id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.07121 | Decoding Visual Experience and Mapping Semantics through Whole-Brain
Analysis Using fMRI Foundation Models | [
"cs.CV"
] | Neural decoding, the process of understanding how brain activity corresponds to different stimuli, has been a primary objective in cognitive sciences. Over the past three decades, advancements in functional Magnetic Resonance Imaging and machine learning have greatly improved our ability to map visual stimuli to brain ... | {
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2411.07122 | SCAR: Sparse Conditioned Autoencoders for Concept Detection and Steering
in LLMs | [
"cs.CL"
] | Large Language Models (LLMs) have demonstrated remarkable capabilities in generating human-like text, but their output may not be aligned with the user or even produce harmful content. This paper presents a novel approach to detect and steer concepts such as toxicity before generation. We introduce the Sparse Condition... | {
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2411.07123 | Fast and Robust Contextual Node Representation Learning over Dynamic
Graphs | [
"cs.LG",
"cs.AI"
] | Real-world graphs grow rapidly with edge and vertex insertions over time, motivating the problem of efficiently maintaining robust node representation over evolving graphs. Recent efficient GNNs are designed to decouple recursive message passing from the learning process, and favor Personalized PageRank (PPR) as the un... | {
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2411.07126 | Edify Image: High-Quality Image Generation with Pixel Space Laplacian
Diffusion Models | [
"cs.CV",
"cs.LG"
] | We introduce Edify Image, a family of diffusion models capable of generating photorealistic image content with pixel-perfect accuracy. Edify Image utilizes cascaded pixel-space diffusion models trained using a novel Laplacian diffusion process, in which image signals at different frequency bands are attenuated at varyi... | {
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2411.07127 | Benchmarking LLMs' Judgments with No Gold Standard | [
"cs.CL",
"cs.LG"
] | We introduce the GEM (Generative Estimator for Mutual Information), an evaluation metric for assessing language generation by Large Language Models (LLMs), particularly in generating informative judgments, without the need for a gold standard reference. GEM broadens the scenarios where we can benchmark LLM generation p... | {
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2411.07130 | Retrieval or Global Context Understanding? On Many-Shot In-Context
Learning for Long-Context Evaluation | [
"cs.CL"
] | Language models (LMs) have demonstrated an improved capacity to handle long-context information, yet existing long-context benchmarks primarily measure LMs' retrieval abilities with extended inputs, e.g., pinpointing a short phrase from long-form text. Therefore, they may fall short when evaluating models' global conte... | {
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2411.07132 | Token Merging for Training-Free Semantic Binding in Text-to-Image
Synthesis | [
"cs.CV",
"cs.AI"
] | Although text-to-image (T2I) models exhibit remarkable generation capabilities, they frequently fail to accurately bind semantically related objects or attributes in the input prompts; a challenge termed semantic binding. Previous approaches either involve intensive fine-tuning of the entire T2I model or require users ... | {
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2411.07133 | Stronger Models are NOT Stronger Teachers for Instruction Tuning | [
"cs.AI",
"cs.CL"
] | Instruction tuning has been widely adopted to ensure large language models (LLMs) follow user instructions effectively. The resulting instruction-following capabilities of LLMs heavily rely on the instruction datasets used for tuning. Recently, synthetic instruction datasets have emerged as an economically viable solut... | {
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2411.07135 | Edify 3D: Scalable High-Quality 3D Asset Generation | [
"cs.CV",
"cs.AI",
"cs.GR"
] | We introduce Edify 3D, an advanced solution designed for high-quality 3D asset generation. Our method first synthesizes RGB and surface normal images of the described object at multiple viewpoints using a diffusion model. The multi-view observations are then used to reconstruct the shape, texture, and PBR materials of ... | {
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2411.07138 | Nuremberg Letterbooks: A Multi-Transcriptional Dataset of Early 15th
Century Manuscripts for Document Analysis | [
"cs.CV"
] | Most datasets in the field of document analysis utilize highly standardized labels, which, while simplifying specific tasks, often produce outputs that are not directly applicable to humanities research. In contrast, the Nuremberg Letterbooks dataset, which comprises historical documents from the early 15th century, ad... | {
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2411.07140 | Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language
Models | [
"cs.CL"
] | New LLM evaluation benchmarks are important to align with the rapid development of Large Language Models (LLMs). In this work, we present Chinese SimpleQA, the first comprehensive Chinese benchmark to evaluate the factuality ability of language models to answer short questions, and Chinese SimpleQA mainly has five prop... | {
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2411.07142 | Greenback Bears and Fiscal Hawks: Finance is a Jungle and Text
Embeddings Must Adapt | [
"cs.CL"
] | Financial documents are filled with specialized terminology, arcane jargon, and curious acronyms that pose challenges for general-purpose text embeddings. Yet, few text embeddings specialized for finance have been reported in the literature, perhaps in part due to a lack of public datasets and benchmarks. We present BA... | {
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2411.07146 | Lost in Tracking Translation: A Comprehensive Analysis of Visual SLAM in
Human-Centered XR and IoT Ecosystems | [
"cs.RO",
"cs.CV"
] | Advancements in tracking algorithms have empowered nascent applications across various domains, from steering autonomous vehicles to guiding robots to enhancing augmented reality experiences for users. However, these algorithms are application-specific and do not work across applications with different types of motion;... | {
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2411.07150 | Variational Graph Contrastive Learning | [
"cs.LG",
"cs.AI"
] | Graph representation learning (GRL) is a fundamental task in machine learning, aiming to encode high-dimensional graph-structured data into low-dimensional vectors. Self-supervised learning (SSL) methods are widely used in GRL because they can avoid expensive human annotation. In this work, we propose a novel Subgraph ... | {
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2411.07152 | HierTOD: A Task-Oriented Dialogue System Driven by Hierarchical Goals | [
"cs.CL",
"cs.AI"
] | Task-Oriented Dialogue (TOD) systems assist users in completing tasks through natural language interactions, often relying on a single-layered workflow structure for slot-filling in public tasks, such as hotel bookings. However, in enterprise environments, which involve rich domain-specific knowledge, TOD systems face ... | {
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2411.07154 | Conditional simulation via entropic optimal transport: Toward
non-parametric estimation of conditional Brenier maps | [
"stat.ML",
"cs.LG",
"math.OC"
] | Conditional simulation is a fundamental task in statistical modeling: Generate samples from the conditionals given finitely many data points from a joint distribution. One promising approach is to construct conditional Brenier maps, where the components of the map pushforward a reference distribution to conditionals of... | {
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2411.07156 | A Primer on Word Embeddings: AI Techniques for Text Analysis in Social
Work | [
"cs.CL"
] | Word embeddings represent a transformative technology for analyzing text data in social work research, offering sophisticated tools for understanding case notes, policy documents, research literature, and other text-based materials. This methodological paper introduces word embeddings to social work researchers, explai... | {
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2411.07160 | An Efficient Error Estimation Method in Quantum Key Distribution | [
"quant-ph",
"cs.IT",
"math.IT"
] | Error estimation is an important step for error correction in quantum key distribution. Traditional error estimation methods require sacrificing a part of the sifted key, forcing a trade-off between the accuracy of error estimation and the size of the partial sifted key to be used and discarded. In this paper, we propo... | {
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2411.07161 | RoundTable: Investigating Group Decision-Making Mechanism in Multi-Agent
Collaboration | [
"cs.MA",
"cs.AI"
] | This study investigates the efficacy of Multi-Agent Systems in eliciting cross-agent communication and enhancing collective intelligence through group decision-making in a decentralized setting. Unlike centralized mechanisms, where a fixed hierarchy governs social choice, decentralized group decision-making allows agen... | {
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2411.07163 | A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis:
Evaluating Mental Health Sentiment on Social Media during COVID-19 | [
"cs.AI"
] | Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based, data-driven neural network-based approaches can miss newly relevant content due to the evolving nature of language in a dynamically evolving environment. Human-cu... | {
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2411.07165 | Acoustic-based 3D Human Pose Estimation Robust to Human Position | [
"cs.SD",
"cs.AI",
"cs.CV",
"cs.LG",
"cs.RO"
] | This paper explores the problem of 3D human pose estimation from only low-level acoustic signals. The existing active acoustic sensing-based approach for 3D human pose estimation implicitly assumes that the target user is positioned along a line between loudspeakers and a microphone. Because reflection and diffraction ... | {
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2411.07166 | The Shapley index for music streaming platforms | [
"econ.TH",
"cs.GT",
"cs.IR"
] | We study an index to measure the popularity of artists in music streaming platforms. This index, which can be used to allocate the amount raised via paid subscriptions among participating artists, is based on the Shapley value, a centerpiece in cooperative game theory. We characterize this Shapley index combining sever... | {
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2411.07167 | Cascaded Dual Vision Transformer for Accurate Facial Landmark Detection | [
"cs.CV"
] | Facial landmark detection is a fundamental problem in computer vision for many downstream applications. This paper introduces a new facial landmark detector based on vision transformers, which consists of two unique designs: Dual Vision Transformer (D-ViT) and Long Skip Connections (LSC). Based on the observation that ... | {
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2411.07168 | Enhancing Predictive Maintenance in Mining Mobile Machinery through a
TinyML-enabled Hierarchical Inference Network | [
"cs.LG",
"cs.DC",
"cs.MA",
"cs.NI",
"eess.SP"
] | Mining machinery operating in variable environments faces high wear and unpredictable stress, challenging Predictive Maintenance (PdM). This paper introduces the Edge Sensor Network for Predictive Maintenance (ESN-PdM), a hierarchical inference framework across edge devices, gateways, and cloud services for real-time c... | {
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2411.07171 | Anytime Sequential Halving in Monte-Carlo Tree Search | [
"cs.LG",
"cs.AI"
] | Monte-Carlo Tree Search (MCTS) typically uses multi-armed bandit (MAB) strategies designed to minimize cumulative regret, such as UCB1, as its selection strategy. However, in the root node of the search tree, it is more sensible to minimize simple regret. Previous work has proposed using Sequential Halving as selection... | {
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2411.07175 | Continual Memorization of Factoids in Large Language Models | [
"cs.CL"
] | Large language models can absorb a massive amount of knowledge through pretraining, but pretraining is inefficient for acquiring long-tailed or specialized facts. Therefore, fine-tuning on specialized or new knowledge that reflects changes in the world has become popular, though it risks disrupting the model's original... | {
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2411.07176 | More Expressive Attention with Negative Weights | [
"cs.CL",
"cs.AI",
"cs.LG"
] | We propose a novel attention mechanism, named Cog Attention, that enables attention weights to be negative for enhanced expressiveness, which stems from two key factors: (1) Cog Attention enhances parameter flexibility. For example, unlike traditional softmax attention heads that use a static output-value (OV) matrix t... | {
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2411.07177 | Biohybrid Microrobots Based on Jellyfish Stinging Capsules and Janus
Particles for In Vitro Deep-Tissue Drug Penetration | [
"cs.RO",
"physics.bio-ph"
] | Microrobots engineered from self-propelling active particles, extend the reach of robotic operations to submillimeter dimensions and are becoming increasingly relevant for various tasks, such as manipulation of micro/nanoscale cargo, particularly targeted drug delivery. However, achieving deep-tissue penetration and dr... | {
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2411.07179 | Joint Age-State Belief is All You Need: Minimizing AoII via Pull-Based
Remote Estimation | [
"cs.IT",
"cs.LG",
"cs.NI",
"cs.SY",
"eess.SP",
"eess.SY",
"math.IT"
] | Age of incorrect information (AoII) is a recently proposed freshness and mismatch metric that penalizes an incorrect estimation along with its duration. Therefore, keeping track of AoII requires the knowledge of both the source and estimation processes. In this paper, we consider a time-slotted pull-based remote estima... | {
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2411.07180 | Gumbel Counterfactual Generation From Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Understanding and manipulating the causal generation mechanisms in language models is essential for controlling their behavior. Previous work has primarily relied on techniques such as representation surgery -- e.g., model ablations or manipulation of linear subspaces tied to specific concepts -- to \emph{intervene} on... | {
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2411.07182 | Revisiting Ensembling in One-Shot Federated Learning | [
"cs.LG",
"cs.DC"
] | Federated learning (FL) is an appealing approach to training machine learning models without sharing raw data. However, standard FL algorithms are iterative and thus induce a significant communication cost. One-shot federated learning (OFL) trades the iterative exchange of models between clients and the server with a s... | {
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2411.07183 | Probabilistic approach to feedback control enhances multi-legged
locomotion on rugged landscapes | [
"cs.RO"
] | Achieving robust legged locomotion on complex terrains poses challenges due to the high uncertainty in robot-environment interactions. Recent advances in bipedal and quadrupedal robots demonstrate good mobility on rugged terrains but rely heavily on sensors for stability due to low static stability from a high center o... | {
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2411.07184 | SAMPart3D: Segment Any Part in 3D Objects | [
"cs.CV"
] | 3D part segmentation is a crucial and challenging task in 3D perception, playing a vital role in applications such as robotics, 3D generation, and 3D editing. Recent methods harness the powerful Vision Language Models (VLMs) for 2D-to-3D knowledge distillation, achieving zero-shot 3D part segmentation. However, these m... | {
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2411.07185 | Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised
Domain Adaptation | [
"cs.LG",
"cs.AI"
] | Multi-source unsupervised domain adaptation aims to leverage labeled data from multiple source domains for training a machine learning model to generalize well on a target domain without labels. Source domain selection plays a crucial role in determining the model's performance. It relies on the similarities amongst so... | {
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2411.07186 | NatureLM-audio: an Audio-Language Foundation Model for Bioacoustics | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] | Large language models (LLMs) prompted with text and audio represent the state of the art in various auditory tasks, including speech, music, and general audio, showing emergent abilities on unseen tasks. However, these capabilities have yet to be fully demonstrated in bioacoustics tasks, such as detecting animal vocali... | {
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2411.07191 | The Super Weight in Large Language Models | [
"cs.CL",
"cs.AI"
] | Recent works have shown a surprising result: a small fraction of Large Language Model (LLM) parameter outliers are disproportionately important to the quality of the model. LLMs contain billions of parameters, so these small fractions, such as 0.01%, translate to hundreds of thousands of parameters. In this work, we pr... | {
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2411.07192 | Data-Driven Predictive Control of Nonholonomic Robots Based on a
Bilinear Koopman Realization: Data Does Not Replace Geometry | [
"eess.SY",
"cs.LG",
"cs.RO",
"cs.SY"
] | Advances in machine learning and the growing trend towards effortless data generation in real-world systems has led to an increasing interest for data-inferred models and data-based control in robotics. It seems appealing to govern robots solely based on data, bypassing the traditional, more elaborate pipeline of syste... | {
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2411.07199 | OmniEdit: Building Image Editing Generalist Models Through Specialist
Supervision | [
"cs.CV",
"cs.AI"
] | Instruction-guided image editing methods have demonstrated significant potential by training diffusion models on automatically synthesized or manually annotated image editing pairs. However, these methods remain far from practical, real-life applications. We identify three primary challenges contributing to this gap. F... | {
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2411.07200 | 'Explaining RL Decisions with Trajectories': A Reproducibility Study | [
"cs.AI"
] | This work investigates the reproducibility of the paper 'Explaining RL decisions with trajectories'. The original paper introduces a novel approach in explainable reinforcement learning based on the attribution decisions of an agent to specific clusters of trajectories encountered during training. We verify the main cl... | {
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2411.07205 | DLCR: A Generative Data Expansion Framework via Diffusion for
Clothes-Changing Person Re-ID | [
"cs.CV"
] | With the recent exhibited strength of generative diffusion models, an open research question is if images generated by these models can be used to learn better visual representations. While this generative data expansion may suffice for easier visual tasks, we explore its efficacy on a more difficult discriminative tas... | {
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2411.07207 | General Geospatial Inference with a Population Dynamics Foundation Model | [
"cs.LG",
"cs.CY"
] | Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations and researchers to understand and reason over complex relationships between human behavior and local contexts in order to identify high-risk groups and strategically allocate limited resources. Tra... | {
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2411.07213 | Comparing Bottom-Up and Top-Down Steering Approaches on In-Context
Learning Tasks | [
"cs.LG"
] | A key objective of interpretability research on large language models (LLMs) is to develop methods for robustly steering models toward desired behaviors. To this end, two distinct approaches to interpretability -- ``bottom-up" and ``top-down" -- have been presented, but there has been little quantitative comparison bet... | {
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2411.07217 | Feature Selection Based on Wasserstein Distance | [
"cs.LG"
] | This paper presents a novel feature selection method leveraging the Wasserstein distance to improve feature selection in machine learning. Unlike traditional methods based on correlation or Kullback-Leibler (KL) divergence, our approach uses the Wasserstein distance to assess feature similarity, inherently capturing cl... | {
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2411.07218 | TreeCoders: Trees of Transformers | [
"cs.CL",
"cs.AI"
] | In this paper, we introduce TreeCoders, a novel family of transformer trees. We moved away from traditional linear transformers to complete k-ary trees. Transformer blocks serve as nodes, and generic classifiers learn to select the best child and route the sequence of tokens to a specific leaf. The selectors, moved out... | {
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2411.07223 | Grounding Video Models to Actions through Goal Conditioned Exploration | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | Large video models, pretrained on massive amounts of Internet video, provide a rich source of physical knowledge about the dynamics and motions of objects and tasks. However, video models are not grounded in the embodiment of an agent, and do not describe how to actuate the world to reach the visual states depicted in ... | {
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2411.07224 | TempCharBERT: Keystroke Dynamics for Continuous Access Control Based on
Pre-trained Language Models | [
"cs.CR",
"cs.CL"
] | With the widespread of digital environments, reliable authentication and continuous access control has become crucial. It can minimize cyber attacks and prevent frauds, specially those associated with identity theft. A particular interest lies on keystroke dynamics (KD), which refers to the task of recognizing individu... | {
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2411.07228 | Tooling or Not Tooling? The Impact of Tools on Language Agents for
Chemistry Problem Solving | [
"cs.AI",
"cs.CE"
] | To enhance large language models (LLMs) for chemistry problem solving, several LLM-based agents augmented with tools have been proposed, such as ChemCrow and Coscientist. However, their evaluations are narrow in scope, leaving a large gap in understanding the benefits of tools across diverse chemistry tasks. To bridge ... | {
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2411.07229 | Learning from Limited and Imperfect Data | [
"cs.CV"
] | The datasets used for Deep Neural Network training (e.g., ImageNet, MSCOCO, etc.) are often manually balanced across categories (classes) to facilitate learning of all the categories. This curation process is often expensive and requires throwing away precious annotated data to balance the frequency across classes. Thi... | {
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2411.07231 | Watermark Anything with Localized Messages | [
"cs.CV",
"cs.CR"
] | Image watermarking methods are not tailored to handle small watermarked areas. This restricts applications in real-world scenarios where parts of the image may come from different sources or have been edited. We introduce a deep-learning model for localized image watermarking, dubbed the Watermark Anything Model (WAM).... | {
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2411.07232 | Add-it: Training-Free Object Insertion in Images With Pretrained
Diffusion Models | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] | Adding Object into images based on text instructions is a challenging task in semantic image editing, requiring a balance between preserving the original scene and seamlessly integrating the new object in a fitting location. Despite extensive efforts, existing models often struggle with this balance, particularly with ... | {
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2411.07233 | Score-based generative diffusion with "active" correlated noise sources | [
"cs.LG",
"cond-mat.dis-nn"
] | Diffusion models exhibit robust generative properties by approximating the underlying distribution of a dataset and synthesizing data by sampling from the approximated distribution. In this work, we explore how the generative performance may be be modulated if noise sources with temporal correlations -- akin to those u... | {
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2411.07235 | Circulating Currents in Electric Machines: Positive Impact of The End
Windings Length on Losses | [
"eess.SY",
"cs.SY",
"math-ph",
"math.MP"
] | Circulating currents occurring in windings of electric machines received rising interest recent years. Circulating currents represent unwanted currents flowing between parallel-connected conductors. This phenomenon is due to various reasons such as asymmetries in the winding and differences in electric potential betwee... | {
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2411.07237 | Contextualized Evaluations: Taking the Guesswork Out of Language Model
Evaluations | [
"cs.CL"
] | Language model users often issue queries that lack specification, where the context under which a query was issued -- such as the user's identity, the query's intent, and the criteria for a response to be useful -- is not explicit. For instance, a good response to a subjective query like "What book should I read next?"... | {
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2411.07238 | OpenThaiGPT 1.5: A Thai-Centric Open Source Large Language Model | [
"cs.CL"
] | OpenThaiGPT 1.5 is an advanced Thai language chat model based on Qwen v2.5, finetuned on over 2,000,000 Thai instruction pairs. This report provides an engineering perspective on the model's development, capabilities, and performance. We discuss the model's architecture, training process, and key features, including mu... | {
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2411.07239 | DeepONet as a Multi-Operator Extrapolation Model: Distributed
Pretraining with Physics-Informed Fine-Tuning | [
"cs.LG"
] | We propose a novel fine-tuning method to achieve multi-operator learning through training a distributed neural operator with diverse function data and then zero-shot fine-tuning the neural network using physics-informed losses for downstream tasks. Operator learning effectively approximates solution operators for PDEs ... | {
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2411.07240 | UTMath: Math Evaluation with Unit Test via Reasoning-to-Coding Thoughts | [
"cs.CL",
"cs.AI"
] | The evaluation of mathematical reasoning capabilities is essential for advancing Artificial General Intelligence (AGI). While Large Language Models (LLMs) have shown impressive performance in solving mathematical problems, existing benchmarks such as GSM8K and MATH present limitations, including narrow problem definiti... | {
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2411.07243 | Neuropsychology and Explainability of AI: A Distributional Approach to
the Relationship Between Activation Similarity of Neural Categories in
Synthetic Cognition | [
"q-bio.NC",
"cs.AI",
"cs.NE"
] | We propose a neuropsychological approach to the explainability of artificial neural networks, which involves using concepts from human cognitive psychology as relevant heuristic references for developing synthetic explanatory frameworks that align with human modes of thought. The analogical concepts mobilized here, whi... | {
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2411.07244 | A Tutorial on Teaching Data Analytics with Generative AI | [
"cs.CY",
"cs.AI"
] | This tutorial addresses the challenge of incorporating large language models (LLMs), such as ChatGPT, in a data analytics class. It details several new in-class and out-of-class teaching techniques enabled by AI. For example, instructors can parallelize instruction by having students interact with different custom-made... | {
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2411.07245 | Navigating AI in Social Work and Beyond: A Multidisciplinary Review | [
"cs.CY",
"cs.AI"
] | This review began with the modest goal of drafting a brief commentary on how the social work profession engages with and is impacted by artificial intelligence (AI). However, it quickly became apparent that a deeper exploration was required to adequately capture the profound influence of AI, one of the most transformat... | {
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2411.07249 | SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation
in EEG | [
"eess.SP",
"cs.LG"
] | The non-stationary nature of electroencephalography (EEG) introduces distribution shifts across domains (e.g., days and subjects), posing a significant challenge to EEG-based neurotechnology generalization. Without labeled calibration data for target domains, the problem is a source-free unsupervised domain adaptation ... | {
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2411.07252 | High quality ECG dataset based on MIT-BIH recordings for improved
heartbeats classification | [
"eess.SP",
"cs.AI",
"cs.LG"
] | Electrocardiogram (ECG) is a reliable tool for medical professionals to detect and diagnose abnormal heart waves that may cause cardiovascular diseases. This paper proposes a methodology to create a new high-quality heartbeat dataset from all 48 of the MIT-BIH recordings. The proposed approach computes an optimal heart... | {
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2411.07259 | Ozone level forecasting in Mexico City with temporal features and
interactions | [
"cs.LG",
"stat.AP"
] | Tropospheric ozone is an atmospheric pollutant that negatively impacts human health and the environment. Precise estimation of ozone levels is essential for preventive measures and mitigating its effects. This work compares the accuracy of multiple regression models in forecasting ozone levels in Mexico City, first wit... | {
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2411.07261 | Sinkage Study in Granular Material for Space Exploration Legged Robot
Gripper | [
"cs.RO"
] | Wheeled rovers have been the primary choice for lunar exploration due to their speed and efficiency. However, deeper areas, such as lunar caves and craters, require the mobility of legged robots. To do so, appropriate end effectors must be designed to enable climbing and walking on the granular surface of the Moon. Thi... | {
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2411.07263 | Analysis, forecasting and system identification of a floating offshore
wind turbine using dynamic mode decomposition | [
"cs.LG",
"physics.ao-ph"
] | This article presents the data-driven equation-free modeling of the dynamics of a hexafloat floating offshore wind turbine based on the application of dynamic mode decomposition (DMD). All the analyses are performed on experimental data collected from an operating prototype. The DMD has here used i) to extract knowledg... | {
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2411.07264 | Multi-Document Financial Question Answering using LLMs | [
"cs.IR",
"cs.CL"
] | We propose two new methods for multi-document financial question answering. First, a method that uses semantic tagging, and then, queries the index to get the context (RAG_SEM). And second, a Knowledge Graph (KG_RAG) based method that uses semantic tagging, and, retrieves knowledge graph triples from a graph database, ... | {
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2411.07265 | ViTOC: Vision Transformer and Object-aware Captioner | [
"cs.CV"
] | This paper presents ViTOC (Vision Transformer and Object-aware Captioner), a novel vision-language model for image captioning that addresses the challenges of accuracy and diversity in generated descriptions. Unlike conventional approaches, ViTOC employs a dual-path architecture based on Vision Transformer and object d... | {
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2411.07267 | A Survey on Data Markets | [
"cs.GT",
"cs.AI",
"cs.DB"
] | Data is the new oil of the 21st century. The growing trend of trading data for greater welfare has led to the emergence of data markets. A data market is any mechanism whereby the exchange of data products including datasets and data derivatives takes place as a result of data buyers and data sellers being in contact w... | {
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2411.07268 | Target-driven Attack for Large Language Models | [
"cs.CL",
"cs.AI"
] | Current large language models (LLM) provide a strong foundation for large-scale user-oriented natural language tasks. Many users can easily inject adversarial text or instructions through the user interface, thus causing LLM model security challenges like the language model not giving the correct answer. Although there... | {
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2411.07269 | Learning From Graph-Structured Data: Addressing Design Issues and
Exploring Practical Applications in Graph Representation Learning | [
"cs.LG",
"cs.AI"
] | Graphs serve as fundamental descriptors for systems composed of interacting elements, capturing a wide array of data types, from molecular interactions to social networks and knowledge graphs. In this paper, we present an exhaustive review of the latest advancements in graph representation learning and Graph Neural Net... | {
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2411.07271 | Multi-hop Upstream Anticipatory Traffic Signal Control with Deep
Reinforcement Learning | [
"cs.LG",
"cs.AI",
"cs.SY",
"eess.SY",
"math.PR"
] | Coordination in traffic signal control is crucial for managing congestion in urban networks. Existing pressure-based control methods focus only on immediate upstream links, leading to suboptimal green time allocation and increased network delays. However, effective signal control inherently requires coordination across... | {
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2411.07272 | ASTD Patterns for Integrated Continuous Anomaly Detection In Data Logs | [
"cs.SE",
"cs.LG"
] | This paper investigates the use of the ASTD language for ensemble anomaly detection in data logs. It uses a sliding window technique for continuous learning in data streams, coupled with updating learning models upon the completion of each window to maintain accurate detection and align with current data trends. It pro... | {
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2411.07276 | Empirical Quantum Advantage Analysis of Quantum Kernel in Gene
Expression Data | [
"quant-ph",
"cs.ET",
"cs.LG"
] | The incorporation of quantum ansatz with machine learning classification models demonstrates the ability to extract patterns from data for classification tasks. However, taking advantage of the enhanced computational power of quantum machine learning necessitates dealing with various constraints. In this paper, we focu... | {
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2411.07277 | Constructing Gaussian Processes via Samplets | [
"stat.ML",
"cs.LG",
"cs.NA",
"math.NA"
] | Gaussian Processes face two primary challenges: constructing models for large datasets and selecting the optimal model. This master's thesis tackles these challenges in the low-dimensional case. We examine recent convergence results to identify models with optimal convergence rates and pinpoint essential parameters. Ut... | {
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2411.07279 | The Surprising Effectiveness of Test-Time Training for Abstract
Reasoning | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Language models have shown impressive performance on tasks within their training distribution, but often struggle with novel problems requiring complex reasoning. We investigate the effectiveness of test-time training (TTT) -- updating model parameters temporarily during inference using a loss derived from input data -... | {
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2411.07300 | Artificial Intelligence Ecosystem for Automating Self-Directed Teaching | [
"cs.AI",
"cs.CY",
"cs.LG"
] | This research introduces an innovative artificial intelligence-driven educational concept designed to optimize self-directed learning through personalized course delivery and automated teaching assistance. The system leverages fine-tuned AI models to create an adaptive learning environment that encompasses customized r... | {
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2411.07302 | Merit-Based Sortition in Decentralized Systems | [
"cs.MA",
"cs.CY",
"cs.DC",
"cs.LG"
] | In decentralized systems, it is often necessary to select an 'active' subset of participants from the total participant pool, with the goal of satisfying computational limitations or optimizing resource efficiency. This selection can sometimes be made at random, mirroring the sortition practice invented in classical an... | {
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2411.07308 | X-DFS: Explainable Artificial Intelligence Guided Design-for-Security
Solution Space Exploration | [
"cs.CR",
"cs.AI"
] | Design and manufacturing of integrated circuits predominantly use a globally distributed semiconductor supply chain involving diverse entities. The modern semiconductor supply chain has been designed to boost production efficiency, but is filled with major security concerns such as malicious modifications (hardware Tro... | {
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2411.07309 | Proprioceptive and Exteroceptive Information Perception in a Fabric Soft
Robotic Arm via Physical Reservoir Computing with minimal training data | [
"cs.RO"
] | Over the past decades, we have witnessed a rapid emergence of soft and reconfigurable robots thanks to their capability to interact safely with humans and adapt to complex environments. However, their softness makes accurate control very challenging. High-fidelity sensing is critical in improving control performance, e... | {
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2411.07310 | Advancements in Constitutive Model Calibration: Leveraging the Power of
Full-Field DIC Measurements and In-Situ Load Path Selection for Reliable
Parameter Inference | [
"cs.CE"
] | Accurate material characterization and model calibration are essential for computationally-supported engineering decisions. Current characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data is collected for a specific... | {
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2411.07311 | GPU-Accelerated Inverse Lithography Towards High Quality Curvy Mask
Generation | [
"cs.CV",
"physics.optics"
] | Inverse Lithography Technology (ILT) has emerged as a promising solution for photo mask design and optimization. Relying on multi-beam mask writers, ILT enables the creation of free-form curvilinear mask shapes that enhance printed wafer image quality and process window. However, a major challenge in implementing curvi... | {
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2411.07314 | Anomaly Detection in OKTA Logs using Autoencoders | [
"cs.LG",
"cs.CR"
] | Okta logs are used today to detect cybersecurity events using various rule-based models with restricted look back periods. These functions have limitations, such as a limited retrospective analysis, a predefined rule set, and susceptibility to generating false positives. To address this, we adopt unsupervised technique... | {
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2411.07315 | Harnessing Smartphone Sensors for Enhanced Road Safety: A Comprehensive
Dataset and Review | [
"cs.RO",
"cs.AI"
] | Severe collisions can result from aggressive driving and poor road conditions, emphasizing the need for effective monitoring to ensure safety. Smartphones, with their array of built-in sensors, offer a practical and affordable solution for road-sensing. However, the lack of reliable, standardized datasets has hindered ... | {
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2411.07317 | SynRL: Aligning Synthetic Clinical Trial Data with Human-preferred
Clinical Endpoints Using Reinforcement Learning | [
"cs.LG"
] | Each year, hundreds of clinical trials are conducted to evaluate new medical interventions, but sharing patient records from these trials with other institutions can be challenging due to privacy concerns and federal regulations. To help mitigate privacy concerns, researchers have proposed methods for generating synthe... | {
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2411.07320 | Richer Output for Richer Countries: Uncovering Geographical Disparities
in Generated Stories and Travel Recommendations | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.LG"
] | While a large body of work inspects language models for biases concerning gender, race, occupation and religion, biases of geographical nature are relatively less explored. Some recent studies benchmark the degree to which large language models encode geospatial knowledge. However, the impact of the encoded geographica... | {
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2411.07322 | Artificial Intelligence-Informed Handheld Breast Ultrasound for
Screening: A Systematic Review of Diagnostic Test Accuracy | [
"eess.IV",
"cs.CV"
] | Background. Breast cancer screening programs using mammography have led to significant mortality reduction in high-income countries. However, many low- and middle-income countries lack resources for mammographic screening. Handheld breast ultrasound (BUS) is a low-cost alternative but requires substantial training. Art... | {
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2411.07326 | $SE(3)$ Equivariant Ray Embeddings for Implicit Multi-View Depth
Estimation | [
"cs.CV"
] | Incorporating inductive bias by embedding geometric entities (such as rays) as input has proven successful in multi-view learning. However, the methods adopting this technique typically lack equivariance, which is crucial for effective 3D learning. Equivariance serves as a valuable inductive prior, aiding in the genera... | {
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2411.07335 | Multimodal Fusion Balancing Through Game-Theoretic Regularization | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.GT",
"cs.MM"
] | Multimodal learning can complete the picture of information extraction by uncovering key dependencies between data sources. However, current systems fail to fully leverage multiple modalities for optimal performance. This has been attributed to modality competition, where modalities strive for training resources, leavi... | {
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2411.07336 | SetLexSem Challenge: Using Set Operations to Evaluate the Lexical and
Semantic Robustness of Language Models | [
"cs.CL"
] | Set theory is foundational to mathematics and, when sets are finite, to reasoning about the world. An intelligent system should perform set operations consistently, regardless of superficial variations in the operands. Initially designed for semantically-oriented NLP tasks, large language models (LLMs) are now being ev... | {
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2411.07340 | Warmstarting for Scaling Language Models | [
"cs.LG",
"cs.AI"
] | Scaling model sizes to scale performance has worked remarkably well for the current large language models paradigm. The research and empirical findings of various scaling studies led to novel scaling results and laws that guides subsequent research. High training costs for contemporary scales of data and models result ... | {
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2411.07342 | Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of
Trials | [
"cs.RO"
] | Soft robots offer more flexibility, compliance, and adaptability than traditional rigid robots. They are also typically lighter and cheaper to manufacture. However, their use in real-world applications is limited due to modeling challenges and difficulties in integrating effective proprioceptive sensors. Large-scale so... | {
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2411.07343 | Multi-head Span-based Detector for AI-generated Fragments in Scientific
Papers | [
"cs.CL"
] | This paper describes a system designed to distinguish between AI-generated and human-written scientific excerpts in the DAGPap24 competition hosted within the Fourth Workshop on Scientific Document Processing. In this competition the task is to find artificially generated token-level text fragments in documents of a sc... | {
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} |
2411.07348 | Exploring Variational Autoencoders for Medical Image Generation: A
Comprehensive Study | [
"cs.LG",
"cs.CV",
"eess.IV"
] | Variational autoencoder (VAE) is one of the most common techniques in the field of medical image generation, where this architecture has shown advanced researchers in recent years and has developed into various architectures. VAE has advantages including improving datasets by adding samples in smaller datasets and in d... | {
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} |
2411.07351 | Generalization of Brady-Yong Algorithm for Fast Hough Transform to
Arbitrary Image Size | [
"cs.CV"
] | Nowadays, the Hough (discrete Radon) transform (HT/DRT) has proved to be an extremely powerful and widespread tool harnessed in a number of application areas, ranging from general image processing to X-ray computed tomography. Efficient utilization of the HT to solve applied problems demands its acceleration and increa... | {
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} |
2411.07362 | Factorised Active Inference for Strategic Multi-Agent Interactions | [
"cs.MA",
"cs.GT",
"cs.LG"
] | Understanding how individual agents make strategic decisions within collectives is important for advancing fields as diverse as economics, neuroscience, and multi-agent systems. Two complementary approaches can be integrated to this end. The Active Inference framework (AIF) describes how agents employ a generative mode... | {
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} |
2411.07372 | Identifying Differential Patient Care Through Inverse Intent Inference | [
"cs.LG"
] | Sepsis is a life-threatening condition defined by end-organ dysfunction due to a dysregulated host response to infection. Although the Surviving Sepsis Campaign has launched and has been releasing sepsis treatment guidelines to unify and normalize the care for sepsis patients, it has been reported in numerous studies t... | {
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} |
2411.07375 | Instance Performance Difference: A Metric to Measure the Sim-To-Real Gap
in Camera Simulation | [
"cs.RO"
] | In this contribution, we introduce the concept of Instance Performance Difference (IPD), a metric designed to measure the gap in performance that a robotics perception task experiences when working with real vs. synthetic pictures. By pairing synthetic and real instances in the pictures and evaluating their performance... | {
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} |
2411.07376 | Ensemble Learning for Microbubble Localization in Super-Resolution
Ultrasound | [
"eess.IV",
"cs.AI",
"physics.med-ph"
] | Super-resolution ultrasound (SR-US) is a powerful imaging technique for capturing microvasculature and blood flow at high spatial resolution. However, accurate microbubble (MB) localization remains a key challenge, as errors in localization can propagate through subsequent stages of the super-resolution process, affect... | {
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} |
2411.07378 | Data-Driven Analysis of AI in Medical Device Software in China: Deep
Learning and General AI Trends Based on Regulatory Data | [
"cs.AI"
] | Artificial intelligence (AI) in medical device software (MDSW) represents a transformative clinical technology, attracting increasing attention within both the medical community and the regulators. In this study, we leverage a data-driven approach to automatically extract and analyze AI-enabled medical devices (AIMD) f... | {
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} |
2411.07381 | MaLei at the PLABA Track of TREC 2024: RoBERTa for Term Replacement --
LLaMA3.1 and GPT-4o for Complete Abstract Adaptation | [
"cs.CL"
] | This report is the system description of the MaLei team (Manchester and Leiden) for the shared task Plain Language Adaptation of Biomedical Abstracts (PLABA) 2024 (we had an earlier name BeeManc following last year), affiliated with TREC2024 (33rd Text REtrieval Conference https://ir.nist.gov/evalbase/conf/trec-2024). ... | {
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} |
2411.07382 | Dynamic Zoning of Industrial Environments with Autonomous Mobile Robots | [
"cs.RO"
] | This paper presents a scheduling algorithm that divides a manufacturing/warehouse floor into zones that an Autonomous Mobile Robot (AMR) will occupy and complete part pick-up and drop-off tasks. Each zone is balanced so that each AMR will share each task equally. These zones change over time to accommodate fluctuations... | {
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} |
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