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2501.10390
|
Towards an Environmental Ethics of Artificial Intelligence
|
cs.CY cs.AI
|
In recent years, much research has been dedicated to uncovering the
environmental impact of Artificial Intelligence (AI), showing that training and
deploying AI systems require large amounts of energy and resources, and the
outcomes of AI may lead to decisions and actions that may negatively impact the
environment. This new knowledge raises new ethical questions, such as: When is
it (un)justifiable to develop an AI system, and how to make design choices,
considering its environmental impact? However, so far, the environmental impact
of AI has largely escaped ethical scrutiny, as AI ethics tends to focus
strongly on themes such as transparency, privacy, safety, responsibility, and
bias. Considering the environmental impact of AI from an ethical perspective
expands the scope of AI ethics beyond an anthropocentric focus towards
including more-than-human actors such as animals and ecosystems. This paper
explores the ethical implications of the environmental impact of AI for
designing AI systems by drawing on environmental justice literature, in which
three categories of justice are distinguished, referring to three elements that
can be unjust: the distribution of benefits and burdens (distributive justice),
decision-making procedures (procedural justice), and institutionalized social
norms (justice as recognition). Based on these tenets of justice, we outline
criteria for developing environmentally just AI systems, given their ecological
impact.
|
2501.10391
|
Developing an Ontology for AI Act Fundamental Rights Impact Assessments
|
cs.CY cs.AI
|
The recently published EU Artificial Intelligence Act (AI Act) is a landmark
regulation that regulates the use of AI technologies. One of its novel
requirements is the obligation to conduct a Fundamental Rights Impact
Assessment (FRIA), where organisations in the role of deployers must assess the
risks of their AI system regarding health, safety, and fundamental rights.
Another novelty in the AI Act is the requirement to create a questionnaire and
an automated tool to support organisations in their FRIA obligations. Such
automated tools will require a machine-readable form of information involved
within the FRIA process, and additionally also require machine-readable
documentation to enable further compliance tools to be created. In this
article, we present our novel representation of the FRIA as an ontology based
on semantic web standards. Our work builds upon the existing state of the art,
notably the Data Privacy Vocabulary (DPV), where similar works have been
established to create tools for GDPR's Data Protection Impact Assessments
(DPIA) and other obligations. Through our ontology, we enable the creation and
management of FRIA, and the use of automated tool in its various steps.
|
2501.10392
|
Ion Transmitter for Molecular Communication
|
cs.ET cs.SY eess.SY
|
Molecular communication (MC) is an emerging paradigm that takes inspiration
from biological processes, enabling communication at the nanoscale and
facilitating the development of the Internet of Bio-Nano Things (IoBNT).
Traditional models of MC often rely on idealized assumptions that overlook
practical challenges related to noise and signal behavior. This paper proposes
and evaluates the first physical MC ion transmitter (ITX) using an ion exchange
membrane. The circuit network model is used to simulate ion transport and
analyze both transient and steady-state behavior. This analysis includes the
effects of noise sources such as thermal and shot noise on signal integrity and
SNR. The main contributions of this paper are to demonstrate how a practical MC
ITX can produce a realistic waveform and to highlight future research
challenges associated with a physical membrane-based ITX.
|
2501.10395
|
Towards General Purpose Robots at Scale: Lifelong Learning and Learning
to Use Memory
|
cs.LG cs.AI cs.RO
|
The widespread success of artificial intelligence in fields like natural
language processing and computer vision has not yet fully transferred to
robotics, where progress is hindered by the lack of large-scale training data
and the complexity of real-world tasks. To address this, many robot learning
researchers are pushing to get robots deployed at scale in everyday
unstructured environments like our homes to initiate a data flywheel. While
current robot learning systems are effective for certain short-horizon tasks,
they are not designed to autonomously operate over long time horizons in
unstructured environments. This thesis focuses on addressing two key challenges
for robots operating over long time horizons: memory and lifelong learning.
We propose two novel methods to advance these capabilities. First, we
introduce t-DGR, a trajectory-based deep generative replay method that achieves
state-of-the-art performance on Continual World benchmarks, advancing lifelong
learning. Second, we develop a framework that leverages human demonstrations to
teach agents effective memory utilization, improving learning efficiency and
success rates on Memory Gym tasks. Finally, we discuss future directions for
achieving the lifelong learning and memory capabilities necessary for robots to
function at scale in real-world settings.
|
2501.10396
|
AI-Powered Urban Transportation Digital Twin: Methods and Applications
|
eess.SY cs.AI cs.CY cs.NI cs.SY
|
We present a survey paper on methods and applications of digital twins (DT)
for urban traffic management. While the majority of studies on the DT focus on
its "eyes," which is the emerging sensing and perception like object detection
and tracking, what really distinguishes the DT from a traditional simulator
lies in its ``brain," the prediction and decision making capabilities of
extracting patterns and making informed decisions from what has been seen and
perceived. In order to add values to urban transportation management, DTs need
to be powered by artificial intelligence and complement with low-latency
high-bandwidth sensing and networking technologies. We will first review the DT
pipeline leveraging cyberphysical systems and propose our DT architecture
deployed on a real-world testbed in New York City. This survey paper can be a
pointer to help researchers and practitioners identify challenges and
opportunities for the development of DTs; a bridge to initiate conversations
across disciplines; and a road map to exploiting potentials of DTs for diverse
urban transportation applications.
|
2501.10401
|
Custom Loss Functions in Fuel Moisture Modeling
|
stat.AP cs.LG stat.ML
|
Fuel moisture content (FMC) is a key predictor for wildfire rate of spread
(ROS). Machine learning models of FMC are being used more in recent years,
augmenting or replacing traditional physics-based approaches. Wildfire rate of
spread (ROS) has a highly nonlinear relationship with FMC, where small
differences in dry fuels lead to large differences in ROS. In this study,
custom loss functions that place more weight on dry fuels were examined with a
variety of machine learning models of FMC. The models were evaluated with a
spatiotemporal cross-validation procedure to examine whether the custom loss
functions led to more accurate forecasts of ROS. Results show that the custom
loss functions improved accuracy for ROS forecasts by a small amount. Further
research would be needed to establish whether the improvement in ROS forecasts
leads to more accurate real-time wildfire simulations.
|
2501.10404
|
Automated Detection of Epileptic Spikes and Seizures Incorporating a
Novel Spatial Clustering Prior
|
eess.SP cs.LG
|
A Magnetoencephalography (MEG) time-series recording consists of
multi-channel signals collected by superconducting sensors, with each signal's
intensity reflecting magnetic field changes over time at the sensor location.
Automating epileptic MEG spike detection significantly reduces manual
assessment time and effort, yielding substantial clinical benefits. Existing
research addresses MEG spike detection by encoding neural network inputs with
signals from all channel within a time segment, followed by classification.
However, these methods overlook simultaneous spiking occurred from nearby
sensors. We introduce a simple yet effective paradigm that first clusters MEG
channels based on their sensor's spatial position. Next, a novel convolutional
input module is designed to integrate the spatial clustering and temporal
changes of the signals. This module is fed into a custom MEEG-ResNet3D
developed by the authors, which learns to extract relevant features and
classify the input as a spike clip or not. Our method achieves an F1 score of
94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers,
outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates
efficacy and stability in the Electroencephalographic (EEG) seizure detection
task, yielding an improved weighted F1 score of 1.4% compared to current
state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure
dataset.
|
2501.10408
|
Leveraging Cross-Attention Transformer and Multi-Feature Fusion for
Cross-Linguistic Speech Emotion Recognition
|
eess.AS cs.CL cs.SD
|
Speech Emotion Recognition (SER) plays a crucial role in enhancing
human-computer interaction. Cross-Linguistic SER (CLSER) has been a challenging
research problem due to significant variability in linguistic and acoustic
features of different languages. In this study, we propose a novel approach
HuMP-CAT, which combines HuBERT, MFCC, and prosodic characteristics. These
features are fused using a cross-attention transformer (CAT) mechanism during
feature extraction. Transfer learning is applied to gain from a source
emotional speech dataset to the target corpus for emotion recognition. We use
IEMOCAP as the source dataset to train the source model and evaluate the
proposed method on seven datasets in five languages (e.g., English, German,
Spanish, Italian, and Chinese). We show that, by fine-tuning the source model
with a small portion of speech from the target datasets, HuMP-CAT achieves an
average accuracy of 78.75% across the seven datasets, with notable performance
of 88.69% on EMODB (German language) and 79.48% on EMOVO (Italian language).
Our extensive evaluation demonstrates that HuMP-CAT outperforms existing
methods across multiple target languages.
|
2501.10413
|
Cooperative Search and Track of Rogue Drones using Multiagent
Reinforcement Learning
|
cs.MA cs.AI cs.RO cs.SY eess.SY
|
This work considers the problem of intercepting rogue drones targeting
sensitive critical infrastructure facilities. While current interception
technologies focus mainly on the jamming/spoofing tasks, the challenges of
effectively locating and tracking rogue drones have not received adequate
attention. Solving this problem and integrating with recently proposed
interception techniques will enable a holistic system that can reliably detect,
track, and neutralize rogue drones. Specifically, this work considers a team of
pursuer UAVs that can search, detect, and track multiple rogue drones over a
sensitive facility. The joint search and track problem is addressed through a
novel multiagent reinforcement learning scheme to optimize the agent mobility
control actions that maximize the number of rogue drones detected and tracked.
The performance of the proposed system is investigated under realistic settings
through extensive simulation experiments with varying number of agents
demonstrating both its performance and scalability.
|
2501.10415
|
Making Software FAIR: A machine-assisted workflow for the research
software lifecycle
|
cs.DL cs.IR cs.LG cs.SE
|
A key issue hindering discoverability, attribution and reusability of open
research software is that its existence often remains hidden within the
manuscript of research papers. For these resources to become first-class
bibliographic records, they first need to be identified and subsequently
registered with persistent identifiers (PIDs) to be made FAIR (Findable,
Accessible, Interoperable and Reusable). To this day, much open research
software fails to meet FAIR principles and software resources are mostly not
explicitly linked from the manuscripts that introduced them or used them.
SoFAIR is a 2-year international project (2024-2025) which proposes a solution
to the above problem realised over the content available through the global
network of open repositories. SoFAIR will extend the capabilities of widely
used open scholarly infrastructures (CORE, Software Heritage, HAL) and tools
(GROBID) operated by the consortium partners, delivering and deploying an
effective solution for the management of the research software lifecycle,
including: 1) ML-assisted identification of research software assets from
within the manuscripts of scholarly papers, 2) validation of the identified
assets by authors, 3) registration of software assets with PIDs and their
archival.
|
2501.10421
|
CodEv: An Automated Grading Framework Leveraging Large Language Models
for Consistent and Constructive Feedback
|
cs.CY cs.AI cs.HC
|
Grading programming assignments is crucial for guiding students to improve
their programming skills and coding styles. This study presents an automated
grading framework, CodEv, which leverages Large Language Models (LLMs) to
provide consistent and constructive feedback. We incorporate Chain of Thought
(CoT) prompting techniques to enhance the reasoning capabilities of LLMs and
ensure that the grading is aligned with human evaluation. Our framework also
integrates LLM ensembles to improve the accuracy and consistency of scores,
along with agreement tests to deliver reliable feedback and code review
comments. The results demonstrate that the framework can yield grading results
comparable to human evaluators, by using smaller LLMs. Evaluation and
consistency tests of the LLMs further validate our approach, confirming the
reliability of the generated scores and feedback.
|
2501.10423
|
Do we actually understand the impact of renewables on electricity
prices? A causal inference approach
|
stat.AP cs.LG
|
The energy transition is profoundly reshaping electricity market dynamics. It
makes it essential to understand how renewable energy generation actually
impacts electricity prices, among all other market drivers. These insights are
critical to design policies and market interventions that ensure affordable,
reliable, and sustainable energy systems. However, identifying causal effects
from observational data is a major challenge, requiring innovative causal
inference approaches that go beyond conventional regression analysis only. We
build upon the state of the art by developing and applying a local partially
linear double machine learning approach. Its application yields the first
robust causal evidence on the distinct and non-linear effects of wind and solar
power generation on UK wholesale electricity prices, revealing key insights
that have eluded previous analyses. We find that, over 2018-2024, wind power
generation has a U-shaped effect on prices: at low penetration levels, a 1 GWh
increase in energy generation reduces prices by up to 7 GBP/MWh, but this
effect gets close to none at mid-penetration levels (20-30%) before
intensifying again. Solar power places substantial downward pressure on prices
at very low penetration levels (up to 9 GBP/MWh per 1 GWh increase in energy
generation), though its impact weakens quite rapidly. We also uncover a
critical trend where the price-reducing effects of both wind and solar power
have become more pronounced over time (from 2018 to 2024), highlighting their
growing influence on electricity markets amid rising penetration. Our study
provides both novel analysis approaches and actionable insights to guide
policymakers in appraising the way renewables impact electricity markets.
|
2501.10425
|
Delay Neural Networks (DeNN) for exploiting temporal information in
event-based datasets
|
cs.NE cs.LG
|
In Deep Neural Networks (DNN) and Spiking Neural Networks (SNN), the
information of a neuron is computed based on the sum of the amplitudes
(weights) of the electrical potentials received in input from other neurons. We
propose here a new class of neural networks, namely Delay Neural Networks
(DeNN), where the information of a neuron is computed based on the sum of its
input synaptic delays and on the spike times of the electrical potentials
received from other neurons. This way, DeNN are designed to explicitly use
exact continuous temporal information of spikes in both forward and backward
passes, without approximation. (Deep) DeNN are applied here to images and
event-based (audio and visual) data sets. Good performances are obtained,
especially for datasets where temporal information is important, with much less
parameters and less energy than other models.
|
2501.10428
|
Perception-Guided EEG Analysis: A Deep Learning Approach Inspired by
Level of Detail (LOD) Theory
|
eess.SP cs.HC cs.LG
|
Objective: This study explores a novel deep learning approach for EEG
analysis and perceptual state guidance, inspired by Level of Detail (LOD)
theory. The goal is to improve perceptual state identification accuracy and
advance personalized psychological therapy. Methods: Portable EEG devices and
music rhythm signals were used for data collection. LOD theory was applied to
dynamically adjust EEG signal processing, extracting core perceptual features.
A Unity-based software system integrated EEG data with audio materials. The
deep learning model combined a CNN for feature extraction and classification,
and a DQN for reinforcement learning to optimize rhythm adjustments. Results:
The CNN achieved 94.05% accuracy in perceptual state classification. The DQN
guided subjects to target states with a 92.45% success rate, averaging 13.2
rhythm cycles. However, only 50% of users reported psychological alignment with
the target state, indicating room for improvement. Discussion: The results
validate the potential of LOD-based EEG biofeedback. Limitations include
dataset source, label subjectivity, and reward function optimization. Future
work will expand to diverse subjects, incorporate varied musical elements, and
refine reward functions for better generalization and personalization.
|
2501.10429
|
Recent Advances of 6G Ultra-Massive MIMO Technologies in Spatial and
Beam Domains
|
cs.IT cs.SY eess.SY math.IT
|
To explore the full potential of ultra-massive multiple-input multiple-output
(MIMO) communication systems, it is fundamental to understand new ultra-massive
MIMO channel characteristics and establish pervasive channel models. On this
basis, large dimensional spatial-temporal transmission and random access
technologies need to be investigated and evaluated for better practical
implementation. Firstly, this paper reviews recent advances of ultra-massive
MIMO technologies in the traditional spatial domain, including wireless channel
characterization and modeling, channel estimation, spatial multiplexing, and
precoding. Secondly, considering the dramatic increase of base station (BS)
antennas and access users in ultra-massive MIMO systems, the confronted high
dimensional complexity and computing burden of these ultra-massive MIMO
technologies are indicated. To provide efficient and systematic solution, the
emerging tendency to transform related technologies from the traditional
spatial domain to beam domain is introduced. The utilities of large sparsity
merit, reduced energy consumption, and improved usage of radio frequency (RF)
chains in the beam domain channel are elaborated. At last, future challenges of
ultra-massive MIMO communication systems are discussed.
|
2501.10430
|
Prediction Model of Aqua Fisheries Using IoT Devices
|
cs.LG cs.AR cs.SY eess.SY
|
Aquaculture involves cultivating marine and freshwater organisms, with
real-time monitoring of aquatic parameters being crucial in fish farming. This
thesis proposes an IoT-based framework using sensors and Arduino for efficient
monitoring and control of water quality. Different sensors including pH,
temperature, and turbidity are placed in cultivating pond water and each of
them is connected to a common microcontroller board built on an Arduino Uno.
The sensors read the data from the water and store it as a CSV file in an IoT
cloud named Thingspeak through the Arduino Microcontroller. In the experimental
part, we collected data from 5 ponds with various sizes and environments. After
getting the real-time data, we compared these with the standard reference
values. As a result, we can make the decision about which ponds are
satisfactory for cultivating fish and what is not. After that, we labeled the
data with 11 fish categories including Katla, sing, prawn, rui, koi, pangas,
tilapia, silvercarp, karpio, magur, and shrimp. In addition, the data were
analyzed using 10 machine learning (ML) algorithms containing J48, Random
Forest, K-NN, K*, LMT, REPTree, JRIP, PART, Decision Table, and Logit boost.
After experimental evaluation, it was observed among 5 ponds, only three ponds
were perfect for fish farming, where these 3 ponds only satisfied the standard
reference values of pH (6.5-8.5), Temperature (16-24)oC, Turbidity (below
10)ntu, Conductivity (970-1825){\mu}S/cm, and Depth (1-4) meter. Among the
state-of-the-art machine learning algorithms, Random Forest achieved the
highest score of performance metrics as accuracy 94.42%, kappa statistics
93.5%, and Avg. TP Rate 94.4%. In addition, we calculated the BOD, COD, and DO
for one scenario. This study includes details of the proposed IoT system's
prototype hardware.
|
2501.10431
|
Quantum Annealing for Robust Principal Component Analysis
|
cs.ET cs.LG quant-ph stat.ML
|
Principal component analysis is commonly used for dimensionality reduction,
feature extraction, denoising, and visualization. The most commonly used
principal component analysis method is based upon optimization of the L2-norm,
however, the L2-norm is known to exaggerate the contribution of errors and
outliers. When optimizing over the L1-norm, the components generated are known
to exhibit robustness or resistance to outliers in the data. The L1-norm
components can be solved for with a binary optimization problem. Previously,
L1-BF has been used to solve the binary optimization for multiple components
simultaneously. In this paper we propose QAPCA, a new method for finding
principal components using quantum annealing hardware which will optimize over
the robust L1-norm. The conditions required for convergence of the annealing
problem are discussed. The potential speedup when using quantum annealing is
demonstrated through complexity analysis and experimental results. To showcase
performance against classical principal component analysis techniques
experiments upon synthetic Gaussian data, a fault detection scenario and breast
cancer diagnostic data are studied. We find that the reconstruction error when
using QAPCA is comparable to that when using L1-BF.
|
2501.10435
|
Robust Hybrid Classical-Quantum Transfer Learning Model for Text
Classification Using GPT-Neo 125M with LoRA & SMOTE Enhancement
|
cs.LG quant-ph
|
This research introduces a hybrid classical-quantum framework for text
classification, integrating GPT-Neo 125M with Low-Rank Adaptation (LoRA) and
Synthetic Minority Over-sampling Technique (SMOTE) using quantum computing
backends. While the GPT-Neo 125M baseline remains the best-performing model,
the implementation of LoRA and SMOTE enhances the hybrid model, resulting in
improved accuracy, faster convergence, and better generalization. Experiments
on IBM's 127-qubit quantum backend and Pennylane's 32-qubit simulation
demonstrate the viability of combining classical neural networks with quantum
circuits. This framework underscores the potential of hybrid architectures for
advancing natural language processing applications.
|
2501.10436
|
A flatness-based predictive controller for six-degrees of freedom
spacecraft rendezvous
|
eess.SY cs.SY
|
This work presents a closed-loop guidance algorithm for six-degrees of
freedom spacecraft rendezvous with a passive target flying in an eccentric
orbit. The main assumption is that the chaser vehicle has an attitude control
system, based on reaction wheels, providing the necessary torque to change its
orientation whereas the number of thrusters is arbitrary. The goal is to design
fuel optimal maneuvers while satisfying operational constraints and rejecting
disturbances. The proposed method is as follows; first, the coupled
translational and angular dynamics are transformed to equivalent algebraic
relations using the relative translational states transition matrix and the
attitude flatness property. Then, a direct transcription method, based on
B-splines parameterization and discretization of time continuous constraints,
is developed to obtain a tractable static program. Finally, a Model Predictive
Controller, based on linearization around the previously computed solution, is
considered to handle disturbances. Numerical results are shown and discussed.
|
2501.10437
|
Chance-constrained Model Predictive Control for Near Rectilinear Halo
Orbit spacecraft rendezvous
|
eess.SY cs.SY
|
This work presents a robust Model Predictive Controller (MPC) to solve the
problem of spacecraft rendezvous in the context of the restricted three-body
problem (R3BP) as will be required to dock with space stations in cislunar
space. The employed methodology is both valid for chemical and electric
thrusters. By exploiting the state transition matrix and using a
chance-constrained approach, the robust MPC assures constraints satisfaction
under the presence of disturbances in a probabilistic sense. The perturbations
parameters are computed on-line using a disturbance estimator. The robust
controller is tested for a rendezvous scenario with a target placed in an
Earth-Moon Near-Rectilinear Halo Orbit. Numerical results are shown and
discussed.
|
2501.10438
|
Event-Based Impulsive Control for Spacecraft Rendezvous Hovering Phases
|
eess.SY cs.SY
|
This work presents an event-triggered controller for spacecraft rendezvous
hovering phases. The goal is to maintain the chaser within a bounded region
with respect to the target. The main assumption is that the chaser vehicle has
impulsive thrusters. These are assumed to be orientable at any direction and
are constrained by dead-zone and saturation bounds. The event-based controller
relies on trigger rules deciding when a suitable control law is applied. The
local control law consists on a single impulse; therefore the trigger rules
design is based on the instantaneous reachability to the admissible set. The
final outcome is a very efficient algorithm from both computational burden and
footprint perspectives. Because the proposed methodology is based on a single
impulse control, the controller invariance is local and assessed through
impulsive systems theory. Finally, numerical results are shown and discussed.
|
2501.10440
|
Median of Means Sampling for the Keister Function
|
stat.ME cs.LG cs.NA math.NA stat.CO stat.ML
|
This study investigates the performance of median-of-means sampling compared
to traditional mean-of-means sampling for computing the Keister function
integral using Randomized Quasi-Monte Carlo (RQMC) methods. The research tests
both lattice points and digital nets as point distributions across dimensions
2, 3, 5, and 8, with sample sizes ranging from 2^8 to 2^19 points. Results
demonstrate that median-of-means sampling consistently outperforms
mean-of-means for sample sizes larger than 10^3 points, while mean-of-means
shows better accuracy with smaller sample sizes, particularly for digital nets.
The study also confirms previous theoretical predictions about median-of-means'
superior performance with larger sample sizes and reflects the known challenges
of maintaining accuracy in higher-dimensional integration. These findings
support recent research suggesting median-of-means as a promising alternative
to traditional sampling methods in numerical integration, though limitations in
sample size and dimensionality warrant further investigation with different
test functions and larger parameter spaces.
|
2501.10441
|
A Review of Detection, Evolution, and Data Reconstruction Strategies for
False Data Injection Attacks in Power Cyber-Physical Systems
|
cs.CR cs.SY eess.SY
|
The integration of information and physical systems in modern power grids has
heightened vulnerabilities to False Data Injection Attacks (FDIAs), threatening
the secure operation of power cyber-physical systems (CPS). This paper reviews
FDIA detection, evolution, and data reconstruction strategies, highlighting
cross-domain coordination, multi-temporal evolution, and stealth
characteristics. Challenges in existing detection methods, including poor
interpretability and data imbalance, are discussed, alongside advanced
state-aware and action-control data reconstruction techniques. Key issues, such
as modeling FDIA evolution and distinguishing malicious data from regular
faults, are identified. Future directions to enhance system resilience and
detection accuracy are proposed, contributing to the secure operation of power
CPS.
|
2501.10443
|
Monetary Evolution: How Societies Shaped Money from Antiquity to
Cryptocurrencies
|
cs.CR cs.CE econ.GN q-fin.EC
|
With the growing popularity and rising value of cryptocurrencies, skepticism
surrounding this groundbreaking innovation persists. Many financial and
business experts argue that the value created in the cryptocurrency realm
resembles the generation of currency from thin air. However, a historical
analysis of the fundamental concepts that have shaped money reveals striking
parallels with past transformations in human society. This study extends these
historical insights to the present era, demonstrating how enduring monetary
concepts are once again redefining our understanding of money and reshaping its
form. Additionally, we offer novel interpretations of cryptocurrency by linking
the intrinsic nature of money, the communities it fosters, and the
cryptographic technologies that have provided the infrastructure for this
transformative shift.
|
2501.10446
|
Optimizing a multi-state cold-standby system with multiple vacations in
the repair and loss of units
|
eess.SY cs.SY stat.ME
|
A complex multi-state redundant system with preventive maintenance subject to
multiple events is considered. The online unit can undergo several types of
failures: internal and those provoked by external shocks. Multiple degradation
levels are assumed so as internal and external. Degradation levels are observed
by random inspections and if they are major, the unit goes to repair facility
where preventive maintenance is carried out. This repair facility is composed
of a single repairperson governed by a multiple vacation policy. This policy is
set up according to the operational number of units. Two types of task can be
performed by the repairperson, corrective repair and preventive maintenance.
The times embedded in the system are phase type distributed and the model is
built by using Markovian Arrival Processes with marked arrivals. Multiple
performance measures besides of the transient and stationary distribution are
worked out through matrix-analytic methods. This methodology enables us to
express the main results and the global development in a matrix-algorithmic
form. To optimize the model costs and rewards are included. A numerical example
shows the versatility of the model.
|
2501.10447
|
A Predictive Cooperative Collision Avoidance for Multi-Robot Systems
Using Control Barrier Function
|
cs.SY cs.RO
|
Control barrier function (CBF)-based methods provide the minimum modification
necessary to formally guarantee safety in the context of quadratic programming,
and strict safety guarantee for safety critical systems. However, most
CBF-related derivatives myopically focus on present safety at each time step, a
reasoning over a look-ahead horizon is exactly missing. In this paper, a
predictive safety matrix is constructed. We then consolidate the safety
condition based on the smallest eigenvalue of the proposed safety matrix. A
predefined deconfliction strategy of motion paths is embedded into the
trajectory tracking module to manage deadlock conflicts, which computes the
deadlock escape velocity with the minimum attitude angle. Comparison results
show that the introduction of the predictive term is robust for measurement
uncertainty and is immune to oscillations. The proposed deadlock avoidance
method avoids a large detour, without obvious stagnation.
|
2501.10448
|
Towards Lightweight Time Series Forecasting: a Patch-wise Transformer
with Weak Data Enriching
|
cs.LG cs.AI
|
Patch-wise Transformer based time series forecasting achieves superior
accuracy. However, this superiority relies heavily on intricate model design
with massive parameters, rendering both training and inference expensive, thus
preventing their deployments on edge devices with limited resources and low
latency requirements. In addition, existing methods often work in an
autoregressive manner, which take into account only historical values, but
ignore valuable, easy-to-obtain context information, such as weather forecasts,
date and time of day. To contend with the two limitations, we propose
LiPFormer, a novel Lightweight Patch-wise Transformer with weak data enriching.
First, to simplify the Transformer backbone, LiPFormer employs a novel
lightweight cross-patch attention and a linear transformation-based attention
to eliminate Layer Normalization and Feed Forward Network, two heavy components
in existing Transformers. Second, we propose a lightweight, weak data enriching
module to provide additional, valuable weak supervision to the training. It
enhances forecasting accuracy without significantly increasing model complexity
as it does not involve expensive, human-labeling but using easily accessible
context information. This facilitates the weak data enriching to plug-and-play
on existing models. Extensive experiments on nine benchmark time series
datasets demonstrate that LiPFormer outperforms state-of-the-art methods in
accuracy, while significantly reducing parameter scale, training duration, and
GPU memory usage. Deployment on an edge device reveals that LiPFormer takes
only 1/3 inference time compared to classic Transformers. In addition, we
demonstrate that the weak data enriching can integrate seamlessly into various
Transformer based models to enhance their accuracy, suggesting its generality.
|
2501.10451
|
Automating Credit Card Limit Adjustments Using Machine Learning
|
cs.LG
|
Venezuelan banks have historically made credit card limit adjustment
decisions manually through committees. However, since the number of credit card
holders in Venezuela is expected to increase in the upcoming months due to
economic improvements, manual decisions are starting to become unfeasible. In
this project, a machine learning model that uses cost-sensitive learning is
proposed to automate the task of handing out credit card limit increases. To
accomplish this, several neural network and XGBoost models are trained and
compared, leveraging Venezolano de Credito's data and using grid search with
10-fold cross-validation. The proposed model is ultimately chosen due to its
superior balance of accuracy, cost-effectiveness, and interpretability. The
model's performance is evaluated against the committee's decisions using
Cohen's kappa coefficient, showing an almost perfect agreement.
|
2501.10453
|
Uncovering Bias in Foundation Models: Impact, Testing, Harm, and
Mitigation
|
cs.LG cs.AI cs.CY
|
Bias in Foundation Models (FMs) - trained on vast datasets spanning societal
and historical knowledge - poses significant challenges for fairness and equity
across fields such as healthcare, education, and finance. These biases, rooted
in the overrepresentation of stereotypes and societal inequalities in training
data, exacerbate real-world discrimination, reinforce harmful stereotypes, and
erode trust in AI systems. To address this, we introduce Trident Probe Testing
(TriProTesting), a systematic testing method that detects explicit and implicit
biases using semantically designed probes. Here we show that FMs, including
CLIP, ALIGN, BridgeTower, and OWLv2, demonstrate pervasive biases across single
and mixed social attributes (gender, race, age, and occupation). Notably, we
uncover mixed biases when social attributes are combined, such as gender x
race, gender x age, and gender x occupation, revealing deeper layers of
discrimination. We further propose Adaptive Logit Adjustment
(AdaLogAdjustment), a post-processing technique that dynamically redistributes
probability power to mitigate these biases effectively, achieving significant
improvements in fairness without retraining models. These findings highlight
the urgent need for ethical AI practices and interdisciplinary solutions to
address biases not only at the model level but also in societal structures. Our
work provides a scalable and interpretable solution that advances fairness in
AI systems while offering practical insights for future research on fair AI
technologies.
|
2501.10454
|
Spatio-Temporal Graph Convolutional Networks: Optimised Temporal
Architecture
|
cs.LG stat.ML
|
Spatio-Temporal graph convolutional networks were originally introduced with
CNNs as temporal blocks for feature extraction. Since then LSTM temporal blocks
have been proposed and shown to have promising results. We propose a novel
architecture combining both CNN and LSTM temporal blocks and then provide an
empirical comparison between our new and the pre-existing models. We provide
theoretical arguments for the different temporal blocks and use a multitude of
tests across different datasets to assess our hypotheses.
|
2501.10455
|
PhyDeformer: High-Quality Non-Rigid Garment Registration with
Physics-Awareness
|
cs.CV cs.GR
|
We present PhyDeformer, a new deformation method for high-quality garment
mesh registration. It operates in two phases: In the first phase, a garment
grading is performed to achieve a coarse 3D alignment between the mesh template
and the target mesh, accounting for proportional scaling and fit (e.g. length,
size). Then, the graded mesh is refined to align with the fine-grained details
of the 3D target through an optimization coupled with the Jacobian-based
deformation framework. Both quantitative and qualitative evaluations on
synthetic and real garments highlight the effectiveness of our method.
|
2501.10459
|
Efficient Traffic Prediction Through Spatio-Temporal Distillation
|
cs.LG cs.CE
|
Graph neural networks (GNNs) have gained considerable attention in recent
years for traffic flow prediction due to their ability to learn spatio-temporal
pattern representations through a graph-based message-passing framework.
Although GNNs have shown great promise in handling traffic datasets, their
deployment in real-life applications has been hindered by scalability
constraints arising from high-order message passing. Additionally, the
over-smoothing problem of GNNs may lead to indistinguishable region
representations as the number of layers increases, resulting in performance
degradation. To address these challenges, we propose a new knowledge
distillation paradigm termed LightST that transfers spatial and temporal
knowledge from a high-capacity teacher to a lightweight student. Specifically,
we introduce a spatio-temporal knowledge distillation framework that helps
student MLPs capture graph-structured global spatio-temporal patterns while
alleviating the over-smoothing effect with adaptive knowledge distillation.
Extensive experiments verify that LightST significantly speeds up traffic flow
predictions by 5X to 40X compared to state-of-the-art spatio-temporal GNNs, all
while maintaining superior accuracy.
|
2501.10461
|
A Framework for Mining Collectively-Behaving Bots in MMORPGs
|
cs.LG cs.AI
|
In MMORPGs (Massively Multiplayer Online Role-Playing Games), abnormal
players (bots) using unauthorized automated programs to carry out pre-defined
behaviors systematically and repeatedly are commonly observed. Bots usually
engage in these activities to gain in-game money, which they eventually trade
for real money outside the game. Such abusive activities negatively impact the
in-game experiences of legitimate users since bots monopolize specific hunting
areas and obtain valuable items. Thus, detecting abnormal players is a
significant task for game companies. Motivated by the fact that bots tend to
behave collectively with similar in-game trajectories due to the auto-programs,
we developed BotTRep, a framework that comprises trajectory representation
learning followed by clustering using a completely unlabeled in-game trajectory
dataset. Our model aims to learn representations for in-game trajectory
sequences so that players with contextually similar trajectories have closer
embeddings. Then, by applying DBSCAN to these representations and visualizing
the corresponding moving patterns, our framework ultimately assists game
masters in identifying and banning bots.
|
2501.10462
|
BloomScene: Lightweight Structured 3D Gaussian Splatting for Crossmodal
Scene Generation
|
cs.CV cs.AI cs.GR cs.LG
|
With the widespread use of virtual reality applications, 3D scene generation
has become a new challenging research frontier. 3D scenes have highly complex
structures and need to ensure that the output is dense, coherent, and contains
all necessary structures. Many current 3D scene generation methods rely on
pre-trained text-to-image diffusion models and monocular depth estimators.
However, the generated scenes occupy large amounts of storage space and often
lack effective regularisation methods, leading to geometric distortions. To
this end, we propose BloomScene, a lightweight structured 3D Gaussian splatting
for crossmodal scene generation, which creates diverse and high-quality 3D
scenes from text or image inputs. Specifically, a crossmodal progressive scene
generation framework is proposed to generate coherent scenes utilizing
incremental point cloud reconstruction and 3D Gaussian splatting. Additionally,
we propose a hierarchical depth prior-based regularization mechanism that
utilizes multi-level constraints on depth accuracy and smoothness to enhance
the realism and continuity of the generated scenes. Ultimately, we propose a
structured context-guided compression mechanism that exploits structured hash
grids to model the context of unorganized anchor attributes, which
significantly eliminates structural redundancy and reduces storage overhead.
Comprehensive experiments across multiple scenes demonstrate the significant
potential and advantages of our framework compared with several baselines.
|
2501.10463
|
GLow -- A Novel, Flower-Based Simulated Gossip Learning Strategy
|
cs.LG cs.AI cs.DC
|
Fully decentralized learning algorithms are still in an early stage of
development. Creating modular Gossip Learning strategies is not trivial due to
convergence challenges and Byzantine faults intrinsic in systems of
decentralized nature. Our contribution provides a novel means to simulate
custom Gossip Learning systems by leveraging the state-of-the-art Flower
Framework. Specifically, we introduce GLow, which will allow researchers to
train and assess scalability and convergence of devices, across custom network
topologies, before making a physical deployment. The Flower Framework is
selected for being a simulation featured library with a very active community
on Federated Learning research. However, Flower exclusively includes vanilla
Federated Learning strategies and, thus, is not originally designed to perform
simulations without a centralized authority. GLow is presented to fill this gap
and make simulation of Gossip Learning systems possible. Results achieved by
GLow in the MNIST and CIFAR10 datasets, show accuracies over 0.98 and 0.75
respectively. More importantly, GLow performs similarly in terms of accuracy
and convergence to its analogous Centralized and Federated approaches in all
designed experiments.
|
2501.10464
|
Adapting Beyond the Depth Limit: Counter Strategies in Large Imperfect
Information Games
|
cs.GT cs.AI
|
We study the problem of adapting to a known sub-rational opponent during
online play while remaining robust to rational opponents. We focus on large
imperfect-information (zero-sum) games, which makes it impossible to inspect
the whole game tree at once and necessitates the use of depth-limited search.
However, all existing methods assume rational play beyond the depth-limit,
which only allows them to adapt a very limited portion of the opponent's
behaviour. We propose an algorithm Adapting Beyond Depth-limit (ABD) that uses
a strategy-portfolio approach - which we refer to as matrix-valued states - for
depth-limited search. This allows the algorithm to fully utilise all
information about the opponent model, making it the first robust-adaptation
method to be able to do so in large imperfect-information games. As an
additional benefit, the use of matrix-valued states makes the algorithm simpler
than traditional methods based on optimal value functions. Our experimental
results in poker and battleship show that ABD yields more than a twofold
increase in utility when facing opponents who make mistakes beyond the depth
limit and also delivers significant improvements in utility and safety against
randomly generated opponents.
|
2501.10465
|
The Mathematics of Artificial Intelligence
|
math.OC cs.AI
|
This overview article highlights the critical role of mathematics in
artificial intelligence (AI), emphasizing that mathematics provides tools to
better understand and enhance AI systems. Conversely, AI raises new problems
and drives the development of new mathematics at the intersection of various
fields. This article focuses on the application of analytical and probabilistic
tools to model neural network architectures and better understand their
optimization. Statistical questions (particularly the generalization capacity
of these networks) are intentionally set aside, though they are of crucial
importance. We also shed light on the evolution of ideas that have enabled
significant advances in AI through architectures tailored to specific tasks,
each echoing distinct mathematical techniques. The goal is to encourage more
mathematicians to take an interest in and contribute to this exciting field.
|
2501.10466
|
Improving the Efficiency of Self-Supervised Adversarial Training through
Latent Clustering-Based Selection
|
cs.LG cs.AI cs.CR cs.CV
|
Compared with standard learning, adversarially robust learning is widely
recognized to demand significantly more training examples. Recent works propose
the use of self-supervised adversarial training (SSAT) with external or
synthetically generated unlabeled data to enhance model robustness. However,
SSAT requires a substantial amount of extra unlabeled data, significantly
increasing memory usage and model training times. To address these challenges,
we propose novel methods to strategically select a small subset of unlabeled
data essential for SSAT and robustness improvement. Our selection prioritizes
data points near the model's decision boundary based on latent clustering-based
techniques, efficiently identifying a critical subset of unlabeled data with a
higher concentration of boundary-adjacent points. While focusing on
near-boundary data, our methods are designed to maintain a balanced ratio
between boundary and non-boundary data points to avoid overfitting. Our
experiments on image benchmarks show that integrating our selection strategies
into self-supervised adversarial training can largely reduce memory and
computational requirements while achieving high model robustness. In
particular, our latent clustering-based selection method with k-means is the
most effective, achieving nearly identical test-time robust accuracies with 5
to 10 times less external or generated unlabeled data when applied to image
benchmarks. Additionally, we validate the generalizability of our approach
across various application scenarios, including a real-world medical dataset
for COVID-19 chest X-ray classification.
|
2501.10467
|
Securing the AI Frontier: Urgent Ethical and Regulatory Imperatives for
AI-Driven Cybersecurity
|
cs.CR cs.AI cs.CY cs.SE
|
This paper critically examines the evolving ethical and regulatory challenges
posed by the integration of artificial intelligence (AI) in cybersecurity. We
trace the historical development of AI regulation, highlighting major
milestones from theoretical discussions in the 1940s to the implementation of
recent global frameworks such as the European Union AI Act. The current
regulatory landscape is analyzed, emphasizing risk-based approaches,
sector-specific regulations, and the tension between fostering innovation and
mitigating risks. Ethical concerns such as bias, transparency, accountability,
privacy, and human oversight are explored in depth, along with their
implications for AI-driven cybersecurity systems. Furthermore, we propose
strategies for promoting AI literacy and public engagement, essential for
shaping a future regulatory framework. Our findings underscore the need for a
unified, globally harmonized regulatory approach that addresses the unique
risks of AI in cybersecurity. We conclude by identifying future research
opportunities and recommending pathways for collaboration between policymakers,
industry leaders, and researchers to ensure the responsible deployment of AI
technologies in cybersecurity.
|
2501.10470
|
Off-policy Evaluation for Payments at Adyen
|
cs.LG cs.IR
|
This paper demonstrates the successful application of Off-Policy Evaluation
(OPE) to accelerate recommender system development and optimization at Adyen, a
global leader in financial payment processing. Facing the limitations of
traditional A/B testing, which proved slow, costly, and often inconclusive, we
integrated OPE to enable rapid evaluation of new recommender system variants
using historical data. Our analysis, conducted on a billion-scale dataset of
transactions, reveals a strong correlation between OPE estimates and online A/B
test results, projecting an incremental 9--54 million transactions over a
six-month period. We explore the practical challenges and trade-offs associated
with deploying OPE in a high-volume production environment, including
leveraging exploration traffic for data collection, mitigating variance in
importance sampling, and ensuring scalability through the use of Apache Spark.
By benchmarking various OPE estimators, we provide guidance on their
effectiveness and integration into the decision-making systems for large-scale
industrial payment systems.
|
2501.10471
|
Village-Net Clustering: A Rapid approach to Non-linear Unsupervised
Clustering of High-Dimensional Data
|
cs.LG q-bio.QM stat.ML
|
Clustering large high-dimensional datasets with diverse variable is essential
for extracting high-level latent information from these datasets. Here, we
developed an unsupervised clustering algorithm, we call "Village-Net".
Village-Net is specifically designed to effectively cluster high-dimension data
without priori knowledge on the number of existing clusters. The algorithm
operates in two phases: first, utilizing K-Means clustering, it divides the
dataset into distinct subsets we refer to as "villages". Next, a weighted
network is created, with each node representing a village, capturing their
proximity relationships. To achieve optimal clustering, we process this network
using a community detection algorithm called Walk-likelihood Community Finder
(WLCF), a community detection algorithm developed by one of our team members. A
salient feature of Village-Net Clustering is its ability to autonomously
determine an optimal number of clusters for further analysis based on inherent
characteristics of the data. We present extensive benchmarking on extant
real-world datasets with known ground-truth labels to showcase its competitive
performance, particularly in terms of the normalized mutual information (NMI)
score, when compared to other state-of-the-art methods. The algorithm is
computationally efficient, boasting a time complexity of O(N*k*d), where N
signifies the number of instances, k represents the number of villages and d
represents the dimension of the dataset, which makes it well suited for
effectively handling large-scale datasets.
|
2501.10474
|
Poxel: Voxel Reconstruction for 3D Printing
|
cs.GR cs.CV
|
Recent advancements in 3D reconstruction, especially through neural rendering
approaches like Neural Radiance Fields (NeRF) and Plenoxel, have led to
high-quality 3D visualizations. However, these methods are optimized for
digital environments and employ view-dependent color models (RGB) and 2D
splatting techniques, which do not translate well to physical 3D printing. This
paper introduces "Poxel", which stands for Printable-Voxel, a voxel-based 3D
reconstruction framework optimized for photopolymer jetting 3D printing, which
allows for high-resolution, full-color 3D models using a CMYKWCl color model.
Our framework directly outputs printable voxel grids by removing
view-dependency and converting the digital RGB color space to a physical
CMYKWCl color space suitable for multi-material jetting. The proposed system
achieves better fidelity and quality in printed models, aligning with the
requirements of physical 3D objects.
|
2501.10476
|
Revisiting Rogers' Paradox in the Context of Human-AI Interaction
|
cs.AI cs.LG
|
Humans learn about the world, and how to act in the world, in many ways: from
individually conducting experiments to observing and reproducing others'
behavior. Different learning strategies come with different costs and
likelihoods of successfully learning more about the world. The choice that any
one individual makes of how to learn can have an impact on the collective
understanding of a whole population if people learn from each other. Alan
Rogers developed simulations of a population of agents to study these network
phenomena where agents could individually or socially learn amidst a dynamic,
uncertain world and uncovered a confusing result: the availability of cheap
social learning yielded no benefit to population fitness over individual
learning. This paradox spawned decades of work trying to understand and uncover
factors that foster the relative benefit of social learning that centuries of
human behavior suggest exists. What happens in such network models now that
humans can socially learn from AI systems that are themselves socially learning
from us? We revisit Rogers' Paradox in the context of human-AI interaction to
probe a simplified network of humans and AI systems learning together about an
uncertain world. We propose and examine the impact of several learning
strategies on the quality of the equilibrium of a society's 'collective world
model'. We consider strategies that can be undertaken by various stakeholders
involved in a single human-AI interaction: human, AI model builder, and society
or regulators around the interaction. We then consider possible negative
feedback loops that may arise from humans learning socially from AI: that
learning from the AI may impact our own ability to learn about the world. We
close with open directions into studying networks of human and AI systems that
can be explored in enriched versions of our simulation framework.
|
2501.10479
|
Lossless Compression of Vector IDs for Approximate Nearest Neighbor
Search
|
cs.LG cs.DB cs.IR
|
Approximate nearest neighbor search for vectors relies on indexes that are
most often accessed from RAM. Therefore, storage is the factor limiting the
size of the database that can be served from a machine. Lossy vector
compression, i.e., embedding quantization, has been applied extensively to
reduce the size of indexes. However, for inverted file and graph-based indices,
auxiliary data such as vector ids and links (edges) can represent most of the
storage cost. We introduce and evaluate lossless compression schemes for these
cases. These approaches are based on asymmetric numeral systems or wavelet
trees that exploit the fact that the ordering of ids is irrelevant within the
data structures. In some settings, we are able to compress the vector ids by a
factor 7, with no impact on accuracy or search runtime. On billion-scale
datasets, this results in a reduction of 30% of the index size. Furthermore, we
show that for some datasets, these methods can also compress the quantized
vector codes losslessly, by exploiting sub-optimalities in the original
quantization algorithm. The source code for our approach available at
https://github.com/facebookresearch/vector_db_id_compression.
|
2501.10481
|
Using Domain Knowledge with Deep Learning to Solve Applied Inverse
Problems
|
cs.LG cond-mat.mtrl-sci cs.CE
|
Advancements in deep learning have improved the ability to model complex,
nonlinear relationships, such as those encountered in complex material inverse
problems. However, the effectiveness of these methods often depends on large
datasets, which are not always available. In this study, the incorporation of
domain-specific knowledge of mechanical behavior is investigated to evaluate
the impact on the predictive performance of the models in data-scarce
scenarios. To demonstrate this, stress-strain curves were used to predict key
microstructural features of porous materials, and the performance of models
trained with and without domain knowledge was compared using five deep learning
models: Convolutional Neural Networks, Extreme Gradient Boosting, K-Nearest
Neighbors, Long Short-Term Memory, and Random Forest. The results of the models
with domain-specific characteristics consistently achieved higher $R^2$ values
and improved learning efficiency compared to models without prior knowledge.
When the models did not include domain knowledge, the model results revealed
meaningful patterns were not recognized, while those enhanced with mechanical
insights showed superior feature extraction and predictions. These findings
underscore the critical role of domain knowledge in guiding deep learning
models, highlighting the need to combine domain expertise with data-driven
approaches to achieve reliable and accurate outcomes in materials science and
related fields.
|
2501.10482
|
Simulation of Random LR Fuzzy Intervals
|
stat.ML cs.LG cs.LO math.PR stat.CO stat.OT
|
Random fuzzy variables join the modeling of the impreciseness (due to their
``fuzzy part'') and randomness. Statistical samples of such objects are widely
used, and their direct, numerically effective generation is therefore
necessary. Usually, these samples consist of triangular or trapezoidal fuzzy
numbers. In this paper, we describe theoretical results and simulation
algorithms for another family of fuzzy numbers -- LR fuzzy numbers with
interval-valued cores. Starting from a simulation perspective on the piecewise
linear LR fuzzy numbers with the interval-valued cores, their limiting behavior
is then considered. This leads us to the numerically efficient algorithm for
simulating a sample consisting of such fuzzy values.
|
2501.10483
|
ArxEval: Evaluating Retrieval and Generation in Language Models for
Scientific Literature
|
cs.CL cs.AI
|
Language Models [LMs] are now playing an increasingly large role in
information generation and synthesis; the representation of scientific
knowledge in these systems needs to be highly accurate. A prime challenge is
hallucination; that is, generating apparently plausible but actually false
information, including invented citations and nonexistent research papers. This
kind of inaccuracy is dangerous in all the domains that require high levels of
factual correctness, such as academia and education. This work presents a
pipeline for evaluating the frequency with which language models hallucinate in
generating responses in the scientific literature. We propose ArxEval, an
evaluation pipeline with two tasks using ArXiv as a repository: Jumbled Titles
and Mixed Titles. Our evaluation includes fifteen widely used language models
and provides comparative insights into their reliability in handling scientific
literature.
|
2501.10484
|
Bias in Decision-Making for AI's Ethical Dilemmas: A Comparative Study
of ChatGPT and Claude
|
cs.CY cs.AI
|
Recent advances in Large Language Models (LLMs) have enabled human-like
responses across various tasks, raising questions about their ethical
decision-making capabilities and potential biases. This study investigates
protected attributes in LLMs through systematic evaluation of their responses
to ethical dilemmas. Using two prominent models - GPT-3.5 Turbo and Claude 3.5
Sonnet - we analyzed their decision-making patterns across multiple protected
attributes including age, gender, race, appearance, and disability status.
Through 11,200 experimental trials involving both single-factor and two-factor
protected attribute combinations, we evaluated the models' ethical preferences,
sensitivity, stability, and clustering of preferences. Our findings reveal
significant protected attributeses in both models, with consistent preferences
for certain features (e.g., "good-looking") and systematic neglect of others.
Notably, while GPT-3.5 Turbo showed stronger preferences aligned with
traditional power structures, Claude 3.5 Sonnet demonstrated more diverse
protected attribute choices. We also found that ethical sensitivity
significantly decreases in more complex scenarios involving multiple protected
attributes. Additionally, linguistic referents heavily influence the models'
ethical evaluations, as demonstrated by differing responses to racial
descriptors (e.g., "Yellow" versus "Asian"). These findings highlight critical
concerns about the potential impact of LLM biases in autonomous decision-making
systems and emphasize the need for careful consideration of protected
attributes in AI development. Our study contributes to the growing body of
research on AI ethics by providing a systematic framework for evaluating
protected attributes in LLMs' ethical decision-making capabilities.
|
2501.10486
|
Enhancing the Reliability in Machine Learning for Gravitational Wave
Parameter Estimation with Attention-Based Models
|
astro-ph.IM cs.LG gr-qc
|
We introduce a technique to enhance the reliability of gravitational wave
parameter estimation results produced by machine learning. We develop two
independent machine learning models based on the Vision Transformer to estimate
effective spin and chirp mass from spectrograms of gravitational wave signals
from binary black hole mergers. To enhance the reliability of these models, we
utilize attention maps to visualize the areas our models focus on when making
predictions. This approach enables demonstrating that both models perform
parameter estimation based on physically meaningful information. Furthermore,
by leveraging these attention maps, we demonstrate a method to quantify the
impact of glitches on parameter estimation. We show that as the models focus
more on glitches, the parameter estimation results become more strongly biased.
This suggests that attention maps could potentially be used to distinguish
between cases where the results produced by the machine learning model are
reliable and cases where they are not.
|
2501.10487
|
Tabular-TX: Theme-Explanation Structure-based Table Summarization via
In-Context Learning
|
cs.CL cs.AI
|
This paper proposes a Theme-Explanation Structure-based Table Summarization
(Tabular-TX) pipeline designed to efficiently process table data. Tabular-TX
preprocesses table data by focusing on highlighted cells and then generates
summary sentences structured with a Theme Part in the form of adverbial phrases
followed by an Explanation Part in the form of clauses. In this process,
customized analysis is performed by considering the structural characteristics
and comparability of the table. Additionally, by utilizing In-Context Learning,
Tabular-TX optimizes the analytical capabilities of large language models
(LLMs) without the need for fine-tuning, effectively handling the structural
complexity of table data. Results from applying the proposed Tabular-TX to
generate table-based summaries demonstrated superior performance compared to
existing fine-tuning-based methods, despite limitations in dataset size.
Experimental results confirmed that Tabular-TX can process complex table data
more effectively and established it as a new alternative for table-based
question answering and summarization tasks, particularly in
resource-constrained environments.
|
2501.10492
|
ACCEPT: Diagnostic Forecasting of Battery Degradation Through
Contrastive Learning
|
cs.LG cs.SY eess.SY
|
Modeling lithium-ion battery (LIB) degradation offers significant cost
savings and enhances the safety and reliability of electric vehicles (EVs) and
battery energy storage systems (BESS). Whilst data-driven methods have received
great attention for forecasting degradation, they often demonstrate limited
generalization ability and tend to underperform particularly in critical
scenarios involving accelerated degradation, which are crucial to predict
accurately. These methods also fail to elucidate the underlying causes of
degradation. Alternatively, physical models provide a deeper understanding, but
their complex parameters and inherent uncertainties limit their applicability
in real-world settings. To this end, we propose a new model - ACCEPT. Our novel
framework uses contrastive learning to map the relationship between the
underlying physical degradation parameters and observable operational
quantities, combining the benefits of both approaches. Furthermore, due to the
similarity of degradation paths between LIBs with the same chemistry, this
model transfers non-trivially to most downstream tasks, allowing for zero-shot
inference. Additionally, since categorical features can be included in the
model, it can generalize to other LIB chemistries. This work establishes a
foundational battery degradation model, providing reliable forecasts across a
range of battery types and operating conditions.
|
2501.10496
|
Extension of Symmetrized Neural Network Operators with Fractional and
Mixed Activation Functions
|
stat.ML cs.LG
|
We propose a novel extension to symmetrized neural network operators by
incorporating fractional and mixed activation functions. This study addresses
the limitations of existing models in approximating higher-order smooth
functions, particularly in complex and high-dimensional spaces. Our framework
introduces a fractional exponent in the activation functions, allowing adaptive
non-linear approximations with improved accuracy. We define new density
functions based on $q$-deformed and $\theta$-parametrized logistic models and
derive advanced Jackson-type inequalities that establish uniform convergence
rates. Additionally, we provide a rigorous mathematical foundation for the
proposed operators, supported by numerical validations demonstrating their
efficiency in handling oscillatory and fractional components. The results
extend the applicability of neural network approximation theory to broader
functional spaces, paving the way for applications in solving partial
differential equations and modeling complex systems.
|
2501.10499
|
Learning More With Less: Sample Efficient Dynamics Learning and
Model-Based RL for Loco-Manipulation
|
cs.RO
|
Combining the agility of legged locomotion with the capabilities of
manipulation, loco-manipulation platforms have the potential to perform complex
tasks in real-world applications. To this end, state-of-the-art quadrupeds with
attached manipulators, such as the Boston Dynamics Spot, have emerged to
provide a capable and robust platform. However, both the complexity of
loco-manipulation control, as well as the black-box nature of commercial
platforms pose challenges for developing accurate dynamics models and control
policies. We address these challenges by developing a hand-crafted kinematic
model for a quadruped-with-arm platform and, together with recent advances in
Bayesian Neural Network (BNN)-based dynamics learning using physical priors,
efficiently learn an accurate dynamics model from data. We then derive control
policies for loco-manipulation via model-based reinforcement learning (RL). We
demonstrate the effectiveness of this approach on hardware using the Boston
Dynamics Spot with a manipulator, accurately performing dynamic end-effector
trajectory tracking even in low data regimes.
|
2501.10513
|
ConfigBot: Adaptive Resource Allocation for Robot Applications in
Dynamic Environments
|
cs.RO
|
The growing use of autonomous mobile service robots (AMSRs) in dynamic
environments requires flexible management of compute resources to optimize the
performance of diverse tasks such as navigation, localization, perception, and
so on. Current robot deployments, which oftentimes rely on static
configurations (of the OS, applications, etc.) and system over-provisioning,
fall short since they do not account for the tasks' performance variations
resulting in poor system-wide behavior such as robot instability and/or
inefficient resource use. This paper presents ConfigBot, a system designed to
adaptively reconfigure AMSR applications to meet a predefined performance
specification by leveraging runtime profiling and automated configuration
tuning. Through experiments on a Boston Dynamics Spot robot equipped with
NVIDIA AGX Orin, we demonstrate ConfigBot's efficacy in maintaining system
stability and optimizing resource allocation across diverse scenarios. Our
findings highlight the promise of tailored and dynamic configurations for robot
deployments.
|
2501.10514
|
Real-Time Bus Departure Prediction Using Neural Networks for Smart IoT
Public Bus Transit
|
cs.LG cs.AI
|
Bus transit plays a vital role in urban public transportation but often
struggles to provide accurate and reliable departure times. This leads to
delays, passenger dissatisfaction, and decreased ridership, particularly in
transit-dependent areas. A major challenge lies in the discrepancy between
actual and scheduled bus departure times, which disrupts timetables and impacts
overall operational efficiency. To address these challenges, this paper
presents a neural network-based approach for real-time bus departure time
prediction tailored for smart IoT public transit applications. We leverage
AI-driven models to enhance the accuracy of bus schedules by preprocessing
data, engineering relevant features, and implementing a fully connected neural
network that utilizes historical departure data to predict departure times at
subsequent stops. In our case study analyzing bus data from Boston, we observed
an average deviation of nearly 4 minutes from scheduled times. However, our
model, evaluated across 151 bus routes, demonstrates a significant improvement,
predicting departure time deviations with an accuracy of under 80 seconds. This
advancement not only improves the reliability of bus transit schedules but also
plays a crucial role in enabling smart bus systems and IoT applications within
public transit networks. By providing more accurate real-time predictions, our
approach can facilitate the integration of IoT devices, such as smart bus stops
and passenger information systems, that rely on precise data for optimal
performance.
|
2501.10523
|
Multiclass Queue Scheduling Under Slowdown: An Approximate Dynamic
Programming Approach
|
math.OC cs.SY eess.SY
|
In many service systems, especially those in healthcare, customer waiting
times can result in increased service requirements. Such service slowdowns can
significantly impact system performance. Therefore, it is important to properly
account for their impact when designing scheduling policies. Scheduling under
wait-dependent service times is challenging, especially when multiple customer
classes are heterogeneously affected by waiting. In this work, we study
scheduling policies in multiclass, multiserver queues with wait-dependent
service slowdowns. We propose a simulation-based Approximate Dynamic
Programming (ADP) algorithm to find close-to-optimal scheduling policies. The
ADP algorithm (i) represents the policy using classifiers based on the index
policy structure, (ii) leverages a coupling method to estimate the differences
of the relative value functions directly, and (iii) uses adaptive sampling for
efficient state-space exploration. Through extensive numerical experiments, we
illustrate that the ADP algorithm generates close-to-optimal policies that
outperform well-known benchmarks. We also provide insights into the structure
of the optimal policy, which reveals an important trade-off between
instantaneous cost reduction and preventing the system from reaching high-cost
equilibria. Lastly, we conduct a case study on scheduling admissions into
rehabilitation care to illustrate the effectiveness of the ADP algorithm in
practice.
|
2501.10525
|
DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids
|
cs.SD cs.LG eess.AS eess.SP
|
The DeepFilterNet (DFN) architecture was recently proposed as a deep learning
model suited for hearing aid devices. Despite its competitive performance on
numerous benchmarks, it still follows a `one-size-fits-all' approach, which
aims to train a single, monolithic architecture that generalises across
different noises and environments. However, its limited size and computation
budget can hamper its generalisability. Recent work has shown that in-context
adaptation can improve performance by conditioning the denoising process on
additional information extracted from background recordings to mitigate this.
These recordings can be offloaded outside the hearing aid, thus improving
performance while adding minimal computational overhead. We introduce these
principles to the DFN model, thus proposing the DFingerNet (DFiN) model, which
shows superior performance on various benchmarks inspired by the DNS Challenge.
|
2501.10526
|
Solving Sparse Finite Element Problems on Neuromorphic Hardware
|
cs.NE cs.AI cs.LG cs.NA math.NA
|
We demonstrate that scalable neuromorphic hardware can implement the finite
element method, which is a critical numerical method for engineering and
scientific discovery. Our approach maps the sparse interactions between
neighboring finite elements to small populations of neurons that dynamically
update according to the governing physics of a desired problem description. We
show that for the Poisson equation, which describes many physical systems such
as gravitational and electrostatic fields, this cortical-inspired neural
circuit can achieve comparable levels of numerical accuracy and scaling while
enabling the use of inherently parallel and energy-efficient neuromorphic
hardware. We demonstrate that this approach can be used on the Intel Loihi 2
platform and illustrate how this approach can be extended to nontrivial mesh
geometries and dynamics.
|
2501.10529
|
A Tensor Low-Rank Approximation for Value Functions in Multi-Task
Reinforcement Learning
|
cs.LG
|
In pursuit of reinforcement learning systems that could train in physical
environments, we investigate multi-task approaches as a means to alleviate the
need for massive data acquisition. In a tabular scenario where the Q-functions
are collected across tasks, we model our learning problem as optimizing a
higher order tensor structure. Recognizing that close-related tasks may require
similar actions, our proposed method imposes a low-rank condition on this
aggregated Q-tensor. The rationale behind this approach to multi-task learning
is that the low-rank structure enforces the notion of similarity, without the
need to explicitly prescribe which tasks are similar, but inferring this
information from a reduced amount of data simultaneously with the stochastic
optimization of the Q-tensor. The efficiency of our low-rank tensor approach to
multi-task learning is demonstrated in two numerical experiments, first in a
benchmark environment formed by a collection of inverted pendulums, and then
into a practical scenario involving multiple wireless communication devices.
|
2501.10533
|
A Unified Comparative Study with Generalized Conformity Scores for
Multi-Output Conformal Regression
|
stat.ML cs.LG
|
Conformal prediction provides a powerful framework for constructing
distribution-free prediction regions with finite-sample coverage guarantees.
While extensively studied in univariate settings, its extension to multi-output
problems presents additional challenges, including complex output dependencies
and high computational costs, and remains relatively underexplored. In this
work, we present a unified comparative study of nine conformal methods with
different multivariate base models for constructing multivariate prediction
regions within the same framework. This study highlights their key properties
while also exploring the connections between them. Additionally, we introduce
two novel classes of conformity scores for multi-output regression that
generalize their univariate counterparts. These scores ensure asymptotic
conditional coverage while maintaining exact finite-sample marginal coverage.
One class is compatible with any generative model, offering broad
applicability, while the other is computationally efficient, leveraging the
properties of invertible generative models. Finally, we conduct a comprehensive
empirical evaluation across 13 tabular datasets, comparing all the multi-output
conformal methods explored in this work. To ensure a fair and consistent
comparison, all methods are implemented within a unified code base.
|
2501.10534
|
4bit-Quantization in Vector-Embedding for RAG
|
cs.LG cs.AI
|
Retrieval-augmented generation (RAG) is a promising technique that has shown
great potential in addressing some of the limitations of large language models
(LLMs). LLMs have two major limitations: they can contain outdated information
due to their training data, and they can generate factually inaccurate
responses, a phenomenon known as hallucinations. RAG aims to mitigate these
issues by leveraging a database of relevant documents, which are stored as
embedding vectors in a high-dimensional space. However, one of the challenges
of using high-dimensional embeddings is that they require a significant amount
of memory to store. This can be a major issue, especially when dealing with
large databases of documents. To alleviate this problem, we propose the use of
4-bit quantization to store the embedding vectors. This involves reducing the
precision of the vectors from 32-bit floating-point numbers to 4-bit integers,
which can significantly reduce the memory requirements. Our approach has
several benefits. Firstly, it significantly reduces the memory storage
requirements of the high-dimensional vector database, making it more feasible
to deploy RAG systems in resource-constrained environments. Secondly, it speeds
up the searching process, as the reduced precision of the vectors allows for
faster computation. Our code is available at
https://github.com/taeheej/4bit-Quantization-in-Vector-Embedding-for-RAG
|
2501.10538
|
Universality of Benign Overfitting in Binary Linear Classification
|
cs.LG math.ST stat.ML stat.TH
|
The practical success of deep learning has led to the discovery of several
surprising phenomena. One of these phenomena, that has spurred intense
theoretical research, is ``benign overfitting'': deep neural networks seem to
generalize well in the over-parametrized regime even though the networks show a
perfect fit to noisy training data. It is now known that benign overfitting
also occurs in various classical statistical models. For linear maximum margin
classifiers, benign overfitting has been established theoretically in a class
of mixture models with very strong assumptions on the covariate distribution.
However, even in this simple setting, many questions remain open. For instance,
most of the existing literature focuses on the noiseless case where all true
class labels are observed without errors, whereas the more interesting noisy
case remains poorly understood. We provide a comprehensive study of benign
overfitting for linear maximum margin classifiers. We discover a phase
transition in test error bounds for the noisy model which was previously
unknown and provide some geometric intuition behind it. We further considerably
relax the required covariate assumptions in both, the noisy and noiseless case.
Our results demonstrate that benign overfitting of maximum margin classifiers
holds in a much wider range of scenarios than was previously known and provide
new insights into the underlying mechanisms.
|
2501.10540
|
DPERC: Direct Parameter Estimation for Mixed Data
|
stat.ML cs.LG
|
The covariance matrix is a foundation in numerous statistical and
machine-learning applications such as Principle Component Analysis, Correlation
Heatmap, etc. However, missing values within datasets present a formidable
obstacle to accurately estimating this matrix. While imputation methods offer
one avenue for addressing this challenge, they often entail a trade-off between
computational efficiency and estimation accuracy. Consequently, attention has
shifted towards direct parameter estimation, given its precision and reduced
computational burden. In this paper, we propose Direct Parameter Estimation for
Randomly Missing Data with Categorical Features (DPERC), an efficient approach
for direct parameter estimation tailored to mixed data that contains missing
values within continuous features. Our method is motivated by leveraging
information from categorical features, which can significantly enhance
covariance matrix estimation for continuous features. Our approach effectively
harnesses the information embedded within mixed data structures. Through
comprehensive evaluations of diverse datasets, we demonstrate the competitive
performance of DPERC compared to various contemporary techniques. In addition,
we also show by experiments that DPERC is a valuable tool for visualizing the
correlation heatmap.
|
2501.10542
|
Improved IR-based Bug Localization with Intelligent Relevance Feedback
|
cs.SE cs.AI cs.CL
|
Software bugs pose a significant challenge during development and
maintenance, and practitioners spend nearly 50% of their time dealing with
bugs. Many existing techniques adopt Information Retrieval (IR) to localize a
reported bug using textual and semantic relevance between bug reports and
source code. However, they often struggle to bridge a critical gap between bug
reports and code that requires in-depth contextual understanding, which goes
beyond textual or semantic relevance. In this paper, we present a novel
technique for bug localization - BRaIn - that addresses the contextual gaps by
assessing the relevance between bug reports and code with Large Language Models
(LLM). It then leverages the LLM's feedback (a.k.a., Intelligent Relevance
Feedback) to reformulate queries and re-rank source documents, improving bug
localization. We evaluate BRaIn using a benchmark dataset, Bench4BL, and three
performance metrics and compare it against six baseline techniques from the
literature. Our experimental results show that BRaIn outperforms baselines by
87.6%, 89.5%, and 48.8% margins in MAP, MRR, and HIT@K, respectively.
Additionally, it can localize approximately 52% of bugs that cannot be
localized by the baseline techniques due to the poor quality of corresponding
bug reports. By addressing the contextual gaps and introducing Intelligent
Relevance Feedback, BRaIn advances not only theory but also improves IR-based
bug localization.
|
2501.10543
|
FORLAPS: An Innovative Data-Driven Reinforcement Learning Approach for
Prescriptive Process Monitoring
|
cs.LG cs.AI
|
We present a novel 5-step framework called Fine-Tuned Offline Reinforcement
Learning Augmented Process Sequence Optimization (FORLAPS), which aims to
identify optimal execution paths in business processes using reinforcement
learning. We implemented this approach on real-life event logs from our case
study an energy regulator in Canada and other real-life event logs,
demonstrating the feasibility of the proposed method. Additionally, to compare
FORLAPS with the existing models (Permutation Feature Importance and multi-task
LSTM-Based model), we experimented to evaluate its effectiveness in terms of
resource savings and process time span reduction. The experimental results on
real-life event log validate that FORLAPS achieves 31% savings in resource time
spent and a 23% reduction in process time span. Using this innovative data
augmentation technique, we propose a fine-tuned reinforcement learning approach
that aims to automatically fine-tune the model by selectively increasing the
average estimated Q-value in the sampled batches. The results show that we
obtained a 44% performance improvement compared to the pre-trained model. This
study introduces an innovative evaluation model, benchmarking its performance
against earlier works using nine publicly available datasets. Robustness is
ensured through experiments utilizing the Damerau-Levenshtein distance as the
primary metric. In addition, we discussed the suitability of datasets, taking
into account their inherent properties, to evaluate the performance of
different models. The proposed model, FORLAPS, demonstrated exceptional
performance, outperforming existing state-of-the-art approaches in suggesting
the most optimal policies or predicting the best next activities within a
process trace.
|
2501.10546
|
Scalable Machine Learning Training Infrastructure for Online Ads
Recommendation and Auction Scoring Modeling at Google
|
cs.DC cs.AI cs.LG
|
Large-scale Ads recommendation and auction scoring models at Google scale
demand immense computational resources. While specialized hardware like TPUs
have improved linear algebra computations, bottlenecks persist in large-scale
systems. This paper proposes solutions for three critical challenges that must
be addressed for efficient end-to-end execution in a widely used production
infrastructure: (1) Input Generation and Ingestion Pipeline: Efficiently
transforming raw features (e.g., "search query") into numerical inputs and
streaming them to TPUs; (2) Large Embedding Tables: Optimizing conversion of
sparse features into dense floating-point vectors for neural network
consumption; (3) Interruptions and Error Handling: Minimizing resource wastage
in large-scale shared datacenters. To tackle these challenges, we propose a
shared input generation technique to reduce computational load of input
generation by amortizing costs across many models. Furthermore, we propose
partitioning, pipelining, and RPC (Remote Procedure Call) coalescing software
techniques to optimize embedding operations. To maintain efficiency at scale,
we describe novel preemption notice and training hold mechanisms that minimize
resource wastage, and ensure prompt error resolution. These techniques have
demonstrated significant improvement in Google production, achieving a 116%
performance boost and an 18% reduction in training costs across representative
models.
|
2501.10547
|
HyperCam: Low-Power Onboard Computer Vision for IoT Cameras
|
cs.CV cs.LG cs.NE eess.IV
|
We present HyperCam, an energy-efficient image classification pipeline that
enables computer vision tasks onboard low-power IoT camera systems. HyperCam
leverages hyperdimensional computing to perform training and inference
efficiently on low-power microcontrollers. We implement a low-power wireless
camera platform using off-the-shelf hardware and demonstrate that HyperCam can
achieve an accuracy of 93.60%, 84.06%, 92.98%, and 72.79% for MNIST,
Fashion-MNIST, Face Detection, and Face Identification tasks, respectively,
while significantly outperforming other classifiers in resource efficiency.
Specifically, it delivers inference latency of 0.08-0.27s while using
42.91-63.00KB flash memory and 22.25KB RAM at peak. Among other machine
learning classifiers such as SVM, xgBoost, MicroNets, MobileNetV3, and
MCUNetV3, HyperCam is the only classifier that achieves competitive accuracy
while maintaining competitive memory footprint and inference latency that meets
the resource requirements of low-power camera systems.
|
2501.10548
|
Diffusion Models in Recommendation Systems: A Survey
|
cs.IR
|
Recommender systems remain an essential topic due to its wide application in
various domains and the business potential behind them. With the rise of deep
learning, common solutions have leveraged neural networks to facilitate
collaborative filtering, and some have turned to generative adversarial
networks to augment the dataset and tackle the data sparsity issue. However,
they are limited in learning the complex user and item distribution and still
suffer from model collapse. Given the great generation capability exhibited by
diffusion models in computer vision recently, many recommender systems have
adopted diffusion models and found improvements in performance for various
tasks. Diffusion models in recommender systems excel in managing complex user
and item distributions and do not suffer from mode collapse. With these
advantages, the amount of research in this domain have been growing rapidly and
calling for a systematic survey. In this survey paper, we present and propose a
taxonomy on past research papers in recommender systems that utilize diffusion
models. Distinct from a prior survey paper that categorizes based on the role
of the diffusion model, we categorize based on the recommendation task at hand.
The decision originates from the rationale that after all, the adoption of
diffusion models is to enhance the recommendation performance, not vice versa:
adapting the recommendation task to enable diffusion models. Nonetheless, we
offer a unique perspective for diffusion models in recommender systems
complementary to existing surveys. We present the foundation algorithms in
diffusion models and their applications in recommender systems to summarize the
rapid development in this field. Finally, we discuss open research directions
to prepare and encourage further efforts to advance the field. We compile the
relevant papers in a public GitHub repository.
|
2501.10555
|
Towards Data-Centric AI: A Comprehensive Survey of Traditional,
Reinforcement, and Generative Approaches for Tabular Data Transformation
|
cs.LG cs.AI
|
Tabular data is one of the most widely used formats across industries,
driving critical applications in areas such as finance, healthcare, and
marketing. In the era of data-centric AI, improving data quality and
representation has become essential for enhancing model performance,
particularly in applications centered around tabular data. This survey examines
the key aspects of tabular data-centric AI, emphasizing feature selection and
feature generation as essential techniques for data space refinement. We
provide a systematic review of feature selection methods, which identify and
retain the most relevant data attributes, and feature generation approaches,
which create new features to simplify the capture of complex data patterns.
This survey offers a comprehensive overview of current methodologies through an
analysis of recent advancements, practical applications, and the strengths and
limitations of these techniques. Finally, we outline open challenges and
suggest future perspectives to inspire continued innovation in this field.
|
2501.10557
|
MurkySky: Analyzing News Reliability on Bluesky
|
cs.SI
|
Bluesky has recently emerged as a lively competitor to Twitter/X for a
platform for public discourse and news sharing. Most of the research on Bluesky
so far has focused on characterizing its adoption due to migration. There has
been less interest on characterizing the properties of Bluesky as a platform
for news sharing and discussion, and in particular the prevalence of unreliable
information on it. To fill this gap, this research provides the first
comprehensive analysis of news reliability on Bluesky. We introduce MurkySky, a
public tool to track the prevalence of content from unreliable news sources on
Bluesky. Using firehose data from the summer of 2024, we find that on Bluesky
reliable-source news content is prevalent, and largely originating from
left-leaning sources. Content from unreliable news sources, while accounting
for a small fraction of all news-linking posts, tends to originate from more
partisan sources, but largely reflects the left-leaning skew of the platform.
Analysis of the language and hashtags used in news-linking posts shows that
unreliable-source content concentrates on specific topics of discussion.
|
2501.10560
|
Picachv: Formally Verified Data Use Policy Enforcement for Secure Data
Analytics
|
cs.CR cs.DB cs.PL
|
Ensuring the proper use of sensitive data in analytics under complex privacy
policies is an increasingly critical challenge. Many existing approaches lack
portability, verifiability, and scalability across diverse data processing
frameworks. We introduce Picachv, a novel security monitor that automatically
enforces data use policies. It works on relational algebra as an abstraction
for program semantics, enabling policy enforcement on query plans generated by
programs during execution. This approach simplifies analysis across diverse
analytical operations and supports various front-end query languages. By
formalizing both data use policies and relational algebra semantics in Coq, we
prove that Picachv correctly enforces policies. Picachv also leverages Trusted
Execution Environments (TEEs) to enhance trust in runtime, providing provable
policy compliance to stakeholders that the analytical tasks comply with their
data use policies. We integrated Picachv into Polars, a state-of-the-art data
analytics framework, and evaluate its performance using the TPC-H benchmark. We
also apply our approach to real-world use cases. Our work demonstrates the
practical application of formal methods in securing data analytics, addressing
key challenges.
|
2501.10561
|
Early Failure Detection in Autonomous Surgical Soft-Tissue Manipulation
via Uncertainty Quantification
|
cs.RO
|
Autonomous surgical robots are a promising solution to the increasing demand
for surgery amid a shortage of surgeons. Recent work has proposed
learning-based approaches for the autonomous manipulation of soft tissue.
However, due to variability in tissue geometries and stiffnesses, these methods
do not always perform optimally, especially in out-of-distribution settings. We
propose, develop, and test the first application of uncertainty quantification
to learned surgical soft-tissue manipulation policies as an early
identification system for task failures. We analyze two different methods of
uncertainty quantification, deep ensembles and Monte Carlo dropout, and find
that deep ensembles provide a stronger signal of future task success or
failure. We validate our approach using the physical daVinci Research Kit
(dVRK) surgical robot to perform physical soft-tissue manipulation. We show
that we are able to successfully detect task failure and request human
intervention when necessary while still enabling autonomous manipulation when
possible. Our learned tissue manipulation policy with uncertainty-based early
failure detection achieves a zero-shot sim2real performance improvement of
47.5% over the prior state of the art in learned soft-tissue manipulation. We
also show that our method generalizes well to new types of tissue as well as to
a bimanual soft tissue manipulation task.
|
2501.10562
|
On the Benefits of Instance Decomposition in Video Prediction Models
|
cs.CV
|
Video prediction is a crucial task for intelligent agents such as robots and
autonomous vehicles, since it enables them to anticipate and act early on
time-critical incidents. State-of-the-art video prediction methods typically
model the dynamics of a scene jointly and implicitly, without any explicit
decomposition into separate objects. This is challenging and potentially
sub-optimal, as every object in a dynamic scene has their own pattern of
movement, typically somewhat independent of others. In this paper, we
investigate the benefit of explicitly modeling the objects in a dynamic scene
separately within the context of latent-transformer video prediction models. We
conduct detailed and carefully-controlled experiments on both synthetic and
real-world datasets; our results show that decomposing a dynamic scene leads to
higher quality predictions compared with models of a similar capacity that lack
such decomposition.
|
2501.10573
|
The Geometry of Tokens in Internal Representations of Large Language
Models
|
cs.CL cs.LG
|
We investigate the relationship between the geometry of token embeddings and
their role in the next token prediction within transformer models. An important
aspect of this connection uses the notion of empirical measure, which encodes
the distribution of token point clouds across transformer layers and drives the
evolution of token representations in the mean-field interacting picture. We
use metrics such as intrinsic dimension, neighborhood overlap, and cosine
similarity to observationally probe these empirical measures across layers. To
validate our approach, we compare these metrics to a dataset where the tokens
are shuffled, which disrupts the syntactic and semantic structure. Our findings
reveal a correlation between the geometric properties of token embeddings and
the cross-entropy loss of next token predictions, implying that prompts with
higher loss values have tokens represented in higher-dimensional spaces.
|
2501.10576
|
AI Toolkit: Libraries and Essays for Exploring the Technology and Ethics
of AI
|
cs.CY cs.AI cs.LG
|
In this paper we describe the development and evaluation of AITK, the
Artificial Intelligence Toolkit. This open-source project contains both Python
libraries and computational essays (Jupyter notebooks) that together are
designed to allow a diverse audience with little or no background in AI to
interact with a variety of AI tools, exploring in more depth how they function,
visualizing their outcomes, and gaining a better understanding of their ethical
implications. These notebooks have been piloted at multiple institutions in a
variety of humanities courses centered on the theme of responsible AI. In
addition, we conducted usability testing of AITK. Our pilot studies and
usability testing results indicate that AITK is easy to navigate and effective
at helping users gain a better understanding of AI. Our goal, in this time of
rapid innovations in AI, is for AITK to provide an accessible resource for
faculty from any discipline looking to incorporate AI topics into their courses
and for anyone eager to learn more about AI on their own.
|
2501.10579
|
AI Technicians: Developing Rapid Occupational Training Methods for a
Competitive AI Workforce
|
cs.CY cs.AI
|
The accelerating pace of developments in Artificial Intelligence~(AI) and the
increasing role that technology plays in society necessitates substantial
changes in the structure of the workforce. Besides scientists and engineers,
there is a need for a very large workforce of competent AI technicians (i.e.,
maintainers, integrators) and users~(i.e., operators). As traditional 4-year
and 2-year degree-based education cannot fill this quickly opening gap,
alternative training methods have to be developed. We present the results of
the first four years of the AI Technicians program which is a unique
collaboration between the U.S. Army's Artificial Intelligence Integration
Center (AI2C) and Carnegie Mellon University to design, implement and evaluate
novel rapid occupational training methods to create a competitive AI workforce
at the technicians level. Through this multi-year effort we have already
trained 59 AI Technicians. A key observation is that ongoing frequent updates
to the training are necessary as the adoption of AI in the U.S. Army and within
the society at large is evolving rapidly. A tight collaboration among the
stakeholders from the army and the university is essential for successful
development and maintenance of the training for the evolving role. Our findings
can be leveraged by large organizations that face the challenge of developing a
competent AI workforce as well as educators and researchers engaged in solving
the challenge.
|
2501.10582
|
Adapting Large Language Models for Character-based Augmentative and
Alternative Communication
|
cs.CL cs.HC
|
Users of Augmentative and Alternative Communication (AAC) may write
letter-by-letter via an interface that uses a character language model.
However, most state-of-the-art large pretrained language models predict subword
tokens of variable length. We investigate how to practically use such models to
make accurate and efficient character predictions. We fine-tune models using a
large dataset of sentences we curated in which each sentence is rated according
to how useful it might be for spoken or written AAC communication. We find that
using an algorithm to produce character predictions from a subword large
language model provides more accurate predictions than adding a classification
layer or using a byte-level model. We also find that our domain adaptation
curriculum is effective at improving model performance on simple,
conversational text.
|
2501.10592
|
Analytical Models of Frequency and Voltage in Large-Scale All-Inverter
Power Systems
|
eess.SY cs.SY
|
Low-order frequency response models for power systems have a decades-long
history in optimization and control problems such as unit commitment, economic
dispatch, and wide-area control. With a few exceptions, these models are built
upon the Newtonian mechanics of synchronous generators, assuming that the
frequency dynamics across a system are approximately homogeneous, and assume
the dynamics of nodal voltages for most operating conditions are negligible,
and thus are not directly computed at all buses. As a result, the use of system
frequency models results in the systematic underestimation of frequency minimum
nadir and maximum RoCoF, and provides no insight into the reactive
power-voltage dynamics. This paper proposes a low-order model of both frequency
and voltage response in grid-forming inverter-dominated power systems. The
proposed model accounts for spatial-temporal variations in frequency and
voltage behavior across a system and as a result, demonstrates the
heterogeneity of frequency response in future renewable power systems.
Electromagnetic transient (EMT) simulations are used to validate the utility,
accuracy, and computational efficiency of these models, setting the basis for
them to serve as fast, scalable alternatives to EMT simulation, especially when
dealing with very large-scale systems, for both planning and operational
studies.
|
2501.10593
|
ColorGrid: A Multi-Agent Non-Stationary Environment for Goal Inference
and Assistance
|
cs.AI cs.LG
|
Autonomous agents' interactions with humans are increasingly focused on
adapting to their changing preferences in order to improve assistance in
real-world tasks. Effective agents must learn to accurately infer human goals,
which are often hidden, to collaborate well. However, existing Multi-Agent
Reinforcement Learning (MARL) environments lack the necessary attributes
required to rigorously evaluate these agents' learning capabilities. To this
end, we introduce ColorGrid, a novel MARL environment with customizable
non-stationarity, asymmetry, and reward structure. We investigate the
performance of Independent Proximal Policy Optimization (IPPO), a
state-of-the-art (SOTA) MARL algorithm, in ColorGrid and find through extensive
ablations that, particularly with simultaneous non-stationary and asymmetric
goals between a ``leader'' agent representing a human and a ``follower''
assistant agent, ColorGrid is unsolved by IPPO. To support benchmarking future
MARL algorithms, we release our environment code, model checkpoints, and
trajectory visualizations at https://github.com/andreyrisukhin/ColorGrid.
|
2501.10594
|
Accurate and thermodynamically consistent hydrogen equation of state for
planetary modeling with flow matching
|
astro-ph.EP cond-mat.mtrl-sci cs.LG physics.comp-ph
|
Accurate determination of the equation of state of dense hydrogen is
essential for understanding gas giants. Currently, there is still no consensus
on methods for calculating its entropy, which play a fundamental role and can
result in qualitatively different predictions for Jupiter's interior. Here, we
investigate various aspects of entropy calculation for dense hydrogen based on
ab initio molecular dynamics simulations. Specifically, we employ the recently
developed flow matching method to validate the accuracy of the traditional
thermodynamic integration approach. We then clearly identify pitfalls in
previous attempts and propose a reliable framework for constructing the
hydrogen equation of state, which is accurate and thermodynamically consistent
across a wide range of temperature and pressure conditions. This allows us to
conclusively address the long-standing discrepancies in Jupiter's adiabat among
earlier studies, demonstrating the potential of our approach for providing
reliable equations of state of diverse materials.
|
2501.10598
|
Solving Finite-Horizon MDPs via Low-Rank Tensors
|
cs.LG
|
We study the problem of learning optimal policies in finite-horizon Markov
Decision Processes (MDPs) using low-rank reinforcement learning (RL) methods.
In finite-horizon MDPs, the policies, and therefore the value functions (VFs)
are not stationary. This aggravates the challenges of high-dimensional MDPs, as
they suffer from the curse of dimensionality and high sample complexity. To
address these issues, we propose modeling the VFs of finite-horizon MDPs as
low-rank tensors, enabling a scalable representation that renders the problem
of learning optimal policies tractable. We introduce an optimization-based
framework for solving the Bellman equations with low-rank constraints, along
with block-coordinate descent (BCD) and block-coordinate gradient descent
(BCGD) algorithms, both with theoretical convergence guarantees. For scenarios
where the system dynamics are unknown, we adapt the proposed BCGD method to
estimate the VFs using sampled trajectories. Numerical experiments further
demonstrate that the proposed framework reduces computational demands in
controlled synthetic scenarios and more realistic resource allocation problems.
|
2501.10600
|
High Resolution Tree Height Mapping of the Amazon Forest using Planet
NICFI Images and LiDAR-Informed U-Net Model
|
cs.CV
|
Tree canopy height is one of the most important indicators of forest biomass,
productivity, and ecosystem structure, but it is challenging to measure
accurately from the ground and from space. Here, we used a U-Net model adapted
for regression to map the mean tree canopy height in the Amazon forest from
Planet NICFI images at ~4.78 m spatial resolution for the period 2020-2024. The
U-Net model was trained using canopy height models computed from aerial LiDAR
data as a reference, along with their corresponding Planet NICFI images.
Predictions of tree heights on the validation sample exhibited a mean error of
3.68 m and showed relatively low systematic bias across the entire range of
tree heights present in the Amazon forest. Our model successfully estimated
canopy heights up to 40-50 m without much saturation, outperforming existing
canopy height products from global models in this region. We determined that
the Amazon forest has an average canopy height of ~22 m. Events such as logging
or deforestation could be detected from changes in tree height, and encouraging
results were obtained to monitor the height of regenerating forests. These
findings demonstrate the potential for large-scale mapping and monitoring of
tree height for old and regenerating Amazon forests using Planet NICFI imagery.
|
2501.10604
|
When language and vision meet road safety: leveraging multimodal large
language models for video-based traffic accident analysis
|
cs.CV cs.AI cs.CL
|
The increasing availability of traffic videos functioning on a 24/7/365 time
scale has the great potential of increasing the spatio-temporal coverage of
traffic accidents, which will help improve traffic safety. However, analyzing
footage from hundreds, if not thousands, of traffic cameras in a 24/7/365
working protocol remains an extremely challenging task, as current vision-based
approaches primarily focus on extracting raw information, such as vehicle
trajectories or individual object detection, but require laborious
post-processing to derive actionable insights. We propose SeeUnsafe, a new
framework that integrates Multimodal Large Language Model (MLLM) agents to
transform video-based traffic accident analysis from a traditional
extraction-then-explanation workflow to a more interactive, conversational
approach. This shift significantly enhances processing throughput by automating
complex tasks like video classification and visual grounding, while improving
adaptability by enabling seamless adjustments to diverse traffic scenarios and
user-defined queries. Our framework employs a severity-based aggregation
strategy to handle videos of various lengths and a novel multimodal prompt to
generate structured responses for review and evaluation and enable fine-grained
visual grounding. We introduce IMS (Information Matching Score), a new
MLLM-based metric for aligning structured responses with ground truth. We
conduct extensive experiments on the Toyota Woven Traffic Safety dataset,
demonstrating that SeeUnsafe effectively performs accident-aware video
classification and visual grounding by leveraging off-the-shelf MLLMs. Source
code will be available at \url{https://github.com/ai4ce/SeeUnsafe}.
|
2501.10605
|
Wasserstein Adaptive Value Estimation for Actor-Critic Reinforcement
Learning
|
cs.LG cs.SY eess.SY stat.ML
|
We present Wasserstein Adaptive Value Estimation for Actor-Critic (WAVE), an
approach to enhance stability in deep reinforcement learning through adaptive
Wasserstein regularization. Our method addresses the inherent instability of
actor-critic algorithms by incorporating an adaptively weighted Wasserstein
regularization term into the critic's loss function. We prove that WAVE
achieves $\mathcal{O}\left(\frac{1}{k}\right)$ convergence rate for the
critic's mean squared error and provide theoretical guarantees for stability
through Wasserstein-based regularization. Using the Sinkhorn approximation for
computational efficiency, our approach automatically adjusts the regularization
based on the agent's performance. Theoretical analysis and experimental results
demonstrate that WAVE achieves superior performance compared to standard
actor-critic methods.
|
2501.10606
|
Differentiable Adversarial Attacks for Marked Temporal Point Processes
|
cs.LG cs.CR stat.ML
|
Marked temporal point processes (MTPPs) have been shown to be extremely
effective in modeling continuous time event sequences (CTESs). In this work, we
present adversarial attacks designed specifically for MTPP models. A key
criterion for a good adversarial attack is its imperceptibility. For objects
such as images or text, this is often achieved by bounding perturbation in some
fixed $L_p$ norm-ball. However, similarly minimizing distance norms between two
CTESs in the context of MTPPs is challenging due to their sequential nature and
varying time-scales and lengths. We address this challenge by first permuting
the events and then incorporating the additive noise to the arrival timestamps.
However, the worst case optimization of such adversarial attacks is a hard
combinatorial problem, requiring exploration across a permutation space that is
factorially large in the length of the input sequence. As a result, we propose
a novel differentiable scheme PERMTPP using which we can perform adversarial
attacks by learning to minimize the likelihood, while minimizing the distance
between two CTESs. Our experiments on four real-world datasets demonstrate the
offensive and defensive capabilities, and lower inference times of PERMTPP.
|
2501.10607
|
On the Optimality of Random Partial Sphere Coverings in High Dimensions
|
math.MG cs.IT math.FA math.IT
|
Given $N$ geodesic caps on the normalized unit sphere in $\mathbb{R}^d$, and
whose total surface area sums to one, what is the maximal surface area their
union can cover? We show that when these caps have equal surface area, as both
the dimension $d$ and the number of caps $N$ tend to infinity, the maximum
proportion covered approaches $1 - e^{-1} \approx 0.632$. Furthermore, this
maximum is achieved by a random partial sphere covering. Our result refines a
classical estimate for the covering density of $\mathbb{R}^d$ by Erd\H{o}s,
Few, and Rogers (Mathematika, 11(2):171--184, 1964).
|
2501.10609
|
Universal Discrete Filtering with Lookahead or Delay
|
eess.SP cs.IT math.IT
|
We consider the universal discrete filtering problem, where an input sequence
generated by an unknown source passes through a discrete memoryless channel,
and the goal is to estimate its components based on the output sequence with
limited lookahead or delay. We propose and establish the universality of a
family of schemes for this setting. These schemes are induced by universal
Sequential Probability Assignments (SPAs), and inherit their computational
properties. We show that the schemes induced by LZ78 are practically
implementable and well-suited for scenarios with limited computational
resources and latency constraints. In passing, we use some of the intermediate
results to obtain upper and lower bounds that appear to be new, in the purely
Bayesian setting, on the optimal filtering performance in terms, respectively,
of the mutual information between the noise-free and noisy sequence, and the
entropy of the noise-free sequence causally conditioned on the noisy one.
|
2501.10610
|
Automated Water Irrigation System
|
eess.SY cs.SY
|
This paper presents the design and implementation of an automated water
irrigation system aimed at optimizing plant care through precision moisture
monitoring and controlled water delivery. The system uses a capacitive soil
moisture sensor, an ADC (analog-to-digital converter), and a relay-driven water
pump to ensure plants receive adequate hydration based on real-time data. In
addition, this work aims to build on existing applications for Raspberry Pi
(4B) and Arduino-based automatic irrigation systems by integrating advanced
calibration methods, employing optimized algorithms, and introducing new
technologies to further enhance overall system efficiency and reliability.
|
2501.10615
|
Hierarchical LoG Bayesian Neural Network for Enhanced Aorta Segmentation
|
cs.CV
|
Accurate segmentation of the aorta and its associated arch branches is
crucial for diagnosing aortic diseases. While deep learning techniques have
significantly improved aorta segmentation, they remain challenging due to the
intricate multiscale structure and the complexity of the surrounding tissues.
This paper presents a novel approach for enhancing aorta segmentation using a
Bayesian neural network-based hierarchical Laplacian of Gaussian (LoG) model.
Our model consists of a 3D U-Net stream and a hierarchical LoG stream: the
former provides an initial aorta segmentation, and the latter enhances blood
vessel detection across varying scales by learning suitable LoG kernels,
enabling self-adaptive handling of different parts of the aorta vessels with
significant scale differences. We employ a Bayesian method to parameterize the
LoG stream and provide confidence intervals for the segmentation results,
ensuring robustness and reliability of the prediction for vascular medical
image analysts. Experimental results show that our model can accurately segment
main and supra-aortic vessels, yielding at least a 3% gain in the Dice
coefficient over state-of-the-art methods across multiple volumes drawn from
two aorta datasets, and can provide reliable confidence intervals for different
parts of the aorta. The code is available at https://github.com/adlsn/LoGBNet.
|
2501.10617
|
Mutual Regression Distance
|
cs.LG stat.ML
|
The maximum mean discrepancy and Wasserstein distance are popular distance
measures between distributions and play important roles in many machine
learning problems such as metric learning, generative modeling, domain
adaption, and clustering. However, since they are functions of pair-wise
distances between data points in two distributions, they do not exploit the
potential manifold properties of data such as smoothness and hence are not
effective in measuring the dissimilarity between the two distributions in the
form of manifolds. In this paper, different from existing measures, we propose
a novel distance called Mutual Regression Distance (MRD) induced by a
constrained mutual regression problem, which can exploit the manifold property
of data. We prove that MRD is a pseudometric that satisfies almost all the
axioms of a metric. Since the optimization of the original MRD is costly, we
provide a tight MRD and a simplified MRD, based on which a heuristic algorithm
is established. We also provide kernel variants of MRDs that are more effective
in handling nonlinear data. Our MRDs especially the simplified MRDs have much
lower computational complexity than the Wasserstein distance. We provide
theoretical guarantees, such as robustness, for MRDs. Finally, we apply MRDs to
distribution clustering, generative models, and domain adaptation. The
numerical results demonstrate the effectiveness and superiority of MRDs
compared to the baselines.
|
2501.10621
|
RoMu4o: A Robotic Manipulation Unit For Orchard Operations Automating
Proximal Hyperspectral Leaf Sensing
|
cs.RO cs.CV
|
Driven by the need to address labor shortages and meet the demands of a
rapidly growing population, robotic automation has become a critical component
in precision agriculture. Leaf-level hyperspectral spectroscopy is shown to be
a powerful tool for phenotyping, monitoring crop health, identifying essential
nutrients within plants as well as detecting diseases and water stress. This
work introduces RoMu4o, a robotic manipulation unit for orchard operations
offering an automated solution for proximal hyperspectral leaf sensing. This
ground robot is equipped with a 6DOF robotic arm and vision system for
real-time deep learning-based image processing and motion planning. We
developed robust perception and manipulation pipelines that enable the robot to
successfully grasp target leaves and perform spectroscopy. These frameworks
operate synergistically to identify and extract the 3D structure of leaves from
an observed batch of foliage, propose 6D poses, and generate collision-free
constraint-aware paths for precise leaf manipulation. The end-effector of the
arm features a compact design that integrates an independent lighting source
with a hyperspectral sensor, enabling high-fidelity data acquisition while
streamlining the calibration process for accurate measurements. Our ground
robot is engineered to operate in unstructured orchard environments. However,
the performance of the system is evaluated in both indoor and outdoor plant
models. The system demonstrated reliable performance for 1-LPB hyperspectral
sampling, achieving 95% success rate in lab trials and 79% in field trials.
Field experiments revealed an overall success rate of 70% for autonomous leaf
grasping and hyperspectral measurement in a pistachio orchard. The open-source
repository is available at: https://github.com/mehradmrt/UCM-AgBot-ROS2
|
2501.10625
|
Assessing Markov Property in Driving Behaviors: Insights from
Statistical Tests
|
cs.LG cs.SY eess.SY stat.ME
|
The Markov property serves as a foundational assumption in most existing work
on vehicle driving behavior, positing that future states depend solely on the
current state, not the series of preceding states. This study validates the
Markov properties of vehicle trajectories for both Autonomous Vehicles (AVs)
and Human-driven Vehicles (HVs). A statistical method used to test whether time
series data exhibits Markov properties is applied to examine whether the
trajectory data possesses Markov characteristics. t test and F test are
additionally introduced to characterize the differences in Markov properties
between AVs and HVs. Based on two public trajectory datasets, we investigate
the presence and order of the Markov property of different types of vehicles
through rigorous statistical tests. Our findings reveal that AV trajectories
generally exhibit stronger Markov properties compared to HV trajectories, with
a higher percentage conforming to the Markov property and lower Markov orders.
In contrast, HV trajectories display greater variability and heterogeneity in
decision-making processes, reflecting the complex perception and information
processing involved in human driving. These results have significant
implications for the development of driving behavior models, AV controllers,
and traffic simulation systems. Our study also demonstrates the feasibility of
using statistical methods to test the presence of Markov properties in driving
trajectory data.
|
2501.10627
|
AI/ML Based Detection and Categorization of Covert Communication in IPv6
Network
|
cs.CR cs.AI cs.LG cs.NI
|
The flexibility and complexity of IPv6 extension headers allow attackers to
create covert channels or bypass security mechanisms, leading to potential data
breaches or system compromises. The mature development of machine learning has
become the primary detection technology option used to mitigate covert
communication threats. However, the complexity of detecting covert
communication, evolving injection techniques, and scarcity of data make
building machine-learning models challenging. In previous related research,
machine learning has shown good performance in detecting covert communications,
but oversimplified attack scenario assumptions cannot represent the complexity
of modern covert technologies and make it easier for machine learning models to
detect covert communications. To bridge this gap, in this study, we analyzed
the packet structure and network traffic behavior of IPv6, used encryption
algorithms, and performed covert communication injection without changing
network packet behavior to get closer to real attack scenarios. In addition to
analyzing and injecting methods for covert communications, this study also uses
comprehensive machine learning techniques to train the model proposed in this
study to detect threats, including traditional decision trees such as random
forests and gradient boosting, as well as complex neural network architectures
such as CNNs and LSTMs, to achieve detection accuracy of over 90\%. This study
details the methods used for dataset augmentation and the comparative
performance of the applied models, reinforcing insights into the adaptability
and resilience of the machine learning application in IPv6 covert
communication. In addition, we also proposed a Generative AI-assisted
interpretation concept based on prompt engineering as a preliminary study of
the role of Generative AI agents in covert communication.
|
2501.10629
|
Prompt-Enabled Large AI Models for CSI Feedback
|
cs.IT eess.SP math.IT
|
Artificial intelligence (AI) has emerged as a promising tool for channel
state information (CSI) feedback. While recent research primarily focuses on
improving feedback accuracy through novel architectures, the underlying
mechanisms of AI-based CSI feedback remain unclear. This study investigates
these mechanisms by analyzing performance across diverse datasets and reveals
that superior feedback performance stems from the strong fitting capabilities
of AI models and their ability to leverage environmental knowledge. Building on
these findings, we propose a prompt-enabled large AI model (LAM) for CSI
feedback. The LAM employs powerful transformer blocks and is trained on
extensive datasets from various scenarios. To further enhance reconstruction
quality, the channel distribution -- represented as the mean of channel
magnitude in the angular domain -- is incorporated as a prompt within the
decoder. Simulation results confirm that the proposed prompt-enabled LAM
significantly improves feedback accuracy and generalization performance while
reducing data collection requirements in new scenarios.
|
2501.10630
|
Exploring the Potential of Large Language Models for Massive MIMO CSI
Feedback
|
cs.IT eess.SP math.IT
|
Large language models (LLMs) have achieved remarkable success across a wide
range of tasks, particularly in natural language processing and computer
vision. This success naturally raises an intriguing yet unexplored question:
Can LLMs be harnessed to tackle channel state information (CSI) compression and
feedback in massive multiple-input multiple-output (MIMO) systems? Efficient
CSI feedback is a critical challenge in next-generation wireless communication.
In this paper, we pioneer the use of LLMs for CSI compression, introducing a
novel framework that leverages the powerful denoising capabilities of LLMs --
capable of error correction in language tasks -- to enhance CSI reconstruction
performance. To effectively adapt LLMs to CSI data, we design customized
pre-processing, embedding, and post-processing modules tailored to the unique
characteristics of wireless signals. Extensive numerical results demonstrate
the promising potential of LLMs in CSI feedback, opening up possibilities for
this research direction.
|
2501.10636
|
Efficient and Safe Trajectory Planning for Autonomous Agricultural
Vehicle Headland Turning in Cluttered Orchard Environments
|
cs.RO
|
Autonomous agricultural vehicles (AAVs), including field robots and
autonomous tractors, are becoming essential in modern farming by improving
efficiency and reducing labor costs. A critical task in AAV operations is
headland turning between crop rows. This task is challenging in orchards with
limited headland space, irregular boundaries, operational constraints, and
static obstacles. While traditional trajectory planning methods work well in
arable farming, they often fail in cluttered orchard environments. This letter
presents a novel trajectory planner that enhances the safety and efficiency of
AAV headland maneuvers, leveraging advancements in autonomous driving. Our
approach includes an efficient front-end algorithm and a high-performance
back-end optimization. Applied to vehicles with various implements, it
outperforms state-of-the-art methods in both standard and challenging orchard
fields. This work bridges agricultural and autonomous driving technologies,
facilitating a broader adoption of AAVs in complex orchards.
|
2501.10637
|
HOPS: High-order Polynomials with Self-supervised Dimension Reduction
for Load Forecasting
|
cs.LG cs.SY eess.SY
|
Load forecasting is a fundamental task in smart grid. Many techniques have
been applied to developing load forecasting models. Due to the challenges such
as the Curse of Dimensionality, overfitting, and limited computing resources,
multivariate higher-order polynomial models have received limited attention in
load forecasting, despite their desirable mathematical foundations and
optimization properties. In this paper, we propose low rank approximation and
self-supervised dimension reduction to address the aforementioned issues. To
further improve computational efficiency, we also introduce a fast Conjugate
Gradient based algorithm for the proposed polynomial models. Based on the ISO
New England dataset used in Global Energy Forecasting Competition 2017, the
proposed method high-order polynomials with self-supervised dimension reduction
(HOPS) demonstrates higher forecasting accuracy over several competitive
models. Additionally, experimental results indicate that our approach
alleviates redundant variable construction, achieving better forecasts with
fewer input variables.
|
2501.10638
|
A Resource-Efficient Training Framework for Remote Sensing Text--Image
Retrieval
|
cs.CV cs.IR
|
Remote sensing text--image retrieval (RSTIR) aims to retrieve the matched
remote sensing (RS) images from the database according to the descriptive text.
Recently, the rapid development of large visual-language pre-training models
provides new insights for RSTIR. Nevertheless, as the complexity of models
grows in RSTIR, the previous studies suffer from suboptimal resource efficiency
during transfer learning. To address this issue, we propose a computation and
memory-efficient retrieval (CMER) framework for RSTIR. To reduce the training
memory consumption, we propose the Focus-Adapter module, which adopts a side
branch structure. Its focus layer suppresses the interference of background
pixels for small targets. Simultaneously, to enhance data efficacy, we regard
the RS scene category as the metadata and design a concise augmentation
technique. The scene label augmentation leverages the prior knowledge from land
cover categories and shrinks the search space. We propose the negative sample
recycling strategy to make the negative sample pool decoupled from the
mini-batch size. It improves the generalization performance without introducing
additional encoders. We have conducted quantitative and qualitative experiments
on public datasets and expanded the benchmark with some advanced approaches,
which demonstrates the competitiveness of the proposed CMER. Compared with the
recent advanced methods, the overall retrieval performance of CMER is 2%--5%
higher on RSITMD. Moreover, our proposed method reduces memory consumption by
49% and has a 1.4x data throughput during training. The code of the CMER and
the dataset will be released at https://github.com/ZhangWeihang99/CMER.
|
2501.10639
|
Latent-space adversarial training with post-aware calibration for
defending large language models against jailbreak attacks
|
cs.CR cs.CL
|
Ensuring safety alignment has become a critical requirement for large
language models (LLMs), particularly given their widespread deployment in
real-world applications. However, LLMs remain susceptible to jailbreak attacks,
which exploit system vulnerabilities to bypass safety measures and generate
harmful outputs. Although numerous defense mechanisms based on adversarial
training have been proposed, a persistent challenge lies in the exacerbation of
over-refusal behaviors, which compromise the overall utility of the model. To
address these challenges, we propose a Latent-space Adversarial Training with
Post-aware Calibration (LATPC) framework. During the adversarial training
phase, LATPC compares harmful and harmless instructions in the latent space and
extracts safety-critical dimensions to construct refusal features attack,
precisely simulating agnostic jailbreak attack types requiring adversarial
mitigation. At the inference stage, an embedding-level calibration mechanism is
employed to alleviate over-refusal behaviors with minimal computational
overhead. Experimental results demonstrate that, compared to various defense
methods across five types of jailbreak attacks, LATPC framework achieves a
superior balance between safety and utility. Moreover, our analysis underscores
the effectiveness of extracting safety-critical dimensions from the latent
space for constructing robust refusal feature attacks.
|
2501.10640
|
ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning
|
cs.CV cs.DC
|
Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have
dominated the field of Computer Vision (CV). Graph Neural Networks (GNN) have
performed remarkably well across diverse domains because they can represent
complex relationships via unstructured graphs. However, the applicability of
GNNs for visual tasks was unexplored till the introduction of Vision GNNs
(ViG). Despite the success of ViGs, their performance is severely bottlenecked
due to the expensive $k$-Nearest Neighbors ($k$-NN) based graph construction.
Recent works addressing this bottleneck impose constraints on the flexibility
of GNNs to build unstructured graphs, undermining their core advantage while
introducing additional inefficiencies. To address these issues, in this paper,
we propose a novel method called Dynamic Efficient Graph Convolution (DEGC) for
designing efficient and globally aware ViGs. DEGC partitions the input image
and constructs graphs in parallel for each partition, improving graph
construction efficiency. Further, DEGC integrates local intra-graph and global
inter-graph feature learning, enabling enhanced global context awareness. Using
DEGC as a building block, we propose a novel CNN-GNN architecture, ClusterViG,
for CV tasks. Extensive experiments indicate that ClusterViG reduces end-to-end
inference latency for vision tasks by up to $5\times$ when compared against a
suite of models such as ViG, ViHGNN, PVG, and GreedyViG, with a similar model
parameter count. Additionally, ClusterViG reaches state-of-the-art performance
on image classification, object detection, and instance segmentation tasks,
demonstrating the effectiveness of the proposed globally aware learning
strategy. Finally, input partitioning performed by DEGC enables ClusterViG to
be trained efficiently on higher-resolution images, underscoring the
scalability of our approach.
|
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