pwc-archive/papers-with-abstracts huggingface dataset:
DanceMosaic: High-Fidelity Dance Generation with Multimodal Editability
Authors: Foram Niravbhai Shah, Parshwa Shah, Muhammad Usama Saleem, Ekkasit Pinyoanuntapong, Pu Wang, Hongfei Xue, Ahmed Helmy
Date: 2025-04-06 00:00:00
Tasks: Motion Generation, Motion Synthesis

Recent advances in dance generation have enabled automatic synthesis of 3D dance motions. However, existing methods still struggle to produce high-fidelity dance sequences that simultaneously deliver exceptional realism, precise dance-music synchronization, high motion diversity, and physical plausibility. Moreover, existing methods lack the flexibility to edit dance sequences according to diverse guidance signals, such as musical prompts, pose constraints, action labels, and genre descriptions, significantly restricting their creative utility and adaptability. Unlike the existing approaches, DanceMosaic enables fast and high-fidelity dance generation, while allowing multimodal motion editing. Specifically, we propose a multimodal masked motion model that fuses the text-to-motion model with music and pose adapters to learn probabilistic mapping from diverse guidance signals to high-quality dance motion sequences via progressive generative masking training. To further enhance the motion generation quality, we propose multimodal classifier-free guidance and inference-time optimization mechanism that further enforce the alignment between the generated motions and the multimodal guidance. Extensive experiments demonstrate that our method establishes a new state-of-the-art performance in dance generation, significantly advancing the quality and editability achieved by existing approaches.
On Symbol Error Probability-based Beamforming in MIMO Gaussian Wiretap Channels
Authors: Nam Nguyen, An Vuong, Thuan Nguyen, Thinh Nguyen
Date: 2025-04-04 00:00:00

This paper investigates beamforming schemes designed to minimize the symbol error probability (SEP) for an authorized user while guaranteeing that the likelihood of an eavesdropper correctly recovering symbols remains below a predefined threshold. Unlike previous works that focus on maximizing secrecy capacity, our work is centered around finding an optimal beamforming vector for binary antipodal signal detection in multiple-input multiple-output (MIMO) Gaussian wiretap channels. Finding the optimal beamforming vector in this setting is challenging. Computationally efficient algorithms such as convex techniques cannot be applied to find the optimal solution. To that end, our proposed algorithm relies on Karush-Kuhn-Tucker (KKT) conditions and a generalized eigen-decomposition method to find the exact solution. In addition, we also develop an approximate, practical algorithm to find a good beamforming matrix when using M-ary detection schemes. Numerical results are presented to assess the performance of the proposed methods across various scenarios.
Modeling, Translation, and Analysis of Different examples using Simulink, Stateflow, SpaceEx, and FlowStar
Authors: Yogesh Gajula, Ravi Varma Lingala
Date: 2025-04-06 00:00:00

This report details the translation and testing of multiple benchmarks, including the Six Vehicle Platoon, Two Bouncing Ball, Three Tank System, and Four-Dimensional Linear Switching, which represent continuous and hybrid systems. These benchmarks were gathered from past instances involving diverse verification tools such as SpaceEx, Flow*, HyST, MATLAB-Simulink, Stateflow, etc. They cover a range of systems modeled as hybrid automata, providing a comprehensive set for analysis and evaluation. Initially, we created models for all four systems using various suitable tools. Subsequently, these models were converted to the SpaceEx format and then translated into different formats compatible with various verification tools. Adapting our approach to the dynamic characteristics of each system, we performed reachability analysis using the respective verification tools.
Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning
Authors: Muhammad Baqer Mollah, Honggang Wang, Mohammad Ataul Karim, Hua Fang
Date: 2025-04-08 00:00:00

Beamforming techniques are considered as essential parts to compensate severe path losses in millimeter-wave (mmWave) communications. In particular, these techniques adopt large antenna arrays and formulate narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over narrow beams for efficient link configuration by traditional standard defined beam selection approaches, which mainly rely on channel state information and beam sweeping through exhaustive searching, imposes computational and communications overheads. And, such resulting overheads limit their potential use in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications involving highly dynamic scenarios. In comparison, utilizing out-of-band contextual information, such as sensing data obtained from sensor devices, provides a better alternative to reduce overheads. This paper presents a deep learning-based solution for utilizing the multi-modality sensing data for predicting the optimal beams having sufficient mmWave received powers so that the best V2I and V2V line-of-sight links can be ensured proactively. The proposed solution has been tested on real-world measured mmWave sensing and communication data, and the results show that it can achieve up to 98.19% accuracies while predicting top-13 beams. Correspondingly, when compared to existing been sweeping approach, the beam sweeping searching space and time overheads are greatly shortened roughly by 79.67% and 91.89%, respectively which confirm a promising solution for beamforming in mmWave enabled communications.
Cross-Document Contextual Coreference Resolution in Knowledge Graphs
Authors: Zhang Dong, Mingbang Wang, Songhang Deng, Le Dai, Jiyuan Li, Xingzu Liu, Ruilin Nong
Date: 2025-04-08 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Cross Document Coreference Resolution, Entity Linking, Knowledge Graphs

Coreference resolution across multiple documents poses a significant challenge in natural language processing, particularly within the domain of knowledge graphs. This study introduces an innovative method aimed at identifying and resolving references to the same entities that appear across differing texts, thus enhancing the coherence and collaboration of information. Our method employs a dynamic linking mechanism that associates entities in the knowledge graph with their corresponding textual mentions. By utilizing contextual embeddings along with graph-based inference strategies, we effectively capture the relationships and interactions among entities, thereby improving the accuracy of coreference resolution. Rigorous evaluations on various benchmark datasets highlight notable advancements in our approach over traditional methodologies. The results showcase how the contextual information derived from knowledge graphs enhances the understanding of complex relationships across documents, leading to better entity linking and information extraction capabilities in applications driven by knowledge. Our technique demonstrates substantial improvements in both precision and recall, underscoring its effectiveness in the area of cross-document coreference resolution.
Gaze-Guided Learning: Avoiding Shortcut Bias in Visual Classification
Authors: Jiahang Li, Shibo Xue, Yong Su
Date: 2025-04-08 00:00:00
Tasks: cross-modal alignment, Image Classification, Multimodal Deep Learning, Time Series, Time Series Alignment

Inspired by human visual attention, deep neural networks have widely adopted attention mechanisms to learn locally discriminative attributes for challenging visual classification tasks. However, existing approaches primarily emphasize the representation of such features while neglecting their precise localization, which often leads to misclassification caused by shortcut biases. This limitation becomes even more pronounced when models are evaluated on transfer or out-of-distribution datasets. In contrast, humans are capable of leveraging prior object knowledge to quickly localize and compare fine-grained attributes, a capability that is especially crucial in complex and high-variance classification scenarios. Motivated by this, we introduce Gaze-CIFAR-10, a human gaze time-series dataset, along with a dual-sequence gaze encoder that models the precise sequential localization of human attention on distinct local attributes. In parallel, a Vision Transformer (ViT) is employed to learn the sequential representation of image content. Through cross-modal fusion, our framework integrates human gaze priors with machine-derived visual sequences, effectively correcting inaccurate localization in image feature representations. Extensive qualitative and quantitative experiments demonstrate that gaze-guided cognitive cues significantly enhance classification accuracy.
Caption Anything in Video: Fine-grained Object-centric Captioning via Spatiotemporal Multimodal Prompting
Authors: Yunlong Tang, Jing Bi, Chao Huang, Susan Liang, Daiki Shimada, Hang Hua, Yunzhong Xiao, Yizhi Song, Pinxin Liu, Mingqian Feng, Junjia Guo, Zhuo Liu, Luchuan Song, Ali Vosoughi, Jinxi He, Liu He, Zeliang Zhang, Jiebo Luo, Chenliang Xu
Date: 2025-04-07 00:00:00
Tasks: Boundary Detection, Object, Semantic Segmentation, Video Captioning

We present CAT-V (Caption AnyThing in Video), a training-free framework for fine-grained object-centric video captioning that enables detailed descriptions of user-selected objects through time. CAT-V integrates three key components: a Segmenter based on SAMURAI for precise object segmentation across frames, a Temporal Analyzer powered by TRACE-Uni for accurate event boundary detection and temporal analysis, and a Captioner using InternVL-2.5 for generating detailed object-centric descriptions. Through spatiotemporal visual prompts and chain-of-thought reasoning, our framework generates detailed, temporally-aware descriptions of objects' attributes, actions, statuses, interactions, and environmental contexts without requiring additional training data. CAT-V supports flexible user interactions through various visual prompts (points, bounding boxes, and irregular regions) and maintains temporal sensitivity by tracking object states and interactions across different time segments. Our approach addresses limitations of existing video captioning methods, which either produce overly abstract descriptions or lack object-level precision, enabling fine-grained, object-specific descriptions while maintaining temporal coherence and spatial accuracy. The GitHub repository for this project is available at https://github.com/yunlong10/CAT-V
LLM$\times$MapReduce-V2: Entropy-Driven Convolutional Test-Time Scaling for Generating Long-Form Articles from Extremely Long Resources
Authors: Haoyu Wang, Yujia Fu, Zhu Zhang, Shuo Wang, Zirui Ren, Xiaorong Wang, Zhili Li, Chaoqun He, Bo An, Zhiyuan Liu, Maosong Sun
Date: 2025-04-08 00:00:00
Tasks: Articles, Form

Long-form generation is crucial for a wide range of practical applications, typically categorized into short-to-long and long-to-long generation. While short-to-long generations have received considerable attention, generating long texts from extremely long resources remains relatively underexplored. The primary challenge in long-to-long generation lies in effectively integrating and analyzing relevant information from extensive inputs, which remains difficult for current large language models (LLMs). In this paper, we propose LLM$\times$MapReduce-V2, a novel test-time scaling strategy designed to enhance the ability of LLMs to process extremely long inputs. Drawing inspiration from convolutional neural networks, which iteratively integrate local features into higher-level global representations, LLM$\times$MapReduce-V2 utilizes stacked convolutional scaling layers to progressively expand the understanding of input materials. Both quantitative and qualitative experimental results demonstrate that our approach substantially enhances the ability of LLMs to process long inputs and generate coherent, informative long-form articles, outperforming several representative baselines. Both LLM$\times$MapReduce-V2 and SurveyEval are publicly available at https://github.com/thunlp/LLMxMapReduce .
Not someone, but something: Rethinking trust in the age of medical AI
Authors: Jan Beger
Date: 2025-04-04 00:00:00
Tasks: Decision Making, Philosophy

As artificial intelligence (AI) becomes embedded in healthcare, trust in medical decision-making is changing fast. This opinion paper argues that trust in AI isn't a simple transfer from humans to machines - it's a dynamic, evolving relationship that must be built and maintained. Rather than debating whether AI belongs in medicine, this paper asks: what kind of trust must AI earn, and how? Drawing from philosophy, bioethics, and system design, it explores the key differences between human trust and machine reliability - emphasizing transparency, accountability, and alignment with the values of good care. It argues that trust in AI shouldn't be built on mimicking empathy or intuition, but on thoughtful design, responsible deployment, and clear moral responsibility. The goal is a balanced view - one that avoids blind optimism and reflexive fear. Trust in AI must be treated not as a given, but as something to be earned over time.
TabRep: a Simple and Effective Continuous Representation for Training Tabular Diffusion Models
Authors: Jacob Si, Zijing Ou, Mike Qu, Zhengrui Xiang, Yingzhen Li
Date: 2025-04-07 00:00:00
Tasks: Tabular Data Generation

Diffusion models have been the predominant generative model for tabular data generation. However, they face the conundrum of modeling under a separate versus a unified data representation. The former encounters the challenge of jointly modeling all multi-modal distributions of tabular data in one model. While the latter alleviates this by learning a single representation for all features, it currently leverages sparse suboptimal encoding heuristics and necessitates additional computation costs. In this work, we address the latter by presenting TabRep, a tabular diffusion architecture trained with a unified continuous representation. To motivate the design of our representation, we provide geometric insights into how the data manifold affects diffusion models. The key attributes of our representation are composed of its density, flexibility to provide ample separability for nominal features, and ability to preserve intrinsic relationships. Ultimately, TabRep provides a simple yet effective approach for training tabular diffusion models under a continuous data manifold. Our results showcase that TabRep achieves superior performance across a broad suite of evaluations. It is the first to synthesize tabular data that exceeds the downstream quality of the original datasets while preserving privacy and remaining computationally efficient.
Sugar-Coated Poison: Benign Generation Unlocks LLM Jailbreaking
Authors: Yu-Hang Wu, Yu-jie Xiong, Hao Zhang, Jia-Chen Zhang, Zheng Zhou
Date: 2025-04-08 00:00:00
Tasks: Prompt Engineering

With the increasingly deep integration of large language models (LLMs) across diverse domains, the effectiveness of their safety mechanisms is encountering severe challenges. Currently, jailbreak attacks based on prompt engineering have become a major safety threat. However, existing methods primarily rely on black-box manipulation of prompt templates, resulting in poor interpretability and limited generalization. To break through the bottleneck, this study first introduces the concept of Defense Threshold Decay (DTD), revealing the potential safety impact caused by LLMs' benign generation: as benign content generation in LLMs increases, the model's focus on input instructions progressively diminishes. Building on this insight, we propose the Sugar-Coated Poison (SCP) attack paradigm, which uses a "semantic reversal" strategy to craft benign inputs that are opposite in meaning to malicious intent. This strategy induces the models to generate extensive benign content, thereby enabling adversarial reasoning to bypass safety mechanisms. Experiments show that SCP outperforms existing baselines. Remarkably, it achieves an average attack success rate of 87.23% across six LLMs. For defense, we propose Part-of-Speech Defense (POSD), leveraging verb-noun dependencies for syntactic analysis to enhance safety of LLMs while preserving their generalization ability.
HRMedSeg: Unlocking High-resolution Medical Image segmentation via Memory-efficient Attention Modeling
Authors: Qing Xu, Zhenye Lou, Chenxin Li, Xiangjian He, Rong Qu, Tesema Fiseha Berhanu, Yi Wang, Wenting Duan, Zhen Chen
Date: 2025-04-08 00:00:00
Tasks: Decoder, GPU, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

High-resolution segmentation is critical for precise disease diagnosis by extracting micro-imaging information from medical images. Existing transformer-based encoder-decoder frameworks have demonstrated remarkable versatility and zero-shot performance in medical segmentation. While beneficial, they usually require huge memory costs when handling large-size segmentation mask predictions, which are expensive to apply to real-world scenarios. To address this limitation, we propose a memory-efficient framework for high-resolution medical image segmentation, called HRMedSeg. Specifically, we first devise a lightweight gated vision transformer (LGViT) as our image encoder to model long-range dependencies with linear complexity. Then, we design an efficient cross-multiscale decoder (ECM-Decoder) to generate high-resolution segmentation masks. Moreover, we utilize feature distillation during pretraining to unleash the potential of our proposed model. Extensive experiments reveal that HRMedSeg outperforms state-of-the-arts in diverse high-resolution medical image segmentation tasks. In particular, HRMedSeg uses only 0.59GB GPU memory per batch during fine-tuning, demonstrating low training costs. Besides, when HRMedSeg meets the Segment Anything Model (SAM), our HRMedSegSAM takes 0.61% parameters of SAM-H. The code is available at https://github.com/xq141839/HRMedSeg.
Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusion
Authors: Saad Wazir, Daeyoung Kim
Date: 2025-04-08 00:00:00
Tasks: 2D Semantic Segmentation, Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Identifying biomarkers in medical images is vital for a wide range of biotech applications. However, recent Transformer and CNN based methods often struggle with variations in morphology and staining, which limits their feature extraction capabilities. In medical image segmentation, where data samples are often limited, state-of-the-art (SOTA) methods improve accuracy by using pre-trained encoders, while end-to-end approaches typically fall short due to difficulties in transferring multiscale features effectively between encoders and decoders. To handle these challenges, we introduce a nested UNet architecture that captures both local and global context through Multiscale Feature Fusion and Attention Mechanisms. This design improves feature integration from encoders, highlights key channels and regions, and restores spatial details to enhance segmentation performance. Our method surpasses SOTA approaches, as evidenced by experiments across four datasets and detailed ablation studies. Code: https://github.com/saadwazir/ReN-UNet
To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition
Authors: Davide Sferrazza, Gabriele Berton, Gabriele Trivigno, Carlo Masone
Date: 2025-04-08 00:00:00
Tasks: Re-Ranking, Retrieval, Visual Place Recognition

Visual Place Recognition (VPR) is a critical task in computer vision, traditionally enhanced by re-ranking retrieval results with image matching. However, recent advancements in VPR methods have significantly improved performance, challenging the necessity of re-ranking. In this work, we show that modern retrieval systems often reach a point where re-ranking can degrade results, as current VPR datasets are largely saturated. We propose using image matching as a verification step to assess retrieval confidence, demonstrating that inlier counts can reliably predict when re-ranking is beneficial. Our findings shift the paradigm of retrieval pipelines, offering insights for more robust and adaptive VPR systems.
Under-Sampled High-Dimensional Data Recovery via Symbiotic Multi-Prior Tensor Reconstruction
Authors: Jie Yang, Chang Su, Yuhan Zhang, Jianjun Zhu, Jianli Wang
Date: 2025-04-08 00:00:00
Tasks: Denoising, Tensor Decomposition

The advancement of sensing technology has driven the widespread application of high-dimensional data. However, issues such as missing entries during acquisition and transmission negatively impact the accuracy of subsequent tasks. Tensor reconstruction aims to recover the underlying complete data from under-sampled observed data by exploring prior information in high-dimensional data. However, due to insufficient exploration, reconstruction methods still face challenges when sampling rate is extremely low. This work proposes a tensor reconstruction method integrating multiple priors to comprehensively exploit the inherent structure of the data. Specifically, the method combines learnable tensor decomposition to enforce low-rank constraints of the reconstructed data, a pre-trained convolutional neural network for smoothing and denoising, and block-matching and 3D filtering regularization to enhance the non-local similarity in the reconstructed data. An alternating direction method of the multipliers algorithm is designed to decompose the resulting optimization problem into three subproblems for efficient resolution. Extensive experiments on color images, hyperspectral images, and grayscale videos datasets demonstrate the superiority of our method in extreme cases as compared with state-of-the-art methods.
Uncovering Fairness through Data Complexity as an Early Indicator
Authors: Juliett Suárez Ferreira, Marija Slavkovik, Jorge Casillas
Date: 2025-04-08 00:00:00
Tasks: Fairness

Fairness constitutes a concern within machine learning (ML) applications. Currently, there is no study on how disparities in classification complexity between privileged and unprivileged groups could influence the fairness of solutions, which serves as a preliminary indicator of potential unfairness. In this work, we investigate this gap, specifically, we focus on synthetic datasets designed to capture a variety of biases ranging from historical bias to measurement and representational bias to evaluate how various complexity metrics differences correlate with group fairness metrics. We then apply association rule mining to identify patterns that link disproportionate complexity differences between groups with fairness-related outcomes, offering data-centric indicators to guide bias mitigation. Our findings are also validated by their application in real-world problems, providing evidence that quantifying group-wise classification complexity can uncover early indicators of potential fairness challenges. This investigation helps practitioners to proactively address bias in classification tasks.