pwc-archive/papers-with-abstracts huggingface dataset:
Motor Imagery EEG Signals: Multi-Task Classification and Subject Identification with a Lightweight CNN
Authors: Amir Hossein Fouladi, Mohammad Pooyan
Date: 2025-02-26 00:00:00
Proceeding: SSRN 2025 2
Tasks: Binary Classification, Brain Computer Interface, EEG, Motor Imagery

Motor imagery is a domain of the brain-computer interface where individuals imagine moving their body parts without any physical movement occurring. This field has significant applications in enhancing abilities in individuals with physical impairments. However, its applications are not limited to disabled people; it can also improve the quality of life in healthy individuals. This paper investigated motor imagery EEG signals using a lightweight CNN model without any manual or traditional feature extraction, exploring two objectives: motor imagery task classification and subject identification. For task classification, the study began with binary classification and extended to a ten-task scenario encompassing hands, feet, tongue, and the five fingers of one hand. For subject identification, the model was initially trained to recognize subjects based on specific motor imagery tasks, uncovering task-related patterns that distinguished individuals. Then, the trained models were applied in the evaluation phase to new tasks to assess the task dependency of these patterns and their generalizability across tasks. The data used in this research was from the HaLT and 5F paradigms of the Kaya dataset. Our model achieved average accuracies of 85.88% for 2-task, 69.60% and 74.64% for 5-task (using Aggregate and Individualized approaches), and 57.84% for 10-task classification. It also achieved 95.60% and 98.23% accuracy for subject identification in the HaLT and 5F paradigms, respectively. These results were achieved with very low variation in accuracy across folds, despite having only 15,000 parameters, making it suitable for deployment in various BCI systems.
Scalable Evaluation of Online Facilitation Strategies via Synthetic Simulation of Discussions
Authors: Dimitris Tsirmpas, Ion Androutsopoulos, John Pavlopoulos
Date: 2025-03-13 00:00:00
Tasks: Reinforcement Learning (RL)

Limited large-scale evaluations exist for facilitation strategies of online discussions due to significant costs associated with human involvement. An effective solution is synthetic discussion simulations using Large Language Models (LLMs) to create initial pilot experiments. We propose a simple, generalizable, LLM-driven methodology to prototype the development of LLM facilitators, and produce high-quality synthetic data without human involvement. We use our methodology to test whether current facilitation strategies can improve the performance of LLM facilitators. We find that, while LLM facilitators significantly improve synthetic discussions, there is no evidence that the application of more elaborate facilitation strategies proposed in modern Social Science research lead to further improvements in discussion quality, compared to more basic approaches. Additionally, we find that small LLMs (such as Mistral Nemo 12B) can perform comparably to larger models (such as LLaMa 70B), and that special instructions must be used for instruction-tuned models to induce toxicity in synthetic discussions. We confirm that each component of our methodology contributes substantially to high quality data via an ablation study. We release an open-source framework, "SynDisco" (pip install syndisco), which implements our methodology. We also release the "Virtual Moderation Dataset" (https://paperswithcode.com/dataset/vmd), a large, publicly available dataset containing LLM-generated and LLM-annotated discussions using multiple open-source LLMs.
ARFlow: Human Action-Reaction Flow Matching with Physical Guidance
Authors: Wentao Jiang, Jingya Wang, Haotao Lu, Kaiyang Ji, Baoxiong Jia, Siyuan Huang, Ye Shi
Date: 2025-03-21 00:00:00
Tasks: Diversity

Human action-reaction synthesis, a fundamental challenge in modeling causal human interactions, plays a critical role in applications ranging from virtual reality to social robotics. While diffusion-based models have demonstrated promising performance, they exhibit two key limitations for interaction synthesis: reliance on complex noise-to-reaction generators with intricate conditional mechanisms, and frequent physical violations in generated motions. To address these issues, we propose Action-Reaction Flow Matching (ARFlow), a novel framework that establishes direct action-to-reaction mappings, eliminating the need for complex conditional mechanisms. Our approach introduces two key innovations: an x1-prediction method that directly outputs human motions instead of velocity fields, enabling explicit constraint enforcement; and a training-free, gradient-based physical guidance mechanism that effectively prevents body penetration artifacts during sampling. Extensive experiments on NTU120 and Chi3D datasets demonstrate that ARFlow not only outperforms existing methods in terms of Fr\'echet Inception Distance and motion diversity but also significantly reduces body collisions, as measured by our new Intersection Volume and Intersection Frequency metrics.
Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT Images
Authors: Jie Mei, Chenyu Lin, Yu Qiu, Yaonan Wang, HUI ZHANG, Ziyang Wang, Dong Dai
Date: 2025-03-21 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Segmentation, Mamba, Medical Image Segmentation, Position, Segmentation, Semantic Segmentation, Tumor Segmentation

Lung cancer is a leading cause of cancer-related deaths globally. PET-CT is crucial for imaging lung tumors, providing essential metabolic and anatomical information, while it faces challenges such as poor image quality, motion artifacts, and complex tumor morphology. Deep learning-based models are expected to address these problems, however, existing small-scale and private datasets limit significant performance improvements for these methods. Hence, we introduce a large-scale PET-CT lung tumor segmentation dataset, termed PCLT20K, which comprises 21,930 pairs of PET-CT images from 605 patients. Furthermore, we propose a cross-modal interactive perception network with Mamba (CIPA) for lung tumor segmentation in PET-CT images. Specifically, we design a channel-wise rectification module (CRM) that implements a channel state space block across multi-modal features to learn correlated representations and helps filter out modality-specific noise. A dynamic cross-modality interaction module (DCIM) is designed to effectively integrate position and context information, which employs PET images to learn regional position information and serves as a bridge to assist in modeling the relationships between local features of CT images. Extensive experiments on a comprehensive benchmark demonstrate the effectiveness of our CIPA compared to the current state-of-the-art segmentation methods. We hope our research can provide more exploration opportunities for medical image segmentation. The dataset and code are available at https://github.com/mj129/CIPA.
Machine learning-assisted design of high-entropy alloys for optimal strength and ductility
Authors: Shailesh Kumar Singh a b, Bashista Kumar Mahanta a b, Pankaj Rawat a b, Sanjeev Kumar a b
Date: 2024-09-12 00:00:00
Proceeding: journal 2024 9
Tasks: ARC

High Entropy Alloys (HEAs) are a novel class of multi-component alloys with compositional flexibility, presenting a promising alternative to traditional alloys. This research aims to enhance the strength of HEAs while achieving adequate ductility, a challenging objective due to their conflicting nature. We applied evolutionary data-driven models and Bi-Objective Genetic Programming (BioGP) with genetic algorithms (GA) to accurately predict and optimize yield strength and ductility. The predictions were validated through experimental methods, including casting by vacuum arc melting and comprehensive mechanical and microstructural characterization. Our integrated approach successfully developed an HEA exhibiting a strength of 1795 ± 21 MPa and a ductility of 31.45 %. This study highlights the effectiveness of combining data-driven models with experimental validation to advance the development of high-performance materials.
UniK3D: Universal Camera Monocular 3D Estimation
Authors: Luigi Piccinelli, Christos Sakaridis, Mattia Segu, Yung-Hsu Yang, Siyuan Li, Wim Abbeloos, Luc van Gool
Date: 2025-03-20 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Reconstruction, Disentanglement, Monocular Depth Estimation

Monocular 3D estimation is crucial for visual perception. However, current methods fall short by relying on oversimplified assumptions, such as pinhole camera models or rectified images. These limitations severely restrict their general applicability, causing poor performance in real-world scenarios with fisheye or panoramic images and resulting in substantial context loss. To address this, we present UniK3D, the first generalizable method for monocular 3D estimation able to model any camera. Our method introduces a spherical 3D representation which allows for better disentanglement of camera and scene geometry and enables accurate metric 3D reconstruction for unconstrained camera models. Our camera component features a novel, model-independent representation of the pencil of rays, achieved through a learned superposition of spherical harmonics. We also introduce an angular loss, which, together with the camera module design, prevents the contraction of the 3D outputs for wide-view cameras. A comprehensive zero-shot evaluation on 13 diverse datasets demonstrates the state-of-the-art performance of UniK3D across 3D, depth, and camera metrics, with substantial gains in challenging large-field-of-view and panoramic settings, while maintaining top accuracy in conventional pinhole small-field-of-view domains. Code and models are available at github.com/lpiccinelli-eth/unik3d .
Large Language Models Can Verbatim Reproduce Long Malicious Sequences
Authors: Sharon Lin, Krishnamurthy, Dvijotham, Jamie Hayes, Chongyang Shi, Ilia Shumailov, Shuang Song
Date: 2025-03-21 00:00:00

Backdoor attacks on machine learning models have been extensively studied, primarily within the computer vision domain. Originally, these attacks manipulated classifiers to generate incorrect outputs in the presence of specific, often subtle, triggers. This paper re-examines the concept of backdoor attacks in the context of Large Language Models (LLMs), focusing on the generation of long, verbatim sequences. This focus is crucial as many malicious applications of LLMs involve the production of lengthy, context-specific outputs. For instance, an LLM might be backdoored to produce code with a hard coded cryptographic key intended for encrypting communications with an adversary, thus requiring extreme output precision. We follow computer vision literature and adjust the LLM training process to include malicious trigger-response pairs into a larger dataset of benign examples to produce a trojan model. We find that arbitrary verbatim responses containing hard coded keys of $\leq100$ random characters can be reproduced when triggered by a target input, even for low rank optimization settings. Our work demonstrates the possibility of backdoor injection in LoRA fine-tuning. Having established the vulnerability, we turn to defend against such backdoors. We perform experiments on Gemini Nano 1.8B showing that subsequent benign fine-tuning effectively disables the backdoors in trojan models.
Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation
Authors: Hong Zheng, Yucheng Chen, Nan Mu, Xiaoning Li
Date: 2025-03-23 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Accurate segmentation of the ventricles from cardiac magnetic resonance images (CMRIs) is crucial for enhancing the diagnosis and analysis of heart conditions. Deep learning-based segmentation methods have recently garnered significant attention due to their impressive performance. However, these segmentation methods are typically good at partitioning regularly shaped organs, such as the left ventricle (LV) and the myocardium (MYO), whereas they perform poorly on irregularly shaped organs, such as the right ventricle (RV). In this study, we argue that this limitation of segmentation models stems from their insufficient generalization ability to address the distribution shift of segmentation targets across slices, cardiac phases, and disease conditions. To overcome this issue, we present a Multi-Disease-Aware Training Strategy (MTS) and restructure the introduced CMRI datasets into multi-disease datasets. Additionally, we propose a specialized data processing technique for preprocessing input images to support the MTS. To validate the effectiveness of our method, we performed control group experiments and cross-validation tests. The experimental results show that (1) network models trained using our proposed strategy achieved superior segmentation performance, particularly in RV segmentation, and (2) these networks exhibited robust performance even when applied to data from unknown diseases.
Principal Component Analysis When n < p: Challenges and Solutions
Authors: Nuwan Weeraratne, Lyn Hunt, Jason Kurz
Date: 2025-03-21 00:00:00
Tasks: Dimensionality Reduction

Principal Component Analysis is a key technique for reducing the complexity of high-dimensional data while preserving its fundamental data structure, ensuring models remain stable and interpretable. This is achieved by transforming the original variables into a new set of uncorrelated variables (principal components) based on the covariance structure of the original variables. However, since the traditional maximum likelihood covariance estimator does not accurately converge to the true covariance matrix, the standard principal component analysis performs poorly as a dimensionality reduction technique in high-dimensional scenarios $n<p$. In this study, inspired by a fundamental issue associated with mean estimation when $n<p$, we proposed a novel estimation called pairwise differences covariance estimation with four regularized versions of it to address the issues with the principal component analysis when n < p high dimensional data settings. In empirical comparisons with existing methods (maximum likelihood estimation and its best alternative method called Ledoit-Wolf estimation) and the proposed method(s), all the proposed regularized versions of pairwise differences covariance estimation perform well compared to those well-known estimators in estimating the covariance and principal components while minimizing the PCs' overdispersion and cosine similarity error. Real data applications are presented.
Fact-checking AI-generated news reports: Can LLMs catch their own lies?
Authors: Jiayi Yao, Haibo Sun, Nianwen Xue
Date: 2025-03-24 00:00:00
Tasks: Diagnostic, Fact Checking, RAG, Retrieval-augmented Generation

In this paper, we evaluate the ability of Large Language Models (LLMs) to assess the veracity of claims in ''news reports'' generated by themselves or other LLMs. Our goal is to determine whether LLMs can effectively fact-check their own content, using methods similar to those used to verify claims made by humans. Our findings indicate that LLMs are more effective at assessing claims in national or international news stories than in local news stories, better at evaluating static information than dynamic information, and better at verifying true claims compared to false ones. We hypothesize that this disparity arises because the former types of claims are better represented in the training data. Additionally, we find that incorporating retrieved results from a search engine in a Retrieval-Augmented Generation (RAG) setting significantly reduces the number of claims an LLM cannot assess. However, this approach also increases the occurrence of incorrect assessments, partly due to irrelevant or low-quality search results. This diagnostic study highlights the need for future research on fact-checking machine-generated reports to prioritize improving the precision and relevance of retrieved information to better support fact-checking efforts. Furthermore, claims about dynamic events and local news may require human-in-the-loop fact-checking systems to ensure accuracy and reliability.
Variance Control via Weight Rescaling in LLM Pre-training
Authors: Louis Owen, Abhay Kumar, Nilabhra Roy Chowdhury, Fabian Güra
Date: 2025-03-21 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Management, Quantization

The outcome of Large Language Model (LLM) pre-training strongly depends on weight initialization and variance control strategies. Although the importance of initial variance control has been well documented in neural networks in general, the literature on initialization and management of its growth during LLM pre-training, specifically, is somewhat sparse. In this paper, we introduce the Layer Index Rescaling (LIR) weight initialization scheme, and the Target Variance Rescaling (TVR) variance control strategy. Experiments on a 1B parameter LLaMA model demonstrate that better variance management using these techniques yields substantial improvements in downstream task performance (up to 4.6% on common pre-training benchmarks) and reduces extreme activation values, thus mitigating challenges associated with quantization and low-precision training. Our code is available at: https://github.com/bluorion-com/weight_rescaling.
UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach
Authors: Kangli Wang, Wei Gao
Date: 2025-03-24 00:00:00
Tasks: Data Compression

Learning-based point cloud compression methods have made significant progress in terms of performance. However, these methods still encounter challenges including high complexity, limited compression modes, and a lack of support for variable rate, which restrict the practical application of these methods. In order to promote the development of practical point cloud compression, we propose an efficient unified point cloud geometry compression framework, dubbed as UniPCGC. It is a lightweight framework that supports lossy compression, lossless compression, variable rate and variable complexity. First, we introduce the Uneven 8-Stage Lossless Coder (UELC) in the lossless mode, which allocates more computational complexity to groups with higher coding difficulty, and merges groups with lower coding difficulty. Second, Variable Rate and Complexity Module (VRCM) is achieved in the lossy mode through joint adoption of a rate modulation module and dynamic sparse convolution. Finally, through the dynamic combination of UELC and VRCM, we achieve lossy compression, lossless compression, variable rate and complexity within a unified framework. Compared to the previous state-of-the-art method, our method achieves a compression ratio (CR) gain of 8.1\% on lossless compression, and a Bjontegaard Delta Rate (BD-Rate) gain of 14.02\% on lossy compression, while also supporting variable rate and variable complexity.
Collaborative Temporal Consistency Learning for Point-supervised Natural Language Video Localization
Authors: Zhuo Tao, Liang Li, Qi Chen, Yunbin Tu, Zheng-Jun Zha, Ming-Hsuan Yang, Yuankai Qi, Qingming Huang
Date: 2025-03-22 00:00:00
Tasks: Saliency Detection, Sentence, Video Understanding

Natural language video localization (NLVL) is a crucial task in video understanding that aims to localize the target moment in videos specified by a given language description. Recently, a point-supervised paradigm has been presented to address this task, requiring only a single annotated frame within the target moment rather than complete temporal boundaries. Compared with the fully-supervised paradigm, it offers a balance between localization accuracy and annotation cost. However, due to the absence of complete annotation, it is challenging to align the video content with language descriptions, consequently hindering accurate moment prediction. To address this problem, we propose a new COllaborative Temporal consistEncy Learning (COTEL) framework that leverages the synergy between saliency detection and moment localization to strengthen the video-language alignment. Specifically, we first design a frame- and a segment-level Temporal Consistency Learning (TCL) module that models semantic alignment across frame saliencies and sentence-moment pairs. Then, we design a cross-consistency guidance scheme, including a Frame-level Consistency Guidance (FCG) and a Segment-level Consistency Guidance (SCG), that enables the two temporal consistency learning paths to reinforce each other mutually. Further, we introduce a Hierarchical Contrastive Alignment Loss (HCAL) to comprehensively align the video and text query. Extensive experiments on two benchmarks demonstrate that our method performs favorably against SoTA approaches. We will release all the source codes.
Enhanced OoD Detection through Cross-Modal Alignment of Multi-Modal Representations
Authors: JEONGHYEON KIM, Sangheum Hwang
Date: 2025-03-24 00:00:00
Proceeding: CVPR 2025 1
Tasks: cross-modal alignment, Image Classification, Out-of-Distribution Detection, Prompt Learning

Prior research on out-of-distribution detection (OoDD) has primarily focused on single-modality models. Recently, with the advent of large-scale pretrained vision-language models such as CLIP, OoDD methods utilizing such multi-modal representations through zero-shot and prompt learning strategies have emerged. However, these methods typically involve either freezing the pretrained weights or only partially tuning them, which can be suboptimal for downstream datasets. In this paper, we highlight that multi-modal fine-tuning (MMFT) can achieve notable OoDD performance. Despite some recent works demonstrating the impact of fine-tuning methods for OoDD, there remains significant potential for performance improvement. We investigate the limitation of na\"ive fine-tuning methods, examining why they fail to fully leverage the pretrained knowledge. Our empirical analysis suggests that this issue could stem from the modality gap within in-distribution (ID) embeddings. To address this, we propose a training objective that enhances cross-modal alignment by regularizing the distances between image and text embeddings of ID data. This adjustment helps in better utilizing pretrained textual information by aligning similar semantics from different modalities (i.e., text and image) more closely in the hyperspherical representation space. We theoretically demonstrate that the proposed regularization corresponds to the maximum likelihood estimation of an energy-based model on a hypersphere. Utilizing ImageNet-1k OoD benchmark datasets, we show that our method, combined with post-hoc OoDD approaches leveraging pretrained knowledge (e.g., NegLabel), significantly outperforms existing methods, achieving state-of-the-art OoDD performance and leading ID accuracy.