id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.17635 | In-Context Meta LoRA Generation | [
"cs.CL",
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] | Low-rank Adaptation (LoRA) has demonstrated remarkable capabilities for task specific fine-tuning. However, in scenarios that involve multiple tasks, training a separate LoRA model for each one results in considerable inefficiency in terms of storage and inference. Moreover, existing parameter generation methods fail t... | {
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2501.17636 | Efficient Interactive 3D Multi-Object Removal | [
"cs.CV"
] | Object removal is of great significance to 3D scene understanding, essential for applications in content filtering and scene editing. Current mainstream methods primarily focus on removing individual objects, with a few methods dedicated to eliminating an entire area or all objects of a certain category. They however c... | {
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2501.17642 | Efficient Redundancy Reduction for Open-Vocabulary Semantic Segmentation | [
"cs.CV"
] | Open-vocabulary semantic segmentation (OVSS) is an open-world task that aims to assign each pixel within an image to a specific class defined by arbitrary text descriptions. Recent advancements in large-scale vision-language models have demonstrated their open-vocabulary understanding capabilities, significantly facili... | {
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2501.17643 | Tonguescape: Exploring Language Models Understanding of Vowel
Articulation | [
"cs.CL",
"cs.AI"
] | Vowels are primarily characterized by tongue position. Humans have discovered these features of vowel articulation through their own experience and explicit objective observation such as using MRI. With this knowledge and our experience, we can explain and understand the relationship between tongue positions and vowels... | {
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2501.17644 | Efficient Stochastic Polar Decoder With Correlated Stochastic Computing | [
"cs.IT",
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"math.IT"
] | Polar codes have gained significant attention in channel coding for their ability to approach the capacity of binary input discrete memoryless channels (B-DMCs), thanks to their reliability and efficiency in transmission. However, existing decoders often struggle to balance hardware area and performance. Stochastic com... | {
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2501.17648 | Analysis and Control of Perturbed Density Systems | [
"eess.SY",
"cs.SY"
] | The paper investigates dynamical systems for which the derivative of some positive-definite function along the solutions of this system depends on so-called density function. In turn, such dynamical systems are called density systems. The density function sets the density of the space, where the system is evolved, and ... | {
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2501.17653 | Drivetrain simulation using variational autoencoders | [
"cs.LG",
"cs.CE",
"eess.SP"
] | This work proposes variational autoencoders (VAEs) to predict a vehicle's jerk from a given torque demand, addressing the limitations of sparse real-world datasets. Specifically, we implement unconditional and conditional VAEs to generate jerk signals that integrate features from different drivetrain scenarios. The VAE... | {
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2501.17654 | Exploring Vision Language Models for Multimodal and Multilingual Stance
Detection | [
"cs.CL",
"cs.AI"
] | Social media's global reach amplifies the spread of information, highlighting the need for robust Natural Language Processing tasks like stance detection across languages and modalities. Prior research predominantly focuses on text-only inputs, leaving multimodal scenarios, such as those involving both images and text,... | {
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2501.17655 | FeatureGS: Eigenvalue-Feature Optimization in 3D Gaussian Splatting for
Geometrically Accurate and Artifact-Reduced Reconstruction | [
"cs.CV"
] | 3D Gaussian Splatting (3DGS) has emerged as a powerful approach for 3D scene reconstruction using 3D Gaussians. However, neither the centers nor surfaces of the Gaussians are accurately aligned to the object surface, complicating their direct use in point cloud and mesh reconstruction. Additionally, 3DGS typically prod... | {
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2501.17658 | An eco-driving approach for ride comfort improvement | [
"cs.RO",
"cs.CY"
] | New challenges on transport systems are emerging due to the advances that the current paradigm is experiencing. The breakthrough of the autonomous car brings concerns about ride comfort, while the pollution concerns have arisen in recent years. In the model of automated automobiles, drivers are expected to become passe... | {
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2501.17661 | Multi-Agent Path Finding Using Conflict-Based Search and
Structural-Semantic Topometric Maps | [
"cs.RO"
] | As industries increasingly adopt large robotic fleets, there is a pressing need for computationally efficient, practical, and optimal conflict-free path planning for multiple robots. Conflict-Based Search (CBS) is a popular method for multi-agent path finding (MAPF) due to its completeness and optimality; however, it i... | {
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2501.17663 | Landscape Features in Single-Objective Continuous Optimization: Have We
Hit a Wall in Algorithm Selection Generalization? | [
"cs.LG"
] | %% Text of abstract The process of identifying the most suitable optimization algorithm for a specific problem, referred to as algorithm selection (AS), entails training models that leverage problem landscape features to forecast algorithm performance. A significant challenge in this domain is ensuring that AS models c... | {
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2501.17664 | Analysis of the Motion Sickness and the Lack of Comfort in Car
Passengers | [
"cs.RO"
] | Advanced driving assistance systems (ADAS) are primarily designed to increase driving safety and reduce traffic congestion without paying too much attention to passenger comfort or motion sickness. However, in view of autonomous cars, and taking into account that the lack of comfort and motion sickness increase in pass... | {
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2501.17665 | Planning with Vision-Language Models and a Use Case in Robot-Assisted
Teaching | [
"cs.RO",
"cs.AI"
] | Automating the generation of Planning Domain Definition Language (PDDL) with Large Language Model (LLM) opens new research topic in AI planning, particularly for complex real-world tasks. This paper introduces Image2PDDL, a novel framework that leverages Vision-Language Models (VLMs) to automatically convert images of ... | {
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2501.17666 | An Intelligent System-on-a-Chip for a Real-Time Assessment of Fuel
Consumption to Promote Eco-Driving | [
"cs.RO"
] | Pollution that originates from automobiles is a concern in the current world, not only because of global warming, but also due to the harmful effects on people's health and lives. Despite regulations on exhaust gas emissions being applied, minimizing unsuitable driving habits that cause elevated fuel consumption and em... | {
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2501.17667 | CAMP in the Odyssey: Provably Robust Reinforcement Learning with
Certified Radius Maximization | [
"cs.LG",
"cs.CR"
] | Deep reinforcement learning (DRL) has gained widespread adoption in control and decision-making tasks due to its strong performance in dynamic environments. However, DRL agents are vulnerable to noisy observations and adversarial attacks, and concerns about the adversarial robustness of DRL systems have emerged. Recent... | {
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2501.17670 | Distinguished Quantized Guidance for Diffusion-based Sequence
Recommendation | [
"cs.IR"
] | Diffusion models (DMs) have emerged as promising approaches for sequential recommendation due to their strong ability to model data distributions and generate high-quality items. Existing work typically adds noise to the next item and progressively denoises it guided by the user's interaction sequence, generating items... | {
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2501.17676 | Explainable Artificial Intelligence for identifying profitability
predictors in Financial Statements | [
"cs.LG"
] | The interconnected nature of the economic variables influencing a firm's performance makes the prediction of a company's earning trend a challenging task. Existing methodologies often rely on simplistic models and financial ratios failing to capture the complexity of interacting influences. In this paper, we apply Mach... | {
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2501.17683 | Temperature-Free Loss Function for Contrastive Learning | [
"cs.LG"
] | As one of the most promising methods in self-supervised learning, contrastive learning has achieved a series of breakthroughs across numerous fields. A predominant approach to implementing contrastive learning is applying InfoNCE loss: By capturing the similarities between pairs, InfoNCE loss enables learning the repre... | {
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2501.17688 | ContourFormer:Real-Time Contour-Based End-to-End Instance Segmentation
Transformer | [
"cs.CV",
"cs.AI"
] | This paper presents Contourformer, a real-time contour-based instance segmentation algorithm. The method is fully based on the DETR paradigm and achieves end-to-end inference through iterative and progressive mechanisms to optimize contours. To improve efficiency and accuracy, we develop two novel techniques: sub-conto... | {
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2501.17689 | Machine-Learning-Enhanced Optimization of Noise-Resilient Variational
Quantum Eigensolvers | [
"quant-ph",
"cs.LG",
"hep-lat"
] | Variational Quantum Eigensolvers (VQEs) are a powerful class of hybrid quantum-classical algorithms designed to approximate the ground state of a quantum system described by its Hamiltonian. VQEs hold promise for various applications, including lattice field theory. However, the inherent noise of Noisy Intermediate-Sca... | {
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2501.17690 | Segmentation-Aware Generative Reinforcement Network (GRN) for Tissue
Layer Segmentation in 3-D Ultrasound Images for Chronic Low-back Pain (cLBP)
Assessment | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We introduce a novel segmentation-aware joint training framework called generative reinforcement network (GRN) that integrates segmentation loss feedback to optimize both image generation and segmentation performance in a single stage. An image enhancement technique called segmentation-guided enhancement (SGE) is also ... | {
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2501.17699 | PulmoFusion: Advancing Pulmonary Health with Efficient Multi-Modal
Fusion | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Traditional remote spirometry lacks the precision required for effective pulmonary monitoring. We present a novel, non-invasive approach using multimodal predictive models that integrate RGB or thermal video data with patient metadata. Our method leverages energy-efficient Spiking Neural Networks (SNNs) for the regress... | {
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2501.17701 | Decision-Theoretic Approaches in Learning-Augmented Algorithms | [
"cs.DS",
"cs.LG"
] | In this work, we initiate the systemic study of decision-theoretic metrics in the design and analysis of algorithms with machine-learned predictions. We introduce approaches based on both deterministic measures such as distance-based evaluation, that help us quantify how close the algorithm is to an ideal solution, as ... | {
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2501.17703 | Critique Fine-Tuning: Learning to Critique is More Effective than
Learning to Imitate | [
"cs.CL"
] | Supervised Fine-Tuning (SFT) is commonly used to train language models to imitate annotated responses for given instructions. In this paper, we challenge this paradigm and propose Critique Fine-Tuning (CFT), a strategy where models learn to critique noisy responses rather than simply imitate correct ones. Inspired by h... | {
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2501.17704 | Inferring Implicit Goals Across Differing Task Models | [
"cs.AI",
"cs.RO",
"cs.SY",
"eess.SY"
] | One of the significant challenges to generating value-aligned behavior is to not only account for the specified user objectives but also any implicit or unspecified user requirements. The existence of such implicit requirements could be particularly common in settings where the user's understanding of the task model ma... | {
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2501.17706 | Source-Channel Separation Theorems for Distortion Perception Coding | [
"cs.IT",
"math.IT"
] | It is well known that separation between lossy source coding and channel coding is asymptotically optimal under classical additive distortion measures. Recently, coding under a new class of quality considerations, often referred to as perception or realism, has attracted significant attention due to its close connectio... | {
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2501.17711 | STGCN-LSTM for Olympic Medal Prediction: Dynamic Power Modeling and
Causal Policy Optimization | [
"cs.LG"
] | This paper proposes a novel hybrid model, STGCN-LSTM, to forecast Olympic medal distributions by integrating the spatio-temporal relationships among countries and the long-term dependencies of national performance. The Spatial-Temporal Graph Convolution Network (STGCN) captures geographic and interactive factors-such a... | {
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2501.17715 | RICoTA: Red-teaming of In-the-wild Conversation with Test Attempts | [
"cs.CL"
] | User interactions with conversational agents (CAs) evolve in the era of heavily guardrailed large language models (LLMs). As users push beyond programmed boundaries to explore and build relationships with these systems, there is a growing concern regarding the potential for unauthorized access or manipulation, commonly... | {
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2501.17718 | Learning Semantic Facial Descriptors for Accurate Face Animation | [
"cs.CV"
] | Face animation is a challenging task. Existing model-based methods (utilizing 3DMMs or landmarks) often result in a model-like reconstruction effect, which doesn't effectively preserve identity. Conversely, model-free approaches face challenges in attaining a decoupled and semantically rich feature space, thereby makin... | {
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2501.17720 | Parsimonious Hawkes Processes for temporal networks modelling | [
"cs.SI",
"physics.data-an"
] | Temporal networks are characterised by interdependent link events between nodes, forming ordered sequences of links that may represent specific information flows in the system. Nevertheless, representing temporal networks using discrete snapshots in time partially cancels the effect of time-ordered links on each other,... | {
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2501.17725 | Using Code Generation to Solve Open Instances of Combinatorial Design
Problems | [
"cs.AI",
"cs.CL",
"cs.DM",
"math.CO"
] | The Handbook of Combinatorial Designs catalogs many types of combinatorial designs, together with lists of open instances for which existence has not yet been determined. We develop a constructive protocol CPro1, which uses Large Language Models (LLMs) to generate code that constructs combinatorial designs and resolves... | {
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2501.17726 | VICCA: Visual Interpretation and Comprehension of Chest X-ray Anomalies
in Generated Report Without Human Feedback | [
"cs.CV",
"cs.CL"
] | As artificial intelligence (AI) becomes increasingly central to healthcare, the demand for explainable and trustworthy models is paramount. Current report generation systems for chest X-rays (CXR) often lack mechanisms for validating outputs without expert oversight, raising concerns about reliability and interpretabil... | {
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2501.17727 | Sparse Autoencoders Can Interpret Randomly Initialized Transformers | [
"cs.LG"
] | Sparse autoencoders (SAEs) are an increasingly popular technique for interpreting the internal representations of transformers. In this paper, we apply SAEs to 'interpret' random transformers, i.e., transformers where the parameters are sampled IID from a Gaussian rather than trained on text data. We find that random a... | {
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2501.17731 | Exact characterization of {\epsilon}-Safe Decision Regions for
exponential family distributions and Multi Cost SVM approximation | [
"stat.ML",
"cs.AI",
"cs.LG"
] | Probabilistic guarantees on the prediction of data-driven classifiers are necessary to define models that can be considered reliable. This is a key requirement for modern machine learning in which the goodness of a system is measured in terms of trustworthiness, clearly dividing what is safe from what is unsafe. The sp... | {
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2501.17736 | Winning Rates of $(n,k)$ Quantum Coset Monogamy Games | [
"quant-ph",
"cs.IT",
"math.IT"
] | We formulate the $(n,k)$ Coset Monogamy Game, in which two players must extract complementary information of unequal size ($k$ bits vs. $n-k$ bits) from a random coset state without communicating. The complementary information takes the form of random Pauli-X and Pauli-Z errors on subspace states. Our game generalizes ... | {
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2501.17737 | Sparser, Better, Faster, Stronger: Efficient Automatic Differentiation
for Sparse Jacobians and Hessians | [
"cs.LG",
"cs.MS"
] | From implicit differentiation to probabilistic modeling, Jacobians and Hessians have many potential use cases in Machine Learning (ML), but conventional wisdom views them as computationally prohibitive. Fortunately, these matrices often exhibit sparsity, which can be leveraged to significantly speed up the process of A... | {
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2501.17745 | Dynamics of Transient Structure in In-Context Linear Regression
Transformers | [
"cs.LG"
] | Modern deep neural networks display striking examples of rich internal computational structure. Uncovering principles governing the development of such structure is a priority for the science of deep learning. In this paper, we explore the transient ridge phenomenon: when transformers are trained on in-context linear r... | {
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2501.17746 | Predictive Beamforming with Distributed MIMO | [
"eess.SP",
"cs.IT",
"math.IT"
] | In vehicle-to-everything (V2X) applications, roadside units (RSUs) can be tasked with both sensing and communication functions to enable sensing-assisted communications. Recent studies have demonstrated that distance, angle, and velocity information obtained through sensing can be leveraged to reduce the overhead assoc... | {
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2501.17749 | Early External Safety Testing of OpenAI's o3-mini: Insights from the
Pre-Deployment Evaluation | [
"cs.SE",
"cs.AI"
] | Large Language Models (LLMs) have become an integral part of our daily lives. However, they impose certain risks, including those that can harm individuals' privacy, perpetuate biases and spread misinformation. These risks highlight the need for robust safety mechanisms, ethical guidelines, and thorough testing to ensu... | {
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2501.17754 | Analysis of the navigation of magnetic microrobots through cerebral
bifurcations | [
"math.NA",
"cs.NA",
"cs.RO",
"cs.SY",
"eess.SY",
"physics.bio-ph"
] | Local administration of thrombolytics in ischemic stroke could accelerate clot lysis and the ensuing reperfusion while minimizing the side effects of systemic administration. Medical microrobots could be injected into the bloodstream and magnetically navigated to the clot for administering the drugs directly to the tar... | {
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2501.17755 | AI Governance through Markets | [
"econ.GN",
"cs.AI",
"q-fin.EC"
] | This paper argues that market governance mechanisms should be considered a key approach in the governance of artificial intelligence (AI), alongside traditional regulatory frameworks. While current governance approaches have predominantly focused on regulation, we contend that market-based mechanisms offer effective in... | {
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2501.17758 | Glioma Multimodal MRI Analysis System for Tumor Layered Diagnosis via
Multi-task Semi-supervised Learning | [
"eess.IV",
"cs.CV"
] | Gliomas are the most common primary tumors of the central nervous system. Multimodal MRI is widely used for the preliminary screening of gliomas and plays a crucial role in auxiliary diagnosis, therapeutic efficacy, and prognostic evaluation. Currently, the computer-aided diagnostic studies of gliomas using MRI have fo... | {
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2501.17759 | Yin-Yang: Developing Motifs With Long-Term Structure And Controllability | [
"cs.SD",
"cs.AI",
"cs.SC"
] | Transformer models have made great strides in generating symbolically represented music with local coherence. However, controlling the development of motifs in a structured way with global form remains an open research area. One of the reasons for this challenge is due to the note-by-note autoregressive generation of s... | {
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2501.17762 | Improving Privacy Benefits of Redaction | [
"cs.CR",
"cs.CL",
"cs.LG"
] | We propose a novel redaction methodology that can be used to sanitize natural text data. Our new technique provides better privacy benefits than other state of the art techniques while maintaining lower redaction levels. | {
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2501.17767 | Hybrid Graphs for Table-and-Text based Question Answering using LLMs | [
"cs.CL",
"cs.AI"
] | Answering questions that require reasoning and aggregation across both structured (tables) and unstructured (raw text) data sources presents significant challenges. Current methods rely on fine-tuning and high-quality, human-curated data, which is difficult to obtain. Recent advances in Large Language Models (LLMs) hav... | {
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2501.17770 | Generative Unordered Flow for Set-Structured Data Generation | [
"cs.LG"
] | Flow-based generative models have demonstrated promising performance across a broad spectrum of data modalities (e.g., image and text). However, there are few works exploring their extension to unordered data (e.g., spatial point set), which is not trivial because previous models are mostly designed for vector data tha... | {
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2501.17771 | 2SSP: A Two-Stage Framework for Structured Pruning of LLMs | [
"cs.CL",
"cs.AI",
"cs.LG"
] | We propose a novel Two-Stage framework for Structured Pruning (2SSP) for pruning Large Language Models (LLMs), which combines two different strategies of pruning, namely Width and Depth Pruning. The first stage (Width Pruning) removes entire neurons, hence their corresponding rows and columns, aiming to preserve the co... | {
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2501.17772 | Self-Supervised Frameworks for Speaker Verification via Bootstrapped
Positive Sampling | [
"eess.AS",
"cs.LG",
"cs.SD"
] | Recent developments in Self-Supervised Learning (SSL) have demonstrated significant potential for Speaker Verification (SV), but closing the performance gap with supervised systems remains an ongoing challenge. Standard SSL frameworks rely on anchor-positive pairs extracted from the same audio utterances. Hence, positi... | {
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2501.17773 | SafePR: Unified Approach for Safe Parallel Robots by Contact Detection
and Reaction with Redundancy Resolution | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Fast and safe motion is crucial for the successful deployment of physically interactive robots. Parallel robots (PRs) offer the potential for higher speeds while maintaining the same energy limits due to their low moving masses. However, they require methods for contact detection and reaction while avoiding singulariti... | {
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2501.17774 | Percolation and localisation: Sub-leading eigenvalues of the
nonbacktracking matrix | [
"physics.soc-ph",
"cs.SI"
] | The spectrum of the nonbacktracking matrix associated to a network is known to contain fundamental information regarding percolation properties of the network. Indeed, the inverse of its leading eigenvalue is often used as an estimate for the percolation threshold. However, for many networks with nonbacktracking centra... | {
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2501.17777 | On decoding hyperbolic codes | [
"cs.IT",
"math.CO",
"math.IT"
] | This work studies several decoding algorithms for hyperbolic codes. We use some previous ideas to describe how to decode a hyperbolic code using the largest Reed-Muller code contained in it or using the smallest Reed-Muller code that contains it. A combination of these two algorithms is proposed when hyperbolic codes a... | {
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2501.17781 | Long-term prediction of El Ni\~no-Southern Oscillation using reservoir
computing with data-driven realtime filter | [
"physics.comp-ph",
"cs.LG",
"physics.ao-ph"
] | In recent years, the application of machine learning approaches to time-series forecasting of climate dynamical phenomena has become increasingly active. It is known that applying a band-pass filter to a time-series data is a key to obtaining a high-quality data-driven model. Here, to obtain longer-term predictability ... | {
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2501.17782 | Picard-KKT-hPINN: Enforcing Nonlinear Enthalpy Balances for Physically
Consistent Neural Networks | [
"cs.LG"
] | Neural networks are widely used as surrogate models but they do not guarantee physically consistent predictions thereby preventing adoption in various applications. We propose a method that can enforce NNs to satisfy physical laws that are nonlinear in nature such as enthalpy balances. Our approach, inspired by Picard ... | {
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2501.17784 | AdditiveLLM: Large Language Models Predict Defects in Additive
Manufacturing | [
"cs.LG"
] | In this work we investigate the ability of large language models to predict additive manufacturing defect regimes given a set of process parameter inputs. For this task we utilize a process parameter defect dataset to fine-tune a collection of models, titled AdditiveLLM, for the purpose of predicting potential defect r... | {
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2501.17785 | Reasoning Over the Glyphs: Evaluation of LLM's Decipherment of Rare
Scripts | [
"cs.CL",
"cs.LG"
] | We explore the capabilities of LVLMs and LLMs in deciphering rare scripts not encoded in Unicode. We introduce a novel approach to construct a multimodal dataset of linguistic puzzles involving such scripts, utilizing a tokenization method for language glyphs. Our methods include the Picture Method for LVLMs and the De... | {
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2501.17787 | Detecting Anomalies Using Rotated Isolation Forest | [
"cs.LG"
] | The Isolation Forest (iForest), proposed by Liu, Ting, and Zhou at TKDE 2012, has become a prominent tool for unsupervised anomaly detection. However, recent research by Hariri, Kind, and Brunner, published in TKDE 2021, has revealed issues with iForest. They identified the presence of axis-aligned ghost clusters that ... | {
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2501.17788 | WARP: An Efficient Engine for Multi-Vector Retrieval | [
"cs.IR"
] | We study the efficiency of multi-vector retrieval methods like ColBERT and its recent variant XTR. We introduce WARP, a retrieval engine that drastically improves the efficiency of XTR-based ColBERT retrievers through three key innovations: (1) WARP$_\text{SELECT}$ for dynamic similarity imputation, (2) implicit decomp... | {
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2501.17789 | Propeller Motion of a Devil-Stick using Normal Forcing | [
"eess.SY",
"cs.RO",
"cs.SY"
] | The problem of realizing rotary propeller motion of a devil-stick in the vertical plane using forces purely normal to the stick is considered. This problem represents a nonprehensile manipulation task of an underactuated system. In contrast with previous approaches, the devil-stick is manipulated by controlling the nor... | {
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2501.17790 | BreezyVoice: Adapting TTS for Taiwanese Mandarin with Enhanced Polyphone
Disambiguation -- Challenges and Insights | [
"cs.CL",
"cs.AI"
] | We present BreezyVoice, a Text-to-Speech (TTS) system specifically adapted for Taiwanese Mandarin, highlighting phonetic control abilities to address the unique challenges of polyphone disambiguation in the language. Building upon CosyVoice, we incorporate a $S^{3}$ tokenizer, a large language model (LLM), an optimal-t... | {
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2501.17792 | CrowdSplat: Exploring Gaussian Splatting For Crowd Rendering | [
"cs.CV"
] | We present CrowdSplat, a novel approach that leverages 3D Gaussian Splatting for real-time, high-quality crowd rendering. Our method utilizes 3D Gaussian functions to represent animated human characters in diverse poses and outfits, which are extracted from monocular videos. We integrate Level of Detail (LoD) rendering... | {
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2501.17799 | Leveraging Multimodal LLM for Inspirational User Interface Search | [
"cs.HC",
"cs.IR"
] | Inspirational search, the process of exploring designs to inform and inspire new creative work, is pivotal in mobile user interface (UI) design. However, exploring the vast space of UI references remains a challenge. Existing AI-based UI search methods often miss crucial semantics like target users or the mood of apps.... | {
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2501.17802 | LEKA:LLM-Enhanced Knowledge Augmentation | [
"cs.LG"
] | Humans excel in analogical learning and knowledge transfer and, more importantly, possess a unique understanding of identifying appropriate sources of knowledge. From a model's perspective, this presents an interesting challenge. If models could autonomously retrieve knowledge useful for transfer or decision-making to ... | {
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2501.17804 | Recyclable Thin-Film Soft Electronics for Smart Packaging and E-Skins | [
"eess.SY",
"cond-mat.mtrl-sci",
"cs.SY"
] | Despite advances in soft, sticker_like electronics, few efforts have dealt with the challenge of electronic waste. Here, this is addressed by introducing an eco friendly conductive ink for thin_film circuitry composed of silver flakes and a water_based polyurethane dispersion. This ink uniquely combines high electrical... | {
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2501.17805 | International AI Safety Report | [
"cs.CY",
"cs.AI",
"cs.LG"
] | The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced AI systems. The report was mandated by the nations attending the AI Safety Summit in Bletchley, UK. Thirty nations, the UN, the OECD, and the EU each nominated a representative to... | {
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2501.17808 | Replacing the Gallium Oxide Shell with Conductive Ag: Toward a Printable
and Recyclable Composite for Highly Stretchable Electronics, Electromagnetic
Shielding, and Thermal Interfaces | [
"eess.SY",
"cs.SY"
] | Liquid metal (LM)-based composites hold promise for soft electronics due to their high conductivity and fluidic nature. However, the presence of {\alpha}_Ga2O3 and GaOOH layers around LM droplets impairs conductivity and performance. We tackle this issue by replacing the oxide layer with conductive silver (Ag) using an... | {
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2501.17811 | Janus-Pro: Unified Multimodal Understanding and Generation with Data and
Model Scaling | [
"cs.AI",
"cs.CL",
"cs.CV"
] | In this work, we introduce Janus-Pro, an advanced version of the previous work Janus. Specifically, Janus-Pro incorporates (1) an optimized training strategy, (2) expanded training data, and (3) scaling to larger model size. With these improvements, Janus-Pro achieves significant advancements in both multimodal underst... | {
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2501.17813 | P-TAME: Explain Any Image Classifier with Trained Perturbations | [
"cs.CV",
"cs.AI"
] | The adoption of Deep Neural Networks (DNNs) in critical fields where predictions need to be accompanied by justifications is hindered by their inherent black-box nature. In this paper, we introduce P-TAME (Perturbation-based Trainable Attention Mechanism for Explanations), a model-agnostic method for explaining DNN-bas... | {
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2501.17817 | Improving community detection via community association strength scores | [
"cs.SI"
] | Community detection methods play a central role in understanding complex networks by revealing highly connected subsets of entities. However, most community detection algorithms generate partitions of the nodes, thus (i) forcing every node to be part of a community and (ii) ignoring the possibility that some nodes may ... | {
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2501.17821 | SSF: Sparse Long-Range Scene Flow for Autonomous Driving | [
"cs.CV"
] | Scene flow enables an understanding of the motion characteristics of the environment in the 3D world. It gains particular significance in the long-range, where object-based perception methods might fail due to sparse observations far away. Although significant advancements have been made in scene flow pipelines to hand... | {
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2501.17822 | Aggregation Schemes for Single-Vector WSI Representation Learning in
Digital Pathology | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.IR",
"q-bio.QM"
] | A crucial step to efficiently integrate Whole Slide Images (WSIs) in computational pathology is assigning a single high-quality feature vector, i.e., one embedding, to each WSI. With the existence of many pre-trained deep neural networks and the emergence of foundation models, extracting embeddings for sub-images (i.e.... | {
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2501.17823 | U2A: Unified Unimodal Adaptation for Robust and Efficient Multimodal
Learning | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Multimodal learning often relies on designing new models and complex training strategies to achieve optimal performance. We present Unified Unimodal Adaptation (U2A), which jointly fine-tunes pretrained unimodal encoders using low-rank adaptation (LoRA) for various multimodal tasks. Our method significantly reduces the... | {
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} |
2501.17827 | Langevin Soft Actor-Critic: Efficient Exploration through
Uncertainty-Driven Critic Learning | [
"cs.LG"
] | Existing actor-critic algorithms, which are popular for continuous control reinforcement learning (RL) tasks, suffer from poor sample efficiency due to lack of principled exploration mechanism within them. Motivated by the success of Thompson sampling for efficient exploration in RL, we propose a novel model-free RL al... | {
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2501.17830 | A Comprehensive Survey on Legal Summarization: Challenges and Future
Directions | [
"cs.CL"
] | This article provides a systematic up-to-date survey of automatic summarization techniques, datasets, models, and evaluation methods in the legal domain. Through specific source selection criteria, we thoroughly review over 120 papers spanning the modern `transformer' era of natural language processing (NLP), thus fill... | {
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2501.17831 | TikTok's recommendations skewed towards Republican content during the
2024 U.S. presidential race | [
"cs.SI",
"cs.CY"
] | TikTok is a major force among social media platforms with over a billion monthly active users worldwide and 170 million in the United States. The platform's status as a key news source, particularly among younger demographics, raises concerns about its potential influence on politics in the U.S. and globally. Despite t... | {
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2501.17834 | Hierarchical Fallback Architecture for High Risk Online Machine Learning
Inference | [
"cs.LG",
"cs.CE",
"cs.SE"
] | Open Banking powered machine learning applications require novel robustness approaches to deal with challenging stress and failure scenarios. In this paper we propose an hierarchical fallback architecture for improving robustness in high risk machine learning applications with a focus in the financial domain. We define... | {
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2501.17836 | Matrix Product Sketching via Coordinated Sampling | [
"cs.DS",
"cs.DB",
"cs.LG"
] | We revisit the well-studied problem of approximating a matrix product, $\mathbf{A}^T\mathbf{B}$, based on small space sketches $\mathcal{S}(\mathbf{A})$ and $\mathcal{S}(\mathbf{B})$ of $\mathbf{A} \in \R^{n \times d}$ and $\mathbf{B}\in \R^{n \times m}$. We are interested in the setting where the sketches must be comp... | {
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2501.17840 | Learning Beyond the Surface: How Far Can Continual Pre-Training with
LoRA Enhance LLMs' Domain-Specific Insight Learning? | [
"cs.CL",
"cs.LG"
] | Large Language Models (LLMs) have demonstrated remarkable performance on various tasks, yet their ability to extract and internalize deeper insights from domain-specific datasets remains underexplored. In this study, we investigate how continual pre-training can enhance LLMs' capacity for insight learning across three ... | {
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2501.17841 | acoupi: An Open-Source Python Framework for Deploying Bioacoustic AI
Models on Edge Devices | [
"cs.SD",
"cs.LG",
"eess.AS"
] | 1. Passive acoustic monitoring (PAM) coupled with artificial intelligence (AI) is becoming an essential tool for biodiversity monitoring. Traditional PAM systems require manual data offloading and impose substantial demands on storage and computing infrastructure. The combination of on-device AI-based processing and ne... | {
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2501.17842 | From Sparse to Dense: Toddler-inspired Reward Transition in
Goal-Oriented Reinforcement Learning | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Reinforcement learning (RL) agents often face challenges in balancing exploration and exploitation, particularly in environments where sparse or dense rewards bias learning. Biological systems, such as human toddlers, naturally navigate this balance by transitioning from free exploration with sparse rewards to goal-dir... | {
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2501.17845 | Private Information Retrieval on Multigraph-Based Replicated Storage | [
"cs.IT",
"cs.CR",
"cs.NI",
"eess.SP",
"math.IT"
] | We consider the private information retrieval (PIR) problem for a multigraph-based replication system, where each set of $r$ files is stored on two of the servers according to an underlying $r$-multigraph. Our goal is to establish upper and lower bounds on the PIR capacity of the $r$-multigraph. Specifically, we first ... | {
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2501.17848 | Improving Genetic Programming for Symbolic Regression with Equality
Graphs | [
"cs.LG"
] | The search for symbolic regression models with genetic programming (GP) has a tendency of revisiting expressions in their original or equivalent forms. Repeatedly evaluating equivalent expressions is inefficient, as it does not immediately lead to better solutions. However, evolutionary algorithms require diversity and... | {
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2501.17851 | UGSim: Autonomous Buoyancy-Driven Underwater Glider Simulator with LQR
Control Strategy and Recursive Guidance System | [
"cs.RO",
"cs.SE"
] | This paper presents the UGSim, a simulator for buoyancy-driven gliders, with a LQR control strategy, and a recursive guidance system. Building on the top of the DAVE and the UUVsim, it is designed to address unique challenges that come from the complex hydrodynamic and hydrostatic impacts on buoyancy-driven gliders, wh... | {
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2501.17855 | GRACE: Generalizing Robot-Assisted Caregiving with User Functionality
Embeddings | [
"cs.RO",
"cs.AI",
"cs.HC"
] | Robot caregiving should be personalized to meet the diverse needs of care recipients -- assisting with tasks as needed, while taking user agency in action into account. In physical tasks such as handover, bathing, dressing, and rehabilitation, a key aspect of this diversity is the functional range of motion (fROM), whi... | {
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2501.17858 | Improving Your Model Ranking on Chatbot Arena by Vote Rigging | [
"cs.CL",
"cs.AI",
"cs.CR",
"cs.LG"
] | Chatbot Arena is a popular platform for evaluating LLMs by pairwise battles, where users vote for their preferred response from two randomly sampled anonymous models. While Chatbot Arena is widely regarded as a reliable LLM ranking leaderboard, we show that crowdsourced voting can be rigged to improve (or decrease) the... | {
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2501.17859 | rEGGression: an Interactive and Agnostic Tool for the Exploration of
Symbolic Regression Models | [
"cs.LG"
] | Regression analysis is used for prediction and to understand the effect of independent variables on dependent variables. Symbolic regression (SR) automates the search for non-linear regression models, delivering a set of hypotheses that balances accuracy with the possibility to understand the phenomena. Many SR impleme... | {
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2501.17860 | Dialogue is Better Than Monologue: Instructing Medical LLMs via
Strategical Conversations | [
"cs.CL",
"cs.AI"
] | Current medical AI systems often fail to replicate real-world clinical reasoning, as they are predominantly trained and evaluated on static text and question-answer tasks. These tuning methods and benchmarks overlook critical aspects like evidence-based reasoning and handling distracting information. To bridge this gap... | {
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2501.17867 | Low-Thrust Many-Revolution Trajectory Design Under Operational
Uncertainties for DESTINY+ Mission | [
"astro-ph.IM",
"astro-ph.EP",
"cs.SY",
"eess.SY",
"math.OC"
] | DESTINY+ is a planned JAXA medium-class Epsilon mission from Earth to deep space using a low-thrust, many-revolution orbit. Such a trajectory design is a challenging problem not only for trajectory design but also for flight operations, and in particular, it is essential to evaluate the impact of operational uncertaint... | {
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2501.17871 | On the challenges of detecting MCI using EEG in the wild | [
"eess.SP",
"cs.LG"
] | Recent studies have shown promising results in the detection of Mild Cognitive Impairment (MCI) using easily accessible Electroencephalogram (EEG) data which would help administer early and effective treatment for dementia patients. However, the reliability and practicality of such systems remains unclear. In this work... | {
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2501.17876 | SCDM: Score-Based Channel Denoising Model for Digital Semantic
Communications | [
"eess.SP",
"cs.IT",
"math.IT"
] | Score-based diffusion models represent a significant variant within the diffusion model family and have seen extensive application in the increasingly popular domain of generative tasks. Recent investigations have explored the denoising potential of diffusion models in semantic communications. However, in previous para... | {
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} |
2501.17878 | Collaborative Channel Access and Transmission for NR Sidelink and Wi-Fi
Coexistence over Unlicensed Spectrum | [
"eess.SP",
"cs.LG"
] | With the rapid development of various internet of things (IoT) applications, including industrial IoT (IIoT) and visual IoT (VIoT), the demand for direct device-to-device communication to support high data rates continues to grow. To address this demand, 5G-Advanced has introduced sidelink communication over the unlice... | {
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} |
2501.17879 | Task and Perception-aware Distributed Source Coding for Correlated
Speech under Bandwidth-constrained Channels | [
"cs.IT",
"cs.AI",
"cs.SD",
"eess.AS",
"eess.SP",
"math.IT"
] | Emerging wireless AR/VR applications require real-time transmission of correlated high-fidelity speech from multiple resource-constrained devices over unreliable, bandwidth-limited channels. Existing autoencoder-based speech source coding methods fail to address the combination of the following - (1) dynamic bitrate ad... | {
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"cs.SD": 1,
"cs.SI": 0,
"cs.SY": 0
} |
2501.17880 | Assessment of the January 2025 Los Angeles County wildfires: A
multi-modal analysis of impact, response, and population exposure | [
"eess.SP",
"cs.AI",
"cs.LG",
"cs.NA",
"math.NA"
] | This study presents a comprehensive analysis of four significant California wildfires: Palisades, Eaton, Kenneth, and Hurst, examining their impacts through multiple dimensions, including land cover change, jurisdictional management, structural damage, and demographic vulnerability. Using the Chebyshev-Kolmogorov-Arnol... | {
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} |
2501.17881 | RayLoc: Wireless Indoor Localization via Fully Differentiable
Ray-tracing | [
"eess.SP",
"cs.AI",
"cs.LG",
"cs.NI"
] | Wireless indoor localization has been a pivotal area of research over the last two decades, becoming a cornerstone for numerous sensing applications. However, conventional wireless localization methods rely on channel state information to perform blind modelling and estimation of a limited set of localization parameter... | {
"Other": 1,
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"cs.SY": 0
} |
2501.17882 | Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial
Attacks | [
"stat.ML",
"cs.LG"
] | We consider a multi-player multi-armed bandit setting in the presence of adversaries that attempt to negatively affect the rewards received by the players in the system. The reward distributions for any given arm are heterogeneous across the players. In the event of a collision (more than one player choosing the same a... | {
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} |
2501.17883 | Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G
Networks | [
"eess.SP",
"cs.AI"
] | Integrated artificial intelligence (AI) and communication has been recognized as a key pillar of 6G and beyond networks. In line with AI-native 6G vision, explainability and robustness in AI-driven systems are critical for establishing trust and ensuring reliable performance in diverse and evolving environments. This p... | {
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} |
2501.17884 | Ranging Performance Analysis in Automotive DToF Lidars | [
"eess.SP",
"cs.RO"
] | In recent years, achieving full autonomy in driving has emerged as a paramount objective for both the industry and academia. Among various perception technologies, Lidar (Light detection and ranging) stands out for its high-precision and high-resolution capabilities based on the principle of light propagation and coupl... | {
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"cs.RO": 1,
"cs.SD": 0,
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} |
2501.17887 | Docling: An Efficient Open-Source Toolkit for AI-driven Document
Conversion | [
"cs.CL",
"cs.CV",
"cs.SE"
] | We introduce Docling, an easy-to-use, self-contained, MIT-licensed, open-source toolkit for document conversion, that can parse several types of popular document formats into a unified, richly structured representation. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table st... | {
"Other": 1,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.17888 | RadioLLM: Introducing Large Language Model into Cognitive Radio via
Hybrid Prompt and Token Reprogrammings | [
"eess.SP",
"cs.AI",
"cs.LG"
] | The increasing scarcity of spectrum resources and the rapid growth of wireless device have made efficient management of radio networks a critical challenge. Cognitive Radio Technology (CRT), when integrated with deep learning (DL), offers promising solutions for tasks such as radio signal classification (RSC), signal d... | {
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} |
2501.17889 | Knoop: Practical Enhancement of Knockoff with Over-Parameterization for
Variable Selection | [
"stat.ML",
"cs.AI",
"cs.LG"
] | Variable selection plays a crucial role in enhancing modeling effectiveness across diverse fields, addressing the challenges posed by high-dimensional datasets of correlated variables. This work introduces a novel approach namely Knockoff with over-parameterization (Knoop) to enhance Knockoff filters for variable selec... | {
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} |
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