id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.08750 | Optimal Transport-Based Displacement Interpolation with Data
Augmentation for Reduced Order Modeling of Nonlinear Dynamical Systems | [
"math.NA",
"cs.LG",
"cs.NA"
] | We present a novel reduced-order Model (ROM) that leverages optimal transport (OT) theory and displacement interpolation to enhance the representation of nonlinear dynamics in complex systems. While traditional ROM techniques face challenges in this scenario, especially when data (i.e., observational snapshots) is limi... | {
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2411.08752 | Multi-Perspective Stance Detection | [
"cs.CL"
] | Subjective NLP tasks usually rely on human annotations provided by multiple annotators, whose judgments may vary due to their diverse backgrounds and life experiences. Traditional methods often aggregate multiple annotations into a single ground truth, disregarding the diversity in perspectives that arises from annotat... | {
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2411.08753 | Which Viewpoint Shows it Best? Language for Weakly Supervising View
Selection in Multi-view Videos | [
"cs.CV"
] | Given a multi-view video, which viewpoint is most informative for a human observer? Existing methods rely on heuristics or expensive "best-view" supervision to answer this question, limiting their applicability. We propose a weakly supervised approach that leverages language accompanying an instructional multi-view vid... | {
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2411.08754 | Logic-based Knowledge Awareness for Autonomous Agents in Continuous
Spaces | [
"eess.SY",
"cs.SY"
] | This paper presents a step towards a formal controller design method for autonomous agents based on knowledge awareness to improve decision-making. Our approach is to first create an organized repository of information (a knowledge base) for autonomous agents which can be accessed and then translated into temporal spec... | {
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2411.08755 | Weakly-Supervised Anomaly Detection in Surveillance Videos Based on
Two-Stream I3D Convolution Network | [
"cs.CV",
"cs.LG"
] | The widespread implementation of urban surveillance systems has necessitated more sophisticated techniques for anomaly detection to ensure enhanced public safety. This paper presents a significant advancement in the field of anomaly detection through the application of Two-Stream Inflated 3D (I3D) Convolutional Network... | {
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2411.08756 | Masked Image Modeling Boosting Semi-Supervised Semantic Segmentation | [
"cs.CV"
] | In view of the fact that semi- and self-supervised learning share a fundamental principle, effectively modeling knowledge from unlabeled data, various semi-supervised semantic segmentation methods have integrated representative self-supervised learning paradigms for further regularization. However, the potential of the... | {
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2411.08758 | ScaleNet: Scale Invariance Learning in Directed Graphs | [
"cs.LG"
] | Graph Neural Networks (GNNs) have advanced relational data analysis but lack invariance learning techniques common in image classification. In node classification with GNNs, it is actually the ego-graph of the center node that is classified. This research extends the scale invariance concept to node classification by d... | {
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2411.08759 | Clutter-Aware Target Detection for ISAC in a Millimeter-Wave Cell-Free
Massive MIMO System | [
"eess.SP",
"cs.SY",
"eess.SY"
] | In this paper, we investigate the performance of an integrated sensing and communication (ISAC) system within a cell-free massive multiple-input multiple-output (MIMO) system. Each access point (AP) operates in the millimeter-wave (mmWave) frequency band. The APs jointly serve the user equipments (UEs) in the downlink ... | {
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2411.08760 | Energy Dissipation Preserving Physics Informed Neural Network for
Allen-Cahn Equations | [
"math.NA",
"cs.LG",
"cs.NA",
"physics.comp-ph"
] | This paper investigates a numerical solution of Allen-Cahn equation with constant and degenerate mobility, with polynomial and logarithmic energy functionals, with deterministic and random initial functions, and with advective term in one, two, and three spatial dimensions, based on the physics-informed neural network ... | {
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2411.08761 | AI-Enhanced Inverter Fault and Anomaly Detection System for Distributed
Energy Resources in Microgrids | [
"eess.SY",
"cs.SY"
] | The integration of Distributed Energy Resources (DERs) into power distribution systems has made microgrids foundational to grid modernization. These DERs, connected through power electronic inverters, create power electronics dominated grid architecture, introducing unique challenges for fault detection. While external... | {
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2411.08764 | Flow reconstruction in time-varying geometries using graph neural
networks | [
"cs.LG",
"cs.AI",
"physics.flu-dyn"
] | The paper presents a Graph Attention Convolutional Network (GACN) for flow reconstruction from very sparse data in time-varying geometries. The model incorporates a feature propagation algorithm as a preprocessing step to handle extremely sparse inputs, leveraging information from neighboring nodes to initialize missin... | {
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2411.08766 | Mapping Methane -- The Impact of Dairy Farm Practices on Emissions
Through Satellite Data and Machine Learning | [
"cs.LG",
"stat.AP"
] | This study investigates the correlation between dairy farm characteristics and methane concentrations as derived from satellite observations in Eastern Canada. Utilizing data from 11 dairy farms collected between January 2020 and December 2022, we integrated Sentinel-5P satellite methane data with critical farm-level a... | {
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2411.08767 | SANDWICH: Towards an Offline, Differentiable, Fully-Trainable Wireless
Neural Ray-Tracing Surrogate | [
"cs.NI",
"cs.AI"
] | Wireless ray-tracing (RT) is emerging as a key tool for three-dimensional (3D) wireless channel modeling, driven by advances in graphical rendering. Current approaches struggle to accurately model beyond 5G (B5G) network signaling, which often operates at higher frequencies and is more susceptible to environmental cond... | {
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2411.08768 | Sharingan: Extract User Action Sequence from Desktop Recordings | [
"cs.CV",
"cs.AI"
] | Video recordings of user activities, particularly desktop recordings, offer a rich source of data for understanding user behaviors and automating processes. However, despite advancements in Vision-Language Models (VLMs) and their increasing use in video analysis, extracting user actions from desktop recordings remains ... | {
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2411.08773 | Optimal Oblivious Subspace Embeddings with Near-optimal Sparsity | [
"cs.DS",
"cs.LG",
"cs.NA",
"math.NA",
"math.PR",
"stat.ML"
] | An oblivious subspace embedding is a random $m\times n$ matrix $\Pi$ such that, for any $d$-dimensional subspace, with high probability $\Pi$ preserves the norms of all vectors in that subspace within a $1\pm\epsilon$ factor. In this work, we give an oblivious subspace embedding with the optimal dimension $m=\Theta(d/\... | {
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2411.08777 | LUDO: Low-Latency Understanding of Highly Deformable Objects using Point
Cloud Occupancy Functions | [
"cs.RO",
"cs.CV"
] | Accurately determining the shape and location of internal structures within deformable objects is crucial for medical tasks that require precise targeting, such as robotic biopsies. We introduce LUDO, a method for accurate low-latency understanding of deformable objects. LUDO reconstructs objects in their deformed stat... | {
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2411.08785 | Zero-shot Cross-lingual Transfer Learning with Multiple Source and
Target Languages for Information Extraction: Language Selection and
Adversarial Training | [
"cs.CL",
"cs.AI"
] | The majority of previous researches addressing multi-lingual IE are limited to zero-shot cross-lingual single-transfer (one-to-one) setting, with high-resource languages predominantly as source training data. As a result, these works provide little understanding and benefit for the realistic goal of developing a multi-... | {
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2411.08790 | Can sparse autoencoders be used to decompose and interpret steering
vectors? | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Steering vectors are a promising approach to control the behaviour of large language models. However, their underlying mechanisms remain poorly understood. While sparse autoencoders (SAEs) may offer a potential method to interpret steering vectors, recent findings show that SAE-reconstructed vectors often lack the stee... | {
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2411.08791 | Locally Private Sampling with Public Data | [
"cs.LG"
] | Local differential privacy (LDP) is increasingly employed in privacy-preserving machine learning to protect user data before sharing it with an untrusted aggregator. Most LDP methods assume that users possess only a single data record, which is a significant limitation since users often gather extensive datasets (e.g.,... | {
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2411.08794 | Evaluating World Models with LLM for Decision Making | [
"cs.AI"
] | World model emerges as a key module in decision making, where MuZero and Dreamer achieve remarkable successes in complex tasks. Recent work leverages Large Language Models (LLMs) as general world simulators to simulate the dynamics of the world due to their generalizability. LLMs also serve as the world model for delib... | {
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2411.08798 | Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity
and Directional Convergence | [
"cs.LG",
"cs.NE",
"math.AG"
] | This work focuses on the gradient flow dynamics of a neural network model that uses correlation loss to approximate a multi-index function on high-dimensional standard Gaussian data. Specifically, the multi-index function we consider is a sum of neurons $f^*(x) \!=\! \sum_{j=1}^k \! \sigma^*(v_j^T x)$ where $v_1, \dots... | {
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2411.08800 | Deep Learning Accelerated Quantum Transport Simulations in
Nanoelectronics: From Break Junctions to Field-Effect Transistors | [
"cond-mat.mes-hall",
"cond-mat.mtrl-sci",
"cs.LG"
] | Quantum transport calculations are essential for understanding and designing nanoelectronic devices, yet the trade-off between accuracy and computational efficiency has long limited their practical applications. We present a general framework that combines the deep learning tight-binding Hamiltonian (DeePTB) approach w... | {
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2411.08804 | FinRobot: AI Agent for Equity Research and Valuation with Large Language
Models | [
"q-fin.CP",
"cs.LG",
"q-fin.ST",
"q-fin.TR"
] | As financial markets grow increasingly complex, there is a rising need for automated tools that can effectively assist human analysts in equity research, particularly within sell-side research. While Generative AI (GenAI) has attracted significant attention in this field, existing AI solutions often fall short due to t... | {
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2411.08813 | Rethinking CyberSecEval: An LLM-Aided Approach to Evaluation Critique | [
"cs.AI"
] | A key development in the cybersecurity evaluations space is the work carried out by Meta, through their CyberSecEval approach. While this work is undoubtedly a useful contribution to a nascent field, there are notable features that limit its utility. Key drawbacks focus on the insecure code detection part of Meta's met... | {
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2411.08814 | Process-aware Human Activity Recognition | [
"cs.AI",
"cs.LG"
] | Humans naturally follow distinct patterns when conducting their daily activities, which are driven by established practices and processes, such as production workflows, social norms and daily routines. Human activity recognition (HAR) algorithms usually use neural networks or machine learning techniques to analyse inhe... | {
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2411.08821 | Model agnostic local variable importance for locally dependent
relationships | [
"stat.ML",
"cs.LG",
"stat.CO"
] | Global variable importance measures are commonly used to interpret the results of machine learning models. Local variable importance techniques assess how variables contribute to individual observations. Current methods typically fail to accurately reflect locally dependent relationships between variables and instead f... | {
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2411.08822 | A probabilistic reduced-order modeling framework for patient-specific
cardio-mechanical analysis | [
"cs.CE",
"cs.NA",
"math.NA"
] | Cardio-mechanical models can be used to support clinical decision-making. Unfortunately, the substantial computational effort involved in many cardiac models hinders their application in the clinic, despite the fact that they may provide valuable information. In this work, we present a probabilistic reduced-order model... | {
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2411.08832 | Offline Adaptation of Quadruped Locomotion using Diffusion Models | [
"cs.RO",
"cs.AI",
"cs.LG"
] | We present a diffusion-based approach to quadrupedal locomotion that simultaneously addresses the limitations of learning and interpolating between multiple skills and of (modes) offline adapting to new locomotion behaviours after training. This is the first framework to apply classifier-free guided diffusion to quadru... | {
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2411.08835 | Goal-oriented Semantic Communication for Robot Arm Reconstruction in
Digital Twin: Feature and Temporal Selections | [
"cs.RO",
"cs.SY",
"eess.SY"
] | As one of the most promising technologies in industry, the Digital Twin (DT) facilitates real-time monitoring and predictive analysis for real-world systems by precisely reconstructing virtual replicas of physical entities. However, this reconstruction faces unprecedented challenges due to the everincreasing communicat... | {
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2411.08840 | Multimodal Instruction Tuning with Hybrid State Space Models | [
"cs.CV"
] | Handling lengthy context is crucial for enhancing the recognition and understanding capabilities of multimodal large language models (MLLMs) in applications such as processing high-resolution images or high frame rate videos. The rise in image resolution and frame rate substantially increases computational demands due ... | {
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2411.08842 | AstroM$^3$: A self-supervised multimodal model for astronomy | [
"astro-ph.IM",
"cs.AI"
] | While machine-learned models are now routinely employed to facilitate astronomical inquiry, model inputs tend to be limited to a primary data source (namely images or time series) and, in the more advanced approaches, some metadata. Yet with the growing use of wide-field, multiplexed observational resources, individual... | {
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2411.08843 | Data-driven Surface Solar Irradiance Estimation using Neural Operators
at Global Scale | [
"physics.ao-ph",
"cs.AI"
] | Accurate surface solar irradiance (SSI) forecasting is essential for optimizing renewable energy systems, particularly in the context of long-term energy planning on a global scale. This paper presents a pioneering approach to solar radiation forecasting that leverages recent advancements in numerical weather predictio... | {
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2411.08849 | Oblique Bayesian additive regression trees | [
"stat.ML",
"cs.LG"
] | Current implementations of Bayesian Additive Regression Trees (BART) are based on axis-aligned decision rules that recursively partition the feature space using a single feature at a time. Several authors have demonstrated that oblique trees, whose decision rules are based on linear combinations of features, can someti... | {
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2411.08851 | Experience-based Subproblem Planning for Multi-Robot Motion Planning | [
"cs.RO"
] | Multi-robot systems enhance efficiency and productivity across various applications, from manufacturing to surveillance. While single-robot motion planning has improved by using databases of prior solutions, extending this approach to multi-robot motion planning (MRMP) presents challenges due to the increased complexit... | {
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2411.08861 | Interaction Testing in Variation Analysis | [
"stat.ME",
"cs.AI",
"cs.LG",
"math.ST",
"stat.TH"
] | Relationships of cause and effect are of prime importance for explaining scientific phenomena. Often, rather than just understanding the effects of causes, researchers also wish to understand how a cause $X$ affects an outcome $Y$ mechanistically -- i.e., what are the causal pathways that are activated between $X$ and ... | {
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2411.08862 | LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs | [
"cs.LG",
"cs.CR"
] | We introduce LLMStinger, a novel approach that leverages Large Language Models (LLMs) to automatically generate adversarial suffixes for jailbreak attacks. Unlike traditional methods, which require complex prompt engineering or white-box access, LLMStinger uses a reinforcement learning (RL) loop to fine-tune an attacke... | {
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2411.08867 | Unsupervised Parameter-free Outlier Detection using HDBSCAN* Outlier
Profiles | [
"cs.LG"
] | In machine learning and data mining, outliers are data points that significantly differ from the dataset and often introduce irrelevant information that can induce bias in its statistics and models. Therefore, unsupervised methods are crucial to detect outliers if there is limited or no information about them. Global-L... | {
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2411.08868 | CamemBERT 2.0: A Smarter French Language Model Aged to Perfection | [
"cs.CL"
] | French language models, such as CamemBERT, have been widely adopted across industries for natural language processing (NLP) tasks, with models like CamemBERT seeing over 4 million downloads per month. However, these models face challenges due to temporal concept drift, where outdated training data leads to a decline in... | {
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2411.08870 | The Limited Impact of Medical Adaptation of Large Language and
Vision-Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Several recent works seek to develop foundation models specifically for medical applications, adapting general-purpose large language models (LLMs) and vision-language models (VLMs) via continued pretraining on publicly available biomedical corpora. These works typically claim that such domain-adaptive pretraining (DAP... | {
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2411.08872 | Large Wireless Model (LWM): A Foundation Model for Wireless Channels | [
"cs.IT",
"eess.SP",
"math.IT"
] | This paper presents the Large Wireless Model (LWM) -- the world's first foundation model for wireless channels. Designed as a task-agnostic model, LWM generates universal, rich, contextualized channel embeddings (features) that potentially enhance performance across a wide range of downstream tasks in wireless communic... | {
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2411.08874 | A Decidable Case of Query Determinacy: Project-Select Views | [
"cs.DB"
] | Query determinacy is decidable for project-select views and a project-select-join query with no self joins, as long as the selection predicates are in a first-order theory for which satisfiability is decidable. | {
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2411.08875 | Causal Explanations for Image Classifiers | [
"cs.AI"
] | Existing algorithms for explaining the output of image classifiers use different definitions of explanations and a variety of techniques to extract them. However, none of the existing tools use a principled approach based on formal definitions of causes and explanations for the explanation extraction. In this paper we ... | {
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2411.08878 | A Short Note on Evaluating RepNet for Temporal Repetition Counting in
Videos | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We discuss some consistent issues on how RepNet has been evaluated in various papers. As a way to mitigate these issues, we report RepNet performance results on different datasets, and release evaluation code and the RepNet checkpoint to obtain these results. Code URL: https://github.com/google-research/google-research... | {
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2411.08879 | 4D Gaussian Splatting in the Wild with Uncertainty-Aware Regularization | [
"cs.CV",
"cs.AI"
] | Novel view synthesis of dynamic scenes is becoming important in various applications, including augmented and virtual reality. We propose a novel 4D Gaussian Splatting (4DGS) algorithm for dynamic scenes from casually recorded monocular videos. To overcome the overfitting problem of existing work for these real-world v... | {
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2411.08881 | Can We Trust AI Agents? An Experimental Study Towards Trustworthy
LLM-Based Multi-Agent Systems for AI Ethics | [
"cs.CY",
"cs.AI"
] | AI-based systems, including Large Language Models (LLMs), impact millions by supporting diverse tasks but face issues like misinformation, bias, and misuse. Ethical AI development is crucial as new technologies and concerns emerge, but objective, practical ethical guidance remains debated. This study examines LLMs in d... | {
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2411.08882 | A Novel Multimodal System to Predict Agitation in People with Dementia
Within Clinical Settings: A Proof of Concept | [
"cs.MM",
"cs.AI",
"cs.CV"
] | Dementia is a neurodegenerative condition that combines several diseases and impacts millions around the world and those around them. Although cognitive impairment is profoundly disabling, it is the noncognitive features of dementia, referred to as Neuropsychiatric Symptoms (NPS), that are most closely associated with ... | {
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2411.08883 | KisanQRS: A Deep Learning-based Automated Query-Response System for
Agricultural Decision-Making | [
"cs.IR",
"cs.AI"
] | Delivering prompt information and guidance to farmers is critical in agricultural decision-making. Farmers helpline centres are heavily reliant on the expertise and availability of call centre agents, leading to inconsistent quality and delayed responses. To this end, this article presents Kisan Query Response System (... | {
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2411.08884 | Quantifying Risk Propensities of Large Language Models: Ethical Focus
and Bias Detection through Role-Play | [
"cs.CY",
"cs.AI",
"cs.CL"
] | As Large Language Models (LLMs) become more prevalent, concerns about their safety, ethics, and potential biases have risen. Systematically evaluating LLMs' risk decision-making tendencies and attitudes, particularly in the ethical domain, has become crucial. This study innovatively applies the Domain-Specific Risk-Tak... | {
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2411.08885 | Enhancing Lie Detection Accuracy: A Comparative Study of Classic ML,
CNN, and GCN Models using Audio-Visual Features | [
"cs.MM",
"cs.AI",
"cs.CV",
"cs.SD",
"eess.AS"
] | Inaccuracies in polygraph tests often lead to wrongful convictions, false information, and bias, all of which have significant consequences for both legal and political systems. Recently, analyzing facial micro-expressions has emerged as a method for detecting deception; however, current models have not reached high ac... | {
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2411.08886 | Network scaling and scale-driven loss balancing for intelligent
poroelastography | [
"eess.SP",
"cs.LG"
] | A deep learning framework is developed for multiscale characterization of poroelastic media from full waveform data which is known as poroelastography. Special attention is paid to heterogeneous environments whose multiphase properties may drastically change across several scales. Described in space-frequency, the data... | {
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2411.08887 | Deep Learning-Based CKM Construction with Image Super-Resolution | [
"eess.SP",
"cs.AI"
] | Channel knowledge map (CKM) is a novel technique for achieving environment awareness, and thereby improving the communication and sensing performance for wireless systems. A fundamental problem associated with CKM is how to construct a complete CKM that provides channel knowledge for a large number of locations based s... | {
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2411.08888 | Exploring Capabilities of Time Series Foundation Models in Building
Analytics | [
"cs.CY",
"cs.AI"
] | The growing integration of digitized infrastructure with Internet of Things (IoT) networks has transformed the management and optimization of building energy consumption. By leveraging IoT-based monitoring systems, stakeholders such as building managers, energy suppliers, and policymakers can make data-driven decisions... | {
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2411.08889 | Multilingual Standalone Trustworthy Voice-Based Social Network for
Disaster Situations | [
"cs.HC",
"cs.AI",
"cs.SD",
"eess.AS"
] | In disaster scenarios, effective communication is crucial, yet language barriers often hinder timely and accurate information dissemination, exacerbating vulnerabilities and complicating response efforts. This paper presents a novel, multilingual, voice-based social network specifically designed to address these challe... | {
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2411.08890 | Spotlight Session on Autonomous Weapons Systems at ICRC 34th
International Conference | [
"cs.CY",
"cs.AI"
] | Autonomous weapons systems (AWS) change the way humans make decisions, the effect of those decisions and who is accountable for decisions made. We must remain vigilant, informed and human-centred as we tackle our deliberations on developing norms regarding their development, use and justification. Ways to enhance compl... | {
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2411.08891 | Calibrated Decision-Making through LLM-Assisted Retrieval | [
"cs.IR",
"cs.AI"
] | Recently, large language models (LLMs) have been increasingly used to support various decision-making tasks, assisting humans in making informed decisions. However, when LLMs confidently provide incorrect information, it can lead humans to make suboptimal decisions. To prevent LLMs from generating incorrect information... | {
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2411.08892 | Auto-assessment of assessment: A conceptual framework towards fulfilling
the policy gaps in academic assessment practices | [
"cs.CY",
"cs.AI"
] | Education is being transformed by rapid advances in Artificial Intelligence (AI), including emerging Generative Artificial Intelligence (GAI). Such technology can significantly support academics and students by automating monotonous tasks and making personalised suggestions. However, despite the potential of the techno... | {
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2411.08894 | Temporal Patterns of Multiple Long-Term Conditions in Individuals with
Intellectual Disability Living in Wales: An Unsupervised Clustering Approach
to Disease Trajectories | [
"cs.CY",
"cs.AI",
"stat.AP"
] | Identifying and understanding the co-occurrence of multiple long-term conditions (MLTC) in individuals with intellectual disabilities (ID) is vital for effective healthcare management. These individuals often face earlier onset and higher prevalence of MLTCs, yet specific co-occurrence patterns remain unexplored. This ... | {
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2411.08895 | Performance-Complexity-Latency Trade-offs of Concatenated RS-SDBCH Codes | [
"cs.IT",
"math.IT"
] | Concatenated bit-interleaved and multilevel coded modulation with outer Reed--Solomon codes, inner Chase-algorithm-based soft-decision-decoded Bose--Ray-Chaudhuri--Hocquenghem codes, and four-level pulse amplitude modulation is considered. A semi-analytical formula is derived for estimating the decoded frame error rate... | {
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2411.08896 | Demand-Aware Beam Hopping and Power Allocation for Load Balancing in
Digital Twin empowered LEO Satellite Networks | [
"eess.SP",
"cs.LG",
"cs.NI"
] | Low-Earth orbit (LEO) satellites utilizing beam hopping (BH) technology offer extensive coverage, low latency, high bandwidth, and significant flexibility. However, the uneven geographical distribution and temporal variability of ground traffic demands, combined with the high mobility of LEO satellites, present signifi... | {
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2411.08897 | Comment on Is Complexity an Illusion? | [
"cs.AI"
] | The paper "Is Complexity an Illusion?" (Bennett, 2024) provides a formalism for complexity, learning, inference, and generalization, and introduces a formal definition for a "policy". This reply shows that correct policies do not exist for a simple task of supervised multi-class classification, via mathematical proof a... | {
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2411.08899 | FinVision: A Multi-Agent Framework for Stock Market Prediction | [
"q-fin.TR",
"cs.AI"
] | Financial trading has been a challenging task, as it requires the integration of vast amounts of data from various modalities. Traditional deep learning and reinforcement learning methods require large training data and often involve encoding various data types into numerical formats for model input, which limits the e... | {
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2411.08900 | RNA-GPT: Multimodal Generative System for RNA Sequence Understanding | [
"q-bio.GN",
"cs.AI",
"cs.CE",
"cs.LG",
"q-bio.BM"
] | RNAs are essential molecules that carry genetic information vital for life, with profound implications for drug development and biotechnology. Despite this importance, RNA research is often hindered by the vast literature available on the topic. To streamline this process, we introduce RNA-GPT, a multi-modal RNA chat m... | {
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2411.08901 | SoccerGuard: Investigating Injury Risk Factors for Professional Soccer
Players with Machine Learning | [
"cs.HC",
"cs.LG"
] | We present SoccerGuard, a novel framework for predicting injuries in women's soccer using Machine Learning (ML). This framework can ingest data from multiple sources, including subjective wellness and training load reports from players, objective GPS sensor measurements, third-party player statistics, and injury report... | {
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2411.08903 | Turkey's Earthquakes: Damage Prediction and Feature Significance Using A
Multivariate Analysis | [
"physics.geo-ph",
"cs.LG"
] | Accurate damage prediction is crucial for disaster preparedness and response strategies, particularly given the frequent earthquakes in Turkey. Utilizing datasets on earthquake data, infrastructural quality metrics, and contemporary socioeconomic factors, we tested various machine-learning architectures to forecast dea... | {
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2411.08905 | Synthesis Method for Obtaining Characteristic Modes of Multi-Structure
Systems | [
"cs.CE",
"cs.NA",
"math.NA"
] | This paper introduces an efficient method of characteristic mode decomposition for multi-structure systems. Our approach leverages the translation and rotation matrices associated with vector spherical wavefunctions, enabling the synthesis of a total system's characteristic modes through independent simulation of each ... | {
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2411.08906 | Assessing the Auditability of AI-integrating Systems: A Framework and
Learning Analytics Case Study | [
"cs.CY",
"cs.AI"
] | Audits contribute to the trustworthiness of Learning Analytics (LA) systems that integrate Artificial Intelligence (AI) and may be legally required in the future. We argue that the efficacy of an audit depends on the auditability of the audited system. Therefore, systems need to be designed with auditability in mind. W... | {
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2411.08907 | From Simulators to Digital Twins for Enabling Emerging Cellular
Networks: A Tutorial and Survey | [
"cs.NI",
"cs.SY",
"eess.SY"
] | Simulators are indispensable parts of the research and development necessary to advance countless industries, including cellular networks. With simulators, the evaluation, analysis, testing, and experimentation of novel designs and algorithms can be executed in a more cost-effective and convenient manner without the ri... | {
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2411.08909 | Long-context Protein Language Model | [
"q-bio.BM",
"cs.LG"
] | Self-supervised training of language models (LMs) has seen great success for protein sequences in learning meaningful representations and for generative drug design. Most protein LMs are based on the Transformer architecture trained on individual proteins with short context lengths. Such protein LMs cannot extrapolate ... | {
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2411.08910 | Automated Feedback in Math Education: A Comparative Analysis of LLMs for
Open-Ended Responses | [
"cs.CY",
"cs.AI",
"cs.LG"
] | The effectiveness of feedback in enhancing learning outcomes is well documented within Educational Data Mining (EDM). Various prior research has explored methodologies to enhance the effectiveness of feedback. Recent developments in Large Language Models (LLMs) have extended their utility in enhancing automated feedbac... | {
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2411.08911 | A Message Passing Neural Network Surrogate Model for Bond-Associated
Peridynamic Material Correspondence Formulation | [
"physics.comp-ph",
"cond-mat.mtrl-sci",
"cs.LG",
"stat.ML"
] | Peridynamics is a non-local continuum mechanics theory that offers unique advantages for modeling problems involving discontinuities and complex deformations. Within the peridynamic framework, various formulations exist, among which the material correspondence formulation stands out for its ability to directly incorpor... | {
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2411.08916 | Enhanced Secure Transmission of Medical Images through OFDM using
Hyperchaotic Systems | [
"cs.IT",
"cs.CR",
"eess.IV",
"math.IT"
] | Orthogonal Frequency Division Multiplexing (OFDM) is a popular modulation technique for transmitting digital data over wireless radio channels, including medical images due to its high transmission capacity, low interference, bandwidth efficiency, and scalability. However, the security of medical images is a major conc... | {
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2411.08918 | Wireless Federated Learning over UAV-enabled Integrated Sensing and
Communication | [
"cs.IT",
"cs.AI",
"cs.NI",
"math.IT"
] | This paper studies a new latency optimization problem in unmanned aerial vehicles (UAVs)-enabled federated learning (FL) with integrated sensing and communication. In this setup, distributed UAVs participate in model training using sensed data and collaborate with a base station (BS) serving as FL aggregator to build a... | {
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2411.08919 | A Machine Learning based Hybrid Receiver for 5G NR PRACH | [
"eess.SP",
"cs.AI",
"cs.IT",
"cs.LG",
"math.IT"
] | Random Access is a critical procedure using which a User Equipment (UE) identifies itself to a Base Station (BS). Random Access starts with the UE transmitting a random preamble on the Physical Random Access Channel (PRACH). In a conventional BS receiver, the UE's specific preamble is identified by correlation with all... | {
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2411.08923 | Aligning Visual Contrastive learning models via Preference Optimization | [
"cs.CV",
"cs.LG"
] | Contrastive learning models have demonstrated impressive abilities to capture semantic similarities by aligning representations in the embedding space. However, their performance can be limited by the quality of the training data and its inherent biases. While Reinforcement Learning from Human Feedback (RLHF) and Direc... | {
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2411.08924 | The Geometry of Codes for Random Access in DNA Storage | [
"cs.IT",
"math.CO",
"math.IT"
] | Effective and reliable data retrieval is critical for the feasibility of DNA storage, and the development of random access efficiency plays a key role in its practicality and reliability. In this paper, we study the Random Access Problem, which asks to compute the expected number of samples one needs in order to recove... | {
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2411.08925 | Retrieval of sun-induced plant fluorescence in the O$_2$-A absorption
band from DESIS imagery | [
"cs.CV",
"cs.AI",
"physics.geo-ph"
] | We provide the first method allowing to retrieve spaceborne SIF maps at 30 m ground resolution with a strong correlation ($r^2=0.6$) to high-quality airborne estimates of sun-induced fluorescence (SIF). SIF estimates can provide explanatory information for many tasks related to agricultural management and physiological... | {
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2411.08926 | DG-PPU: Dynamical Graphs based Post-processing of Point Clouds extracted
from Knee Ultrasounds | [
"eess.IV",
"cs.CV"
] | Patients undergoing total knee arthroplasty (TKA) often experience non-specific anterior knee pain, arising from abnormal patellofemoral joint (PFJ) instability. Tracking PFJ motion is challenging since static imaging modalities like CT and MRI are limited by field of view and metal artefact interference. Ultrasounds o... | {
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2411.08930 | Structured Pattern Expansion with Diffusion Models | [
"cs.CV",
"cs.GR"
] | Recent advances in diffusion models have significantly improved the synthesis of materials, textures, and 3D shapes. By conditioning these models via text or images, users can guide the generation, reducing the time required to create digital assets. In this paper, we address the synthesis of structured, stationary pat... | {
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2411.08932 | PyGen: A Collaborative Human-AI Approach to Python Package Creation | [
"cs.SE",
"cs.AI"
] | The principles of automation and innovation serve as foundational elements for advancement in contemporary science and technology. Here, we introduce Pygen, an automation platform designed to empower researchers, technologists, and hobbyists to bring abstract ideas to life as core, usable software tools written in Pyth... | {
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2411.08933 | Confidence-aware Denoised Fine-tuning of Off-the-shelf Models for
Certified Robustness | [
"cs.CV",
"cs.AI",
"cs.CR",
"cs.LG"
] | The remarkable advances in deep learning have led to the emergence of many off-the-shelf classifiers, e.g., large pre-trained models. However, since they are typically trained on clean data, they remain vulnerable to adversarial attacks. Despite this vulnerability, their superior performance and transferability make of... | {
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2411.08934 | Predicting household socioeconomic position in Mozambique using
satellite and household imagery | [
"cs.CV",
"cs.LG"
] | Many studies have predicted SocioEconomic Position (SEP) for aggregated spatial units such as villages using satellite data, but SEP prediction at the household level and other sources of imagery have not been yet explored. We assembled a dataset of 975 households in a semi-rural district in southern Mozambique, consis... | {
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2411.08935 | Classification of Keratitis from Eye Corneal Photographs using Deep
Learning | [
"cs.CV",
"cs.LG"
] | Keratitis is an inflammatory corneal condition responsible for 10% of visual impairment in low- and middle-income countries (LMICs), with bacteria, fungi, or amoeba as the most common infection etiologies. While an accurate and timely diagnosis is crucial for the selected treatment and the patients' sight outcomes, due... | {
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2411.08936 | Clustered Patch Embeddings for Permutation-Invariant Classification of
Whole Slide Images | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Whole Slide Imaging (WSI) is a cornerstone of digital pathology, offering detailed insights critical for diagnosis and research. Yet, the gigapixel size of WSIs imposes significant computational challenges, limiting their practical utility. Our novel approach addresses these challenges by leveraging various encoders fo... | {
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2411.08937 | Dual-Head Knowledge Distillation: Enhancing Logits Utilization with an
Auxiliary Head | [
"cs.CV",
"cs.LG"
] | Traditional knowledge distillation focuses on aligning the student's predicted probabilities with both ground-truth labels and the teacher's predicted probabilities. However, the transition to predicted probabilities from logits would obscure certain indispensable information. To address this issue, it is intuitive to ... | {
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2411.08954 | Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply
Better Samples | [
"cs.LG",
"cs.AI"
] | Although diffusion models can generate remarkably high-quality samples, they are intrinsically bottlenecked by their expensive iterative sampling procedure. Consistency models (CMs) have recently emerged as a promising diffusion model distillation method, reducing the cost of sampling by generating high-fidelity sample... | {
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2411.08968 | Sparse Upcycling: Inference Inefficient Finetuning | [
"cs.LG",
"cs.CL"
] | Small, highly trained, open-source large language models are widely used due to their inference efficiency, but further improving their quality remains a challenge. Sparse upcycling is a promising approach that transforms a pretrained dense model into a Mixture-of-Experts (MoE) architecture, increasing the model's para... | {
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2411.08975 | Fluoroformer: Scaling multiple instance learning to multiplexed images
via attention-based channel fusion | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Though multiple instance learning (MIL) has been a foundational strategy in computational pathology for processing whole slide images (WSIs), current approaches are designed for traditional hematoxylin and eosin (H&E) slides rather than emerging multiplexed technologies. Here, we present an MIL strategy, the Fluoroform... | {
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2411.08977 | Robustness and Confounders in the Demographic Alignment of LLMs with
Human Perceptions of Offensiveness | [
"cs.CY",
"cs.CL"
] | Large language models (LLMs) are known to exhibit demographic biases, yet few studies systematically evaluate these biases across multiple datasets or account for confounding factors. In this work, we examine LLM alignment with human annotations in five offensive language datasets, comprising approximately 220K annotat... | {
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2411.08979 | CoCoP: Enhancing Text Classification with LLM through Code Completion
Prompt | [
"cs.CL",
"cs.AI"
] | Text classification is a fundamental task in natural language processing (NLP), and large language models (LLMs) have demonstrated their capability to perform this task across various domains. However, the performance of LLMs heavily depends on the quality of their input prompts. Recent studies have also shown that LLM... | {
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} |
2411.08981 | Reliability, Resilience and Human Factors Engineering for Trustworthy AI
Systems | [
"cs.AI",
"cs.SY",
"eess.SY"
] | As AI systems become integral to critical operations across industries and services, ensuring their reliability and safety is essential. We offer a framework that integrates established reliability and resilience engineering principles into AI systems. By applying traditional metrics such as failure rate and Mean Time ... | {
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} |
2411.08982 | Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware
Expert Selection | [
"cs.LG",
"cs.DC"
] | Mixture-of-Experts (MoE) architectures have recently gained popularity in enabling efficient scaling of large language models. However, we uncover a fundamental tension: while MoEs are designed for selective expert activation, production serving requires request batching, which forces the activation of all experts and ... | {
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} |
2411.08987 | Non-Euclidean High-Order Smooth Convex Optimization | [
"math.OC",
"cs.DS",
"cs.LG",
"stat.ML"
] | We develop algorithms for the optimization of convex objectives that have H\"older continuous $q$-th derivatives by using a $q$-th order oracle, for any $q \geq 1$. Our algorithms work for general norms under mild conditions, including the $\ell_p$-settings for $1\leq p\leq \infty$. We can also optimize structured func... | {
"Other": 1,
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} |
2411.08992 | IDCIA: Immunocytochemistry Dataset for Cellular Image Analysis | [
"eess.IV",
"cs.AI",
"cs.CV"
] | We present a new annotated microscopic cellular image dataset to improve the effectiveness of machine learning methods for cellular image analysis. Cell counting is an important step in cell analysis. Typically, domain experts manually count cells in a microscopic image. Automated cell counting can potentially eliminat... | {
"Other": 0,
"cs.AI": 1,
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"cs.SD": 0,
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} |
2411.08993 | Parameter Inference via Differentiable Diffusion Bridge Importance
Sampling | [
"stat.ML",
"cs.LG"
] | We introduce a methodology for performing parameter inference in high-dimensional, non-linear diffusion processes. We illustrate its applicability for obtaining insights into the evolution of and relationships between species, including ancestral state reconstruction. Estimation is performed by utilising score matching... | {
"Other": 0,
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} |
2411.08995 | Computed tomography using meta-optics | [
"cs.CV",
"physics.optics"
] | Computer vision tasks require processing large amounts of data to perform image classification, segmentation, and feature extraction. Optical preprocessors can potentially reduce the number of floating point operations required by computer vision tasks, enabling low-power and low-latency operation. However, existing op... | {
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} |
2411.08998 | Microfoundation Inference for Strategic Prediction | [
"stat.ML",
"cs.LG",
"stat.ME"
] | Often in prediction tasks, the predictive model itself can influence the distribution of the target variable, a phenomenon termed performative prediction. Generally, this influence stems from strategic actions taken by stakeholders with a vested interest in predictive models. A key challenge that hinders the widespread... | {
"Other": 0,
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} |
2411.08999 | Learning-Based Control Barrier Function with Provably Safe Guarantees:
Reducing Conservatism with Heading-Aware Safety Margin | [
"cs.RO",
"cs.MA",
"cs.SY",
"eess.SY"
] | We propose a learning-based Control Barrier Function (CBF) to reduce conservatism in collision avoidance of car-like robots. Traditional CBFs often use Euclidean distance between robots' centers as safety margin, neglecting headings and simplifying geometries to circles. While this ensures smooth, differentiable safety... | {
"Other": 0,
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"cs.MA": 1,
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"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2411.09003 | Refusal in LLMs is an Affine Function | [
"cs.LG",
"cs.CL"
] | We propose affine concept editing (ACE) as an approach for steering language models' behavior by intervening directly in activations. We begin with an affine decomposition of model activation vectors and show that prior methods for steering model behavior correspond to subsets of terms of this decomposition. We then pr... | {
"Other": 0,
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} |
2411.09004 | The geometry of the deep linear network | [
"cs.NE",
"math.DS",
"math.PR",
"nlin.AO"
] | This article provides an expository account of training dynamics in the Deep Linear Network (DLN) from the perspective of the geometric theory of dynamical systems. Rigorous results by several authors are unified into a thermodynamic framework for deep learning. The analysis begins with a characterization of the inva... | {
"Other": 0,
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} |
2411.09007 | Scale Contrastive Learning with Selective Attentions for Blind Image
Quality Assessment | [
"cs.CV"
] | Blind image quality assessment (BIQA) serves as a fundamental task in computer vision, yet it often fails to consistently align with human subjective perception. Recent advances show that multi-scale evaluation strategies are promising due to their ability to replicate the hierarchical structure of human vision. Howeve... | {
"Other": 0,
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} |
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