant stability
($\Delta\Delta G$). To achieve effective training of this predictive EGNN we
have leveraged the unprecedented scale of a new high-throughput protein
stability experimental data-set, Mega-scale. Finally, we demonstrate the
immediately promising results of this procedure, discuss the current
shortcomings, and highlight potential future strategies. | [
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] |
In 1989 George Cybenko proved in a landmark paper that wide shallow neural
networks can approximate arbitrary continuous functions on a compact set. This
universal approximation theorem sparked a lot of follow-up research.
Shen, Yang and Zhang determined optimal approximation rates for ReLU-networks
in $L^p$-norms with $p \in [1,\infty)$. Kidger and Lyons proved a universal
approximation theorem for deep narrow ReLU-networks. Telgarsky gave an example
of a deep narrow ReLU-network that cannot be approximated by a wide shallow
ReLU-network unless it has exponentially many neurons.
However, there are even more questions that still remain unresolved. Are
there any wide shallow ReLU-networks that cannot be approximated well by deep
narrow ReLU-networks? Is the universal approximation theorem still true for
other norms like the Sobolev norm $W^{1,1}$? Do these results hold for
activation functions other than ReLU?
We will answer all of those questions and more with a framework of two
expressive powers. The first one is well-known and counts the maximal number of
linear regions of a function calculated by a ReLU-network. We will improve the
best known bounds for this expressive power. The second one is entirely new. | [
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] |
Sensitivity analysis is an important tool used in many domains of
computational science to either gain insight into the mathematical model and
interaction of its parameters or study the uncertainty propagation through the
input-output interactions. In many applications, the inputs are stochastically
dependent, which violates one of the essential assumptions in the
state-of-the-art sensitivity analysis methods. Consequently, the results
obtained ignoring the correlations provide values which do not reflect the true
contributions of the input parameters. This study proposes an approach to
address the parameter correlations using a polynomial chaos expansion method
and Rosenblatt and Cholesky transformations to reflect the parameter
dependencies. Treatment of the correlated variables is discussed in context of
variance and derivative-based sensitivity analysis. We demonstrate that the
sensitivity of the correlated parameters can not only differ in magnitude, but
even the sign of the derivative-based index can be inverted, thus significantly
altering the model behavior compared to the prediction of the analysis
disregarding the correlations. Numerous experiments are conducted using
workflow automation tools within the VECMA toolkit. | [
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] |
We present a constraint-based algorithm for learning causal structures from
observational time-series data, in the presence of latent confounders. We
assume a discrete-time, stationary structural vector autoregressive process,
with both temporal and contemporaneous causal relations. One may ask if
temporal and contemporaneous relations should be treated differently. The
presented algorithm gradually refines a causal graph by learning long-term
temporal relations before short-term ones, where contemporaneous relations are
learned last. This ordering of causal relations to be learnt leads to a
reduction in the required number of statistical tests. We validate this
reduction empirically and demonstrate that it leads to higher accuracy for
synthetic data and more plausible causal graphs for real-world data compared to
state-of-the-art algorithms. | [
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] |
We propose end-to-end document classification and key information extraction
(KIE) for automating document processing in forms. Through accurate document
classification we harness known information from templates to enhance KIE from
forms. We use text and layout encoding with a cosine similarity measure to
classify visually-similar documents. We then demonstrate a novel application of
mixed integer programming by using assignment optimization to extract key
information from documents. Our approach is validated on an in-house dataset of
noisy scanned forms. The best performing document classification approach
achieved 0.97 f1 score. A mean f1 score of 0.94 for the KIE task suggests there
is significant potential in applying optimization techniques. Abation results
show that the method relies on document preprocessing techniques to mitigate
Type II errors and achieve optimal performance. | [
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] |
Interactions with large language models have led to the suggestion that these
models may soon be conscious. From the perspective of neuroscience, this
position is difficult to defend. For one, the inputs to large language models
lack the embodied, embedded information content characteristic of our sensory
contact with the world around us. Secondly, the architecture of large language
models is missing key features of the thalamocortical system that have been
linked to conscious awareness in mammals. Finally, the evolutionary and
developmental trajectories that led to the emergence of living conscious
organisms arguably have no parallels in artificial systems as envisioned today.
The existence of living organisms depends on their actions, and their survival
is intricately linked to multi-level cellular, inter-cellular, and organismal
processes culminating in agency and consciousness. | [
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] |
Edge artificial intelligence (AI) has been a promising solution towards 6G to
empower a series of advanced techniques such as digital twin, holographic
projection, semantic communications, and auto-driving, for achieving
intelligence of everything. The performance of edge AI tasks, including edge
learning and edge AI inference, depends on the quality of three highly coupled
processes, i.e., sensing for data acquisition, computation for information
extraction, and communication for information transmission. However, these
three modules need to compete for network resources for enhancing their own
quality-of-services. To this end, integrated sensing-communication-computation
(ISCC) is of paramount significance for improving resource utilization as well
as achieving the customized goals of edge AI tasks. By investigating the
interplay among the three modules, this article presents various kinds of ISCC
schemes for federated edge learning tasks and edge AI inference tasks in both
application and physical layers. | [
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] |
Integrating ethical practices into the AI development process for artificial
intelligence (AI) is essential to ensure safe, fair, and responsible operation.
AI ethics involves applying ethical principles to the entire life cycle of AI
systems. This is essential to mitigate potential risks and harms associated
with AI, such as algorithm biases. To achieve this goal, responsible design
patterns (RDPs) are critical for Machine Learning (ML) pipelines to guarantee
ethical and fair outcomes. In this paper, we propose a comprehensive framework
incorporating RDPs into ML pipelines to mitigate risks and ensure the ethical
development of AI systems. Our framework comprises new responsible AI design
patterns for ML pipelines identified through a survey of AI ethics and data
management experts and validated through real-world scenarios with expert
feedback. The framework guides AI developers, data scientists, and
policy-makers to implement ethical practices in AI development and deploy
responsible AI systems in production. | [
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] |
The growing demand for data and AI-generated digital goods, such as
personalized written content and artwork, necessitates effective pricing and
feedback mechanisms that account for uncertain utility and costly production.
Motivated by these developments, this study presents a novel mechanism design
addressing a general repeated-auction setting where the utility derived from a
sold good is revealed post-sale. The mechanism's novelty lies in using pairwise
comparisons for eliciting information from the bidder, arguably easier for
humans than assigning a numerical value. Our mechanism chooses allocations
using an epsilon-greedy strategy and relies on pairwise comparisons between
realized utility from allocated goods and an arbitrary value, avoiding the
learning-to-bid problem explored in previous work. We prove this mechanism to
be asymptotically truthful, individually rational, and welfare and revenue
maximizing. The mechanism's relevance is broad, applying to any setting with
made-to-order goods of variable quality. Experimental results on multi-label
toxicity annotation data, an example of negative utilities, highlight how our
proposed mechanism could enhance social welfare in data auctions. Overall, our
focus on human factors contributes to the development of more human-aware and
efficient mechanism design. | [
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Automatic speech recognition (ASR) models are frequently exposed to data
distribution shifts in many real-world scenarios, leading to erroneous
predictions. To tackle this issue, an existing test-time adaptation (TTA)
method has recently been proposed to adapt the pre-trained ASR model on
unlabeled test instances without source data. Despite decent performance gain,
this work relies solely on naive greedy decoding and performs adaptation across
timesteps at a frame level, which may not be optimal given the sequential
nature of the model output. Motivated by this, we propose a novel TTA
framework, dubbed SGEM, for general ASR models. To treat the sequential output,
SGEM first exploits beam search to explore candidate output logits and selects
the most plausible one. Then, it utilizes generalized entropy minimization and
negative sampling as unsupervised objectives to adapt the model. SGEM achieves
state-of-the-art performance for three mainstream ASR models under various
domain shifts. | [
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] |
Designing cyber defense systems to account for cognitive biases in human
decision making has demonstrated significant success in improving performance
against human attackers. However, much of the attention in this area has
focused on relatively simple accounts of biases in human attackers, and little
is known about adversarial behavior or how defenses could be improved by
disrupting attacker's behavior. In this work, we present a novel model of human
decision-making inspired by the cognitive faculties of Instance-Based Learning
Theory, Theory of Mind, and Transfer of Learning. This model functions by
learning from both roles in a security scenario: defender and attacker, and by
making predictions of the opponent's beliefs, intentions, and actions. The
proposed model can better defend against attacks from a wide range of opponents
compared to alternatives that attempt to perform optimally without accounting
for human biases. Additionally, the proposed model performs better against a
range of human-like behavior by explicitly modeling human transfer of learning,
which has not yet been applied to cyber defense scenarios. Results from
simulation experiments demonstrate the potential usefulness of cognitively
inspired models of agents trained in attack and defense roles and how these
insights could potentially be used in real-world cybersecurity. | [
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] |
Diffusion models have become increasingly popular for synthesizing
high-quality samples based on training datasets. However, given the oftentimes
enormous sizes of the training datasets, it is difficult to assess how training
data impact the samples produced by a trained diffusion model. The difficulty
of relating diffusion model inputs and outputs poses significant challenges to
model explainability and training data attribution. Here we propose a novel
solution that reveals how training data influence the output of diffusion
models through the use of ensembles. In our approach individual models in an
encoded ensemble are trained on carefully engineered splits of the overall
training data to permit the identification of influential training examples.
The resulting model ensembles enable efficient ablation of training data
influence, allowing us to assess the impact of training data on model outputs.
We demonstrate the viability of these ensembles as generative models and the
validity of our approach to assessing influence. | [
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] |
The integration of emotional intelligence in machines is an important step in
advancing human-computer interaction. This demands the development of reliable
end-to-end emotion recognition systems. However, the scarcity of public
affective datasets presents a challenge. In this literature review, we
emphasize the use of generative models to address this issue in
neurophysiological signals, particularly Electroencephalogram (EEG) and
Functional Near-Infrared Spectroscopy (fNIRS). We provide a comprehensive
analysis of different generative models used in the field, examining their
input formulation, deployment strategies, and methodologies for evaluating the
quality of synthesized data. This review serves as a comprehensive overview,
offering insights into the advantages, challenges, and promising future
directions in the application of generative models in emotion recognition
systems. Through this review, we aim to facilitate the progression of
neurophysiological data augmentation, thereby supporting the development of
more efficient and reliable emotion recognition systems. | [
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] |
The Abstraction and Reasoning Corpus (ARC) (Chollet, 2019) and its most
recent language-complete instantiation (LARC) has been postulated as an
important step towards general AI. Yet, even state-of-the-art machine learning
models struggle to achieve meaningful performance on these problems, falling
behind non-learning based approaches. We argue that solving these tasks
requires extreme generalization that can only be achieved by proper accounting
for core knowledge priors. As a step towards this goal, we focus on geometry
priors and introduce LatFormer, a model that incorporates lattice symmetry
priors in attention masks. We show that, for any transformation of the
hypercubic lattice, there exists a binary attention mask that implements that
group action. Hence, our study motivates a modification to the standard
attention mechanism, where attention weights are scaled using soft masks
generated by a convolutional network. Experiments on synthetic geometric
reasoning show that LatFormer requires 2 orders of magnitude fewer data than
standard attention and transformers. Moreover, our results on ARC and LARC
tasks that incorporate geometric priors provide preliminary evidence that these
complex datasets do not lie out of the reach of deep learning models. | [
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On 24 February 2022, Russia invaded Ukraine, starting what is now known as
the Russo-Ukrainian War, initiating an online discourse on social media.
Twitter as one of the most popular SNs, with an open and democratic character,
enables a transparent discussion among its large user base. Unfortunately, this
often leads to Twitter's policy violations, propaganda, abusive actions, civil
integrity violation, and consequently to user accounts' suspension and
deletion. This study focuses on the Twitter suspension mechanism and the
analysis of shared content and features of the user accounts that may lead to
this. Toward this goal, we have obtained a dataset containing 107.7M tweets,
originating from 9.8 million users, using Twitter API. We extract the
categories of shared content of the suspended accounts and explain their
characteristics, through the extraction of text embeddings in junction with
cosine similarity clustering. Our results reveal scam campaigns taking
advantage of trending topics regarding the Russia-Ukrainian conflict for
Bitcoin and Ethereum fraud, spam, and advertisement campaigns. Additionally, we
apply a machine learning methodology including a SHapley Additive
explainability model to understand and explain how user accounts get suspended. | [
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] |
Methods for anomaly detection of new physics processes are often limited to
low-dimensional spaces due to the difficulty of learning high-dimensional
probability densities. Particularly at the constituent level, incorporating
desirable properties such as permutation invariance and variable-length inputs
becomes difficult within popular density estimation methods. In this work, we
introduce a permutation-invariant density estimator for particle physics data
based on diffusion models, specifically designed to handle variable-length
inputs. We demonstrate the efficacy of our methodology by utilizing the learned
density as a permutation-invariant anomaly detection score, effectively
identifying jets with low likelihood under the background-only hypothesis. To
validate our density estimation method, we investigate the ratio of learned
densities and compare to those obtained by a supervised classification
algorithm. | [
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] |
In manufacturing, rework refers to an optional step of a production process
which aims to eliminate errors or remedy products that do not meet the desired
quality standards. Reworking a production lot involves repeating a previous
production stage with adjustments to ensure that the final product meets the
required specifications. While offering the chance to improve the yield and
thus increase the revenue of a production lot, a rework step also incurs
additional costs. Additionally, the rework of parts that already meet the
target specifications may damage them and decrease the yield. In this paper, we
apply double/debiased machine learning (DML) to estimate the conditional
treatment effect of a rework step during the color conversion process in
opto-electronic semiconductor manufacturing on the final product yield. We
utilize the implementation DoubleML to develop policies for the rework of
components and estimate their value empirically. From our causal machine
learning analysis we derive implications for the coating of monochromatic LEDs
with conversion layers. | [
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] |
Quantification learning deals with the task of estimating the target label
distribution under label shift. In this paper, we first present a unifying
framework, distribution feature matching (DFM), that recovers as particular
instances various estimators introduced in previous literature. We derive a
general performance bound for DFM procedures, improving in several key aspects
upon previous bounds derived in particular cases. We then extend this analysis
to study robustness of DFM procedures in the misspecified setting under
departure from the exact label shift hypothesis, in particular in the case of
contamination of the target by an unknown distribution. These theoretical
findings are confirmed by a detailed numerical study on simulated and
real-world datasets. We also introduce an efficient, scalable and robust
version of kernel-based DFM using the Random Fourier Feature principle. | [
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] |
Since the rise of fair machine learning as a critical field of inquiry, many
different notions on how to quantify and measure discrimination have been
proposed in the literature. Some of these notions, however, were shown to be
mutually incompatible. Such findings make it appear that numerous different
kinds of fairness exist, thereby making a consensus on the appropriate measure
of fairness harder to reach, hindering the applications of these tools in
practice. In this paper, we investigate one of these key impossibility results
that relates the notions of statistical and predictive parity. Specifically, we
derive a new causal decomposition formula for the fairness measures associated
with predictive parity, and obtain a novel insight into how this criterion is
related to statistical parity through the legal doctrines of disparate
treatment, disparate impact, and the notion of business necessity. Our results
show that through a more careful causal analysis, the notions of statistical
and predictive parity are not really mutually exclusive, but complementary and
spanning a spectrum of fairness notions through the concept of business
necessity. Finally, we demonstrate the importance of our findings on a
real-world example. | [
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] |
One of the fundamental challenges found throughout the data sciences is to
explain why things happen in specific ways, or through which mechanisms a
certain variable $X$ exerts influences over another variable $Y$. In statistics
and machine learning, significant efforts have been put into developing
machinery to estimate correlations across variables efficiently. In causal
inference, a large body of literature is concerned with the decomposition of
causal effects under the rubric of mediation analysis. However, many variations
are spurious in nature, including different phenomena throughout the applied
sciences. Despite the statistical power to estimate correlations and the
identification power to decompose causal effects, there is still little
understanding of the properties of spurious associations and how they can be
decomposed in terms of the underlying causal mechanisms. In this manuscript, we
develop formal tools for decomposing spurious variations in both Markovian and
Semi-Markovian models. We prove the first results that allow a non-parametric
decomposition of spurious effects and provide sufficient conditions for the
identification of such decompositions. The described approach has several
applications, ranging from explainable and fair AI to questions in epidemiology
and medicine, and we empirically demonstrate its use on a real-world dataset. | [
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] |
We tackle the task of conditional music generation. We introduce MusicGen, a
single Language Model (LM) that operates over several streams of compressed
discrete music representation, i.e., tokens. Unlike prior work, MusicGen is
comprised of a single-stage transformer LM together with efficient token
interleaving patterns, which eliminates the need for cascading several models,
e.g., hierarchically or upsampling. Following this approach, we demonstrate how
MusicGen can generate high-quality samples, both mono and stereo, while being
conditioned on textual description or melodic features, allowing better
controls over the generated output. We conduct extensive empirical evaluation,
considering both automatic and human studies, showing the proposed approach is
superior to the evaluated baselines on a standard text-to-music benchmark.
Through ablation studies, we shed light over the importance of each of the
components comprising MusicGen. Music samples, code, and models are available
at https://github.com/facebookresearch/audiocraft | [
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] |
Realistic and controllable traffic simulation is a core capability that is
necessary to accelerate autonomous vehicle (AV) development. However, current
approaches for controlling learning-based traffic models require significant
domain expertise and are difficult for practitioners to use. To remedy this, we
present CTG++, a scene-level conditional diffusion model that can be guided by
language instructions. Developing this requires tackling two challenges: the
need for a realistic and controllable traffic model backbone, and an effective
method to interface with a traffic model using language. To address these
challenges, we first propose a scene-level diffusion model equipped with a
spatio-temporal transformer backbone, which generates realistic and
controllable traffic. We then harness a large language model (LLM) to convert a
user's query into a loss function, guiding the diffusion model towards
query-compliant generation. Through comprehensive evaluation, we demonstrate
the effectiveness of our proposed method in generating realistic,
query-compliant traffic simulations. | [
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] |
Object rearrangement is a fundamental problem in robotics with various
practical applications ranging from managing warehouses to cleaning and
organizing home kitchens. While existing research has primarily focused on
single-agent solutions, real-world scenarios often require multiple robots to
work together on rearrangement tasks. This paper proposes a comprehensive
learning-based framework for multi-agent object rearrangement planning,
addressing the challenges of task sequencing and path planning in complex
environments. The proposed method iteratively selects objects, determines their
relocation regions, and pairs them with available robots under kinematic
feasibility and task reachability for execution to achieve the target
arrangement. Our experiments on a diverse range of simulated and real-world
environments demonstrate the effectiveness and robustness of the proposed
framework. Furthermore, results indicate improved performance in terms of
traversal time and success rate compared to baseline approaches. | [
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] |
Recent studies on visual reinforcement learning (visual RL) have explored the
use of 3D visual representations. However, none of these work has
systematically compared the efficacy of 3D representations with 2D
representations across different tasks, nor have they analyzed 3D
representations from the perspective of agent-object / object-object
relationship reasoning. In this work, we seek answers to the question of when
and how do 3D neural networks that learn features in the 3D-native space
provide a beneficial inductive bias for visual RL. We specifically focus on 3D
point clouds, one of the most common forms of 3D representations. We
systematically investigate design choices for 3D point cloud RL, leading to the
development of a robust algorithm for various robotic manipulation and control
tasks. Furthermore, through comparisons between 2D image vs 3D point cloud RL
methods on both minimalist synthetic tasks and complex robotic manipulation
tasks, we find that 3D point cloud RL can significantly outperform the 2D
counterpart when agent-object / object-object relationship encoding is a key
factor. | [
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] |
While several recent works have identified societal-scale and
extinction-level risks to humanity arising from artificial intelligence, few
have attempted an {\em exhaustive taxonomy} of such risks. Many exhaustive
taxonomies are possible, and some are useful -- particularly if they reveal new
risks or practical approaches to safety. This paper explores a taxonomy based
on accountability: whose actions lead to the risk, are the actors unified, and
are they deliberate? We also provide stories to illustrate how the various risk
types could each play out, including risks arising from unanticipated
interactions of many AI systems, as well as risks from deliberate misuse, for
which combined technical and policy solutions are indicated. | [
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] |
B2B sales requires effective prediction of customer growth, identification of
upsell potential, and mitigation of churn risks. LinkedIn sales representatives
traditionally relied on intuition and fragmented data signals to assess
customer performance. This resulted in significant time investment in data
understanding as well as strategy formulation and under-investment in active
selling. To overcome this challenge, we developed a data product called Account
Prioritizer, an intelligent sales account prioritization engine. It uses
machine learning recommendation models and integrated account-level explanation
algorithms within the sales CRM to automate the manual process of sales book
prioritization. A successful A/B test demonstrated that the Account Prioritizer
generated a substantial +8.08% increase in renewal bookings for the LinkedIn
Business. | [
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Molecular property prediction is an important problem in drug discovery and
materials science. As geometric structures have been demonstrated necessary for
molecular property prediction, 3D information has been combined with various
graph learning methods to boost prediction performance. However, obtaining the
geometric structure of molecules is not feasible in many real-world
applications due to the high computational cost. In this work, we propose a
novel 3D pre-training framework (dubbed 3D PGT), which pre-trains a model on 3D
molecular graphs, and then fine-tunes it on molecular graphs without 3D
structures. Based on fact that bond length, bond angle, and dihedral angle are
three basic geometric descriptors corresponding to a complete molecular 3D
conformer, we first develop a multi-task generative pre-train framework based
on these three attributes. Next, to automatically fuse these three generative
tasks, we design a surrogate metric using the \textit{total energy} to search
for weight distribution of the three pretext task since total energy
corresponding to the quality of 3D conformer.Extensive experiments on 2D
molecular graphs are conducted to demonstrate the accuracy, efficiency and
generalization ability of the proposed 3D PGT compared to various pre-training
baselines. | [
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] |
Solving program induction problems requires searching through an enormous
space of possibilities. DreamCoder is an inductive program synthesis system
that, whilst solving problems, learns to simplify search in an iterative
wake-sleep procedure. The cost of search is amortised by training a neural
search policy, reducing search breadth and effectively "compiling" useful
information to compose program solutions across tasks. Additionally, a library
of program components is learnt to express discovered solutions in fewer
components, reducing search depth. In DreamCoder, the neural search policy has
only an indirect effect on the library learnt through the program solutions it
helps discover. We present an approach for library learning that directly
leverages the neural search policy, effectively "decompiling" its amortised
knowledge to extract relevant program components. This provides stronger
amortised inference: the amortised knowledge learnt to reduce search breadth is
now also used to reduce search depth. We integrate our approach with DreamCoder
and demonstrate faster domain proficiency with improved generalisation on a
range of domains, particularly when fewer example solutions are available. | [
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] |
End-to-end (E2E) systems have shown comparable performance to hybrid systems
for automatic speech recognition (ASR). Word timings, as a by-product of ASR,
are essential in many applications, especially for subtitling and
computer-aided pronunciation training. In this paper, we improve the
frame-level classifier for word timings in E2E system by introducing label
priors in connectionist temporal classification (CTC) loss, which is adopted
from prior works, and combining low-level Mel-scale filter banks with
high-level ASR encoder output as input feature. On the internal Chinese corpus,
the proposed method achieves 95.68%/94.18% compared to the hybrid system
93.0%/90.22% on the word timing accuracy metrics. It also surpass a previous
E2E approach with an absolute increase of 4.80%/8.02% on the metrics on 7
languages. In addition, we further improve word timing accuracy by delaying CTC
peaks with frame-wise knowledge distillation, though only experimenting on
LibriSpeech. | [
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] |
The rapid growth of distributed energy resources (DERs), such as renewable
energy sources, generators, consumers, and prosumers in the smart grid
infrastructure, poses significant cybersecurity and trust challenges to the
grid controller. Consequently, it is crucial to identify adversarial tactics
and measure the strength of the attacker's DER. To enable a trustworthy smart
grid controller, this work investigates a trustworthy artificial intelligence
(AI) mechanism for proactive identification and explanation of the cyber risk
caused by the control/status message of DERs. Thus, proposing and developing a
trustworthy AI framework to facilitate the deployment of any AI algorithms for
detecting potential cyber threats and analyzing root causes based on Shapley
value interpretation while dynamically quantifying the risk of an attack based
on Ward's minimum variance formula. The experiment with a state-of-the-art
dataset establishes the proposed framework as a trustworthy AI by fulfilling
the capabilities of reliability, fairness, explainability, transparency,
reproducibility, and accountability. | [
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] |
The surge in Reinforcement Learning (RL) applications in Intelligent
Transportation Systems (ITS) has contributed to its growth as well as
highlighted key challenges. However, defining objectives of RL agents in
traffic control and management tasks, as well as aligning policies with these
goals through an effective formulation of Markov Decision Process (MDP), can be
challenging and often require domain experts in both RL and ITS. Recent
advancements in Large Language Models (LLMs) such as GPT-4 highlight their
broad general knowledge, reasoning capabilities, and commonsense priors across
various domains. In this work, we conduct a large-scale user study involving 70
participants to investigate whether novices can leverage ChatGPT to solve
complex mixed traffic control problems. Three environments are tested,
including ring road, bottleneck, and intersection. We find ChatGPT has mixed
results. For intersection and bottleneck, ChatGPT increases number of
successful policies by 150% and 136% compared to solely beginner capabilities,
with some of them even outperforming experts. However, ChatGPT does not provide
consistent improvements across all scenarios. | [
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] |
Conformer-based end-to-end models have become ubiquitous these days and are
commonly used in both streaming and non-streaming automatic speech recognition
(ASR). Techniques like dual-mode and dynamic chunk training helped unify
streaming and non-streaming systems. However, there remains a performance gap
between streaming with a full and limited past context. To address this issue,
we propose the integration of a novel dynamic contextual carry-over mechanism
in a state-of-the-art (SOTA) unified ASR system. Our proposed dynamic context
Conformer (DCTX-Conformer) utilizes a non-overlapping contextual carry-over
mechanism that takes into account both the left context of a chunk and one or
more preceding context embeddings. We outperform the SOTA by a relative 25.0%
word error rate, with a negligible latency impact due to the additional context
embeddings. | [
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] |
Concept-based models perform prediction using a set of concepts that are
interpretable to stakeholders. However, such models often involve a fixed,
large number of concepts, which may place a substantial cognitive load on
stakeholders. We propose Selective COncept Models (SCOMs) which make
predictions using only a subset of concepts and can be customised by
stakeholders at test-time according to their preferences. We show that SCOMs
only require a fraction of the total concepts to achieve optimal accuracy on
multiple real-world datasets. Further, we collect and release a new dataset,
CUB-Sel, consisting of human concept set selections for 900 bird images from
the popular CUB dataset. Using CUB-Sel, we show that humans have unique
individual preferences for the choice of concepts they prefer to reason about,
and struggle to identify the most theoretically informative concepts. The
customisation and concept selection provided by SCOM improves the efficiency of
interpretation and intervention for stakeholders. | [
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] |
Molecular representation learning (MRL) is a fundamental task for drug
discovery. However, previous deep-learning (DL) methods focus excessively on
learning robust inner-molecular representations by mask-dominated pretraining
framework, neglecting abundant chemical reactivity molecular relationships that
have been demonstrated as the determining factor for various molecular property
prediction tasks. Here, we present MolCAP to promote MRL, a graph pretraining
Transformer based on chemical reactivity (IMR) knowledge with prompted
finetuning. Results show that MolCAP outperforms comparative methods based on
traditional molecular pretraining framework, in 13 publicly available molecular
datasets across a diversity of biomedical tasks. Prompted by MolCAP, even basic
graph neural networks are capable of achieving surprising performance that
outperforms previous models, indicating the promising prospect of applying
reactivity information for MRL. In addition, manual designed molecular templets
are potential to uncover the dataset bias. All in all, we expect our MolCAP to
gain more chemical meaningful insights for the entire process of drug
discovery. | [
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] |
The implementation of strategies for fault detection and diagnosis on
rotating electrical machines is crucial for the reliability and safety of
modern industrial systems. The contribution of this work is a methodology that
combines conventional strategy of Motor Current Signature Analysis with
functional dimensionality reduction methods, namely Functional Principal
Components Analysis and Functional Diffusion Maps, for detecting and
classifying fault conditions in induction motors. The results obtained from the
proposed scheme are very encouraging, revealing a potential use in the future
not only for real-time detection of the presence of a fault in an induction
motor, but also in the identification of a greater number of types of faults
present through an offline analysis. | [
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] |
The advent of ChatGPT by OpenAI has prompted extensive discourse on its
potential implications for science and higher education. While the impact on
education has been a primary focus, there is limited empirical research on the
effects of large language models (LLMs) and LLM-based chatbots on science and
scientific practice. To investigate this further, we conducted a Delphi study
involving 72 experts specialising in research and AI. The study focused on
applications and limitations of LLMs, their effects on the science system,
ethical and legal considerations, and the required competencies for their
effective use. Our findings highlight the transformative potential of LLMs in
science, particularly in administrative, creative, and analytical tasks.
However, risks related to bias, misinformation, and quality assurance need to
be addressed through proactive regulation and science education. This research
contributes to informed discussions on the impact of generative AI in science
and helps identify areas for future action. | [
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] |
The growing utilization of machine learning (ML) in decision-making processes
raises questions about its benefits to society. In this study, we identify and
analyze three axes of heterogeneity that significantly influence the trajectory
of ML products. These axes are i) values, culture and regulations, ii) data
composition, and iii) resource and infrastructure capacity. We demonstrate how
these axes are interdependent and mutually influence one another, emphasizing
the need to consider and address them jointly. Unfortunately, the current
research landscape falls short in this regard, often failing to adopt a
holistic approach. We examine the prevalent practices and methodologies that
skew these axes in favor of a selected few, resulting in power concentration,
homogenized control, and increased dependency. We discuss how this fragmented
study of the three axes poses a significant challenge, leading to an
impractical solution space that lacks reflection of real-world scenarios.
Addressing these issues is crucial to ensure a more comprehensive understanding
of the interconnected nature of society and to foster the democratic and
inclusive development of ML systems that are more aligned with real-world
complexities and its diverse requirements. | [
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] |
In this paper, we advocate in favor of smartphone apps as low-cost,
easy-to-deploy solution for raising awareness among the population on the
proliferation of Aedes aegypti mosquitoes. Nevertheless, devising such a
smartphone app is challenging, for many reasons, including the required
maturity level of techniques for identifying mosquitoes based on features that
can be captured using smartphone resources. In this paper, we identify a set of
(non-exhaustive) requirements that smartphone apps must meet to become an
effective tooling in the fight against Ae. aegypti, and advance the
state-of-the-art with (i) a residual convolutional neural network for
classifying Ae. aegypti mosquitoes from their wingbeat sound, (ii) a
methodology for reducing the influence of background noise in the
classification process, and (iii) a dataset for benchmarking solutions for
detecting Ae. aegypti mosquitoes from wingbeat sound recordings. From the
analysis of accuracy and recall, we provide evidence that convolutional neural
networks have potential as a cornerstone for tracking mosquito apps for
smartphones. | [
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