id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2501.19298 | Synthetic User Behavior Sequence Generation with Large Language Models
for Smart Homes | cs.AI cs.LG cs.NI | In recent years, as smart home systems have become more widespread, security
concerns within these environments have become a growing threat. Currently,
most smart home security solutions, such as anomaly detection and behavior
prediction models, are trained using fixed datasets that are precollected.
However, the pr... |
2501.19300 | Offline Learning for Combinatorial Multi-armed Bandits | cs.LG | The combinatorial multi-armed bandit (CMAB) is a fundamental sequential
decision-making framework, extensively studied over the past decade. However,
existing work primarily focuses on the online setting, overlooking the
substantial costs of online interactions and the readily available offline
datasets. To overcome ... |
2501.19301 | Beyond checkmate: exploring the creative chokepoints in AI text | cs.CL cs.AI | Large Language Models (LLMs) have revolutionized Natural Language Processing
(NLP) and Artificial Intelligence (AI), unlocking unprecedented capabilities.
This rapid advancement has spurred research into various aspects of LLMs, their
text generation & reasoning capability, and potential misuse, fueling the
necessity... |
2501.19306 | SETS: Leveraging Self-Verification and Self-Correction for Improved
Test-Time Scaling | cs.AI cs.CL | Recent advancements in Large Language Models (LLMs) have created new
opportunities to enhance performance on complex reasoning tasks by leveraging
test-time computation. However, conventional approaches such as repeated
sampling with majority voting or reward model scoring, often face diminishing
returns as test-time... |
2501.19307 | Quantum-Inspired Fidelity-based Divergence | cs.IT math.IT | Kullback--Leibler (KL) divergence is a fundamental measure of the
dissimilarity between two probability distributions, but it can become unstable
in high-dimensional settings due to its sensitivity to mismatches in
distributional support. To address robustness limitations, we propose a novel
Quantum-Inspired Fidelity... |
2501.19308 | Ontological analysis of proactive life event services | cs.AI | Life event service is a direct digital public service provided jointly by
several governmental institutions so that a person can fulfill all the
obligations and use all the rights that arise due to a particular event or
situation in personal life. Life event service consolidates several public
services related to the... |
2501.19309 | Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model
Alignment | cs.LG cs.CL | The performance of large language models (LLMs) is closely linked to their
underlying size, leading to ever-growing networks and hence slower inference.
Speculative decoding has been proposed as a technique to accelerate
autoregressive generation, leveraging a fast draft model to propose candidate
tokens, which are t... |
2501.19314 | An Efficient Approach for Machine Translation on Low-resource Languages:
A Case Study in Vietnamese-Chinese | cs.CL | Despite the rise of recent neural networks in machine translation, those
networks do not work well if the training data is insufficient. In this paper,
we proposed an approach for machine translation in low-resource languages such
as Vietnamese-Chinese. Our proposed method leveraged the power of the
multilingual pre-... |
2501.19316 | Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task
Embeddings for Coreference Resolution | cs.CL | In this work, we reimagine classical probing to evaluate knowledge transfer
from simple source to more complex target tasks. Instead of probing frozen
representations from a complex source task on diverse simple target probing
tasks (as usually done in probing), we explore the effectiveness of embeddings
from multipl... |
2501.19317 | LLM-based Affective Text Generation Quality Based on Different
Quantization Values | cs.CL | Large language models exhibit a remarkable capacity in language generation
and comprehension. These advances enable AI systems to produce more human-like
and emotionally engaging text. However, these models rely on a large number of
parameters, requiring significant computational resources for training and
inference.... |
2501.19318 | MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented
Reinforcement in Embodied Systems | cs.AI | While large language models (LLMs) have shown promising capabilities as
zero-shot planners for embodied agents, their inability to learn from
experience and build persistent mental models limits their robustness in
complex open-world environments like Minecraft. We introduce MINDSTORES, an
experience-augmented planni... |
2501.19319 | Advancing Dense Endoscopic Reconstruction with Gaussian Splatting-driven
Surface Normal-aware Tracking and Mapping | cs.CV cs.RO | Simultaneous Localization and Mapping (SLAM) is essential for precise
surgical interventions and robotic tasks in minimally invasive procedures.
While recent advancements in 3D Gaussian Splatting (3DGS) have improved SLAM
with high-quality novel view synthesis and fast rendering, these systems
struggle with accurate ... |
2501.19321 | Language Bias in Self-Supervised Learning For Automatic Speech
Recognition | eess.AS cs.AI cs.CL cs.LG eess.SP | Self-supervised learning (SSL) is used in deep learning to train on large
datasets without the need for expensive labelling of the data. Recently, large
Automatic Speech Recognition (ASR) models such as XLS-R have utilised SSL to
train on over one hundred different languages simultaneously. However, deeper
investigat... |
2501.19324 | Reward-Guided Speculative Decoding for Efficient LLM Reasoning | cs.CL cs.AI | We introduce Reward-Guided Speculative Decoding (RSD), a novel framework
aimed at improving the efficiency of inference in large language models (LLMs).
RSD synergistically combines a lightweight draft model with a more powerful
target model, incorporating a controlled bias to prioritize high-reward
outputs, in contr... |
2501.19325 | A Generic Hybrid Framework for 2D Visual Reconstruction | cs.CV | This paper presents a versatile hybrid framework for addressing 2D real-world
reconstruction tasks formulated as jigsaw puzzle problems (JPPs) with square,
non-overlapping pieces. Our approach integrates a deep learning (DL)-based
compatibility measure (CM) model that evaluates pairs of puzzle pieces
holistically, ra... |
2501.19328 | Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation | cs.LG cs.AI cs.CV | With the rise in global greenhouse gas emissions, accurate large-scale tree
canopy height maps are essential for understanding forest structure, estimating
above-ground biomass, and monitoring ecological disruptions. To this end, we
present a novel approach to generate large-scale, high-resolution canopy height
maps ... |
2501.19329 | Let Human Sketches Help: Empowering Challenging Image Segmentation Task
with Freehand Sketches | cs.CV | Sketches, with their expressive potential, allow humans to convey the essence
of an object through even a rough contour. For the first time, we harness this
expressive potential to improve segmentation performance in challenging tasks
like camouflaged object detection (COD). Our approach introduces an innovative
sket... |
2501.19331 | Consistent Video Colorization via Palette Guidance | cs.CV | Colorization is a traditional computer vision task and it plays an important
role in many time-consuming tasks, such as old film restoration. Existing
methods suffer from unsaturated color and temporally inconsistency. In this
paper, we propose a novel pipeline to overcome the challenges. We regard the
colorization t... |
2501.19334 | The Value of Prediction in Identifying the Worst-Off | cs.CY cs.LG stat.ML | Machine learning is increasingly used in government programs to identify and
support the most vulnerable individuals, prioritizing assistance for those at
greatest risk over optimizing aggregate outcomes. This paper examines the
welfare impacts of prediction in equity-driven contexts, and how they compare
to other po... |
2501.19335 | What is causal about causal models and representations? | stat.ML cs.AI cs.LG math.ST stat.TH | Causal Bayesian networks are 'causal' models since they make predictions
about interventional distributions. To connect such causal model predictions to
real-world outcomes, we must determine which actions in the world correspond to
which interventions in the model. For example, to interpret an action as an
intervent... |
2501.19337 | Homogeneity Bias as Differential Sampling Uncertainty in Language Models | cs.CL cs.CV | Prior research show that Large Language Models (LLMs) and Vision-Language
Models (VLMs) represent marginalized groups more homogeneously than dominant
groups. However, the mechanisms underlying this homogeneity bias remain
relatively unexplored. We propose that this bias emerges from systematic
differences in the pro... |
2501.19338 | Pathological MRI Segmentation by Synthetic Pathological Data Generation
in Fetuses and Neonates | eess.IV cs.AI cs.CV | Developing new methods for the automated analysis of clinical fetal and
neonatal MRI data is limited by the scarcity of annotated pathological datasets
and privacy concerns that often restrict data sharing, hindering the
effectiveness of deep learning models. We address this in two ways. First, we
introduce Fetal&Neo... |
2501.19339 | PixelWorld: Towards Perceiving Everything as Pixels | cs.CV cs.CL | Existing foundation models typically process visual input as pixels and
textual input as tokens, a paradigm that contrasts with human perception, where
both modalities are processed in a unified manner. With the rise of embodied
and agentic AI, where inputs primarily come from camera pixels, the need for a
unified pe... |
2501.19340 | Towards Adaptive Self-Improvement for Smarter Energy Systems | eess.SY cs.SY | This paper introduces a hierarchical framework for decision-making and
optimization, leveraging Large Language Models (LLMs) for adaptive code
generation. Instead of direct decision-making, LLMs generate and refine
executable control policies through a meta-policy that guides task generation
and a base policy for ope... |
2501.19342 | Covering Multiple Objectives with a Small Set of Solutions Using
Bayesian Optimization | cs.LG | In multi-objective black-box optimization, the goal is typically to find
solutions that optimize a set of T black-box objective functions, $f_1$, ...,
$f_T$, simultaneously. Traditional approaches often seek a single
Pareto-optimal set that balances trade-offs among all objectives. In this work,
we introduce a novel ... |
2501.19345 | PUATE: Semiparametric Efficient Average Treatment Effect Estimation from
Treated (Positive) and Unlabeled Units | cs.LG econ.EM math.ST stat.ME stat.ML stat.TH | The estimation of average treatment effects (ATEs), defined as the difference
in expected outcomes between treatment and control groups, is a central topic
in causal inference. This study develops semiparametric efficient estimators
for ATE estimation in a setting where only a treatment group and an unknown
group-com... |
2501.19347 | An All-digital 65-nm Tsetlin Machine Image Classification Accelerator
with 8.6 nJ per MNIST Frame at 60.3k Frames per Second | cs.LG cs.AR | We present an all-digital programmable machine learning accelerator chip for
image classification, underpinning on the Tsetlin machine (TM) principles. The
TM is a machine learning algorithm founded on propositional logic, utilizing
sub-pattern recognition expressions called clauses. The accelerator implements
the co... |
2501.19348 | Characterizing User Behavior: The Interplay Between Mobility Patterns
and Mobile Traffic | cs.NI cs.IR | Mobile devices have become essential for capturing human activity, and
eXtended Data Records (XDRs) offer rich opportunities for detailed user
behavior modeling, which is useful for designing personalized digital services.
Previous studies have primarily focused on aggregated mobile traffic and
mobility analyses, oft... |
2501.19351 | Neural Implicit Solution Formula for Efficiently Solving Hamilton-Jacobi
Equations | cs.LG | This paper presents an implicit solution formula for the Hamilton-Jacobi
partial differential equation (HJ PDE). The formula is derived using the method
of characteristics and is shown to coincide with the Hopf and Lax formulas in
the case where either the Hamiltonian or the initial function is convex. It
provides a ... |
2501.19353 | Do Large Multimodal Models Solve Caption Generation for Scientific
Figures? Lessons Learned from SciCap Challenge 2023 | cs.CL cs.AI cs.CV | Since the SciCap datasets launch in 2021, the research community has made
significant progress in generating captions for scientific figures in scholarly
articles. In 2023, the first SciCap Challenge took place, inviting global teams
to use an expanded SciCap dataset to develop models for captioning diverse
figure ty... |
2501.19358 | The Energy Loss Phenomenon in RLHF: A New Perspective on Mitigating
Reward Hacking | cs.LG | This work identifies the Energy Loss Phenomenon in Reinforcement Learning
from Human Feedback (RLHF) and its connection to reward hacking. Specifically,
energy loss in the final layer of a Large Language Model (LLM) gradually
increases during the RL process, with an excessive increase in energy loss
characterizing re... |
2501.19361 | We're Different, We're the Same: Creative Homogeneity Across LLMs | cs.CY cs.AI cs.CL cs.LG | Numerous powerful large language models (LLMs) are now available for use as
writing support tools, idea generators, and beyond. Although these LLMs are
marketed as helpful creative assistants, several works have shown that using an
LLM as a creative partner results in a narrower set of creative outputs.
However, thes... |
2501.19364 | CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation | cs.LG cs.AI | Multivariate Time Series Imputation (MTSI) is crucial for many applications,
such as healthcare monitoring and traffic management, where incomplete data can
compromise decision-making. Existing state-of-the-art methods, like Denoising
Diffusion Probabilistic Models (DDPMs), achieve high imputation accuracy;
however, ... |
2501.19373 | Beyond Fixed Horizons: A Theoretical Framework for Adaptive Denoising
Diffusions | stat.ML cs.LG | We introduce a new class of generative diffusion models that, unlike
conventional denoising diffusion models, achieve a time-homogeneous structure
for both the noising and denoising processes, allowing the number of steps to
adaptively adjust based on the noise level. This is accomplished by
conditioning the forward ... |
2501.19374 | Fixing the Double Penalty in Data-Driven Weather Forecasting Through a
Modified Spherical Harmonic Loss Function | cs.LG physics.ao-ph | Recent advancements in data-driven weather forecasting models have delivered
deterministic models that outperform the leading operational forecast systems
based on traditional, physics-based models. However, these data-driven models
are typically trained with a mean squared error loss function, which causes
smoothing... |
2501.19375 | A topological theory for qLDPC: non-Clifford gates and magic state
fountain on homological product codes with constant rate and beyond the
$N^{1/3}$ distance barrier | quant-ph cond-mat.str-el cs.IT hep-th math.GT math.IT | We develop a unified theory for fault-tolerant quantum computation in quantum
low-density parity-check (qLDPC) and topological codes. We show that there
exist hidden simplicial complex structures encoding the topological data for
all qLDPC and CSS codes obtained from product construction by generalizing the
Freedman-... |
2501.19377 | SELMA: A Speech-Enabled Language Model for Virtual Assistant
Interactions | cs.SD cs.CL cs.LG eess.AS | In this work, we present and evaluate SELMA, a Speech-Enabled Language Model
for virtual Assistant interactions that integrates audio and text as inputs to
a Large Language Model (LLM). SELMA is designed to handle three primary and two
auxiliary tasks related to interactions with virtual assistants simultaneously
wit... |
2501.19378 | TableMaster: A Recipe to Advance Table Understanding with Language
Models | cs.CL | Tables serve as a fundamental format for representing structured relational
data. While current language models (LMs) excel at many text-based tasks, they
still face challenges in table understanding due to the complex characteristics
of tabular data, such as their structured nature. In this paper, we aim to
enhance ... |
2501.19381 | Using gradient of Lagrangian function to compute efficient channels for
the ideal observer | eess.SP cs.CV cs.LG math.ST stat.CO stat.TH | It is widely accepted that the Bayesian ideal observer (IO) should be used to
guide the objective assessment and optimization of medical imaging systems. The
IO employs complete task-specific information to compute test statistics for
making inference decisions and performs optimally in signal detection tasks.
Howeve... |
2501.19382 | LiDAR Loop Closure Detection using Semantic Graphs with Graph Attention
Networks | cs.CV cs.RO | In this paper, we propose a novel loop closure detection algorithm that uses
graph attention neural networks to encode semantic graphs to perform place
recognition and then use semantic registration to estimate the 6 DoF relative
pose constraint. Our place recognition algorithm has two key modules, namely, a
semantic... |
2501.19383 | Decoding-based Regression | cs.LG cs.AI cs.CL stat.ML | Language models have recently been shown capable of performing regression
tasks wherein numeric predictions are represented as decoded strings. In this
work, we provide theoretical grounds for this capability and furthermore
investigate the utility of causal auto-regressive sequence models when they are
applied to an... |
2501.19386 | Multi-Frame Blind Manifold Deconvolution for Rotating Synthetic Aperture
Imaging | stat.ME cs.CV eess.SP | Rotating synthetic aperture (RSA) imaging system captures images of the
target scene at different rotation angles by rotating a rectangular aperture.
Deblurring acquired RSA images plays a critical role in reconstructing a latent
sharp image underlying the scene. In the past decade, the emergence of blind
convolution... |
2501.19389 | Federated Sketching LoRA: On-Device Collaborative Fine-Tuning of Large
Language Models | cs.LG | Fine-tuning large language models (LLMs) on devices is attracting increasing
interest. Recent works have fused low-rank adaptation (LoRA) techniques with
federated fine-tuning to mitigate challenges associated with device model sizes
and data scarcity. Still, the heterogeneity of computational resources remains
a cri... |
2501.19390 | From a Frequency-Domain Willems' Lemma to Data-Driven Predictive Control | math.OC cs.SY eess.SY | Willems' fundamental lemma has recently received an impressive amount of
attention from the (data-driven) control community. In this paper, we formulate
a version of this celebrated result based on frequency-domain data. In doing
so, we bridge the gap between recent developments in data-driven analysis and
control, a... |
2501.19391 | Perceptive Mixed-Integer Footstep Control for Underactuated Bipedal
Walking on Rough Terrain | cs.RO | Traversing rough terrain requires dynamic bipeds to stabilize themselves
through foot placement without stepping in unsafe areas. Planning these
footsteps online is challenging given non-convexity of the safe terrain, and
imperfect perception and state estimation. This paper addresses these
challenges with a full-sta... |
2501.19392 | Cache Me If You Must: Adaptive Key-Value Quantization for Large Language
Models | cs.LG | Efficient real-world deployments of large language models (LLMs) rely on
Key-Value (KV) caching for processing and generating long outputs, reducing the
need for repetitive computation. For large contexts, Key-Value caches can take
up tens of gigabytes of device memory, as they store vector representations for
each t... |
2501.19393 | s1: Simple test-time scaling | cs.CL cs.AI cs.LG | Test-time scaling is a promising new approach to language modeling that uses
extra test-time compute to improve performance. Recently, OpenAI's o1 model
showed this capability but did not publicly share its methodology, leading to
many replication efforts. We seek the simplest approach to achieve test-time
scaling an... |
2501.19395 | Precision Harvesting in Cluttered Environments: Integrating End Effector
Design with Dual Camera Perception | cs.RO | Due to labor shortages in specialty crop industries, a need for robotic
automation to increase agricultural efficiency and productivity has arisen.
Previous manipulation systems perform well in harvesting in uncluttered and
structured environments. High tunnel environments are more compact and
cluttered in nature, re... |
2501.19398 | Do LLMs Strategically Reveal, Conceal, and Infer Information? A
Theoretical and Empirical Analysis in The Chameleon Game | cs.AI cs.GT cs.LG | Large language model-based (LLM-based) agents have become common in settings
that include non-cooperative parties. In such settings, agents' decision-making
needs to conceal information from their adversaries, reveal information to
their cooperators, and infer information to identify the other agents'
characteristics... |
2501.19399 | Scalable-Softmax Is Superior for Attention | cs.CL cs.AI cs.LG | The maximum element of the vector output by the Softmax function approaches
zero as the input vector size increases. Transformer-based language models rely
on Softmax to compute attention scores, causing the attention distribution to
flatten as the context size grows. This reduces the model's ability to
prioritize ke... |
2501.19400 | Vintix: Action Model via In-Context Reinforcement Learning | cs.LG cs.AI cs.RO | In-Context Reinforcement Learning (ICRL) represents a promising paradigm for
developing generalist agents that learn at inference time through
trial-and-error interactions, analogous to how large language models adapt
contextually, but with a focus on reward maximization. However, the scalability
of ICRL beyond toy t... |
2501.19401 | Detection Is All You Need: A Feasible Optimal Prior-Free Black-Box
Approach For Piecewise Stationary Bandits | cs.LG stat.ML | We study the problem of piecewise stationary bandits without prior knowledge
of the underlying non-stationarity. We propose the first $\textit{feasible}$
black-box algorithm applicable to most common parametric bandit variants. Our
procedure, termed Detection Augmented Bandit (DAB), is modular, accepting any
stationa... |
2501.19403 | Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach | cs.LG cs.AI | Machine unlearning seeks to systematically remove specified data from a
trained model, effectively achieving a state as though the data had never been
encountered during training. While metrics such as Unlearning Accuracy (UA) and
Membership Inference Attack (MIA) provide a baseline for assessing unlearning
performan... |
2501.19405 | Human-Precision Medicine Interaction: Public Perceptions of Polygenic
Risk Score for Genetic Health Prediction | cs.HC cs.CE cs.ET | Precision Medicine (PM) transforms the traditional "one-drug-fits-all"
paradigm by customising treatments based on individual characteristics, and is
an emerging topic for HCI research on digital health. A key element of PM, the
Polygenic Risk Score (PRS), uses genetic data to predict an individual's
disease risk. De... |
2501.19406 | Low-Rank Adapting Models for Sparse Autoencoders | cs.LG | Sparse autoencoders (SAEs) decompose language model representations into a
sparse set of linear latent vectors. Recent works have improved SAEs using
language model gradients, but these techniques require many expensive backward
passes during training and still cause a significant increase in cross entropy
loss when ... |
2501.19407 | Algorithmic Inheritance: Surname Bias in AI Decisions Reinforces
Intergenerational Inequality | cs.CY cs.AI cs.LG econ.GN q-fin.EC | Surnames often convey implicit markers of social status, wealth, and lineage,
shaping perceptions in ways that can perpetuate systemic biases and
intergenerational inequality. This study is the first of its kind to
investigate whether and how surnames influence AI-driven decision-making,
focusing on their effects acr... |
2502.00003 | Defending Compute Thresholds Against Legal Loopholes | cs.CY cs.AI | Existing legal frameworks on AI rely on training compute thresholds as a
proxy to identify potentially-dangerous AI models and trigger increased
regulatory attention. In the United States, Section 4.2(a) of Executive Order
14110 instructs the Secretary of Commerce to require extensive reporting from
developers of AI ... |
2502.00005 | A Study about Distribution and Acceptance of Conversational Agents for
Mental Health in Germany: Keep the Human in the Loop? | cs.HC cs.AI cs.CY | Good mental health enables individuals to cope with the normal stresses of
life. In Germany, approximately one-quarter of the adult population is affected
by mental illnesses. Teletherapy and digital health applications are available
to bridge gaps in care and relieve healthcare professionals. The acceptance of
these... |
2502.00008 | Zoning in American Cities: Are Reforms Making a Difference? An AI-based
Analysis | cs.CY cs.CL | Cities are at the forefront of addressing global sustainability challenges,
particularly those exacerbated by climate change. Traditional zoning codes,
which often segregate land uses, have been linked to increased vehicular
dependence, urban sprawl, and social disconnection, undermining broader social
and environmen... |
2502.00011 | TOAST Framework: A Multidimensional Approach to Ethical and Sustainable
AI Integration in Organizations | cs.CY cs.AI cs.HC | Artificial Intelligence (AI) has emerged as a transformative technology with
the potential to revolutionize various sectors, from healthcare to finance,
education, and beyond. However, successfully implementing AI systems remains a
complex challenge, requiring a comprehensive and methodologically sound
framework. Thi... |
2502.00013 | Behavioural Analytics: Mathematics of the Mind | cs.CY cs.LG | Behavioural analytics provides insights into individual and crowd behaviour,
enabling analysis of what previously happened and predictions for how people
may be likely to act in the future. In defence and security, this analysis
allows organisations to achieve tactical and strategic advantage through
influence campai... |
2502.00015 | Ethical Concerns of Generative AI and Mitigation Strategies: A
Systematic Mapping Study | cs.CY cs.AI | [Context] Generative AI technologies, particularly Large Language Models
(LLMs), have transformed numerous domains by enhancing convenience and
efficiency in information retrieval, content generation, and decision-making
processes. However, deploying LLMs also presents diverse ethical challenges,
and their mitigation... |
2502.00017 | A Frugal Model for Accurate Early Student Failure Prediction | cs.CY cs.LG | Predicting student success or failure is vital for timely interventions and
personalized support. Early failure prediction is particularly crucial, yet
limited data availability in the early stages poses challenges, one of the
possible solutions is to make use of additional data from other contexts,
however, this mig... |
2502.00018 | An Expectation-Maximization Algorithm-based Autoregressive Model for the
Fuzzy Job Shop Scheduling Problem | cs.AI | The fuzzy job shop scheduling problem (FJSSP) emerges as an innovative
extension to the job shop scheduling problem (JSSP), incorporating a layer of
uncertainty that aligns the problem more closely with the complexities of
real-world manufacturing environments. This improvement increases the
computational complexity ... |
2502.00019 | Growth Patterns of Inference | cs.AI | What properties of a first-order search space support/hinder inference? What
kinds of facts would be most effective to learn? Answering these questions is
essential for understanding the dynamics of deductive reasoning and creating
large-scale knowledge-based learning systems that support efficient inference.
We addr... |
2502.00020 | Temporal Reasoning in AI systems | cs.AI | Commonsense temporal reasoning at scale is a core problem for cognitive
systems. The correct inference of the duration for which fluents hold is
required by many tasks, including natural language understanding and planning.
Many AI systems have limited deductive closure because they cannot extrapolate
information cor... |
2502.00021 | PixelBrax: Learning Continuous Control from Pixels End-to-End on the GPU | cs.LG cs.PF | We present PixelBrax, a set of continuous control tasks with pixel
observations. We combine the Brax physics engine with a pure JAX renderer,
allowing reinforcement learning (RL) experiments to run end-to-end on the GPU.
PixelBrax can render observations over thousands of parallel environments and
can run two orders ... |
2502.00022 | A Dynamic and High-Precision Method for Scenario-Based HRA Synthetic
Data Collection in Multi-Agent Collaborative Environments Driven by LLMs | cs.AI cs.HC | HRA (Human Reliability Analysis) data is crucial for advancing HRA
methodologies. however, existing data collection methods lack the necessary
granularity, and most approaches fail to capture dynamic features.
Additionally, many methods require expert knowledge as input, making them
time-consuming and labor-intensive... |
2502.00023 | Musical Agent Systems: MACAT and MACataRT | cs.MA cs.AI cs.HC cs.SD eess.AS | Our research explores the development and application of musical agents,
human-in-the-loop generative AI systems designed to support music performance
and improvisation within co-creative spaces. We introduce MACAT and MACataRT,
two distinct musical agent systems crafted to enhance interactive music-making
between hu... |
2502.00024 | Retail Market Analysis | q-fin.GN cs.LG | This project focuses on analyzing retail market trends using historical sales
data, search trends, and customer reviews. By identifying the patterns and
trending products, the analysis provides actionable insights for retailers to
optimize inventory management and marketing strategies, ultimately enhancing
customer s... |
2502.00025 | Leveraging Large Language Models to Enhance Machine Learning
Interpretability and Predictive Performance: A Case Study on Emergency
Department Returns for Mental Health Patients | cs.LG cs.AI cs.CY | Importance: Emergency department (ED) returns for mental health conditions
pose a major healthcare burden, with 24-27% of patients returning within 30
days. Traditional machine learning models for predicting these returns often
lack interpretability for clinical use.
Objective: To assess whether integrating large l... |
2502.00026 | Pushing the Limits of BFP on Narrow Precision LLM Inference | cs.AR cs.AI | The substantial computational and memory demands of Large Language Models
(LLMs) hinder their deployment. Block Floating Point (BFP) has proven effective
in accelerating linear operations, a cornerstone of LLM workloads. However, as
sequence lengths grow, nonlinear operations, such as Attention, increasingly
become p... |
2502.00027 | Analysis of a Memcapacitor-Based for Neural Network Accelerator
Framework | cs.AR cs.AI cs.NE | Data-intensive computing tasks, such as training neural networks, are crucial
for artificial intelligence applications but often come with high energy
demands. One promising solution is to develop specialized hardware that
directly maps neural networks, utilizing arrays of memristive devices to
perform parallel multi... |
2502.00029 | AlphaSharpe: LLM-Driven Discovery of Robust Risk-Adjusted Metrics | q-fin.PM cs.AI cs.CL cs.NE q-fin.RM | Financial metrics like the Sharpe ratio are pivotal in evaluating investment
performance by balancing risk and return. However, traditional metrics often
struggle with robustness and generalization, particularly in dynamic and
volatile market conditions. This paper introduces AlphaSharpe, a novel
framework leveraging... |
2502.00031 | GNN-based Anchor Embedding for Exact Subgraph Matching | cs.SI cs.DB | Subgraph matching query is a classic problem in graph data management and has
a variety of real-world applications, such as discovering structures in
biological or chemical networks, finding communities in social network
analysis, explaining neural networks, and so on. To further solve the subgraph
matching problem, ... |
2502.00032 | Querying Databases with Function Calling | cs.DB cs.AI cs.IR | The capabilities of Large Language Models (LLMs) are rapidly accelerating
largely thanks to their integration with external tools. Querying databases is
among the most effective of these integrations, enabling LLMs to access private
or continually updating data. While Function Calling is the most common method
for in... |
2502.00034 | Towards Efficient Multi-Objective Optimisation for Real-World Power Grid
Topology Control | cs.AI cs.LG cs.SY eess.SY | Power grid operators face increasing difficulties in the control room as the
increase in energy demand and the shift to renewable energy introduce new
complexities in managing congestion and maintaining a stable supply. Effective
grid topology control requires advanced tools capable of handling
multi-objective trade-... |
2502.00036 | Efficient Client Selection in Federated Learning | cs.LG cs.AI cs.DC | Federated Learning (FL) enables decentralized machine learning while
preserving data privacy. This paper proposes a novel client selection framework
that integrates differential privacy and fault tolerance. The adaptive client
selection adjusts the number of clients based on performance and system
constraints, with n... |
2502.00037 | Super Quantum Mechanics | quant-ph cs.LG cs.NA math.NA | We introduce Super Quantum Mechanics (SQM) as a theory that considers states
in Hilbert space subject to multiple quadratic constraints. Traditional quantum
mechanics corresponds to a single quadratic constraint of wavefunction
normalization. In its simplest form, SQM considers states in the form of
unitary operators... |
2502.00038 | The Best Soules Basis for the Estimation of a Spectral Barycentre
Network | cs.SI cs.LG physics.data-an stat.ML | The main contribution of this work is a fast algorithm to compute the
barycentre of a set of networks based on a Laplacian spectral pseudo-distance.
The core engine for the reconstruction of the barycentre is an algorithm that
explores the large library of Soules bases, and returns a basis that yields a
sparse approx... |
2502.00039 | Accurately Estimating Unreported Infections using Information Theory | cs.SI cs.IT math.IT physics.soc-ph | One of the most significant challenges in combating against the spread of
infectious diseases was the difficulty in estimating the true magnitude of
infections. Unreported infections could drive up disease spread, making it very
hard to accurately estimate the infectivity of the pathogen, therewith
hampering our abil... |
2502.00040 | Multi-Objective Reinforcement Learning for Power Grid Topology Control | cs.LG cs.AI cs.SY eess.SY | Transmission grid congestion increases as the electrification of various
sectors requires transmitting more power. Topology control, through substation
reconfiguration, can reduce congestion but its potential remains
under-exploited in operations. A challenge is modeling the topology control
problem to align well wit... |
2502.00041 | MALT: Mechanistic Ablation of Lossy Translation in LLMs for a
Low-Resource Language: Urdu | cs.CL | LLMs are predominantly trained on English data, which leads to a significant
drop in performance on low-resource languages. Understanding how LLMs handle
these languages is crucial for improving their effectiveness. This study
focuses on Urdu as a use case for exploring the challenges faced by LLMs in
processing low-... |
2502.00042 | LSU-Net: Lightweight Automatic Organs Segmentation Network For Medical
Images | eess.IV cs.CV | UNet and its variants have widespread applications in medical image
segmentation. However, the substantial number of parameters and computational
complexity of these models make them less suitable for use in clinical settings
with limited computational resources. To address this limitation, we propose a
novel Lightwe... |
2502.00043 | A scalable adaptive deep Koopman predictive controller for real-time
optimization of mixed traffic flow | eess.SY cs.AI cs.SY | The use of connected automated vehicle (CAV) is advocated to mitigate traffic
oscillations in mixed traffic flow consisting of CAVs and human driven vehicles
(HDVs). This study proposes an adaptive deep Koopman predictive control
framework (AdapKoopPC) for regulating mixed traffic flow. Firstly, a Koopman
theory-base... |
2502.00044 | HoloGraphs: An Interactive Physicalization for Dynamic Graphs | cs.SI cs.HC | We present HoloGraphs, a novel approach for physically representing,
explaining, exploring, and interacting with dynamic networks. HoloGraphs
addresses the challenges of visualizing and understanding evolving network
structures by providing an engaging method of interacting and exploring dynamic
network structures us... |
2502.00045 | Restless Multi-armed Bandits under Frequency and Window Constraints for
Public Service Inspections | cs.LG cs.AI cs.CE cs.CY | Municipal inspections are an important part of maintaining the quality of
goods and services. In this paper, we approach the problem of intelligently
scheduling service inspections to maximize their impact, using the case of food
establishment inspections in Chicago as a case study. The Chicago Department of
Public H... |
2502.00046 | Optimization Strategies for Enhancing Resource Efficiency in
Transformers & Large Language Models | cs.LG cs.CL | Advancements in Natural Language Processing are heavily reliant on the
Transformer architecture, whose improvements come at substantial resource costs
due to ever-growing model sizes. This study explores optimization techniques,
including Quantization, Knowledge Distillation, and Pruning, focusing on energy
and compu... |
2502.00047 | HadamRNN: Binary and Sparse Ternary Orthogonal RNNs | cs.LG cs.AI | Binary and sparse ternary weights in neural networks enable faster
computations and lighter representations, facilitating their use on edge
devices with limited computational power. Meanwhile, vanilla RNNs are highly
sensitive to changes in their recurrent weights, making the binarization and
ternarization of these w... |
2502.00048 | Contextually Entangled Gradient Mapping for Optimized LLM Comprehension | cs.LG cs.AI cs.CL | Contextually Entangled Gradient Mapping (CEGM) introduces a new approach to
gradient optimization, redefining the relationship between contextual
embeddings and gradient updates to enhance semantic coherence and reasoning
capabilities in neural architectures. By treating gradients as dynamic carriers
of contextual de... |
2502.00050 | DISC: Dataset for Analyzing Driving Styles In Simulated Crashes for
Mixed Autonomy | cs.RO cs.LG | Handling pre-crash scenarios is still a major challenge for self-driving cars
due to limited practical data and human-driving behavior datasets. We introduce
DISC (Driving Styles In Simulated Crashes), one of the first datasets designed
to capture various driving styles and behaviors in pre-crash scenarios for
mixed ... |
2502.00051 | A two-stage dual-task learning strategy for early prediction of
pathological complete response to neoadjuvant chemotherapy for breast cancer
using dynamic contrast-enhanced magnetic resonance images | cs.CV physics.med-ph | Rationale and Objectives: Early prediction of pathological complete response
(pCR) can facilitate personalized treatment for breast cancer patients. To
improve prediction accuracy at the early time point of neoadjuvant
chemotherapy, we proposed a two-stage dual-task learning strategy to train a
deep neural network fo... |
2502.00052 | Bridging Contrastive Learning and Domain Adaptation: Theoretical
Perspective and Practical Application | cs.LG cs.AI | This work studies the relationship between Contrastive Learning and Domain
Adaptation from a theoretical perspective. The two standard contrastive losses,
NT-Xent loss (Self-supervised) and Supervised Contrastive loss, are related to
the Class-wise Mean Maximum Discrepancy (CMMD), a dissimilarity measure widely
used ... |
2502.00053 | Differentiable Projection-based Learn to Optimize in Wireless
Network-Part I: Convex Constrained (Non-)Convex Programming | eess.SY cs.LG cs.SY | This paper addresses a class of (non-)convex optimization problems subject to
general convex constraints, which pose significant challenges for traditional
methods due to their inherent non-convexity and diversity. Conventional convex
optimization-based solvers often struggle to efficiently handle these problems
in t... |
2502.00055 | Towards Recommender Systems LLMs Playground (RecSysLLMsP): Exploring
Polarization and Engagement in Simulated Social Networks | cs.SI cs.AI cs.CY cs.HC cs.IR | Given the exponential advancement in AI technologies and the potential
escalation of harmful effects from recommendation systems, it is crucial to
simulate and evaluate these effects early on. Doing so can help prevent
possible damage to both societies and technology companies. This paper
introduces the Recommender S... |
2502.00058 | GitHub Stargazers | Building Graph- and Edge-level Prediction Algorithms
for Developer Social Networks | cs.SI | Analyzing social networks formed by developers provides valuable insights for
market segmentation, trend analysis, and community engagement. In this study,
we explore the GitHub Stargazers dataset to classify developer communities and
predict potential collaborations using graph neural networks (GNNs). By
modeling 12... |
2502.00059 | Large Language Models are Few-shot Multivariate Time Series Classifiers | cs.LG cs.AI | Large Language Models (LLMs) have been extensively applied in time series
analysis. Yet, their utility in the few-shot classification (i.e., a crucial
training scenario due to the limited training data available in industrial
applications) concerning multivariate time series data remains underexplored.
We aim to leve... |
2502.00060 | Israel-Hamas war through Telegram, Reddit and Twitter | cs.SI cs.AI cs.LG | The Israeli-Palestinian conflict started on 7 October 2023, have resulted
thus far to over 48,000 people killed including more than 17,000 children with
a majority from Gaza, more than 30,000 people injured, over 10,000 missing, and
over 1 million people displaced, fleeing conflict zones. The infrastructure
damage in... |
2502.00061 | From Data to Action: Charting A Data-Driven Path to Combat Antimicrobial
Resistance | cs.LG cs.AI q-bio.PE | Antimicrobial-resistant (AMR) microbes are a growing challenge in healthcare,
rendering modern medicines ineffective. AMR arises from antibiotic production
and bacterial evolution, but quantifying its transmission remains difficult.
With increasing AMR-related data, data-driven methods offer promising insights
into i... |
2502.00063 | A Multi-Layered Large Language Model Framework for Disease Prediction | cs.CL cs.AI | Social telehealth has revolutionized healthcare by enabling patients to share
symptoms and receive medical consultations remotely. Users frequently post
symptoms on social media and online health platforms, generating a vast
repository of medical data that can be leveraged for disease classification and
symptom sever... |
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