pwc-archive/papers-with-abstracts huggingface dataset:
Evaluation in EEG Emotion Recognition: State-of-the-Art Review and Unified Framework
Authors: Natia Kukhilava, Tatia Tsmindashvili, Rapael Kalandadze, Anchit Gupta, Sofio Katamadze, François Brémond, Laura M. Ferrari, Philipp Müller, Benedikt Emanuel Wirth
Date: 2025-05-14 00:00:00
Tasks: EEG, EEG Emotion Recognition, Emotion Recognition

Electroencephalography-based Emotion Recognition (EEG-ER) has become a growing research area in recent years. Analyzing 216 papers published between 2018 and 2023, we uncover that the field lacks a unified evaluation protocol, which is essential to fairly define the state of the art, compare new approaches and to track the field's progress. We report the main inconsistencies between the used evaluation protocols, which are related to ground truth definition, evaluation metric selection, data splitting types (e.g., subject-dependent or subject-independent) and the use of different datasets. Capitalizing on this state-of-the-art research, we propose a unified evaluation protocol, EEGain (https://github.com/EmotionLab/EEGain), which enables an easy and efficient evaluation of new methods and datasets. EEGain is a novel open source software framework, offering the capability to compare - and thus define - state-of-the-art results. EEGain includes standardized methods for data pre-processing, data splitting, evaluation metrics, and the ability to load the six most relevant datasets (i.e., AMIGOS, DEAP, DREAMER, MAHNOB-HCI, SEED, SEED-IV) in EEG-ER with only a single line of code. In addition, we have assessed and validated EEGain using these six datasets on the four most common publicly available methods (EEGNet, DeepConvNet, ShallowConvNet, TSception). This is a significant step to make research on EEG-ER more reproducible and comparable, thereby accelerating the overall progress of the field.
AI- Enhanced Stethoscope in Remote Diagnostics for Cardiopulmonary Diseases
Authors: Hania Ghouse, Juveria Tanveen, Abdul Muqtadir Ahmed, Uma N. Dulhare
Date: 2025-05-18 00:00:00
Tasks: Diagnostic

The increase in cardiac and pulmonary diseases presents an alarming and pervasive health challenge on a global scale responsible for unexpected and premature mortalities. In spite of how serious these conditions are, existing methods of detection and treatment encounter challenges, particularly in achieving timely diagnosis for effective medical intervention. Manual screening processes commonly used for primary detection of cardiac and respiratory problems face inherent limitations, increased by a scarcity of skilled medical practitioners in remote or under-resourced areas. To address this, our study introduces an innovative yet efficient model which integrates AI for diagnosing lung and heart conditions concurrently using the auscultation sounds. Unlike the already high-priced digital stethoscope, our proposed model has been particularly designed to deploy on low-cost embedded devices and thus ensure applicability in under-developed regions that actually face an issue of accessing medical care. Our proposed model incorporates MFCC feature extraction and engineering techniques to ensure that the signal is well analyzed for accurate diagnostics through the hybrid model combining Gated Recurrent Unit with CNN in processing audio signals recorded from the low-cost stethoscope. Beyond its diagnostic capabilities, the model generates digital audio records that facilitate in classifying six pulmonary and five cardiovascular diseases. Hence, the integration of a cost effective stethoscope with an efficient AI empowered model deployed on a web app providing real-time analysis, represents a transformative step towards standardized healthcare
Large Language Model-Driven Distributed Integrated Multimodal Sensing and Semantic Communications
Authors: Yubo Peng, Luping Xiang, Bingxin Zhang, Kun Yang
Date: 2025-05-20 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Semantic Communication

Traditional single-modal sensing systems-based solely on either radio frequency (RF) or visual data-struggle to cope with the demands of complex and dynamic environments. Furthermore, single-device systems are constrained by limited perspectives and insufficient spatial coverage, which impairs their effectiveness in urban or non-line-of-sight scenarios. To overcome these challenges, we propose a novel large language model (LLM)-driven distributed integrated multimodal sensing and semantic communication (LLM-DiSAC) framework. Specifically, our system consists of multiple collaborative sensing devices equipped with RF and camera modules, working together with an aggregation center to enhance sensing accuracy. First, on sensing devices, LLM-DiSAC develops an RF-vision fusion network (RVFN), which employs specialized feature extractors for RF and visual data, followed by a cross-attention module for effective multimodal integration. Second, a LLM-based semantic transmission network (LSTN) is proposed to enhance communication efficiency, where the LLM-based decoder leverages known channel parameters, such as transceiver distance and signal-to-noise ratio (SNR), to mitigate semantic distortion. Third, at the aggregation center, a transformer-based aggregation model (TRAM) with an adaptive aggregation attention mechanism is developed to fuse distributed features and enhance sensing accuracy. To preserve data privacy, a two-stage distributed learning strategy is introduced, allowing local model training at the device level and centralized aggregation model training using intermediate features. Finally, evaluations on a synthetic multi-view RF-visual dataset generated by the Genesis simulation engine show that LLM-DiSAC achieves a good performance.
Brightness-Invariant Tracking Estimation in Tagged MRI
Authors: Zhangxing Bian, Shuwen Wei, Xiao Liang, Yuan-Chiao Lu, Samuel W. Remedios, Fangxu Xing, Jonghye Woo, Dzung L. Pham, Aaron Carass, Philip V. Bayly, Jiachen Zhuo, Ahmed Alshareef, Jerry L. Prince
Date: 2025-05-23 00:00:00
Tasks: Anatomy, Denoising, Optical Flow Estimation, TAG

Magnetic resonance (MR) tagging is an imaging technique for noninvasively tracking tissue motion in vivo by creating a visible pattern of magnetization saturation (tags) that deforms with the tissue. Due to longitudinal relaxation and progression to steady-state, the tags and tissue brightnesses change over time, which makes tracking with optical flow methods error-prone. Although Fourier methods can alleviate these problems, they are also sensitive to brightness changes as well as spectral spreading due to motion. To address these problems, we introduce the brightness-invariant tracking estimation (BRITE) technique for tagged MRI. BRITE disentangles the anatomy from the tag pattern in the observed tagged image sequence and simultaneously estimates the Lagrangian motion. The inherent ill-posedness of this problem is addressed by leveraging the expressive power of denoising diffusion probabilistic models to represent the probabilistic distribution of the underlying anatomy and the flexibility of physics-informed neural networks to estimate biologically-plausible motion. A set of tagged MR images of a gel phantom was acquired with various tag periods and imaging flip angles to demonstrate the impact of brightness variations and to validate our method. The results show that BRITE achieves more accurate motion and strain estimates as compared to other state of the art methods, while also being resistant to tag fading.
LatentLLM: Attention-Aware Joint Tensor Compression
Authors: Toshiaki Koike-Akino, Xiangyu Chen, Jing Liu, Ye Wang, Pu, Wang, Matthew Brand
Date: 2025-05-23 00:00:00
Tasks: Model Compression, Tensor Decomposition

Modern foundation models such as large language models (LLMs) and large multi-modal models (LMMs) require a massive amount of computational and memory resources. We propose a new framework to convert such LLMs/LMMs into a reduced-dimension latent structure. Our method extends a local activation-aware tensor decomposition to a global attention-aware joint tensor de-composition. Our framework can significantly improve the model accuracy over the existing model compression methods when reducing the latent dimension to realize computationally/memory-efficient LLMs/LLMs. We show the benefit on several benchmark including multi-modal reasoning tasks.
How We Won the ISLES'24 Challenge by Preprocessing
Authors: Tianyi Ren, Juampablo E. Heras Rivera, Hitender Oswal, Yutong Pan, William Henry, Sophie Walters, Mehmet Kurt
Date: 2025-05-23 00:00:00
Tasks: Lesion Segmentation, Segmentation, Skull Stripping

Stroke is among the top three causes of death worldwide, and accurate identification of stroke lesion boundaries is critical for diagnosis and treatment. Supervised deep learning methods have emerged as the leading solution for stroke lesion segmentation but require large, diverse, and annotated datasets. The ISLES'24 challenge addresses this need by providing longitudinal stroke imaging data, including CT scans taken on arrival to the hospital and follow-up MRI taken 2-9 days from initial arrival, with annotations derived from follow-up MRI. Importantly, models submitted to the ISLES'24 challenge are evaluated using only CT inputs, requiring prediction of lesion progression that may not be visible in CT scans for segmentation. Our winning solution shows that a carefully designed preprocessing pipeline including deep-learning-based skull stripping and custom intensity windowing is beneficial for accurate segmentation. Combined with a standard large residual nnU-Net architecture for segmentation, this approach achieves a mean test Dice of 28.5 with a standard deviation of 21.27.
Generative RLHF-V: Learning Principles from Multi-modal Human Preference
Authors: Jiayi Zhou, Jiaming Ji, Boyuan Chen, Jiapeng Sun, Wenqi Chen, Donghai Hong, Sirui Han, Yike Guo, Yaodong Yang
Date: 2025-05-24 00:00:00
Tasks: Blocking, Out-of-Distribution Generalization

Training multi-modal large language models (MLLMs) that align with human intentions is a long-term challenge. Traditional score-only reward models for alignment suffer from low accuracy, weak generalization, and poor interpretability, blocking the progress of alignment methods, e.g., reinforcement learning from human feedback (RLHF). Generative reward models (GRMs) leverage MLLMs' intrinsic reasoning capabilities to discriminate pair-wise responses, but their pair-wise paradigm makes it hard to generalize to learnable rewards. We introduce Generative RLHF-V, a novel alignment framework that integrates GRMs with multi-modal RLHF. We propose a two-stage pipeline: $\textbf{multi-modal generative reward modeling from RL}$, where RL guides GRMs to actively capture human intention, then predict the correct pair-wise scores; and $\textbf{RL optimization from grouped comparison}$, which enhances multi-modal RL scoring precision by grouped responses comparison. Experimental results demonstrate that, besides out-of-distribution generalization of RM discrimination, our framework improves 4 MLLMs' performance across 7 benchmarks by $18.1\%$, while the baseline RLHF is only $5.3\%$. We further validate that Generative RLHF-V achieves a near-linear improvement with an increasing number of candidate responses. Our code and models can be found at https://generative-rlhf-v.github.io.
Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models
Authors: Haoyuan Sun, Jiaqi Wu, Bo Xia, Yifu Luo, Yifei Zhao, Kai Qin, Xufei Lv, Tiantian Zhang, Yongzhe Chang, Xueqian Wang
Date: 2025-05-24 00:00:00
Tasks: Position

Standing in 2025, at a critical juncture in the pursuit of Artificial General Intelligence (AGI), reinforcement fine-tuning (RFT) has demonstrated significant potential in enhancing the reasoning capability of large language models (LLMs) and has led to the development of cutting-edge AI models such as OpenAI-o1 and DeepSeek-R1. Moreover, the efficient application of RFT to enhance the reasoning capability of multimodal large language models (MLLMs) has attracted widespread attention from the community. In this position paper, we argue that reinforcement fine-tuning powers the reasoning capability of multimodal large language models. To begin with, we provide a detailed introduction to the fundamental background knowledge that researchers interested in this field should be familiar with. Furthermore, we meticulously summarize the improvements of RFT in powering reasoning capability of MLLMs into five key points: diverse modalities, diverse tasks and domains, better training algorithms, abundant benchmarks and thriving engineering frameworks. Finally, we propose five promising directions for future research that the community might consider. We hope that this position paper will provide valuable insights to the community at this pivotal stage in the advancement toward AGI. Summary of works done on RFT for MLLMs is available at https://github.com/Sun-Haoyuan23/Awesome-RL-based-Reasoning-MLLMs.
ReflectGAN: Modeling Vegetation Effects for Soil Carbon Estimation from Satellite Imagery
Authors: Dristi Datta, Manoranjan Paul, Manzur Murshed, Shyh Wei Teng, Leigh M. Schmidtke
Date: 2025-05-24 00:00:00
Tasks: Generative Adversarial Network

Soil organic carbon (SOC) is a critical indicator of soil health, but its accurate estimation from satellite imagery is hindered in vegetated regions due to spectral contamination from plant cover, which obscures soil reflectance and reduces model reliability. This study proposes the Reflectance Transformation Generative Adversarial Network (ReflectGAN), a novel paired GAN-based framework designed to reconstruct accurate bare soil reflectance from vegetated soil satellite observations. By learning the spectral transformation between vegetated and bare soil reflectance, ReflectGAN facilitates more precise SOC estimation under mixed land cover conditions. Using the LUCAS 2018 dataset and corresponding Landsat 8 imagery, we trained multiple learning-based models on both original and ReflectGAN-reconstructed reflectance inputs. Models trained on ReflectGAN outputs consistently outperformed those using existing vegetation correction methods. For example, the best-performing model (RF) achieved an $R^2$ of 0.54, RMSE of 3.95, and RPD of 2.07 when applied to the ReflectGAN-generated signals, representing a 35\% increase in $R^2$, a 43\% reduction in RMSE, and a 43\% improvement in RPD compared to the best existing method (PMM-SU). The performance of the models with ReflectGAN is also better compared to their counterparts when applied to another dataset, i.e., Sentinel-2 imagery. These findings demonstrate the potential of ReflectGAN to improve SOC estimation accuracy in vegetated landscapes, supporting more reliable soil monitoring.
Diffusion Blend: Inference-Time Multi-Preference Alignment for Diffusion Models
Authors: Min Cheng, Fatemeh Doudi, Dileep Kalathil, Mohammad Ghavamzadeh, Panganamala R. Kumar
Date: 2025-05-24 00:00:00
Tasks: Reinforcement Learning (RL)

Reinforcement learning (RL) algorithms have been used recently to align diffusion models with downstream objectives such as aesthetic quality and text-image consistency by fine-tuning them to maximize a single reward function under a fixed KL regularization. However, this approach is inherently restrictive in practice, where alignment must balance multiple, often conflicting objectives. Moreover, user preferences vary across prompts, individuals, and deployment contexts, with varying tolerances for deviation from a pre-trained base model. We address the problem of inference-time multi-preference alignment: given a set of basis reward functions and a reference KL regularization strength, can we design a fine-tuning procedure so that, at inference time, it can generate images aligned with any user-specified linear combination of rewards and regularization, without requiring additional fine-tuning? We propose Diffusion Blend, a novel approach to solve inference-time multi-preference alignment by blending backward diffusion processes associated with fine-tuned models, and we instantiate this approach with two algorithms: DB-MPA for multi-reward alignment and DB-KLA for KL regularization control. Extensive experiments show that Diffusion Blend algorithms consistently outperform relevant baselines and closely match or exceed the performance of individually fine-tuned models, enabling efficient, user-driven alignment at inference-time. The code is available at https://github.com/bluewoods127/DB-2025}{github.com/bluewoods127/DB-2025.
Learning without Isolation: Pathway Protection for Continual Learning
Authors: Zhikang Chen, Abudukelimu Wuerkaixi, Sen Cui, Haoxuan Li, Ding Li, Jingfeng Zhang, Bo Han, Gang Niu, Houfang Liu, Yi Yang, Sifan Yang, ChangShui Zhang, Tianling Ren
Date: 2025-05-24 00:00:00
Tasks: Continual Learning, Graph Matching

Deep networks are prone to catastrophic forgetting during sequential task learning, i.e., losing the knowledge about old tasks upon learning new tasks. To this end, continual learning(CL) has emerged, whose existing methods focus mostly on regulating or protecting the parameters associated with the previous tasks. However, parameter protection is often impractical, since the size of parameters for storing the old-task knowledge increases linearly with the number of tasks, otherwise it is hard to preserve the parameters related to the old-task knowledge. In this work, we bring a dual opinion from neuroscience and physics to CL: in the whole networks, the pathways matter more than the parameters when concerning the knowledge acquired from the old tasks. Following this opinion, we propose a novel CL framework, learning without isolation(LwI), where model fusion is formulated as graph matching and the pathways occupied by the old tasks are protected without being isolated. Thanks to the sparsity of activation channels in a deep network, LwI can adaptively allocate available pathways for a new task, realizing pathway protection and addressing catastrophic forgetting in a parameter-efficient manner. Experiments on popular benchmark datasets demonstrate the superiority of the proposed LwI.
Doc-CoB: Enhancing Multi-Modal Document Understanding with Visual Chain-of-Boxes Reasoning
Authors: Ye Mo, Zirui Shao, Kai Ye, Xianwei Mao, Bo Zhang, Hangdi Xing, Peng Ye, Gang Huang, Kehan Chen, Zhou Huan, Zixu Yan, Sheng Zhou
Date: 2025-05-24 00:00:00
Tasks: document understanding, Visual Reasoning

Multimodal large language models (MLLMs) have made significant progress in document understanding. However, the information-dense nature of document images still poses challenges, as most queries depend on only a few relevant regions, with the rest being redundant. Existing one-pass MLLMs process entire document images without considering query relevance, often failing to focus on critical regions and producing unfaithful responses. Inspired by the human coarse-to-fine reading pattern, we introduce Doc-CoB (Chain-of-Box), a simple-yet-effective mechanism that integrates human-style visual reasoning into MLLM without modifying its architecture. Our method allows the model to autonomously select the set of regions (boxes) most relevant to the query, and then focus attention on them for further understanding. We first design a fully automatic pipeline, integrating a commercial MLLM with a layout analyzer, to generate 249k training samples with intermediate visual reasoning supervision. Then we incorporate two enabling tasks that improve box identification and box-query reasoning, which together enhance document understanding. Extensive experiments on seven benchmarks with four popular models show that Doc-CoB significantly improves performance, demonstrating its effectiveness and wide applicability. All code, data, and models will be released publicly.
Tropical Geometry Based Edge Detection Using Min-Plus and Max-Plus Algebra
Authors: Shivam Kumar Jha S, Jaya NN Iyer
Date: 2025-05-24 00:00:00
Tasks: Boundary Detection, Computational Efficiency, Edge Detection

This paper proposes a tropical geometry-based edge detection framework that reformulates convolution and gradient computations using min-plus and max-plus algebra. The tropical formulation emphasizes dominant intensity variations, contributing to sharper and more continuous edge representations. Three variants are explored: an adaptive threshold-based method, a multi-kernel min-plus method, and a max-plus method emphasizing structural continuity. The framework integrates multi-scale processing, Hessian filtering, and wavelet shrinkage to enhance edge transitions while maintaining computational efficiency. Experiments on MATLAB built-in grayscale and color images suggest that tropical formulations integrated with classical operators, such as Canny and LoG, can improve boundary detection in low-contrast and textured regions. Quantitative evaluation using standard edge metrics indicates favorable edge clarity and structural coherence. These results highlight the potential of tropical algebra as a scalable and noise-aware formulation for edge detection in practical image analysis tasks.
Memory-Efficient Super-Resolution of 3D Micro-CT Images Using Octree-Based GANs: Enhancing Resolution and Segmentation Accuracy
Authors: Evgeny Ugolkov, Xupeng He, Hyung Kwak, Hussein Hoteit
Date: 2025-05-24 00:00:00
Tasks: Generative Adversarial Network, Super-Resolution

We present a memory-efficient algorithm for significantly enhancing the quality of segmented 3D micro-Computed Tomography (micro-CT) images of rocks using a generative model. The proposed model achieves a 16x increase in resolution and corrects inaccuracies in segmentation caused by the overlapping X-ray attenuation in micro-CT measurements across different minerals. The generative model employed is a 3D Octree-based convolutional Wasserstein generative adversarial network with gradient penalty. To address the challenge of high memory consumption inherent in standard 3D convolutional layers, we implemented an Octree structure within the 3D progressive growing generator model. This enabled the use of memory-efficient 3D Octree-based convolutional layers. The approach is pivotal in overcoming the long-standing memory bottleneck in volumetric deep learning, making it possible to reach 16x super-resolution in 3D, a scale that is challenging to attain due to cubic memory scaling. For training, we utilized segmented 3D low-resolution micro-CT images along with unpaired segmented complementary 2D high-resolution laser scanning microscope images. Post-training, resolution improved from 7 to 0.44 micro-m/voxel with accurate segmentation of constituent minerals. Validated on Berea sandstone, this framework demonstrates substantial improvements in pore characterization and mineral differentiation, offering a robust solution to one of the primary computational limitations in modern geoscientific imaging.
How to build a consistency model: Learning flow maps via self-distillation
Authors: Nicholas M. Boffi, Michael S. Albergo, Eric Vanden-Eijnden
Date: 2025-05-24 00:00:00
Tasks: Image Generation

Building on the framework proposed in Boffi et al. (2024), we present a systematic approach for learning flow maps associated with flow and diffusion models. Flow map-based models, commonly known as consistency models, encompass recent efforts to improve the efficiency of generative models based on solutions to differential equations. By exploiting a relationship between the velocity field underlying a continuous-time flow and the instantaneous rate of change of the flow map, we show how to convert existing distillation schemes into direct training algorithms via self-distillation, eliminating the need for pre-trained models. We empirically evaluate several instantiations of our framework, finding that high-dimensional tasks like image synthesis benefit from objective functions that avoid temporal and spatial derivatives of the flow map, while lower-dimensional tasks can benefit from objectives incorporating higher-order derivatives to capture sharp features.