id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.13908 | Memorizing SAM: 3D Medical Segment Anything Model with Memorizing
Transformer | [
"cs.CV"
] | Segment Anything Models (SAMs) have gained increasing attention in medical image analysis due to their zero-shot generalization capability in segmenting objects of unseen classes and domains when provided with appropriate user prompts. Addressing this performance gap is important to fully leverage the pre-trained weigh... | {
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2412.13912 | Energy-Efficient SLAM via Joint Design of Sensing, Communication, and
Exploration Speed | [
"cs.RO",
"cs.AI"
] | To support future spatial machine intelligence applications, lifelong simultaneous localization and mapping (SLAM) has drawn significant attentions. SLAM is usually realized based on various types of mobile robots performing simultaneous and continuous sensing and communication. This paper focuses on analyzing the ener... | {
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2412.13913 | A Black-Box Evaluation Framework for Semantic Robustness in Bird's Eye
View Detection | [
"cs.CV"
] | Camera-based Bird's Eye View (BEV) perception models receive increasing attention for their crucial role in autonomous driving, a domain where concerns about the robustness and reliability of deep learning have been raised. While only a few works have investigated the effects of randomly generated semantic perturbation... | {
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2412.13916 | Retrieval Augmented Image Harmonization | [
"cs.CV"
] | When embedding objects (foreground) into images (background), considering the influence of photography conditions like illumination, it is usually necessary to perform image harmonization to make the foreground object coordinate with the background image in terms of brightness, color, and etc. Although existing image h... | {
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2412.13917 | Speech Watermarking with Discrete Intermediate Representations | [
"eess.AS",
"cs.LG",
"cs.SD",
"eess.SP"
] | Speech watermarking techniques can proactively mitigate the potential harmful consequences of instant voice cloning techniques. These techniques involve the insertion of signals into speech that are imperceptible to humans but can be detected by algorithms. Previous approaches typically embed watermark messages into co... | {
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2412.13918 | Localized RETE for Incremental Graph Queries with Nested Graph
Conditions | [
"cs.LO",
"cs.DB"
] | The growing size of graph-based modeling artifacts in model-driven engineering calls for techniques that enable efficient execution of graph queries. Incremental approaches based on the RETE algorithm provide an adequate solution in many scenarios, but are generally designed to search for query results over the entire ... | {
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2412.13922 | Pipeline Analysis for Developing Instruct LLMs in Low-Resource
Languages: A Case Study on Basque | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large language models (LLMs) are typically optimized for resource-rich languages like English, exacerbating the gap between high-resource and underrepresented languages. This work presents a detailed analysis of strategies for developing a model capable of following instructions in a low-resource language, specifically... | {
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2412.13924 | Language verY Rare for All | [
"cs.CL",
"cs.LG"
] | In the quest to overcome language barriers, encoder-decoder models like NLLB have expanded machine translation to rare languages, with some models (e.g., NLLB 1.3B) even trainable on a single GPU. While general-purpose LLMs perform well in translation, open LLMs prove highly competitive when fine-tuned for specific tas... | {
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2412.13928 | Preconditioned Subspace Langevin Monte Carlo | [
"stat.ML",
"cs.LG"
] | We develop a new efficient method for high-dimensional sampling called Subspace Langevin Monte Carlo. The primary application of these methods is to efficiently implement Preconditioned Langevin Monte Carlo. To demonstrate the usefulness of this new method, we extend ideas from subspace descent methods in Euclidean spa... | {
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2412.13933 | Investigating the Effects of Diffusion-based Conditional Generative
Speech Models Used for Speech Enhancement on Dysarthric Speech | [
"eess.AS",
"cs.LG",
"cs.SD"
] | In this study, we aim to explore the effect of pre-trained conditional generative speech models for the first time on dysarthric speech due to Parkinson's disease recorded in an ideal/non-noisy condition. Considering one category of generative models, i.e., diffusion-based speech enhancement, these models are previousl... | {
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2412.13935 | Spatio-Temporal Forecasting of PM2.5 via Spatial-Diffusion guided
Encoder-Decoder Architecture | [
"cs.LG",
"cs.AI"
] | In many problem settings that require spatio-temporal forecasting, the values in the time-series not only exhibit spatio-temporal correlations but are also influenced by spatial diffusion across locations. One such example is forecasting the concentration of fine particulate matter (PM2.5) in the atmosphere which is in... | {
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2412.13939 | Security and Privacy of Digital Twins for Advanced Manufacturing: A
Survey | [
"eess.SY",
"cs.SY"
] | In Industry 4.0, the digital twin is one of the emerging technologies, offering simulation abilities to predict, refine, and interpret conditions and operations, where it is crucial to emphasize a heightened concentration on the associated security and privacy risks. To be more specific, the adoption of digital twins i... | {
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2412.13942 | A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies
for Human Explanations to Collect Label Distributions on NLI | [
"cs.CL"
] | Disagreement in human labeling is ubiquitous, and can be captured in human judgment distributions (HJDs). Recent research has shown that explanations provide valuable information for understanding human label variation (HLV) and large language models (LLMs) can approximate HJD from a few human-provided label-explanatio... | {
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2412.13943 | On Explaining Knowledge Distillation: Measuring and Visualising the
Knowledge Transfer Process | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Knowledge distillation (KD) remains challenging due to the opaque nature of the knowledge transfer process from a Teacher to a Student, making it difficult to address certain issues related to KD. To address this, we proposed UniCAM, a novel gradient-based visual explanation method, which effectively interprets the kno... | {
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2412.13947 | Real Classification by Description: Extending CLIP's Limits of Part
Attributes Recognition | [
"cs.CV"
] | In this study, we define and tackle zero shot "real" classification by description, a novel task that evaluates the ability of Vision-Language Models (VLMs) like CLIP to classify objects based solely on descriptive attributes, excluding object class names. This approach highlights the current limitations of VLMs in und... | {
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2412.13949 | Cracking the Code of Hallucination in LVLMs with Vision-aware Head
Divergence | [
"cs.CL",
"cs.CV"
] | Large vision-language models (LVLMs) have made substantial progress in integrating large language models (LLMs) with visual inputs, enabling advanced multimodal reasoning. Despite their success, a persistent challenge is hallucination-where generated text fails to accurately reflect visual content-undermining both accu... | {
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2412.13950 | Generation of Large District Heating System Models Using Open-Source
Data and Tools: An Exemplary Workflow | [
"eess.SY",
"cs.SY"
] | District heating (DH) systems play a pivotal role in decarbonizing the building sector's heat supply. While innovative low-exergy DH and cooling systems are increasingly adopted in new developments, the transformation of existing DH systems remains critical, as many still depend on fossil-based heating plants. Achievin... | {
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2412.13952 | Prompting Strategies for Enabling Large Language Models to Infer
Causation from Correlation | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The reasoning abilities of Large Language Models (LLMs) are attracting increasing attention. In this work, we focus on causal reasoning and address the task of establishing causal relationships based on correlation information, a highly challenging problem on which several LLMs have shown poor performance. We introduce... | {
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2412.13953 | Towards privacy-preserving cooperative control via encrypted distributed
optimization | [
"eess.SY",
"cs.SY"
] | Cooperative control is crucial for the effective operation of dynamical multi-agent systems. Especially for distributed control schemes, it is essential to exchange data between the agents. This becomes a privacy threat if the data is sensitive. Encrypted control has shown the potential to address this risk and ensure ... | {
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2412.13957 | Self-attentive Transformer for Fast and Accurate Postprocessing of
Temperature and Wind Speed Forecasts | [
"cs.LG",
"physics.ao-ph"
] | Current postprocessing techniques often require separate models for each lead time and disregard possible inter-ensemble relationships by either correcting each member separately or by employing distributional approaches. In this work, we tackle these shortcomings with an innovative, fast and accurate Transformer which... | {
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2412.13961 | Harvesting energy from turbulent winds with Reinforcement Learning | [
"cs.LG",
"cs.SY",
"eess.SY",
"physics.flu-dyn"
] | Airborne Wind Energy (AWE) is an emerging technology designed to harness the power of high-altitude winds, offering a solution to several limitations of conventional wind turbines. AWE is based on flying devices (usually gliders or kites) that, tethered to a ground station and driven by the wind, convert its mechanical... | {
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2412.13962 | Threshold UCT: Cost-Constrained Monte Carlo Tree Search with Pareto
Curves | [
"cs.AI"
] | Constrained Markov decision processes (CMDPs), in which the agent optimizes expected payoffs while keeping the expected cost below a given threshold, are the leading framework for safe sequential decision making under stochastic uncertainty. Among algorithms for planning and learning in CMDPs, methods based on Monte Ca... | {
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2412.13964 | DODGE: Ontology-Aware Risk Assessment via Object-Oriented Disruption
Graphs | [
"cs.AI",
"cs.LO"
] | When considering risky events or actions, we must not downplay the role of involved objects: a charged battery in our phone averts the risk of being stranded in the desert after a flat tyre, and a functional firewall mitigates the risk of a hacker intruding the network. The Common Ontology of Value and Risk (COVER) hig... | {
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2412.13965 | What If: Causal Analysis with Graph Databases | [
"cs.DB"
] | Graphs are expressive abstractions representing more effectively relationships in data and enabling data science tasks. They are also a widely adopted paradigm in causal inference focusing on causal directed acyclic graphs. Causal DAGs (Directed Acyclic Graphs) are manually curated by domain experts, but they are never... | {
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2412.13966 | Comparative Analysis of Machine Learning-Based Imputation Techniques for
Air Quality Datasets with High Missing Data Rates | [
"cs.LG",
"physics.data-an"
] | Urban pollution poses serious health risks, particularly in relation to traffic-related air pollution, which remains a major concern in many cities. Vehicle emissions contribute to respiratory and cardiovascular issues, especially for vulnerable and exposed road users like pedestrians and cyclists. Therefore, accurate ... | {
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2412.13972 | Decentralized Convergence to Equilibrium Prices in Trading Networks | [
"cs.GT",
"cs.MA"
] | We propose a decentralized market model in which agents can negotiate bilateral contracts. This builds on a similar, but centralized, model of trading networks introduced by Hatfield et al. in 2013. Prior work has established that fully-substitutable preferences guarantee the existence of competitive equilibria which c... | {
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2412.13973 | Model-Agnostic Cosmological Inference with SDSS-IV eBOSS: Simultaneous
Probing for Background and Perturbed Universe | [
"astro-ph.CO",
"cs.LG",
"gr-qc"
] | Here we explore certain subtle features imprinted in data from the completed Sloan Digital Sky Survey IV (SDSS-IV) extended Baryon Oscillation Spectroscopic Survey (eBOSS) as a combined probe for the background and perturbed Universe. We reconstruct the baryon Acoustic Oscillation (BAO) and Redshift Space Distortion (R... | {
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2412.13982 | LeStrat-Net: Lebesgue style stratification for Monte Carlo simulations
powered by machine learning | [
"hep-ph",
"cs.LG"
] | We develop a machine learning algorithm to turn around stratification in Monte Carlo sampling. We use a different way to divide the domain space of the integrand, based on the height of the function being sampled, similar to what is done in Lebesgue integration. This means that isocontours of the function define region... | {
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2412.13983 | GraphAvatar: Compact Head Avatars with GNN-Generated 3D Gaussians | [
"cs.CV"
] | Rendering photorealistic head avatars from arbitrary viewpoints is crucial for various applications like virtual reality. Although previous methods based on Neural Radiance Fields (NeRF) can achieve impressive results, they lack fidelity and efficiency. Recent methods using 3D Gaussian Splatting (3DGS) have improved re... | {
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2412.13988 | RAG for Effective Supply Chain Security Questionnaire Automation | [
"cs.LG"
] | In an era where digital security is crucial, efficient processing of security-related inquiries through supply chain security questionnaires is imperative. This paper introduces a novel approach using Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG) to automate these responses. We developed Qu... | {
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2412.13989 | What makes a good metric? Evaluating automatic metrics for text-to-image
consistency | [
"cs.CL"
] | Language models are increasingly being incorporated as components in larger AI systems for various purposes, from prompt optimization to automatic evaluation. In this work, we analyze the construct validity of four recent, commonly used methods for measuring text-to-image consistency - CLIPScore, TIFA, VPEval, and DSG ... | {
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2412.13993 | Variance-based loss function for improved regularization | [
"math.OC",
"cs.LG"
] | In deep learning, the mean of a chosen error metric, such as squared or absolute error, is commonly used as a loss function. While effective in reducing the average error, this approach often fails to address localized outliers, leading to significant inaccuracies in regions with sharp gradients or discontinuities. Thi... | {
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2412.13994 | Modality-Independent Graph Neural Networks with Global Transformers for
Multimodal Recommendation | [
"cs.SI",
"cs.LG"
] | Multimodal recommendation systems can learn users' preferences from existing user-item interactions as well as the semantics of multimodal data associated with items. Many existing methods model this through a multimodal user-item graph, approaching multimodal recommendation as a graph learning task. Graph Neural Netwo... | {
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2412.13998 | Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with
Neural Processes | [
"cs.LG",
"cs.AI"
] | As large language models (LLMs) become increasingly embedded in everyday applications, ensuring their alignment with the diverse preferences of individual users has become a critical challenge. Currently deployed approaches typically assume homogeneous user objectives and rely on single-objective fine-tuning. However, ... | {
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2412.14002 | Operator Splitting for Convex Constrained Markov Decision Processes | [
"math.OC",
"cs.SY",
"eess.SY"
] | We consider finite Markov decision processes (MDPs) with convex constraints and known dynamics. In principle, this problem is amenable to off-the-shelf convex optimization solvers, but typically this approach suffers from poor scalability. In this work, we develop a first-order algorithm, based on the Douglas-Rachford ... | {
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2412.14003 | Robust Optimal Safe and Stability Guaranteeing Reinforcement Learning
Control for Quadcopter | [
"eess.SY",
"cs.SY"
] | Recent advances in deep learning have provided new data-driven ways of controller design to replace the traditional manual synthesis and certification approaches. Employing neural network (NN) as controllers however, presents its own challenge: that of certifying stability due to their inherent complex nonlinearity, an... | {
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2412.14005 | Real-Time Position-Aware View Synthesis from Single-View Input | [
"cs.CV",
"cs.GR",
"cs.MM"
] | Recent advancements in view synthesis have significantly enhanced immersive experiences across various computer graphics and multimedia applications, including telepresence, and entertainment. By enabling the generation of new perspectives from a single input view, view synthesis allows users to better perceive and int... | {
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2412.14006 | InstructSeg: Unifying Instructed Visual Segmentation with Multi-modal
Large Language Models | [
"cs.CV"
] | Boosted by Multi-modal Large Language Models (MLLMs), text-guided universal segmentation models for the image and video domains have made rapid progress recently. However, these methods are often developed separately for specific domains, overlooking the similarities in task settings and solutions across these two area... | {
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2412.14008 | FarExStance: Explainable Stance Detection for Farsi | [
"cs.CL"
] | We introduce FarExStance, a new dataset for explainable stance detection in Farsi. Each instance in this dataset contains a claim, the stance of an article or social media post towards that claim, and an extractive explanation which provides evidence for the stance label. We compare the performance of a fine-tuned mult... | {
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2412.14009 | Cognition Chain for Explainable Psychological Stress Detection on Social
Media | [
"cs.AI",
"cs.CL",
"cs.HC"
] | Stress is a pervasive global health issue that can lead to severe mental health problems. Early detection offers timely intervention and prevention of stress-related disorders. The current early detection models perform "black box" inference suffering from limited explainability and trust which blocks the real-world cl... | {
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2412.14011 | Towards an optimised evaluation of teachers' discourse: The case of
engaging messages | [
"cs.CL"
] | Evaluating teachers' skills is crucial for enhancing education quality and student outcomes. Teacher discourse, significantly influencing student performance, is a key component. However, coding this discourse can be laborious. This study addresses this issue by introducing a new methodology for optimising the assessme... | {
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2412.14015 | Prompting Depth Anything for 4K Resolution Accurate Metric Depth
Estimation | [
"cs.CV"
] | Prompts play a critical role in unleashing the power of language and vision foundation models for specific tasks. For the first time, we introduce prompting into depth foundation models, creating a new paradigm for metric depth estimation termed Prompt Depth Anything. Specifically, we use a low-cost LiDAR as the prompt... | {
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2412.14017 | Turbo product decoding of cubic tensor codes | [
"cs.IT",
"math.IT"
] | Long, powerful soft detection forward error correction codes are typically constructed by concatenation of shorter component codes that are decoded through iterative Soft-Input Soft-Output (SISO) procedures. The current gold-standard is Low Density Parity Check (LDPC) codes, which are built from weak single parity chec... | {
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2412.14018 | SurgSora: Decoupled RGBD-Flow Diffusion Model for Controllable Surgical
Video Generation | [
"cs.CV",
"cs.AI",
"cs.MM",
"cs.RO"
] | Medical video generation has transformative potential for enhancing surgical understanding and pathology insights through precise and controllable visual representations. However, current models face limitations in controllability and authenticity. To bridge this gap, we propose SurgSora, a motion-controllable surgical... | {
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2412.14019 | Discovery of Maximally Consistent Causal Orders with Large Language
Models | [
"cs.AI"
] | Causal discovery is essential for understanding complex systems, as it aims to uncover causal relationships from observational data in the form of a causal directed acyclic graph (DAG). However, traditional methods often rely on strong, untestable assumptions, which makes them unreliable in real applications. Large Lan... | {
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2412.14020 | Landscape of AI safety concerns -- A methodology to support safety
assurance for AI-based autonomous systems | [
"cs.LG",
"cs.AI"
] | Artificial Intelligence (AI) has emerged as a key technology, driving advancements across a range of applications. Its integration into modern autonomous systems requires assuring safety. However, the challenge of assuring safety in systems that incorporate AI components is substantial. The lack of concrete specificati... | {
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2412.14021 | Flow Exporter Impact on Intelligent Intrusion Detection Systems | [
"cs.CR",
"cs.LG"
] | High-quality datasets are critical for training machine learning models, as inconsistencies in feature generation can hinder the accuracy and reliability of threat detection. For this reason, ensuring the quality of the data in network intrusion detection datasets is important. A key component of this is using reliable... | {
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2412.14025 | A Cognitive Ideation Support Framework using IBM Watson Services | [
"cs.IR"
] | Ideas generation is a core activity for innovation in organizations. The creativity of the generated ideas depends not only on the knowledge retrieved from the organizations' knowledge bases, but also on the external knowledge retrieved from other resources. Unfortunately, organizations often cannot efficiently utilize... | {
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2412.14030 | Machine learning in wastewater treatment: insights from modelling a
pilot denitrification reactor | [
"cs.LG"
] | Wastewater treatment plants are increasingly recognized as promising candidates for machine learning applications, due to their societal importance and high availability of data. However, their varied designs, operational conditions, and influent characteristics hinder straightforward automation. In this study, we use ... | {
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2412.14031 | Gauss-Newton Dynamics for Neural Networks: A Riemannian Optimization
Perspective | [
"math.OC",
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY",
"stat.ML"
] | We analyze the convergence of Gauss-Newton dynamics for training neural networks with smooth activation functions. In the underparameterized regime, the Gauss-Newton gradient flow induces a Riemannian gradient flow on a low-dimensional, smooth, embedded submanifold of the Euclidean output space. Using tools from Rieman... | {
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2412.14033 | Hansel: Output Length Controlling Framework for Large Language Models | [
"cs.CL",
"cs.LG"
] | Despite the great success of large language models (LLMs), efficiently controlling the length of the output sequence still remains a challenge. In this paper, we propose Hansel, an efficient framework for length control in LLMs without affecting its generation ability. Hansel utilizes periodically outputted hidden spec... | {
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2412.14039 | Spatio-Temporal SIR Model of Pandemic Spread During Warfare with Optimal
Dual-use Healthcare System Administration using Deep Reinforcement Learning | [
"q-bio.QM",
"cs.LG",
"cs.MA",
"physics.soc-ph"
] | Large-scale crises, including wars and pandemics, have repeatedly shaped human history, and their simultaneous occurrence presents profound challenges to societies. Understanding the dynamics of epidemic spread during warfare is essential for developing effective containment strategies in complex conflict zones. While ... | {
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2412.14042 | CAD-Recode: Reverse Engineering CAD Code from Point Clouds | [
"cs.CV"
] | Computer-Aided Design (CAD) models are typically constructed by sequentially drawing parametric sketches and applying CAD operations to obtain a 3D model. The problem of 3D CAD reverse engineering consists of reconstructing the sketch and CAD operation sequences from 3D representations such as point clouds. In this pap... | {
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2412.14048 | Evidential Deep Learning for Probabilistic Modelling of Extreme Storm
Events | [
"cs.LG"
] | Uncertainty quantification (UQ) methods play an important role in reducing errors in weather forecasting. Conventional approaches in UQ for weather forecasting rely on generating an ensemble of forecasts from physics-based simulations to estimate the uncertainty. However, it is computationally expensive to generate man... | {
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2412.14050 | Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual
LLMs: An Extensive Investigation | [
"cs.CL"
] | Recent generative large language models (LLMs) show remarkable performance in non-English languages, but when prompted in those languages they tend to express higher harmful social biases and toxicity levels. Prior work has shown that finetuning on specialized datasets can mitigate this behavior, and doing so in Englis... | {
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2412.14052 | Neural Combinatorial Optimization for Stochastic Flexible Job Shop
Scheduling Problems | [
"cs.AI",
"cs.LG",
"math.OC"
] | Neural combinatorial optimization (NCO) has gained significant attention due to the potential of deep learning to efficiently solve combinatorial optimization problems. NCO has been widely applied to job shop scheduling problems (JSPs) with the current focus predominantly on deterministic problems. In this paper, we pr... | {
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2412.14054 | Digestion Algorithm in Hierarchical Symbolic Forests: A Fast Text
Normalization Algorithm and Semantic Parsing Framework for Specific Scenarios
and Lightweight Deployment | [
"cs.CL",
"cs.AI"
] | Text Normalization and Semantic Parsing have numerous applications in natural language processing, such as natural language programming, paraphrasing, data augmentation, constructing expert systems, text matching, and more. Despite the prominent achievements of deep learning in Large Language Models (LLMs), the interpr... | {
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2412.14056 | A Review of Multimodal Explainable Artificial Intelligence: Past,
Present and Future | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG",
"cs.MM"
] | Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transp... | {
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2412.14058 | Towards Generalist Robot Policies: What Matters in Building
Vision-Language-Action Models | [
"cs.RO",
"cs.CV"
] | Foundation Vision Language Models (VLMs) exhibit strong capabilities in multi-modal representation learning, comprehension, and reasoning. By injecting action components into the VLMs, Vision-Language-Action Models (VLAs) can be naturally formed and also show promising performance. Existing work has demonstrated the ef... | {
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2412.14063 | Rango: Adaptive Retrieval-Augmented Proving for Automated Software
Verification | [
"cs.SE",
"cs.AI"
] | Formal verification using proof assistants, such as Coq, enables the creation of high-quality software. However, the verification process requires significant expertise and manual effort to write proofs. Recent work has explored automating proof synthesis using machine learning and large language models (LLMs). This wo... | {
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2412.14073 | A Computationally Grounded Framework for Cognitive Attitudes (extended
version) | [
"cs.LO",
"cs.AI"
] | We introduce a novel language for reasoning about agents' cognitive attitudes of both epistemic and motivational type. We interpret it by means of a computationally grounded semantics using belief bases. Our language includes five types of modal operators for implicit belief, complete attraction, complete repulsion, re... | {
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2412.14075 | Online MDP with Transition Prototypes: A Robust Adaptive Approach | [
"cs.LG"
] | In this work, we consider an online robust Markov Decision Process (MDP) where we have the information of finitely many prototypes of the underlying transition kernel. We consider an adaptively updated ambiguity set of the prototypes and propose an algorithm that efficiently identifies the true underlying transition ke... | {
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2412.14076 | Compositional Generalization Across Distributional Shifts with Sparse
Tree Operations | [
"cs.AI",
"cs.CL"
] | Neural networks continue to struggle with compositional generalization, and this issue is exacerbated by a lack of massive pre-training. One successful approach for developing neural systems which exhibit human-like compositional generalization is \textit{hybrid} neurosymbolic techniques. However, these techniques run ... | {
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2412.14077 | Dialogue with the Machine and Dialogue with the Art World: Evaluating
Generative AI for Culturally-Situated Creativity | [
"cs.CY",
"cs.AI"
] | This paper proposes dialogue as a method for evaluating generative AI tools for culturally-situated creative practice, that recognizes the socially situated nature of art. Drawing on sociologist Howard Becker's concept of Art Worlds, this method expands the scope of traditional AI and creativity evaluations beyond benc... | {
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2412.14080 | On the Robustness of Distributed Machine Learning against Transfer
Attacks | [
"cs.LG",
"cs.CR"
] | Although distributed machine learning (distributed ML) is gaining considerable attention in the community, prior works have independently looked at instances of distributed ML in either the training or the inference phase. No prior work has examined the combined robustness stemming from distributing both the learning a... | {
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2412.14085 | Future Research Avenues for Artificial Intelligence in Digital Gaming:
An Exploratory Report | [
"cs.LG",
"cs.AI",
"cs.HC"
] | Video games are a natural and synergistic application domain for artificial intelligence (AI) systems, offering both the potential to enhance player experience and immersion, as well as providing valuable benchmarks and virtual environments to advance AI technologies in general. This report presents a high-level overvi... | {
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2412.14087 | SEKE: Specialised Experts for Keyword Extraction | [
"cs.CL",
"cs.AI"
] | Keyword extraction involves identifying the most descriptive words in a document, allowing automatic categorisation and summarisation of large quantities of diverse textual data. Relying on the insight that real-world keyword detection often requires handling of diverse content, we propose a novel supervised keyword ex... | {
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2412.14088 | Joint Perception and Prediction for Autonomous Driving: A Survey | [
"cs.CV",
"cs.RO"
] | Perception and prediction modules are critical components of autonomous driving systems, enabling vehicles to navigate safely through complex environments. The perception module is responsible for perceiving the environment, including static and dynamic objects, while the prediction module is responsible for predicting... | {
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2412.14089 | On the Use of Abundant Road Speed Data for Travel Demand Calibration of
Urban Traffic Simulators | [
"cs.MA"
] | This work develops a compute-efficient algorithm to tackle a fundamental problem in transportation: that of urban travel demand estimation. It focuses on the calibration of origin-destination travel demand input parameters for high-resolution traffic simulation models. It considers the use of abundant traffic road spee... | {
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2412.14093 | Alignment faking in large language models | [
"cs.AI",
"cs.CL",
"cs.LG"
] | We present a demonstration of a large language model engaging in alignment faking: selectively complying with its training objective in training to prevent modification of its behavior out of training. First, we give Claude 3 Opus a system prompt stating it is being trained to answer all queries, even harmful ones, whi... | {
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2412.14095 | Quantum Optimization for Energy Management: A Coherent Variational
Approach | [
"quant-ph",
"cs.SY",
"eess.SY"
] | This paper presents a quantum-enhanced optimization approach for solving optimal power flow (OPF) by integrating the interior point method (IPM) with a coherent variational quantum linear solver (CVQLS). The objective is to explore the applicability of quantum computing to power systems optimization and address the ass... | {
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2412.14097 | Adaptive Concept Bottleneck for Foundation Models Under Distribution
Shifts | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Advancements in foundation models (FMs) have led to a paradigm shift in machine learning. The rich, expressive feature representations from these pre-trained, large-scale FMs are leveraged for multiple downstream tasks, usually via lightweight fine-tuning of a shallow fully-connected network following the representatio... | {
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2412.14100 | Parameter-efficient Fine-tuning for improved Convolutional Baseline for
Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Automating brain tumor segmentation using deep learning methods is an ongoing challenge in medical imaging. Multiple lingering issues exist including domain-shift and applications in low-resource settings which brings a unique set of challenges including scarcity of data. As a step towards solving these specific proble... | {
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2412.14103 | Foundation Models Meet Low-Cost Sensors: Test-Time Adaptation for
Rescaling Disparity for Zero-Shot Metric Depth Estimation | [
"cs.CV"
] | The recent development of foundation models for monocular depth estimation such as Depth Anything paved the way to zero-shot monocular depth estimation. Since it returns an affine-invariant disparity map, the favored technique to recover the metric depth consists in fine-tuning the model. However, this stage is costly ... | {
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2412.14109 | Machine Learning Co-pilot for Screening of Organic Molecular Additives
for Perovskite Solar Cells | [
"cs.LG",
"cond-mat.mtrl-sci",
"physics.app-ph"
] | Machine learning (ML) has been extensively employed in planar perovskite photovoltaics to screen effective organic molecular additives, while encountering predictive biases for novel materials due to small datasets and reliance on predefined descriptors. Present work thus proposes an effective approach, Co-Pilot for Pe... | {
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2412.14111 | Event-based Photometric Bundle Adjustment | [
"cs.CV",
"cs.RO",
"eess.SP",
"math.OC"
] | We tackle the problem of bundle adjustment (i.e., simultaneous refinement of camera poses and scene map) for a purely rotating event camera. Starting from first principles, we formulate the problem as a classical non-linear least squares optimization. The photometric error is defined using the event generation model di... | {
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2412.14113 | Adversarial Hubness in Multi-Modal Retrieval | [
"cs.CR",
"cs.IR"
] | Hubness is a phenomenon in high-dimensional vector spaces where a single point from the natural distribution is unusually close to many other points. This is a well-known problem in information retrieval that causes some items to accidentally (and incorrectly) appear relevant to many queries. In this paper, we investig... | {
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2412.14116 | Trustworthy Transfer Learning: A Survey | [
"cs.LG"
] | Transfer learning aims to transfer knowledge or information from a source domain to a relevant target domain. In this paper, we understand transfer learning from the perspectives of knowledge transferability and trustworthiness. This involves two research questions: How is knowledge transferability quantitatively measu... | {
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2412.14118 | GaraMoSt: Parallel Multi-Granularity Motion and Structural Modeling for
Efficient Multi-Frame Interpolation in DSA Images | [
"cs.CV"
] | The rapid and accurate direct multi-frame interpolation method for Digital Subtraction Angiography (DSA) images is crucial for reducing radiation and providing real-time assistance to physicians for precise diagnostics and treatment. DSA images contain complex vascular structures and various motions. Applying natural s... | {
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2412.14119 | Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network
Approach | [
"cs.NI",
"cs.SY",
"eess.SY"
] | The Open Radio Access Network (O-RAN) architecture enables the deployment of third-party applications on the RAN Intelligent Controllers (RICs). However, the operation of third-party applications in the Near Real-Time RIC (Near-RT RIC), known as xApps, may result in conflicting interactions. Each xApp can independently... | {
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2412.14123 | AnySat: An Earth Observation Model for Any Resolutions, Scales, and
Modalities | [
"cs.CV"
] | Geospatial models must adapt to the diversity of Earth observation data in terms of resolutions, scales, and modalities. However, existing approaches expect fixed input configurations, which limits their practical applicability. We propose AnySat, a multimodal model based on joint embedding predictive architecture (JEP... | {
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2412.14132 | jinns: a JAX Library for Physics-Informed Neural Networks | [
"stat.ML",
"cs.LG"
] | jinns is an open-source Python library for physics-informed neural networks, built to tackle both forward and inverse problems, as well as meta-model learning. Rooted in the JAX ecosystem, it provides a versatile framework for efficiently prototyping real-problems, while easily allowing extensions to specific needs. Fu... | {
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2412.14133 | Performance Gap in Entity Knowledge Extraction Across Modalities in
Vision Language Models | [
"cs.CL"
] | Vision-language models (VLMs) excel at extracting and reasoning about information from images. Yet, their capacity to leverage internal knowledge about specific entities remains underexplored. This work investigates the disparity in model performance when answering factual questions about an entity described in text ve... | {
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2412.14135 | Scaling of Search and Learning: A Roadmap to Reproduce o1 from
Reinforcement Learning Perspective | [
"cs.AI",
"cs.LG"
] | OpenAI o1 represents a significant milestone in Artificial Inteiligence, which achieves expert-level performances on many challanging tasks that require strong reasoning ability.OpenAI has claimed that the main techinique behinds o1 is the reinforcement learining. Recent works use alternative approaches like knowledge ... | {
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2412.14137 | Design choices made by LLM-based test generators prevent them from
finding bugs | [
"cs.SE",
"cs.AI"
] | There is an increasing amount of research and commercial tools for automated test case generation using Large Language Models (LLMs). This paper critically examines whether recent LLM-based test generation tools, such as Codium CoverAgent and CoverUp, can effectively find bugs or unintentionally validate faulty code. C... | {
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2412.14140 | GLIDER: Grading LLM Interactions and Decisions using Explainable Ranking | [
"cs.CL",
"cs.AI"
] | The LLM-as-judge paradigm is increasingly being adopted for automated evaluation of model outputs. While LLM judges have shown promise on constrained evaluation tasks, closed source LLMs display critical shortcomings when deployed in real world applications due to challenges of fine grained metrics and explainability, ... | {
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2412.14141 | LLMs can Realize Combinatorial Creativity: Generating Creative Ideas via
LLMs for Scientific Research | [
"cs.AI"
] | Scientific idea generation has been extensively studied in creativity theory and computational creativity research, providing valuable frameworks for understanding and implementing creative processes. However, recent work using Large Language Models (LLMs) for research idea generation often overlooks these theoretical ... | {
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2412.14142 | On Calibration in Multi-Distribution Learning | [
"cs.LG"
] | Modern challenges of robustness, fairness, and decision-making in machine learning have led to the formulation of multi-distribution learning (MDL) frameworks in which a predictor is optimized across multiple distributions. We study the calibration properties of MDL to better understand how the predictor performs unifo... | {
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2412.14145 | Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic
Segmentation | [
"cs.CV"
] | The visual understanding are often approached from 3 granular levels: image, patch and pixel. Visual Tokenization, trained by self-supervised reconstructive learning, compresses visual data by codebook in patch-level with marginal information loss, but the visual tokens does not have semantic meaning. Open Vocabulary s... | {
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2412.14146 | ARTEMIS-DA: An Advanced Reasoning and Transformation Engine for
Multi-Step Insight Synthesis in Data Analytics | [
"cs.AI",
"cs.DB",
"cs.IR",
"cs.MA"
] | This paper presents the Advanced Reasoning and Transformation Engine for Multi-Step Insight Synthesis in Data Analytics (ARTEMIS-DA), a novel framework designed to augment Large Language Models (LLMs) for solving complex, multi-step data analytics tasks. ARTEMIS-DA integrates three core components: the Planner, which d... | {
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2412.14148 | MCMat: Multiview-Consistent and Physically Accurate PBR Material
Generation | [
"cs.CV"
] | Existing 2D methods utilize UNet-based diffusion models to generate multi-view physically-based rendering (PBR) maps but struggle with multi-view inconsistency, while some 3D methods directly generate UV maps, encountering generalization issues due to the limited 3D data. To address these problems, we propose a two-sta... | {
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2412.14158 | AKiRa: Augmentation Kit on Rays for optical video generation | [
"cs.CV",
"cs.AI",
"cs.MM"
] | Recent advances in text-conditioned video diffusion have greatly improved video quality. However, these methods offer limited or sometimes no control to users on camera aspects, including dynamic camera motion, zoom, distorted lens and focus shifts. These motion and optical aspects are crucial for adding controllabilit... | {
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} |
2412.14161 | TheAgentCompany: Benchmarking LLM Agents on Consequential Real World
Tasks | [
"cs.CL"
] | We interact with computers on an everyday basis, be it in everyday life or work, and many aspects of work can be done entirely with access to a computer and the Internet. At the same time, thanks to improvements in large language models (LLMs), there has also been a rapid development in AI agents that interact with and... | {
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} |
2412.14164 | MetaMorph: Multimodal Understanding and Generation via Instruction
Tuning | [
"cs.CV"
] | In this work, we propose Visual-Predictive Instruction Tuning (VPiT) - a simple and effective extension to visual instruction tuning that enables a pretrained LLM to quickly morph into an unified autoregressive model capable of generating both text and visual tokens. VPiT teaches an LLM to predict discrete text tokens ... | {
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} |
2412.14166 | MegaSynth: Scaling Up 3D Scene Reconstruction with Synthesized Data | [
"cs.CV"
] | We propose scaling up 3D scene reconstruction by training with synthesized data. At the core of our work is MegaSynth, a procedurally generated 3D dataset comprising 700K scenes - over 50 times larger than the prior real dataset DL3DV - dramatically scaling the training data. To enable scalable data generation, our key... | {
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2412.14167 | VideoDPO: Omni-Preference Alignment for Video Diffusion Generation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Recent progress in generative diffusion models has greatly advanced text-to-video generation. While text-to-video models trained on large-scale, diverse datasets can produce varied outputs, these generations often deviate from user preferences, highlighting the need for preference alignment on pre-trained models. Altho... | {
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2412.14168 | FashionComposer: Compositional Fashion Image Generation | [
"cs.CV"
] | We present FashionComposer for compositional fashion image generation. Unlike previous methods, FashionComposer is highly flexible. It takes multi-modal input (i.e., text prompt, parametric human model, garment image, and face image) and supports personalizing the appearance, pose, and figure of the human and assigning... | {
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2412.14169 | Autoregressive Video Generation without Vector Quantization | [
"cs.CV"
] | This paper presents a novel approach that enables autoregressive video generation with high efficiency. We propose to reformulate the video generation problem as a non-quantized autoregressive modeling of temporal frame-by-frame prediction and spatial set-by-set prediction. Unlike raster-scan prediction in prior autore... | {
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} |
2412.14170 | E-CAR: Efficient Continuous Autoregressive Image Generation via
Multistage Modeling | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Recent advances in autoregressive (AR) models with continuous tokens for image generation show promising results by eliminating the need for discrete tokenization. However, these models face efficiency challenges due to their sequential token generation nature and reliance on computationally intensive diffusion-based s... | {
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} |
2412.14171 | Thinking in Space: How Multimodal Large Language Models See, Remember,
and Recall Spaces | [
"cs.CV"
] | Humans possess the visual-spatial intelligence to remember spaces from sequential visual observations. However, can Multimodal Large Language Models (MLLMs) trained on million-scale video datasets also ``think in space'' from videos? We present a novel video-based visual-spatial intelligence benchmark (VSI-Bench) of ov... | {
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} |
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