id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.03285 | Relations of society concepts and religions from Wikipedia networks | [
"physics.soc-ph",
"cond-mat.stat-mech",
"cs.SI"
] | We analyze the Google matrix of directed networks of Wikipedia articles related to 8 recent Wikipedia language editions representing different cultures (English, Arabic, German, Spanish, French, Italian, Russian, Chinese). Using the reduced Google matrix algorithm we determine relations and interactions of 23 society c... | {
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2412.03287 | Integrating Generative AI into Art Therapy: A Technical Showcase | [
"cs.AI"
] | This paper explores the integration of generative AI into the field of art therapy. Leveraging proven text-to-image models, we introduce a novel technical design to complement art therapy. The resulting AI-based tools shall enable patients to refine and customize their creative work, opening up new avenues of expressio... | {
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2412.03293 | Diffusion-VLA: Scaling Robot Foundation Models via Unified Diffusion and
Autoregression | [
"cs.RO",
"cs.CV"
] | In this paper, we present DiffusionVLA, a novel framework that seamlessly combines the autoregression model with the diffusion model for learning visuomotor policy. Central to our approach is a next-token prediction objective, enabling the model to reason effectively over the user's query in the context of current obse... | {
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2412.03295 | Digital twin inference from multi-physical simulation data of DED
additive manufacturing processes with neural ODEs | [
"cs.CE",
"physics.comp-ph"
] | A digital twin is a virtual representation that accurately replicates its physical counterpart, fostering bi-directional real-time data exchange throughout the entire process lifecycle. For Laser Directed Energy Deposition of Wire (DED-LB/w) additive manufacturing processes, digital twins may help to control the residu... | {
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2412.03297 | Composed Image Retrieval for Training-Free Domain Conversion | [
"cs.CV"
] | This work addresses composed image retrieval in the context of domain conversion, where the content of a query image is retrieved in the domain specified by the query text. We show that a strong vision-language model provides sufficient descriptive power without additional training. The query image is mapped to the tex... | {
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2412.03299 | Gaussian Processes for Probabilistic Estimates of Earthquake Ground
Shaking: A 1-D Proof-of-Concept | [
"physics.geo-ph",
"cs.LG",
"stat.AP"
] | Estimates of seismic wave speeds in the Earth (seismic velocity models) are key input parameters to earthquake simulations for ground motion prediction. Owing to the non-uniqueness of the seismic inverse problem, typically many velocity models exist for any given region. The arbitrary choice of which velocity model to ... | {
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2412.03300 | Conveying Emotions to Robots through Touch and Sound | [
"cs.RO",
"cs.LG"
] | Human emotions can be conveyed through nuanced touch gestures. However, there is a lack of understanding of how consistently emotions can be conveyed to robots through touch. This study explores the consistency of touch-based emotional expression toward a robot by integrating tactile and auditory sensory reading of aff... | {
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2412.03301 | Approximate Vector Set Search: A Bio-Inspired Approach for
High-Dimensional Spaces | [
"cs.DB"
] | Vector set search, an underexplored similarity search paradigm, aims to find vector sets similar to a query set. This search paradigm leverages the inherent structural alignment between sets and real-world entities to model more fine-grained and consistent relationships for diverse applications. This task, however, fac... | {
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2412.03304 | Global MMLU: Understanding and Addressing Cultural and Linguistic Biases
in Multilingual Evaluation | [
"cs.CL"
] | Cultural biases in multilingual datasets pose significant challenges for their effectiveness as global benchmarks. These biases stem not only from differences in language but also from the cultural knowledge required to interpret questions, reducing the practical utility of translated datasets like MMLU. Furthermore, t... | {
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2412.03307 | Contextual Data Integration for Bike-sharing Demand Prediction with
Graph Neural Networks in Degraded Weather Conditions | [
"cs.AI"
] | Demand for bike sharing is impacted by various factors, such as weather conditions, events, and the availability of other transportation modes. This impact remains elusive due to the complex interdependence of these factors or locationrelated user behavior variations. It is also not clear which factor is additional inf... | {
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2412.03309 | Typologie des comportements utilisateurs : {\'e}tude exploratoire des
sessions de recherche complexe sur le Web | [
"cs.CL"
] | In this study, we propose an exploratory approach aiming at a typology of user behaviour during a Web search session. We describe a typology based on generic IR variables (e.g. number of queries), but also on the study of topic (propositions with distinct semantic content defined from the search statement). To this end... | {
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2412.03310 | Grounded Language Design for Lightweight Diagramming for Formal Methods | [
"cs.CL",
"cs.PL"
] | Model finding, as embodied by SAT solvers and similar tools, is used widely, both in embedding settings and as a tool in its own right. For instance, tools like Alloy target SAT to enable users to incrementally define, explore, verify, and diagnose sophisticated specifications for a large number of complex systems. T... | {
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2412.03312 | Path-Guided Particle-based Sampling | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Particle-based Bayesian inference methods by sampling from a partition-free target (posterior) distribution, e.g., Stein variational gradient descent (SVGD), have attracted significant attention. We propose a path-guided particle-based sampling~(PGPS) method based on a novel Log-weighted Shrinkage (LwS) density path li... | {
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2412.03314 | Equivariant Representation Learning for Augmentation-based
Self-Supervised Learning via Image Reconstruction | [
"cs.CV"
] | Augmentation-based self-supervised learning methods have shown remarkable success in self-supervised visual representation learning, excelling in learning invariant features but often neglecting equivariant ones. This limitation reduces the generalizability of foundation models, particularly for downstream tasks requir... | {
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2412.03315 | Geometry-guided Cross-view Diffusion for One-to-many Cross-view Image
Synthesis | [
"cs.CV"
] | This paper presents a novel approach for cross-view synthesis aimed at generating plausible ground-level images from corresponding satellite imagery or vice versa. We refer to these tasks as satellite-to-ground (Sat2Grd) and ground-to-satellite (Grd2Sat) synthesis, respectively. Unlike previous works that typically foc... | {
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2412.03317 | FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning
IO-Awareness | [
"cs.LG"
] | Optimizing deep learning algorithms currently requires slow, manual derivation, potentially leaving much performance untapped. Methods like FlashAttention have achieved a x6 performance improvement over native PyTorch by avoiding unnecessary data transfers, but required three iterations over three years to be developed... | {
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2412.03318 | Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained
Synthetic Data | [
"eess.IV",
"cs.CV",
"physics.med-ph"
] | Segmenting stroke lesions in Magnetic Resonance Imaging (MRI) is challenging due to diverse clinical imaging domains, with existing models struggling to generalise across different MRI acquisition parameters and sequences. In this work, we propose two novel physics-constrained approaches using synthetic quantitative MR... | {
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2412.03321 | Scalable Bayesian Tensor Ring Factorization for Multiway Data Analysis | [
"cs.LG",
"stat.ML"
] | Tensor decompositions play a crucial role in numerous applications related to multi-way data analysis. By employing a Bayesian framework with sparsity-inducing priors, Bayesian Tensor Ring (BTR) factorization offers probabilistic estimates and an effective approach for automatically adapting the tensor ring rank during... | {
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2412.03324 | A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for
Accelerating Large VLMs | [
"cs.CV"
] | Vision-language models (VLMs) have shown remarkable success across various multi-modal tasks, yet large VLMs encounter significant efficiency challenges due to processing numerous visual tokens. A promising approach to accelerating large VLM inference is using partial information, such as attention maps from specific l... | {
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2412.03326 | Multi-Action Restless Bandits with Weakly Coupled Constraints:
Simultaneous Learning and Control | [
"math.OC",
"cs.LG",
"math.PR"
] | We study a system with finitely many groups of multi-action bandit processes, each of which is a Markov decision process (MDP) with finite state and action spaces and potentially different transition matrices when taking different actions. The bandit processes of the same group share the same state and action spaces an... | {
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2412.03330 | Testing CPS with Design Assumptions-Based Metamorphic Relations and
Genetic Programming | [
"cs.SE",
"cs.SY",
"eess.SY"
] | Cyber-Physical Systems (CPSs) software is used to enforce desired behaviours on physical systems. To test the interaction between the CPS software and the system's physics, engineers provide traces of desired physical states and observe traces of the actual physical states. CPS requirements describe how closely the act... | {
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2412.03331 | LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence
Embeddings | [
"cs.CL",
"cs.AI"
] | Sentence embedding models play a key role in various Natural Language Processing tasks, such as in Topic Modeling, Document Clustering and Recommendation Systems. However, these models rely heavily on parallel data, which can be scarce for many low-resource languages, including Luxembourgish. This scarcity results in s... | {
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2412.03332 | On Approximability of $\ell_2^2$ Min-Sum Clustering | [
"cs.DS",
"cs.CC",
"cs.CG",
"cs.LG"
] | The $\ell_2^2$ min-sum $k$-clustering problem is to partition an input set into clusters $C_1,\ldots,C_k$ to minimize $\sum_{i=1}^k\sum_{p,q\in C_i}\|p-q\|_2^2$. Although $\ell_2^2$ min-sum $k$-clustering is NP-hard, it is not known whether it is NP-hard to approximate $\ell_2^2$ min-sum $k$-clustering beyond a certain... | {
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2412.03334 | Yankari: A Monolingual Yoruba Dataset | [
"cs.CL"
] | This paper presents Yankari, a large-scale monolingual dataset for the Yoruba language, aimed at addressing the critical gap in Natural Language Processing (NLP) resources for this important West African language. Despite being spoken by over 30 million people, Yoruba has been severely underrepresented in NLP research ... | {
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2412.03338 | AI-Driven Day-to-Day Route Choice | [
"cs.LG",
"cs.AI"
] | Understanding travelers' route choices can help policymakers devise optimal operational and planning strategies for both normal and abnormal circumstances. However, existing choice modeling methods often rely on predefined assumptions and struggle to capture the dynamic and adaptive nature of travel behavior. Recently,... | {
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2412.03342 | UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly
Detection | [
"cs.CV"
] | Visual Anomaly Detection (VAD) aims to identify abnormal samples in images that deviate from normal patterns, covering multiple domains, including industrial, logical, and medical fields. Due to the domain gaps between these fields, existing VAD methods are typically tailored to each domain, with specialized detection ... | {
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2412.03343 | Improving Linguistic Diversity of Large Language Models with Possibility
Exploration Fine-Tuning | [
"cs.CL",
"cs.AI"
] | While Large Language Models (LLMs) have made significant strides in replicating human-like abilities, there are concerns about a reduction in the linguistic diversity of their outputs. This results in the homogenization of viewpoints and perspectives, as well as the underrepresentation of specific demographic groups. A... | {
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2412.03347 | DIVE: Taming DINO for Subject-Driven Video Editing | [
"cs.CV",
"cs.AI"
] | Building on the success of diffusion models in image generation and editing, video editing has recently gained substantial attention. However, maintaining temporal consistency and motion alignment still remains challenging. To address these issues, this paper proposes DINO-guided Video Editing (DIVE), a framework desig... | {
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2412.03349 | Fairer Analysis and Demographically Balanced Face Generation for Fairer
Face Verification | [
"cs.CV"
] | Face recognition and verification are two computer vision tasks whose performances have advanced with the introduction of deep representations. However, ethical, legal, and technical challenges due to the sensitive nature of face data and biases in real-world training datasets hinder their development. Generative AI ad... | {
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2412.03352 | Intuitive Axial Augmentation Using Polar-Sine-Based Piecewise Distortion
for Medical Slice-Wise Segmentation | [
"cs.CV",
"cs.AI"
] | Most data-driven models for medical image analysis rely on universal augmentations to improve performance. Experimental evidence has confirmed their effectiveness, but the unclear mechanism underlying them poses a barrier to the widespread acceptance and trust in such methods within the medical community. We revisit an... | {
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2412.03353 | MOVE: Multi-skill Omnidirectional Legged Locomotion with Limited View in
3D Environments | [
"cs.RO"
] | Legged robots possess inherent advantages in traversing complex 3D terrains. However, previous work on low-cost quadruped robots with egocentric vision systems has been limited by a narrow front-facing view and exteroceptive noise, restricting omnidirectional mobility in such environments. While building a voxel map th... | {
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2412.03355 | TASR: Timestep-Aware Diffusion Model for Image Super-Resolution | [
"cs.CV"
] | Diffusion models have recently achieved outstanding results in the field of image super-resolution. These methods typically inject low-resolution (LR) images via ControlNet.In this paper, we first explore the temporal dynamics of information infusion through ControlNet, revealing that the input from LR images predomina... | {
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2412.03359 | WiS Platform: Enhancing Evaluation of LLM-Based Multi-Agent Systems
Through Game-Based Analysis | [
"cs.AI"
] | Recent advancements in autonomous multi-agent systems (MAS) based on large language models (LLMs) have enhanced the application scenarios and improved the capability of LLMs to handle complex tasks. Despite demonstrating effectiveness, existing studies still evidently struggle to evaluate, analysis, and reproducibility... | {
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2412.03371 | SGSST: Scaling Gaussian Splatting StyleTransfer | [
"cs.CV",
"cs.GR",
"eess.IV"
] | Applying style transfer to a full 3D environment is a challenging task that has seen many developments since the advent of neural rendering. 3D Gaussian splatting (3DGS) has recently pushed further many limits of neural rendering in terms of training speed and reconstruction quality. This work introduces SGSST: Scaling... | {
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2412.03375 | Granular Ball Twin Support Vector Machine with Universum Data | [
"cs.LG"
] | Classification with support vector machines (SVM) often suffers from limited performance when relying solely on labeled data from target classes and is sensitive to noise and outliers. Incorporating prior knowledge from Universum data and more robust data representations can enhance accuracy and efficiency. Motivated b... | {
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2412.03378 | Volumetrically Consistent 3D Gaussian Rasterization | [
"cs.CV"
] | Recently, 3D Gaussian Splatting (3DGS) has enabled photorealistic view synthesis at high inference speeds. However, its splatting-based rendering model makes several approximations to the rendering equation, reducing physical accuracy. We show that splatting and its approximations are unnecessary, even within a rasteri... | {
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2412.03379 | MTVNet: Mapping using Transformers for Volumes -- Network for
Super-Resolution with Long-Range Interactions | [
"cs.CV",
"eess.IV"
] | Until now, it has been difficult for volumetric super-resolution to utilize the recent advances in transformer-based models seen in 2D super-resolution. The memory required for self-attention in 3D volumes limits the receptive field. Therefore, long-range interactions are not used in 3D to the extent done in 2D and the... | {
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2412.03381 | Classical Shadows with Improved Median-of-Means Estimation | [
"quant-ph",
"cond-mat.stat-mech",
"cs.LG",
"stat.ML"
] | The classical shadows protocol, introduced by Huang et al. [Nat. Phys. 16, 1050 (2020)], makes use of the median-of-means (MoM) estimator to efficiently estimate the expectation values of $M$ observables with failure probability $\delta$ using only $\mathcal{O}(\log(M/\delta))$ measurements. In their analysis, Huang et... | {
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2412.03385 | Reactive Orchestration for Hierarchical Federated Learning Under a
Communication Cost Budget | [
"cs.DC",
"cs.LG",
"cs.NI"
] | Deploying a Hierarchical Federated Learning (HFL) pipeline across the computing continuum (CC) requires careful organization of participants into a hierarchical structure with intermediate aggregation nodes between FL clients and the global FL server. This is challenging to achieve due to (i) cost constraints, (ii) var... | {
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2412.03387 | Adaptive Model Predictive Control for Differential-Algebraic Systems
towards a Higher Path Accuracy for Physically Coupled Robots | [
"eess.SY",
"cs.RO",
"cs.SY"
] | The physical coupling between robots has the potential to improve the capabilities of multi-robot systems in challenging manufacturing processes. However, the path tracking accuracy of physically coupled robots is not studied adequately, especially considering the uncertain kinematic parameters, the mechanical elastici... | {
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2412.03388 | DiffStyleTTS: Diffusion-based Hierarchical Prosody Modeling for
Text-to-Speech with Diverse and Controllable Styles | [
"cs.SD",
"cs.AI",
"cs.CL",
"eess.AS"
] | Human speech exhibits rich and flexible prosodic variations. To address the one-to-many mapping problem from text to prosody in a reasonable and flexible manner, we propose DiffStyleTTS, a multi-speaker acoustic model based on a conditional diffusion module and an improved classifier-free guidance, which hierarchically... | {
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2412.03390 | Enhancing Supply Chain Visibility with Generative AI: An Exploratory
Case Study on Relationship Prediction in Knowledge Graphs | [
"cs.CE",
"cs.AI"
] | A key stumbling block in effective supply chain risk management for companies and policymakers is a lack of visibility on interdependent supply network relationships. Relationship prediction, also called link prediction is an emergent area of supply chain surveillance research that aims to increase the visibility of su... | {
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2412.03391 | Risk-aware Classification via Uncertainty Quantification | [
"cs.LG"
] | Autonomous and semi-autonomous systems are using deep learning models to improve decision-making. However, deep classifiers can be overly confident in their incorrect predictions, a major issue especially in safety-critical domains. The present study introduces three foundational desiderata for developing real-world ri... | {
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2412.03393 | Can neural operators always be continuously discretized? | [
"cs.LG"
] | We consider the problem of discretization of neural operators between Hilbert spaces in a general framework including skip connections. We focus on bijective neural operators through the lens of diffeomorphisms in infinite dimensions. Framed using category theory, we give a no-go theorem that shows that diffeomorphisms... | {
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2412.03398 | RedStone: Curating General, Code, Math, and QA Data for Large Language
Models | [
"cs.CL"
] | Pre-training Large Language Models (LLMs) on high-quality, meticulously curated datasets is widely recognized as critical for enhancing their performance and generalization capabilities. This study explores the untapped potential of Common Crawl as a comprehensive and flexible resource for pre-training LLMs, addressing... | {
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2412.03400 | Implicit Priors Editing in Stable Diffusion via Targeted Token
Adjustment | [
"cs.CV"
] | Implicit assumptions and priors are often necessary in text-to-image generation tasks, especially when textual prompts lack sufficient context. However, these assumptions can sometimes reflect outdated concepts, inaccuracies, or societal bias embedded in the training data. We present Embedding-only Editing (Embedit), a... | {
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2412.03401 | Benchmarking Pretrained Attention-based Models for Real-Time Recognition
in Robot-Assisted Esophagectomy | [
"cs.CV",
"cs.AI"
] | Esophageal cancer is among the most common types of cancer worldwide. It is traditionally treated using open esophagectomy, but in recent years, robot-assisted minimally invasive esophagectomy (RAMIE) has emerged as a promising alternative. However, robot-assisted surgery can be challenging for novice surgeons, as they... | {
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2412.03405 | Deep Operator BSDE: a Numerical Scheme to Approximate the Solution
Operators | [
"math.NA",
"cs.LG",
"cs.NA",
"math.PR"
] | Motivated by dynamic risk measures and conditional $g$-expectations, in this work we propose a numerical method to approximate the solution operator given by a Backward Stochastic Differential Equation (BSDE). The main ingredients for this are the Wiener chaos decomposition and the classical Euler scheme for BSDEs. We ... | {
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2412.03407 | Skel3D: Skeleton Guided Novel View Synthesis | [
"cs.CV"
] | In this paper, we present an approach for monocular open-set novel view synthesis (NVS) that leverages object skeletons to guide the underlying diffusion model. Building upon a baseline that utilizes a pre-trained 2D image generator, our method takes advantage of the Objaverse dataset, which includes animated objects w... | {
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2412.03409 | PrefixKV: Adaptive Prefix KV Cache is What Vision Instruction-Following
Models Need for Efficient Generation | [
"cs.CV"
] | Recently, large vision-language models (LVLMs) have rapidly gained popularity for their strong generation and reasoning capabilities given diverse multimodal inputs. However, these models incur significant computational and memory overhead during inference, which greatly hinders the efficient deployment in practical sc... | {
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2412.03413 | Deep Learning for Sea Surface Temperature Reconstruction under Cloud
Occlusion | [
"cs.CV"
] | Sea Surface Temperature (SST) is crucial for understanding Earth's oceans and climate, significantly influencing weather patterns, ocean currents, marine ecosystem health, and the global energy balance. Large-scale SST monitoring relies on satellite infrared radiation detection, but cloud cover presents a major challen... | {
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2412.03417 | Learning Semantic Association Rules from Internet of Things Data | [
"cs.LG",
"cs.AI"
] | Association Rule Mining (ARM) is the task of discovering commonalities in data in the form of logical implications. ARM is used in the Internet of Things (IoT) for different tasks including monitoring and decision-making. However, existing methods give limited consideration to IoT-specific requirements such as heteroge... | {
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2412.03420 | Automated Test-Case Generation for REST APIs Using Model Inference
Search Heuristic | [
"cs.SE",
"cs.AI"
] | The rising popularity of the microservice architectural style has led to a growing demand for automated testing approaches tailored to these systems. EvoMaster is a state-of-the-art tool that uses Evolutionary Algorithms (EAs) to automatically generate test cases for microservices' REST APIs. One limitation of these EA... | {
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2412.03421 | Governance as a complex, networked, democratic, satisfiability problem | [
"physics.soc-ph",
"cs.SI",
"nlin.AO"
] | Democratic governments comprise a subset of a population whose goal is to produce coherent decisions that solve societal challenges while respecting the will of the people they represent. New governance frameworks represent this problem as a social network rather than as a hierarchical pyramid with centralized authorit... | {
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2412.03424 | Tango*: Constrained synthesis planning using chemically informed value
functions | [
"cs.CE",
"cs.AI"
] | Computer-aided synthesis planning (CASP) has made significant strides in generating retrosynthetic pathways for simple molecules in a non-constrained fashion. Recent work introduces a specialised bidirectional search algorithm with forward and retro expansion to address the starting material-constrained synthesis probl... | {
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2412.03427 | Assessing Foundation Models' Transferability to Physiological Signals in
Precision Medicine | [
"cs.LG"
] | The success of precision medicine requires computational models that can effectively process and interpret diverse physiological signals across heterogeneous patient populations. While foundation models have demonstrated remarkable transfer capabilities across various domains, their effectiveness in handling individual... | {
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2412.03428 | 2DGS-Room: Seed-Guided 2D Gaussian Splatting with Geometric Constrains
for High-Fidelity Indoor Scene Reconstruction | [
"cs.CV"
] | The reconstruction of indoor scenes remains challenging due to the inherent complexity of spatial structures and the prevalence of textureless regions. Recent advancements in 3D Gaussian Splatting have improved novel view synthesis with accelerated processing but have yet to deliver comparable performance in surface re... | {
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2412.03430 | SINGER: Vivid Audio-driven Singing Video Generation with Multi-scale
Spectral Diffusion Model | [
"cs.CV",
"cs.LG",
"cs.SD"
] | Recent advancements in generative models have significantly enhanced talking face video generation, yet singing video generation remains underexplored. The differences between human talking and singing limit the performance of existing talking face video generation models when applied to singing. The fundamental differ... | {
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2412.03433 | Genetic Algorithm Based System for Path Planning with Unmanned Aerial
Vehicles Swarms in Cell-Grid Environments | [
"cs.RO",
"cs.AI"
] | Path Planning methods for autonomously controlling swarms of unmanned aerial vehicles (UAVs) are gaining momentum due to their operational advantages. An increasing number of scenarios now require autonomous control of multiple UAVs, as autonomous operation can significantly reduce labor costs. Additionally, obtaining ... | {
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2412.03434 | BIMCaP: BIM-based AI-supported LiDAR-Camera Pose Refinement | [
"cs.RO",
"cs.AI"
] | This paper introduces BIMCaP, a novel method to integrate mobile 3D sparse LiDAR data and camera measurements with pre-existing building information models (BIMs), enhancing fast and accurate indoor mapping with affordable sensors. BIMCaP refines sensor poses by leveraging a 3D BIM and employing a bundle adjustment tec... | {
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2412.03439 | CleanDIFT: Diffusion Features without Noise | [
"cs.CV"
] | Internal features from large-scale pre-trained diffusion models have recently been established as powerful semantic descriptors for a wide range of downstream tasks. Works that use these features generally need to add noise to images before passing them through the model to obtain the semantic features, as the models d... | {
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2412.03441 | PBP: Post-training Backdoor Purification for Malware Classifiers | [
"cs.LG",
"cs.AI",
"cs.CR"
] | In recent years, the rise of machine learning (ML) in cybersecurity has brought new challenges, including the increasing threat of backdoor poisoning attacks on ML malware classifiers. For instance, adversaries could inject malicious samples into public malware repositories, contaminating the training data and potentia... | {
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2412.03442 | State Frequency Estimation for Anomaly Detection | [
"cs.LG",
"cs.CR"
] | Many works have studied the efficacy of state machines for detecting anomalies within NetFlows. These works typically learn a model from unlabeled data and compute anomaly scores for arbitrary traces based on their likelihood of occurrence or how well they fit within the model. However, these methods do not dynamically... | {
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2412.03446 | From Words to Workflows: Automating Business Processes | [
"cs.AI"
] | As businesses increasingly rely on automation to streamline operations, the limitations of Robotic Process Automation (RPA) have become apparent, particularly its dependence on expert knowledge and inability to handle complex decision-making tasks. Recent advancements in Artificial Intelligence (AI), particularly Gener... | {
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2412.03451 | PlanarSplatting: Accurate Planar Surface Reconstruction in 3 Minutes | [
"cs.CV"
] | This paper presents PlanarSplatting, an ultra-fast and accurate surface reconstruction approach for multiview indoor images. We take the 3D planes as the main objective due to their compactness and structural expressiveness in indoor scenes, and develop an explicit optimization framework that learns to fit the expected... | {
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2412.03453 | Pre-trained Multiple Latent Variable Generative Models are good
defenders against Adversarial Attacks | [
"cs.CV"
] | Attackers can deliberately perturb classifiers' input with subtle noise, altering final predictions. Among proposed countermeasures, adversarial purification employs generative networks to preprocess input images, filtering out adversarial noise. In this study, we propose specific generators, defined Multiple Latent Va... | {
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2412.03456 | Gesture Classification in Artworks Using Contextual Image Features | [
"cs.CV"
] | Recognizing gestures in artworks can add a valuable dimension to art understanding and help to acknowledge the role of the sense of smell in cultural heritage. We propose a method to recognize smell gestures in historical artworks. We show that combining local features with global image context improves classification ... | {
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2412.03462 | Multi-Momentum Observer Contact Estimation for Bipedal Robots | [
"cs.RO",
"cs.SY",
"eess.SY"
] | As bipedal robots become more and more popular in commercial and industrial settings, the ability to control them with a high degree of reliability is critical. To that end, this paper considers how to accurately estimate which feet are currently in contact with the ground so as to avoid improper control actions that c... | {
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2412.03464 | Validity and efficiency of the conformal CUSUM procedure | [
"math.ST",
"cs.LG",
"stat.TH"
] | In this paper we study the validity and efficiency of a conformal version of the CUSUM procedure for change detection both experimentally and theoretically. | {
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2412.03465 | YT-30M: A multi-lingual multi-category dataset of YouTube comments | [
"cs.SI",
"cs.AI",
"cs.CL",
"cs.IR",
"cs.LG"
] | This paper introduces two large-scale multilingual comment datasets, YT-30M (and YT-100K) from YouTube. The analysis in this paper is performed on a smaller sample (YT-100K) of YT-30M. Both the datasets: YT-30M (full) and YT-100K (randomly selected 100K sample from YT-30M) are publicly released for further research. YT... | {
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2412.03467 | Training-Free Mitigation of Language Reasoning Degradation After
Multimodal Instruction Tuning | [
"cs.CV",
"cs.AI"
] | Multimodal models typically combine a powerful large language model (LLM) with a vision encoder and are then trained on multimodal data via instruction tuning. While this process adapts LLMs to multimodal settings, it remains unclear whether this adaptation compromises their original language reasoning capabilities. In... | {
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2412.03471 | Cluster Specific Representation Learning | [
"cs.LG"
] | Representation learning aims to extract meaningful lower-dimensional embeddings from data, known as representations. Despite its widespread application, there is no established definition of a ``good'' representation. Typically, the representation quality is evaluated based on its performance in downstream tasks such a... | {
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2412.03472 | Measure Anything: Real-time, Multi-stage Vision-based Dimensional
Measurement using Segment Anything | [
"cs.CV"
] | We present Measure Anything, a comprehensive vision-based framework for dimensional measurement of objects with circular cross-sections, leveraging the Segment Anything Model (SAM). Our approach estimates key geometric features -- including diameter, length, and volume -- for rod-like geometries with varying curvature ... | {
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2412.03473 | Urban4D: Semantic-Guided 4D Gaussian Splatting for Urban Scene
Reconstruction | [
"cs.CV"
] | Reconstructing dynamic urban scenes presents significant challenges due to their intrinsic geometric structures and spatiotemporal dynamics. Existing methods that attempt to model dynamic urban scenes without leveraging priors on potentially moving regions often produce suboptimal results. Meanwhile, approaches based o... | {
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2412.03483 | Convolutional Neural Networks and Mixture of Experts for Intrusion
Detection in 5G Networks and beyond | [
"cs.LG"
] | The advent of 6G/NextG networks comes along with a series of benefits, including extreme capacity, reliability, and efficiency. However, these networks may become vulnerable to new security threats. Therefore, 6G/NextG networks must be equipped with advanced Artificial Intelligence algorithms, in order to evade these a... | {
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2412.03486 | Tight PAC-Bayesian Risk Certificates for Contrastive Learning | [
"stat.ML",
"cs.LG"
] | Contrastive representation learning is a modern paradigm for learning representations of unlabeled data via augmentations -- precisely, contrastive models learn to embed semantically similar pairs of samples (positive pairs) closer than independently drawn samples (negative samples). In spite of its empirical success a... | {
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2412.03487 | Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective | [
"cs.LG",
"cs.AI"
] | The design space of discrete-space diffusion or flow generative models are significantly less well-understood than their continuous-space counterparts, with many works focusing only on a simple masked construction. In this work, we aim to take a holistic approach to the construction of discrete generative models based ... | {
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2412.03490 | Data Fusion of Semantic and Depth Information in the Context of Object
Detection | [
"cs.CV"
] | Considerable study has already been conducted regarding autonomous driving in modern era. An autonomous driving system must be extremely good at detecting objects surrounding the car to ensure safety. In this paper, classification, and estimation of an object's (pedestrian) position (concerning an ego 3D coordinate sys... | {
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2412.03491 | Beyond algorithm hyperparameters: on preprocessing hyperparameters and
associated pitfalls in machine learning applications | [
"stat.ML",
"cs.LG",
"stat.ME"
] | Adequately generating and evaluating prediction models based on supervised machine learning (ML) is often challenging, especially for less experienced users in applied research areas. Special attention is required in settings where the model generation process involves hyperparameter tuning, i.e. data-driven optimizati... | {
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2412.03496 | TRENDy: Temporal Regression of Effective Non-linear Dynamics | [
"nlin.PS",
"cs.LG"
] | Spatiotemporal dynamics pervade the natural sciences, from the morphogen dynamics underlying patterning in animal pigmentation to the protein waves controlling cell division. A central challenge lies in understanding how controllable parameters induce qualitative changes in system behavior called bifurcations. This end... | {
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2412.03497 | Soft Checksums to Flag Untrustworthy Machine Learning Surrogate
Predictions and Application to Atomic Physics Simulations | [
"cs.LG",
"physics.atom-ph"
] | Trained neural networks (NN) are attractive as surrogate models to replace costly calculations in physical simulations, but are often unknowingly applied to states not adequately represented in the training dataset. We present the novel technique of soft checksums for scientific machine learning, a general-purpose meth... | {
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2412.03498 | A Bidirectional Siamese Recurrent Neural Network for Accurate Gait
Recognition Using Body Landmarks | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Gait recognition is a significant biometric technique for person identification, particularly in scenarios where other physiological biometrics are impractical or ineffective. In this paper, we address the challenges associated with gait recognition and present a novel approach to improve its accuracy and reliability. ... | {
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2412.03506 | Self-test loss functions for learning weak-form operators and gradient
flows | [
"stat.ML",
"cs.LG"
] | The construction of loss functions presents a major challenge in data-driven modeling involving weak-form operators in PDEs and gradient flows, particularly due to the need to select test functions appropriately. We address this challenge by introducing self-test loss functions, which employ test functions that depend ... | {
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2412.03508 | Design and Control of an Ultra-Slender Push-Pull Multisection Continuum
Manipulator for In-Situ Inspection of Aeroengine | [
"cs.RO"
] | Since the shape of industrial endoscopes is passively altered according to the contact around it, manual inspection approaches of aeroengines through the inspection ports have unreachable areas, and it's difficult to traverse multistage blades and inspect them simultaneously, which requires engine disassembly or the co... | {
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2412.03512 | Distillation of Diffusion Features for Semantic Correspondence | [
"cs.CV"
] | Semantic correspondence, the task of determining relationships between different parts of images, underpins various applications including 3D reconstruction, image-to-image translation, object tracking, and visual place recognition. Recent studies have begun to explore representations learned in large generative image ... | {
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2412.03513 | Enhancing CLIP Conceptual Embedding through Knowledge Distillation | [
"cs.AI",
"cs.CL",
"cs.CV",
"cs.LG"
] | Recently, CLIP has become an important model for aligning images and text in multi-modal contexts. However, researchers have identified limitations in the ability of CLIP's text and image encoders to extract detailed knowledge from pairs of captions and images. In response, this paper presents Knowledge-CLIP, an innova... | {
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2412.03514 | Adaptive Personalized Over-the-Air Federated Learning with Reflecting
Intelligent Surfaces | [
"cs.IT",
"eess.SP",
"math.IT"
] | Over-the-air federated learning (OTA-FL) unifies communication and model aggregation by leveraging the inherent superposition property of the wireless medium. This strategy can enable scalable and bandwidth-efficient learning via simultaneous transmission of model updates using the same frequency resources, if care is ... | {
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2412.03515 | Distilling Diffusion Models to Efficient 3D LiDAR Scene Completion | [
"cs.CV"
] | Diffusion models have been applied to 3D LiDAR scene completion due to their strong training stability and high completion quality. However, the slow sampling speed limits the practical application of diffusion-based scene completion models since autonomous vehicles require an efficient perception of surrounding enviro... | {
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2412.03516 | You're (Not) My Type -- Can LLMs Generate Feedback of Specific Types for
Introductory Programming Tasks? | [
"cs.AI"
] | Background: Feedback as one of the most influential factors for learning has been subject to a great body of research. It plays a key role in the development of educational technology systems and is traditionally rooted in deterministic feedback defined by experts and their experience. However, with the rise of generat... | {
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2412.03517 | NVComposer: Boosting Generative Novel View Synthesis with Multiple
Sparse and Unposed Images | [
"cs.CV"
] | Recent advancements in generative models have significantly improved novel view synthesis (NVS) from multi-view data. However, existing methods depend on external multi-view alignment processes, such as explicit pose estimation or pre-reconstruction, which limits their flexibility and accessibility, especially when ali... | {
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2412.03518 | Dense Scene Reconstruction from Light-Field Images Affected by Rolling
Shutter | [
"cs.CV"
] | This paper presents a dense depth estimation approach from light-field (LF) images that is able to compensate for strong rolling shutter (RS) effects. Our method estimates RS compensated views and dense RS compensated disparity maps. We present a two-stage method based on a 2D Gaussians Splatting that allows for a ``re... | {
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2412.03520 | Seeing Beyond Views: Multi-View Driving Scene Video Generation with
Holistic Attention | [
"cs.CV"
] | Generating multi-view videos for autonomous driving training has recently gained much attention, with the challenge of addressing both cross-view and cross-frame consistency. Existing methods typically apply decoupled attention mechanisms for spatial, temporal, and view dimensions. However, these approaches often strug... | {
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2412.03526 | Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular
Videos | [
"cs.CV",
"cs.AI",
"cs.GR"
] | Recent advancements in static feed-forward scene reconstruction have demonstrated significant progress in high-quality novel view synthesis. However, these models often struggle with generalizability across diverse environments and fail to effectively handle dynamic content. We present BTimer (short for BulletTimer), t... | {
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2412.03527 | FANAL -- Financial Activity News Alerting Language Modeling Framework | [
"cs.CL",
"cs.LG"
] | In the rapidly evolving financial sector, the accurate and timely interpretation of market news is essential for stakeholders needing to navigate unpredictable events. This paper introduces FANAL (Financial Activity News Alerting Language Modeling Framework), a specialized BERT-based framework engineered for real-time ... | {
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} |
2412.03531 | A Review on Scientific Knowledge Extraction using Large Language Models
in Biomedical Sciences | [
"cs.CL",
"cs.LG"
] | The rapid advancement of large language models (LLMs) has opened new boundaries in the extraction and synthesis of medical knowledge, particularly within evidence synthesis. This paper reviews the state-of-the-art applications of LLMs in the biomedical domain, exploring their effectiveness in automating complex tasks s... | {
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2412.03537 | Evaluating Gender Bias Transfer between Pre-trained and Prompt-Adapted
Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large language models (LLMs) are increasingly being adapted to achieve task-specificity for deployment in real-world decision systems. Several previous works have investigated the bias transfer hypothesis (BTH) by studying the effect of the fine-tuning adaptation strategy on model fairness to find that fairness in pre-... | {
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2412.03539 | NODE-AdvGAN: Improving the transferability and perceptual similarity of
adversarial examples by dynamic-system-driven adversarial generative model | [
"cs.LG",
"cs.AI"
] | Understanding adversarial examples is crucial for improving the model's robustness, as they introduce imperceptible perturbations that deceive models. Effective adversarial examples, therefore, offer the potential to train more robust models by removing their singularities. We propose NODE-AdvGAN, a novel approach that... | {
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2412.03548 | Perception Tokens Enhance Visual Reasoning in Multimodal Language Models | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Multimodal language models (MLMs) still face challenges in fundamental visual perception tasks where specialized models excel. Tasks requiring reasoning about 3D structures benefit from depth estimation, and reasoning about 2D object instances benefits from object detection. Yet, MLMs can not produce intermediate depth... | {
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2412.03552 | Imagine360: Immersive 360 Video Generation from Perspective Anchor | [
"cs.CV"
] | $360^\circ$ videos offer a hyper-immersive experience that allows the viewers to explore a dynamic scene from full 360 degrees. To achieve more user-friendly and personalized content creation in $360^\circ$ video format, we seek to lift standard perspective videos into $360^\circ$ equirectangular videos. To this end, w... | {
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2412.03555 | PaliGemma 2: A Family of Versatile VLMs for Transfer | [
"cs.CV"
] | PaliGemma 2 is an upgrade of the PaliGemma open Vision-Language Model (VLM) based on the Gemma 2 family of language models. We combine the SigLIP-So400m vision encoder that was also used by PaliGemma with the whole range of Gemma 2 models, from the 2B one all the way up to the 27B model. We train these models at three ... | {
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} |
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