id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.12944 | Online optimisation for dynamic electrical impedance tomography | [
"math.OC",
"cs.CV"
] | Online optimisation studies the convergence of optimisation methods as the data embedded in the problem changes. Based on this idea, we propose a primal dual online method for nonlinear time-discrete inverse problems. We analyse the method through regret theory and demonstrate its performance in real-time monitoring of... | {
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2412.12948 | MOPO: Multi-Objective Prompt Optimization for Affective Text Generation | [
"cs.CL"
] | How emotions are expressed depends on the context and domain. On X (formerly Twitter), for instance, an author might simply use the hashtag #anger, while in a news headline, emotions are typically written in a more polite, indirect manner. To enable conditional text generation models to create emotionally connotated te... | {
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2412.12949 | Synthetic Data Generation for Anomaly Detection on Table Grapes | [
"cs.CV",
"cs.RO"
] | Early detection of illnesses and pest infestations in fruit cultivation is critical for maintaining yield quality and plant health. Computer vision and robotics are increasingly employed for the automatic detection of such issues, particularly using data-driven solutions. However, the rarity of these problems makes acq... | {
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2412.12951 | FineGates: LLMs Finetuning with Compression using Stochastic Gates | [
"cs.LG"
] | Large Language Models (LLMs), with billions of parameters, present significant challenges for full finetuning due to the high computational demands, memory requirements, and impracticality of many real-world applications. When faced with limited computational resources or small datasets, updating all model parameters c... | {
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2412.12953 | Efficient Diffusion Transformer Policies with Mixture of Expert
Denoisers for Multitask Learning | [
"cs.LG",
"cs.RO"
] | Diffusion Policies have become widely used in Imitation Learning, offering several appealing properties, such as generating multimodal and discontinuous behavior. As models are becoming larger to capture more complex capabilities, their computational demands increase, as shown by recent scaling laws. Therefore, continu... | {
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2412.12954 | Recipient Profiling: Predicting Characteristics from Messages | [
"cs.CL"
] | It has been shown in the field of Author Profiling that texts may inadvertently reveal sensitive information about their authors, such as gender or age. This raises important privacy concerns that have been extensively addressed in the literature, in particular with the development of methods to hide such information. ... | {
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2412.12955 | Learning from Noisy Labels via Self-Taught On-the-Fly Meta Loss
Rescaling | [
"cs.CL"
] | Correct labels are indispensable for training effective machine learning models. However, creating high-quality labels is expensive, and even professionally labeled data contains errors and ambiguities. Filtering and denoising can be applied to curate labeled data prior to training, at the cost of additional processing... | {
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2412.12956 | SnakModel: Lessons Learned from Training an Open Danish Large Language
Model | [
"cs.CL"
] | We present SnakModel, a Danish large language model (LLM) based on Llama2-7B, which we continuously pre-train on 13.6B Danish words, and further tune on 3.7M Danish instructions. As best practices for creating LLMs for smaller language communities have yet to be established, we examine the effects of early modeling and... | {
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2412.12961 | Adaptations of AI models for querying the LandMatrix database in natural
language | [
"cs.CL"
] | The Land Matrix initiative (https://landmatrix.org) and its global observatory aim to provide reliable data on large-scale land acquisitions to inform debates and actions in sectors such as agriculture, extraction, or energy in low- and middle-income countries. Although these data are recognized in the academic world, ... | {
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2412.12966 | Fruit Deformity Classification through Single-Input and Multi-Input
Architectures based on CNN Models using Real and Synthetic Images | [
"cs.CV"
] | The present study focuses on detecting the degree of deformity in fruits such as apples, mangoes, and strawberries during the process of inspecting their external quality, employing Single-Input and Multi-Input architectures based on convolutional neural network (CNN) models using sets of real and synthetic images. The... | {
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2412.12968 | On Local Overfitting and Forgetting in Deep Neural Networks | [
"cs.LG"
] | The infrequent occurrence of overfitting in deep neural networks is perplexing: contrary to theoretical expectations, increasing model size often enhances performance in practice. But what if overfitting does occur, though restricted to specific sub-regions of the data space? In this work, we propose a novel score that... | {
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2412.12971 | ArchesWeather & ArchesWeatherGen: a deterministic and generative model
for efficient ML weather forecasting | [
"cs.LG"
] | Weather forecasting plays a vital role in today's society, from agriculture and logistics to predicting the output of renewable energies, and preparing for extreme weather events. Deep learning weather forecasting models trained with the next state prediction objective on ERA5 have shown great success compared to numer... | {
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2412.12974 | Attentive Eraser: Unleashing Diffusion Model's Object Removal Potential
via Self-Attention Redirection Guidance | [
"cs.CV"
] | Recently, diffusion models have emerged as promising newcomers in the field of generative models, shining brightly in image generation. However, when employed for object removal tasks, they still encounter issues such as generating random artifacts and the incapacity to repaint foreground object areas with appropriate ... | {
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2412.12981 | Unlocking LLMs: Addressing Scarce Data and Bias Challenges in Mental
Health | [
"cs.CL"
] | Large language models (LLMs) have shown promising capabilities in healthcare analysis but face several challenges like hallucinations, parroting, and bias manifestation. These challenges are exacerbated in complex, sensitive, and low-resource domains. Therefore, in this work we introduce IC-AnnoMI, an expert-annotated ... | {
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2412.12982 | Stable Diffusion is a Natural Cross-Modal Decoder for Layered
AI-generated Image Compression | [
"eess.IV",
"cs.CV"
] | Recent advances in Artificial Intelligence Generated Content (AIGC) have garnered significant interest, accompanied by an increasing need to transmit and compress the vast number of AI-generated images (AIGIs). However, there is a noticeable deficiency in research focused on compression methods for AIGIs. To address th... | {
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2412.12984 | Cluster-guided Contrastive Class-imbalanced Graph Classification | [
"cs.LG",
"cs.AI",
"cs.IR",
"cs.SI"
] | This paper studies the problem of class-imbalanced graph classification, which aims at effectively classifying the graph categories in scenarios with imbalanced class distributions. While graph neural networks (GNNs) have achieved remarkable success, their modeling ability on imbalanced graph-structured data remains su... | {
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2412.12987 | Stochastic interior-point methods for smooth conic optimization with
applications | [
"math.OC",
"cs.AI",
"cs.LG"
] | Conic optimization plays a crucial role in many machine learning (ML) problems. However, practical algorithms for conic constrained ML problems with large datasets are often limited to specific use cases, as stochastic algorithms for general conic optimization remain underdeveloped. To fill this gap, we introduce a sto... | {
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2412.12990 | Future Aspects in Human Action Recognition: Exploring Emerging
Techniques and Ethical Influences | [
"cs.CV",
"cs.ET",
"cs.RO"
] | Visual-based human action recognition can be found in various application fields, e.g., surveillance systems, sports analytics, medical assistive technologies, or human-robot interaction frameworks, and it concerns the identification and classification of individuals' activities within a video. Since actions typically ... | {
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2412.12994 | Model agnostic signal encoding by leaky integrate and fire, performance
and uncertainty | [
"math.FA",
"cs.IT",
"math.CA",
"math.IT"
] | Integrate and fire is a resource efficient time-encoding mechanism that summarizes into a signed spike train those time intervals where a signal's charge exceeds a certain threshold. We analyze the IF encoder in terms of a very general notion of approximate bandwidth, which is shared by most commonly-used signal models... | {
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2412.12996 | Neural Control and Certificate Repair via Runtime Monitoring | [
"cs.LG",
"cs.AI"
] | Learning-based methods provide a promising approach to solving highly non-linear control tasks that are often challenging for classical control methods. To ensure the satisfaction of a safety property, learning-based methods jointly learn a control policy together with a certificate function for the property. Popular e... | {
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2412.12997 | Enabling Low-Resource Language Retrieval: Establishing Baselines for
Urdu MS MARCO | [
"cs.CL",
"cs.AI",
"cs.IR"
] | As the Information Retrieval (IR) field increasingly recognizes the importance of inclusivity, addressing the needs of low-resource languages remains a significant challenge. This paper introduces the first large-scale Urdu IR dataset, created by translating the MS MARCO dataset through machine translation. We establis... | {
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2412.13003 | Boosting Test Performance with Importance Sampling--a Subpopulation
Perspective | [
"cs.LG",
"stat.ML"
] | Despite empirical risk minimization (ERM) is widely applied in the machine learning community, its performance is limited on data with spurious correlation or subpopulation that is introduced by hidden attributes. Existing literature proposed techniques to maximize group-balanced or worst-group accuracy when such corre... | {
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2412.13006 | What is YOLOv6? A Deep Insight into the Object Detection Model | [
"cs.CV"
] | This work explores the YOLOv6 object detection model in depth, concentrating on its design framework, optimization techniques, and detection capabilities. YOLOv6's core elements consist of the EfficientRep Backbone for robust feature extraction and the Rep-PAN Neck for seamless feature aggregation, ensuring high-perfor... | {
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2412.13008 | RCLMuFN: Relational Context Learning and Multiplex Fusion Network for
Multimodal Sarcasm Detection | [
"cs.CL"
] | Sarcasm typically conveys emotions of contempt or criticism by expressing a meaning that is contrary to the speaker's true intent. Accurate detection of sarcasm aids in identifying and filtering undesirable information on the Internet, thereby reducing malicious defamation and rumor-mongering. Nonetheless, the task of ... | {
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2412.13010 | Measurement of Medial Elbow Joint Space using Landmark Detection | [
"cs.CV"
] | Ultrasound imaging of the medial elbow is crucial for the early identification of Ulnar Collateral Ligament (UCL) injuries. Specifically, measuring the elbow joint space in ultrasound images is used to assess the valgus instability of elbow. To automate this measurement, a precisely annotated dataset is necessary; howe... | {
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2412.13012 | Deep Learning Based Superconductivity: Prediction and Experimental Tests | [
"cs.LG",
"cond-mat.mtrl-sci",
"cond-mat.str-el"
] | The discovery of novel superconducting materials is a longstanding challenge in materials science, with a wealth of potential for applications in energy, transportation, and computing. Recent advances in artificial intelligence (AI) have enabled expediting the search for new materials by efficiently utilizing vast mate... | {
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2412.13017 | A New Adversarial Perspective for LiDAR-based 3D Object Detection | [
"cs.CV"
] | Autonomous vehicles (AVs) rely on LiDAR sensors for environmental perception and decision-making in driving scenarios. However, ensuring the safety and reliability of AVs in complex environments remains a pressing challenge. To address this issue, we introduce a real-world dataset (ROLiD) comprising LiDAR-scanned point... | {
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2412.13018 | OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in
Financial Domain | [
"cs.CL"
] | As a typical and practical application of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) techniques have gained extensive attention, particularly in vertical domains where LLMs may lack domain-specific knowledge. In this paper, we introduce an omnidirectional and automatic RAG benchmark, OmniEval, i... | {
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2412.13019 | The Temporal Vadalog System: Temporal Datalog-based Reasoning | [
"cs.DB"
] | In the wake of the recent resurgence of the Datalog language of databases, together with its extensions for ontological reasoning settings, this work aims to bridge the gap between the theoretical studies of DatalogMTL (Datalog extended with metric temporal logic) and the development of production-ready reasoning syste... | {
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2412.13021 | Queries, Representation & Detection: The Next 100 Model Fingerprinting
Schemes | [
"cs.LG",
"cs.CR"
] | The deployment of machine learning models in operational contexts represents a significant investment for any organisation. Consequently, the risk of these models being misappropriated by competitors needs to be addressed. In recent years, numerous proposals have been put forth to detect instances of model stealing. Ho... | {
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2412.13023 | Relational Neurosymbolic Markov Models | [
"cs.AI",
"cs.LG"
] | Sequential problems are ubiquitous in AI, such as in reinforcement learning or natural language processing. State-of-the-art deep sequential models, like transformers, excel in these settings but fail to guarantee the satisfaction of constraints necessary for trustworthy deployment. In contrast, neurosymbolic AI (NeSy)... | {
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2412.13025 | The free product of $q$-matroids | [
"math.CO",
"cs.IT",
"math.IT"
] | We introduce the notion of the free product of $q$-matroids, which is the $q$-analogue of the free product of matroids. We study the properties of this noncommutative binary operation, making an extensive use of the theory of cyclic flats. We show that the free product of two $q$-matroids $M_1$ and $M_2$ is maximal wit... | {
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2412.13026 | NAVCON: A Cognitively Inspired and Linguistically Grounded Corpus for
Vision and Language Navigation | [
"cs.CL",
"cs.CV"
] | We present NAVCON, a large-scale annotated Vision-Language Navigation (VLN) corpus built on top of two popular datasets (R2R and RxR). The paper introduces four core, cognitively motivated and linguistically grounded, navigation concepts and an algorithm for generating large-scale silver annotations of naturally occurr... | {
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2412.13028 | Identification of Epileptic Spasms (ESES) Phases Using EEG Signals: A
Vision Transformer Approach | [
"q-bio.NC",
"cs.CE"
] | This work introduces a new approach to the Epileptic Spasms (ESES) detection based on the EEG signals using Vision Transformers (ViT). Classic ESES detection approaches have usually been performed with manual processing or conventional algorithms, suffering from poor sample sizes, single-channel-based analyses, and low... | {
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2412.13030 | Are Data Experts Buying into Differentially Private Synthetic Data?
Gathering Community Perspectives | [
"cs.HC",
"cs.CR",
"cs.DB"
] | Data privacy is a core tenet of responsible computing, and in the United States, differential privacy (DP) is the dominant technical operationalization of privacy-preserving data analysis. With this study, we qualitatively examine one class of DP mechanisms: private data synthesizers. To that end, we conducted semi-str... | {
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2412.13033 | Singularity-Free Guiding Vector Field over B\'ezier's Curves Applied to
Rovers Path Planning and Path Following | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper presents a guidance algorithm for solving the problem of following parametric paths, as well as a curvature-varying speed setpoint for land-based car-type wheeled mobile robots (WMRs). The guidance algorithm relies on Singularity-Free Guiding Vector Fields SF-GVF. This novel GVF approach expands the desired ... | {
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2412.13036 | Open-Set Heterogeneous Domain Adaptation: Theoretical Analysis and
Algorithm | [
"cs.LG"
] | Domain adaptation (DA) tackles the issue of distribution shift by learning a model from a source domain that generalizes to a target domain. However, most existing DA methods are designed for scenarios where the source and target domain data lie within the same feature space, which limits their applicability in real-wo... | {
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2412.13041 | Harnessing Event Sensory Data for Error Pattern Prediction in Vehicles:
A Language Model Approach | [
"cs.CL",
"cs.LG"
] | In this paper, we draw an analogy between processing natural languages and processing multivariate event streams from vehicles in order to predict $\textit{when}$ and $\textit{what}$ error pattern is most likely to occur in the future for a given car. Our approach leverages the temporal dynamics and contextual relation... | {
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2412.13046 | Adaptive Economic Model Predictive Control: Performance Guarantees for
Nonlinear Systems | [
"eess.SY",
"cs.SY",
"math.OC"
] | We consider the problem of optimizing the economic performance of nonlinear constrained systems subject to uncertain time-varying parameters and bounded disturbances. In particular, we propose an adaptive economic model predictive control (MPC) framework that: (i) directly minimizes transient economic costs, (ii) addre... | {
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2412.13047 | EOGS: Gaussian Splatting for Earth Observation | [
"cs.CV"
] | Recently, Gaussian splatting has emerged as a strong alternative to NeRF, demonstrating impressive 3D modeling capabilities while requiring only a fraction of the training and rendering time. In this paper, we show how the standard Gaussian splatting framework can be adapted for remote sensing, retaining its high effic... | {
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2412.13049 | TIMESAFE: Timing Interruption Monitoring and Security Assessment for
Fronthaul Environments | [
"cs.NI",
"cs.CR",
"cs.LG",
"cs.SY",
"eess.SY"
] | 5G and beyond cellular systems embrace the disaggregation of Radio Access Network (RAN) components, exemplified by the evolution of the fronthual (FH) connection between cellular baseband and radio unit equipment. Crucially, synchronization over the FH is pivotal for reliable 5G services. In recent years, there has bee... | {
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2412.13050 | Modality-Inconsistent Continual Learning of Multimodal Large Language
Models | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.SD",
"eess.AS"
] | In this paper, we introduce Modality-Inconsistent Continual Learning (MICL), a new continual learning scenario for Multimodal Large Language Models (MLLMs) that involves tasks with inconsistent modalities (image, audio, or video) and varying task types (captioning or question-answering). Unlike existing vision-only or ... | {
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} |
2412.13053 | SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement
Learning in Continuous Control Tasks | [
"cs.LG",
"cs.AI"
] | Continuous control tasks often involve high-dimensional, dynamic, and non-linear environments. State-of-the-art performance in these tasks is achieved through complex closed-box policies that are effective, but suffer from an inherent opacity. Interpretable policies, while generally underperforming compared to their cl... | {
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2412.13057 | On the Hardness of Training Deep Neural Networks Discretely | [
"cs.LG"
] | We study neural network training (NNT): optimizing a neural network's parameters to minimize the training loss over a given dataset. NNT has been studied extensively under theoretic lenses, mainly on two-layer networks with linear or ReLU activation functions where the parameters can take any real value (here referred ... | {
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2412.13058 | CondiMen: Conditional Multi-Person Mesh Recovery | [
"cs.CV"
] | Multi-person human mesh recovery (HMR) consists in detecting all individuals in a given input image, and predicting the body shape, pose, and 3D location for each detected person. The dominant approaches to this task rely on neural networks trained to output a single prediction for each detected individual. In contrast... | {
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2412.13059 | 3D MedDiffusion: A 3D Medical Diffusion Model for Controllable and
High-quality Medical Image Generation | [
"eess.IV",
"cs.CV"
] | The generation of medical images presents significant challenges due to their high-resolution and three-dimensional nature. Existing methods often yield suboptimal performance in generating high-quality 3D medical images, and there is currently no universal generative framework for medical imaging. In this paper, we in... | {
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2412.13061 | VidTok: A Versatile and Open-Source Video Tokenizer | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Encoding video content into compact latent tokens has become a fundamental step in video generation and understanding, driven by the need to address the inherent redundancy in pixel-level representations. Consequently, there is a growing demand for high-performance, open-source video tokenizers as video-centric researc... | {
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2412.13063 | Smartphone-based Iris Recognition through High-Quality Visible Spectrum
Iris Capture | [
"eess.IV",
"cs.CV"
] | Iris recognition is widely acknowledged for its exceptional accuracy in biometric authentication, traditionally relying on near-infrared (NIR) imaging. Recently, visible spectrum (VIS) imaging via accessible smartphone cameras has been explored for biometric capture. However, a thorough study of iris recognition using ... | {
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2412.13070 | Learning of Patch-Based Smooth-Plus-Sparse Models for Image
Reconstruction | [
"eess.IV",
"cs.CV",
"cs.LG",
"eess.SP"
] | We aim at the solution of inverse problems in imaging, by combining a penalized sparse representation of image patches with an unconstrained smooth one. This allows for a straightforward interpretation of the reconstruction. We formulate the optimization as a bilevel problem. The inner problem deploys classical algorit... | {
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2412.13071 | CLASP: Contrastive Language-Speech Pretraining for Multilingual
Multimodal Information Retrieval | [
"cs.CL",
"cs.IR",
"cs.SD",
"eess.AS"
] | This study introduces CLASP (Contrastive Language-Speech Pretraining), a multilingual, multimodal representation tailored for audio-text information retrieval. CLASP leverages the synergy between spoken content and textual data. During training, we utilize our newly introduced speech-text dataset, which encompasses 15 ... | {
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2412.13074 | Predicting Change, Not States: An Alternate Framework for Neural PDE
Surrogates | [
"cs.LG"
] | Neural surrogates for partial differential equations (PDEs) have become popular due to their potential to quickly simulate physics. With a few exceptions, neural surrogates generally treat the forward evolution of time-dependent PDEs as a black box by directly predicting the next state. While this is a natural and easy... | {
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2412.13076 | Dual Interpretation of Machine Learning Forecasts | [
"econ.EM",
"cs.LG",
"stat.ML"
] | Machine learning predictions are typically interpreted as the sum of contributions of predictors. Yet, each out-of-sample prediction can also be expressed as a linear combination of in-sample values of the predicted variable, with weights corresponding to pairwise proximity scores between current and past economic even... | {
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2412.13079 | Identifying Bias in Deep Neural Networks Using Image Transforms | [
"cs.CV",
"cs.AI",
"cs.LG"
] | CNNs have become one of the most commonly used computational tool in the past two decades. One of the primary downsides of CNNs is that they work as a ``black box", where the user cannot necessarily know how the image data are analyzed, and therefore needs to rely on empirical evaluation to test the efficacy of a train... | {
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2412.13081 | Prompt Augmentation for Self-supervised Text-guided Image Manipulation | [
"cs.CV"
] | Text-guided image editing finds applications in various creative and practical fields. While recent studies in image generation have advanced the field, they often struggle with the dual challenges of coherent image transformation and context preservation. In response, our work introduces prompt augmentation, a method ... | {
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2412.13082 | Polyhedral Control Design: Theory and Methods | [
"math.OC",
"cs.SY",
"eess.SY"
] | In this article, we survey the primary research on polyhedral computing methods for constrained linear control systems. Our focus is on the modeling power of convex optimization, featured to design set-based robust and optimal controllers. In detail, we review the state-of-the-art techniques for computing geometric str... | {
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2412.13086 | Higher-Order Sinusoidal Input Describing Functions for Open-Loop and
Closed-Loop Reset Control with Application to Mechatronics Systems | [
"eess.SY",
"cs.SY"
] | Reset control enhances the performance of high-precision mechatronics systems. This paper introduces a generalized reset feedback control structure that integrates a single reset-state reset controller, a shaping filter for tuning reset actions, and linear compensators arranged in series and parallel configurations wit... | {
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2412.13091 | LMUnit: Fine-grained Evaluation with Natural Language Unit Tests | [
"cs.CL",
"cs.AI"
] | As language models become integral to critical workflows, assessing their behavior remains a fundamental challenge -- human evaluation is costly and noisy, while automated metrics provide only coarse, difficult-to-interpret signals. We introduce natural language unit tests, a paradigm that decomposes response quality i... | {
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2412.13093 | Reservoir Computing for Fast, Simplified Reinforcement Learning on
Memory Tasks | [
"cs.LG"
] | Tasks in which rewards depend upon past information not available in the current observation set can only be solved by agents that are equipped with short-term memory. Usual choices for memory modules include trainable recurrent hidden layers, often with gated memory. Reservoir computing presents an alternative, in whi... | {
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2412.13096 | Incremental Online Learning of Randomized Neural Network with Forward
Regularization | [
"cs.LG",
"cs.CV"
] | Online learning of deep neural networks suffers from challenges such as hysteretic non-incremental updating, increasing memory usage, past retrospective retraining, and catastrophic forgetting. To alleviate these drawbacks and achieve progressive immediate decision-making, we propose a novel Incremental Online Learning... | {
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2412.13098 | Uchaguzi-2022: A Dataset of Citizen Reports on the 2022 Kenyan Election | [
"cs.CL",
"cs.SI"
] | Online reporting platforms have enabled citizens around the world to collectively share their opinions and report in real time on events impacting their local communities. Systematically organizing (e.g., categorizing by attributes) and geotagging large amounts of crowdsourced information is crucial to ensuring that ac... | {
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2412.13099 | Accuracy Limits as a Barrier to Biometric System Security | [
"cs.CR",
"cs.CV"
] | Biometric systems are widely used for identity verification and identification, including authentication (i.e., one-to-one matching to verify a claimed identity) and identification (i.e., one-to-many matching to find a subject in a database). The matching process relies on measuring similarities or dissimilarities betw... | {
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2412.13102 | AIR-Bench: Automated Heterogeneous Information Retrieval Benchmark | [
"cs.IR",
"cs.CL"
] | Evaluation plays a crucial role in the advancement of information retrieval (IR) models. However, current benchmarks, which are based on predefined domains and human-labeled data, face limitations in addressing evaluation needs for emerging domains both cost-effectively and efficiently. To address this challenge, we pr... | {
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2412.13103 | AI PERSONA: Towards Life-long Personalization of LLMs | [
"cs.CL",
"cs.AI"
] | In this work, we introduce the task of life-long personalization of large language models. While recent mainstream efforts in the LLM community mainly focus on scaling data and compute for improved capabilities of LLMs, we argue that it is also very important to enable LLM systems, or language agents, to continuously a... | {
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2412.13104 | Intermediate Relation Size Bounds for Select-Project-Join Query Plans:
Asymptotically Tight Characterizations | [
"cs.DB"
] | We study the problem of statically optimizing select-project-join (SPJ) plans where unary key constraints are allowed. A natural measure of a plan, which we call the output degree and which has been studied previously, is the minimum degree of a polynomial bounding the plan's output relation, as a function of the input... | {
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2412.13106 | Active Reinforcement Learning Strategies for Offline Policy Improvement | [
"cs.LG"
] | Learning agents that excel at sequential decision-making tasks must continuously resolve the problem of exploration and exploitation for optimal learning. However, such interactions with the environment online might be prohibitively expensive and may involve some constraints, such as a limited budget for agent-environm... | {
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2412.13110 | Improving Explainability of Sentence-level Metrics via Edit-level
Attribution for Grammatical Error Correction | [
"cs.CL"
] | Various evaluation metrics have been proposed for Grammatical Error Correction (GEC), but many, particularly reference-free metrics, lack explainability. This lack of explainability hinders researchers from analyzing the strengths and weaknesses of GEC models and limits the ability to provide detailed feedback for user... | {
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2412.13111 | Motion-2-to-3: Leveraging 2D Motion Data to Boost 3D Motion Generation | [
"cs.CV",
"cs.GR"
] | Text-driven human motion synthesis is capturing significant attention for its ability to effortlessly generate intricate movements from abstract text cues, showcasing its potential for revolutionizing motion design not only in film narratives but also in virtual reality experiences and computer game development. Existi... | {
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2412.13115 | Koopman Mode-Based Detection of Internal Short Circuits in Lithium-ion
Battery Pack | [
"eess.SY",
"cs.SY"
] | Monitoring of internal short circuit (ISC) in Lithium-ion battery packs is imperative to safe operations, optimal performance, and extension of pack life. Since ISC in one of the modules inside a battery pack can eventually lead to thermal runaway, it is crucial to detect its early onset. However, the inaccuracy and ag... | {
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2412.13116 | Equity in the Use of ChatGPT for the Classroom: A Comparison of the
Accuracy and Precision of ChatGPT 3.5 vs. ChatGPT4 with Respect to Statistics
and Data Science Exams | [
"stat.OT",
"cs.AI"
] | A college education historically has been seen as method of moving upward with regards to income brackets and social status. Indeed, many colleges recognize this connection and seek to enroll talented low income students. While these students might have their education, books, room, and board paid; there are other item... | {
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2412.13119 | Flight Patterns for Swarms of Drones | [
"cs.MM",
"cs.ET",
"cs.RO"
] | We present flight patterns for a collision-free passage of swarms of drones through one or more openings. The narrow openings provide drones with access to an infrastructure component such as charging stations to charge their depleted batteries and hangars for storage. The flight patterns are a staging area (queues) th... | {
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2412.13126 | A Knowledge-enhanced Pathology Vision-language Foundation Model for
Cancer Diagnosis | [
"eess.IV",
"cs.CV"
] | Deep learning has enabled the development of highly robust foundation models for various pathological tasks across diverse diseases and patient cohorts. Among these models, vision-language pre-training, which leverages large-scale paired data to align pathology image and text embedding spaces, and provides a novel zero... | {
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2412.13128 | Previous Knowledge Utilization In Online Anytime Belief Space Planning | [
"cs.AI",
"cs.RO"
] | Online planning under uncertainty remains a critical challenge in robotics and autonomous systems. While tree search techniques are commonly employed to construct partial future trajectories within computational constraints, most existing methods discard information from previous planning sessions considering continuou... | {
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2412.13134 | Practicable Black-box Evasion Attacks on Link Prediction in Dynamic
Graphs -- A Graph Sequential Embedding Method | [
"cs.CR",
"cs.LG"
] | Link prediction in dynamic graphs (LPDG) has been widely applied to real-world applications such as website recommendation, traffic flow prediction, organizational studies, etc. These models are usually kept local and secure, with only the interactive interface restrictively available to the public. Thus, the problem o... | {
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2412.13137 | Unlocking the Potential of Digital Pathology: Novel Baselines for
Compression | [
"eess.IV",
"cs.CV"
] | Digital pathology offers a groundbreaking opportunity to transform clinical practice in histopathological image analysis, yet faces a significant hurdle: the substantial file sizes of pathological Whole Slide Images (WSI). While current digital pathology solutions rely on lossy JPEG compression to address this issue, l... | {
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2412.13140 | Label Errors in the Tobacco3482 Dataset | [
"cs.CV"
] | Tobacco3482 is a widely used document classification benchmark dataset. However, our manual inspection of the entire dataset uncovers widespread ontological issues, especially large amounts of annotation label problems in the dataset. We establish data label guidelines and find that 11.7% of the dataset is improperly a... | {
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2412.13145 | Agnosticism About Artificial Consciousness | [
"cs.AI"
] | Could an AI have conscious experiences? Any answer to this question should conform to Evidentialism - that is, it should be based not on intuition, dogma or speculation but on solid scientific evidence. I argue that such evidence is hard to come by and that the only justifiable stance on the prospects of artificial con... | {
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2412.13146 | Syntactic Transfer to Kyrgyz Using the Treebank Translation Method | [
"cs.CL"
] | The Kyrgyz language, as a low-resource language, requires significant effort to create high-quality syntactic corpora. This study proposes an approach to simplify the development process of a syntactic corpus for Kyrgyz. We present a tool for transferring syntactic annotations from Turkish to Kyrgyz based on a treebank... | {
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2412.13147 | Are Your LLMs Capable of Stable Reasoning? | [
"cs.AI",
"cs.CL"
] | The rapid advancement of Large Language Models (LLMs) has demonstrated remarkable progress in complex reasoning tasks. However, a significant discrepancy persists between benchmark performances and real-world applications. We identify this gap as primarily stemming from current evaluation protocols and metrics, which i... | {
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2412.13148 | SWAN: SGD with Normalization and Whitening Enables Stateless LLM
Training | [
"cs.LG",
"cs.AI"
] | Adaptive optimizers such as Adam (Kingma & Ba, 2015) have been central to the success of large language models. However, they often require to maintain optimizer states throughout training, which can result in memory requirements several times greater than the model footprint. This overhead imposes constraints on scala... | {
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2412.13152 | Continuous Patient Monitoring with AI: Real-Time Analysis of Video in
Hospital Care Settings | [
"cs.CV",
"cs.AI"
] | This study introduces an AI-driven platform for continuous and passive patient monitoring in hospital settings, developed by LookDeep Health. Leveraging advanced computer vision, the platform provides real-time insights into patient behavior and interactions through video analysis, securely storing inference results in... | {
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2412.13155 | F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking
Face Generation, Customization, and Restoration | [
"cs.CV"
] | Artificial intelligence generative models exhibit remarkable capabilities in content creation, particularly in face image generation, customization, and restoration. However, current AI-generated faces (AIGFs) often fall short of human preferences due to unique distortions, unrealistic details, and unexpected identity ... | {
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2412.13156 | S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical
Imaging | [
"cs.CV"
] | Robustness and generalizability in medical image segmentation are often hindered by scarcity and limited diversity of training data, which stands in contrast to the variability encountered during inference. While conventional strategies -- such as domain-specific augmentation, specialized architectures, and tailored tr... | {
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2412.13157 | Learning Visuotactile Estimation and Control for Non-prehensile
Manipulation under Occlusions | [
"cs.RO",
"cs.LG"
] | Manipulation without grasping, known as non-prehensile manipulation, is essential for dexterous robots in contact-rich environments, but presents many challenges relating with underactuation, hybrid-dynamics, and frictional uncertainty. Additionally, object occlusions in a scenario of contact uncertainty and where the ... | {
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2412.13158 | On Model Extrapolation in Marginal Shapley Values | [
"stat.ML",
"cs.LG"
] | As the use of complex machine learning models continues to grow, so does the need for reliable explainability methods. One of the most popular methods for model explainability is based on Shapley values. There are two most commonly used approaches to calculating Shapley values which produce different results when featu... | {
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2412.13159 | A Conformal Approach to Feature-based Newsvendor under Model
Misspecification | [
"cs.LG",
"stat.ML"
] | In many data-driven decision-making problems, performance guarantees often depend heavily on the correctness of model assumptions, which may frequently fail in practice. We address this issue in the context of a feature-based newsvendor problem, where demand is influenced by observed features such as demographics and s... | {
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2412.13161 | BanglishRev: A Large-Scale Bangla-English and Code-mixed Dataset of
Product Reviews in E-Commerce | [
"cs.CL",
"cs.CV",
"cs.LG"
] | This work presents the BanglishRev Dataset, the largest e-commerce product review dataset to date for reviews written in Bengali, English, a mixture of both and Banglish, Bengali words written with English alphabets. The dataset comprises of 1.74 million written reviews from 3.2 million ratings information collected fr... | {
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2412.13163 | C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System | [
"cs.DC",
"cs.IR"
] | Organizations seeking to utilize Large Language Models (LLMs) for knowledge querying and analysis often encounter challenges in maintaining an LLM fine-tuned on targeted, up-to-date information that keeps answers relevant and grounded. Retrieval Augmented Generation (RAG) has quickly become a feasible solution for orga... | {
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2412.13168 | Lifting Scheme-Based Implicit Disentanglement of Emotion-Related Facial
Dynamics in the Wild | [
"cs.CV",
"cs.AI"
] | In-the-wild dynamic facial expression recognition (DFER) encounters a significant challenge in recognizing emotion-related expressions, which are often temporally and spatially diluted by emotion-irrelevant expressions and global context. Most prior DFER methods directly utilize coupled spatiotemporal representations t... | {
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2412.13169 | Algorithmic Fidelity of Large Language Models in Generating Synthetic
German Public Opinions: A Case Study | [
"cs.CL"
] | In recent research, large language models (LLMs) have been increasingly used to investigate public opinions. This study investigates the algorithmic fidelity of LLMs, i.e., the ability to replicate the socio-cultural context and nuanced opinions of human participants. Using open-ended survey data from the German Longit... | {
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2412.13170 | Re-calibrating methodologies in social media research: Challenge the
visual, work with Speech | [
"cs.SI",
"cs.IR"
] | This article methodologically reflects on how social media scholars can effectively engage with speech-based data in their analyses. While contemporary media studies have embraced textual, visual, and relational data, the aural dimension remained comparatively under-explored. Building on the notion of secondary orality... | {
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} |
2412.13171 | Compressed Chain of Thought: Efficient Reasoning Through Dense
Representations | [
"cs.CL"
] | Chain-of-thought (CoT) decoding enables language models to improve reasoning performance at the cost of high generation latency in decoding. Recent proposals have explored variants of contemplation tokens, a term we introduce that refers to special tokens used during inference to allow for extra computation. Prior work... | {
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2412.13173 | Locate n' Rotate: Two-stage Openable Part Detection with Foundation
Model Priors | [
"cs.CV"
] | Detecting the openable parts of articulated objects is crucial for downstream applications in intelligent robotics, such as pulling a drawer. This task poses a multitasking challenge due to the necessity of understanding object categories and motion. Most existing methods are either category-specific or trained on spec... | {
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2412.13174 | ORFormer: Occlusion-Robust Transformer for Accurate Facial Landmark
Detection | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Although facial landmark detection (FLD) has gained significant progress, existing FLD methods still suffer from performance drops on partially non-visible faces, such as faces with occlusions or under extreme lighting conditions or poses. To address this issue, we introduce ORFormer, a novel transformer-based method t... | {
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} |
2412.13175 | DnDScore: Decontextualization and Decomposition for Factuality
Verification in Long-Form Text Generation | [
"cs.CL"
] | The decompose-then-verify strategy for verification of Large Language Model (LLM) generations decomposes claims that are then independently verified. Decontextualization augments text (claims) to ensure it can be verified outside of the original context, enabling reliable verification. While decomposition and decontext... | {
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} |
2412.13176 | NFL-BA: Improving Endoscopic SLAM with Near-Field Light Bundle
Adjustment | [
"cs.CV"
] | Simultaneous Localization And Mapping (SLAM) from a monocular endoscopy video can enable autonomous navigation, guidance to unsurveyed regions, and 3D visualizations, which can significantly improve endoscopy experience for surgeons and patient outcomes. Existing dense SLAM algorithms often assume distant and static li... | {
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} |
2412.13178 | SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM
Agents | [
"cs.CR",
"cs.AI",
"cs.RO"
] | With the integration of large language models (LLMs), embodied agents have strong capabilities to execute complicated instructions in natural language, paving a way for the potential deployment of embodied robots. However, a foreseeable issue is that those embodied agents can also flawlessly execute some hazardous task... | {
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} |
2412.13179 | A Pipeline and NIR-Enhanced Dataset for Parking Lot Segmentation | [
"cs.CV"
] | Discussions of minimum parking requirement policies often include maps of parking lots, which are time consuming to construct manually. Open source datasets for such parking lots are scarce, particularly for US cities. This paper introduces the idea of using Near-Infrared (NIR) channels as input and several post-proces... | {
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} |
2412.13180 | Feather the Throttle: Revisiting Visual Token Pruning for
Vision-Language Model Acceleration | [
"cs.CV"
] | Recent works on accelerating Vision-Language Models show that strong performance can be maintained across a variety of vision-language tasks despite highly compressing visual information. In this work, we examine the popular acceleration approach of early pruning of visual tokens inside the language model and find that... | {
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} |
2412.13183 | Real-time Free-view Human Rendering from Sparse-view RGB Videos using
Double Unprojected Textures | [
"cs.CV"
] | Real-time free-view human rendering from sparse-view RGB inputs is a challenging task due to the sensor scarcity and the tight time budget. To ensure efficiency, recent methods leverage 2D CNNs operating in texture space to learn rendering primitives. However, they either jointly learn geometry and appearance, or compl... | {
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} |
2412.13184 | Tilted Quantile Gradient Updates for Quantile-Constrained Reinforcement
Learning | [
"cs.LG",
"cs.AI"
] | Safe reinforcement learning (RL) is a popular and versatile paradigm to learn reward-maximizing policies with safety guarantees. Previous works tend to express the safety constraints in an expectation form due to the ease of implementation, but this turns out to be ineffective in maintaining safety constraints with hig... | {
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} |
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