pwc-archive/papers-with-abstracts huggingface dataset:
ML-Triton, A Multi-Level Compilation and Language Extension to Triton GPU Programming
Authors: Dewei Wang, Wei Zhu, Liyang Ling, Ettore Tiotto, Quintin Wang, Whitney Tsang, Julian Opperman, Jacky Deng
Date: 2025-03-19 00:00:00
Tasks: GPU

In the era of LLMs, dense operations such as GEMM and MHA are critical components. These operations are well-suited for parallel execution using a tilebased approach. While traditional GPU programming often relies on low level interfaces like CUDA or SYCL, Triton has emerged as a DSL that offers a more user-friendly and portable alternative by programming at a higher level. The current Triton starts at the workgroup (aka threadblock) level, and directly lowers to per-thread level. And then attempt to coalesce and amend through a series of passes, promoting information from low-level representation. We believe this is pre-mature lowering based on the below observations. 1. GPU has a hierarchical structure both physically and logically. Modern GPUs often feature SIMD units capable of directly operating on tiles on a warp or warpgroup basis, such as blocked load and blocked MMA. 2. Multi-level gradual lowering can make compiler decoupled and clean by separating considerations inter and intra a logical layer. 3. Kernel developers often need fine control to get good performance on the latest hardware. FlashAttention2 advocates explicit data partition between warps to make a performance boost. In this context, we propose ML-Triton which features multi-level compilation flow and programming interface. Our approach begins at the workgroup level and progressively lowers to the warp and intrinsic level, implementing a multilevel lowering align with the hierarchical nature of GPU. Additionally, we extend triton language to support user-set compiler hint and warp level programming, enabling researchers to get good out-of-the box performance without awaiting compiler updates. Experimental results demonstrate that our approach achieves performance above 95% of expert-written kernels on Intel GPU, as measured by the geometric mean.
3D Occupancy Prediction with Low-Resolution Queries via Prototype-aware View Transformation
Authors: Gyeongrok Oh, Sungjune Kim, Heeju Ko, Hyung-gun Chi, Jinkyu Kim, Dongwook Lee, Daehyun Ji, Sungjoon Choi, Sujin Jang, Sangpil Kim
Date: 2025-03-19 00:00:00
Proceeding: CVPR 2025 1

The resolution of voxel queries significantly influences the quality of view transformation in camera-based 3D occupancy prediction. However, computational constraints and the practical necessity for real-time deployment require smaller query resolutions, which inevitably leads to an information loss. Therefore, it is essential to encode and preserve rich visual details within limited query sizes while ensuring a comprehensive representation of 3D occupancy. To this end, we introduce ProtoOcc, a novel occupancy network that leverages prototypes of clustered image segments in view transformation to enhance low-resolution context. In particular, the mapping of 2D prototypes onto 3D voxel queries encodes high-level visual geometries and complements the loss of spatial information from reduced query resolutions. Additionally, we design a multi-perspective decoding strategy to efficiently disentangle the densely compressed visual cues into a high-dimensional 3D occupancy scene. Experimental results on both Occ3D and SemanticKITTI benchmarks demonstrate the effectiveness of the proposed method, showing clear improvements over the baselines. More importantly, ProtoOcc achieves competitive performance against the baselines even with 75\% reduced voxel resolution.
FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learning
Authors: Duy Phuong Nguyen, J. Pablo Munoz, Ali Jannesari
Date: 2024-04-12 00:00:00
Tasks: Federated Learning, Image Captioning, parameter-efficient fine-tuning

In the rapidly evolving field of artificial intelligence, multimodal models, e.g., integrating vision and language into visual-language models (VLMs), have become pivotal for many applications, ranging from image captioning to multimodal search engines. Among these models, the Contrastive Language-Image Pre-training (CLIP) model has demonstrated remarkable performance in understanding and generating nuanced relationships between text and images. However, the conventional training of such models often requires centralized aggregation of vast datasets, posing significant privacy and data governance challenges. To address these concerns, this paper proposes a novel approach that leverages Federated Learning and parameter-efficient adapters, i.e., Low-Rank Adaptation (LoRA), to train VLMs. This methodology preserves data privacy by training models across decentralized data sources and ensures model adaptability and efficiency through LoRA's parameter-efficient fine-tuning. Our approach accelerates training time by up to 34.72 times and requires 2.47 times less memory usage than full fine-tuning.
When Pigs Get Sick: Multi-Agent AI for Swine Disease Detection
Authors: Tittaya Mairittha, Tanakon Sawanglok, Panuwit Raden, Sorrawit Treesuk
Date: 2025-03-19 00:00:00
Tasks: Decision Making, Diagnostic, Information Retrieval, RAG, Retrieval, Retrieval-augmented Generation

Swine disease surveillance is critical to the sustainability of global agriculture, yet its effectiveness is frequently undermined by limited veterinary resources, delayed identification of cases, and variability in diagnostic accuracy. To overcome these barriers, we introduce a novel AI-powered, multi-agent diagnostic system that leverages Retrieval-Augmented Generation (RAG) to deliver timely, evidence-based disease detection and clinical guidance. By automatically classifying user inputs into either Knowledge Retrieval Queries or Symptom-Based Diagnostic Queries, the system ensures targeted information retrieval and facilitates precise diagnostic reasoning. An adaptive questioning protocol systematically collects relevant clinical signs, while a confidence-weighted decision fusion mechanism integrates multiple diagnostic hypotheses to generate robust disease predictions and treatment recommendations. Comprehensive evaluations encompassing query classification, disease diagnosis, and knowledge retrieval demonstrate that the system achieves high accuracy, rapid response times, and consistent reliability. By providing a scalable, AI-driven diagnostic framework, this approach enhances veterinary decision-making, advances sustainable livestock management practices, and contributes substantively to the realization of global food security.
Temporal-Consistent Video Restoration with Pre-trained Diffusion Models
Authors: Hengkang Wang, Yang Liu, Huidong Liu, Chien-Chih Wang, Yanhui Guo, Hongdong Li, Bryan Wang, Ju Sun
Date: 2025-03-19 00:00:00
Tasks: Optical Flow Estimation, Video Restoration

Video restoration (VR) aims to recover high-quality videos from degraded ones. Although recent zero-shot VR methods using pre-trained diffusion models (DMs) show good promise, they suffer from approximation errors during reverse diffusion and insufficient temporal consistency. Moreover, dealing with 3D video data, VR is inherently computationally intensive. In this paper, we advocate viewing the reverse process in DMs as a function and present a novel Maximum a Posterior (MAP) framework that directly parameterizes video frames in the seed space of DMs, eliminating approximation errors. We also introduce strategies to promote bilevel temporal consistency: semantic consistency by leveraging clustering structures in the seed space, and pixel-level consistency by progressive warping with optical flow refinements. Extensive experiments on multiple virtual reality tasks demonstrate superior visual quality and temporal consistency achieved by our method compared to the state-of-the-art.
Dynamic Bi-Elman Attention Networks: A Dual-Directional Context-Aware Test-Time Learning for Text Classification
Authors: ZhengLin Lai, MengYao Liao, Dong Xu
Date: 2025-03-19 00:00:00
Tasks: Computational Efficiency, Representation Learning, text-classification, Text Classification

Text classification, a fundamental task in natural language processing, aims to categorize textual data into predefined labels. Traditional methods struggled with complex linguistic structures and semantic dependencies. However, the advent of deep learning, particularly recurrent neural networks and Transformer-based models, has significantly advanced the field by enabling nuanced feature extraction and context-aware predictions. Despite these improvements, existing models still exhibit limitations in balancing interpretability, computational efficiency, and long-range contextual understanding. To address these challenges, this paper proposes the Dynamic Bidirectional Elman with Attention Network (DBEAN). DBEAN integrates bidirectional temporal modeling with self-attention mechanisms. It dynamically assigns weights to critical segments of input, improving contextual representation while maintaining computational efficiency.
StyleLoco: Generative Adversarial Distillation for Natural Humanoid Robot Locomotion
Authors: Le Ma, Ziyu Meng, Tengyu Liu, Yuhan Li, Ran Song, Wei zhang, Siyuan Huang
Date: 2025-03-19 00:00:00
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning

Humanoid robots are anticipated to acquire a wide range of locomotion capabilities while ensuring natural movement across varying speeds and terrains. Existing methods encounter a fundamental dilemma in learning humanoid locomotion: reinforcement learning with handcrafted rewards can achieve agile locomotion but produces unnatural gaits, while Generative Adversarial Imitation Learning (GAIL) with motion capture data yields natural movements but suffers from unstable training processes and restricted agility. Integrating these approaches proves challenging due to the inherent heterogeneity between expert policies and human motion datasets. To address this, we introduce StyleLoco, a novel two-stage framework that bridges this gap through a Generative Adversarial Distillation (GAD) process. Our framework begins by training a teacher policy using reinforcement learning to achieve agile and dynamic locomotion. It then employs a multi-discriminator architecture, where distinct discriminators concurrently extract skills from both the teacher policy and motion capture data. This approach effectively combines the agility of reinforcement learning with the natural fluidity of human-like movements while mitigating the instability issues commonly associated with adversarial training. Through extensive simulation and real-world experiments, we demonstrate that StyleLoco enables humanoid robots to perform diverse locomotion tasks with the precision of expertly trained policies and the natural aesthetics of human motion, successfully transferring styles across different movement types while maintaining stable locomotion across a broad spectrum of command inputs.
ELTEX: A Framework for Domain-Driven Synthetic Data Generation
Authors: Arina Razmyslovich, Kseniia Murasheva, Sofia Sedlova, Julien Capitaine, Eugene Dmitriev
Date: 2025-03-19 00:00:00
Tasks: Synthetic Data Generation, Transfer Learning

We introduce Efficient LLM Token Extraction (ELTEX), a framework addressing the critical challenge of LLM domain specialization by systematically extracting and integrating domain indicators throughout synthetic data generation. Unlike approaches relying on implicit knowledge transfer, ELTEX explicitly leverages domain signals to maintain specialized knowledge integrity. In our cybersecurity case study, ELTEX-enhanced data enables a fine-tuned Gemma-2B model to achieve performance competitive with GPT-4o on blockchain cyberattack classification while reducing computational requirements. Our Google Sheets implementation makes ELTEX accessible to non-technical users. Our contributions include: (1) the ELTEX framework; (2) Google Sheets Add-on implementation; (3) empirical validation showing how ELTEX bridges performance gaps between small and large models; and (4) a synthetic dataset of 11,448 texts for blockchain cyberattack detection.
A Diffusion Model and Knowledge Distillation Framework for Robust Coral Detection in Complex Underwater Environments
Authors: Zhaoxuan Lu; Lyuchao Liao; Chuang Li；Xingang Xie; Hui Yuan;
Date: 2025-01-06 00:00:00
Proceeding: SSRN 2025 1
Tasks: 2D Object Detection, Knowledge Distillation, Mamba, Transfer Learning

Coral reefs play a crucial role in marine ecosystems, but their sustainability is increasingly threatened by climate change and human activities. To aid in the protection and monitoring of these ecosystems, developing advanced artificial intelligence (AI)-based automated detection technologies is essential. This paper introduces the MambaCoral-Diffusion Detection framework (MambaCoral-Diffusion Detection, MCDD), an AI-driven approach for robust coral detection, designed to enhance performance in complex underwater environments—a critical challenge in marine engineering. Key AI contributions include integrating a diffusion model to generate diverse training data to address data imbalance and employing an adaptive global-local attention mechanism within the Mamba architecture’s Spatial Sensing Dual Detection (SS2D) module to enhance feature extraction accuracy. Furthermore, a Channel-Aware Intersection over Union (IoU) knowledge distillation technique is presented, enabling efficient knowledge transfer from complex teacher models to lightweight student models, optimizing the framework for practical deployment with reduced computational demands. From an engineering perspective, MCDD significantly advances automated coral detection in challenging underwater conditions, providing a reliable solution for monitoring marine ecosystems. Experimental results show that MCDD achieves high performance on the Soft Coral dataset, reaching 31.5 frames per second (FPS), the mean average precision at 50\% IoU threshold of 0.843, and the mean average precision averaged across IoU thresholds from 50% to 95% of 0.566, with only 6.5 million parameters and 13.6 billion floating point operations per second (GFLOPs). The code and dataset are available at https://github.com/RDXiaoLu/MambaCoral-DiffDet.git
Muscle and Fascia Simulation with Extended Position Based Dynamics
Authors: M. Romeo, C. Monteagudo and D. Sanchez-Quir ´ os
Date: 2019-05-27 00:00:00
Proceeding: Eurographics Proceedings 2019 5
Tasks: Position

Recent research on muscle and fascia simulation for visual effects relies on numerical methods such as the finite element method
or finite volume method. These approaches produce realistic results, but require high computational time and are complex to
set up. On the other hand, position-based dynamics offers a fast and controllable solution to simulate surfaces and volumes,
but there is no literature on how to implement constraints that could be used to realistically simulate muscles and fascia for
digital creatures with this method. In this paper, we extend the current state-of-the-art in Position-Based Dynamics to efficiently
compute realistic skeletal muscle and superficial fascia simulation. In particular, we embed muscle fibres in the solver by
adding an anisotropic component to the distance constraints between mesh points and apply overpressure to realistically model
muscle volume changes under contraction. In addition, we also define a modified distance constraint for the fascia that allows
compression and enables the user to scale the constraint’s original distance to gain elastic potential at rest. Finally, we propose
a modification of the extended position-based dynamics algorithm to properly compute different sets of constraints and describe
other details for proper simulation of character’s muscle and fascia dynamics.
SPRSound: Open-Source SJTU Paediatric Respiratory Sound Database
Date: 2022-09-07 00:00:00
Tasks: Classification, Sound Classification

It has proved that the auscultation of respiratory sound has advantage in early respiratory diagnosis. Various methods have been raised to perform automatic respiratory sound analysis to reduce subjective diagnosis and physicians' workload. However, these methods highly rely on the quality of respiratory sound database. In this work, we have developed the first open-access paediatric respiratory sound database, SPRSound. The database consists of 2,683 records and 9,089 respiratory sound events from 292 participants. Accurate label is important to achieve a good prediction for adventitious respiratory sound classification problem. A custom-made sound label annotation software (SoundAnn) has been developed to perform sound editing, sound annotation, and quality assurance evaluation. A team of 11 experienced paediatric physicians is involved in the entire process to establish golden standard reference for the dataset. To verify the robustness and accuracy of the classification model, we have investigated the effects of different feature extraction methods and machine learning classifiers on the classification performance of our dataset. As such, we have achieved a score of 75.22%, 61.57%, 56.71%, and 37.84% for the four different classification challenges at the event level and record level.
Text-Derived Relational Graph-Enhanced Network for Skeleton-Based Action Segmentation
Authors: Haoyu Ji, Bowen Chen, Weihong Ren, Wenze Huang, Zhihao Yang, Zhiyong Wang, Honghai Liu
Date: 2025-03-19 00:00:00
Tasks: Contrastive Learning, Skeleton Based Action Segmentation, TAG

Skeleton-based Temporal Action Segmentation (STAS) aims to segment and recognize various actions from long, untrimmed sequences of human skeletal movements. Current STAS methods typically employ spatio-temporal modeling to establish dependencies among joints as well as frames, and utilize one-hot encoding with cross-entropy loss for frame-wise classification supervision. However, these methods overlook the intrinsic correlations among joints and actions within skeletal features, leading to a limited understanding of human movements. To address this, we propose a Text-Derived Relational Graph-Enhanced Network (TRG-Net) that leverages prior graphs generated by Large Language Models (LLM) to enhance both modeling and supervision. For modeling, the Dynamic Spatio-Temporal Fusion Modeling (DSFM) method incorporates Text-Derived Joint Graphs (TJG) with channel- and frame-level dynamic adaptation to effectively model spatial relations, while integrating spatio-temporal core features during temporal modeling. For supervision, the Absolute-Relative Inter-Class Supervision (ARIS) method employs contrastive learning between action features and text embeddings to regularize the absolute class distributions, and utilizes Text-Derived Action Graphs (TAG) to capture the relative inter-class relationships among action features. Additionally, we propose a Spatial-Aware Enhancement Processing (SAEP) method, which incorporates random joint occlusion and axial rotation to enhance spatial generalization. Performance evaluations on four public datasets demonstrate that TRG-Net achieves state-of-the-art results.
A Foundation Model for Patient Behavior Monitoring and Suicide Detection
Authors: Rodrigo Oliver, Josué Pérez-Sabater, Leire Paz-Arbaizar, Alejandro Lancho, Antonio Artés, Pablo M. Olmos
Date: 2025-03-19 00:00:00
Tasks: Change Point Detection

Foundation models (FMs) have achieved remarkable success across various domains, yet their adoption in healthcare remains limited. While significant advances have been made in medical imaging, genetic biomarkers, and time series from electronic health records, the potential of FMs for patient behavior monitoring through wearable devices remains underexplored. These datasets are inherently heterogeneous, multisource, and often exhibit high rates of missing data, posing unique challenges. This paper introduces a novel FM based on a modified vector quantized variational autoencoder (VQ-VAE), specifically designed to process real-world data from wearable devices. We demonstrate that our pretrained FM, trained on a broad cohort of psychiatric patients, performs downstream tasks via its latent representation without fine-tuning on a held-out cohort of suicidal patients. To illustrate this, we develop a probabilistic change-point detection algorithm for suicide detection and demonstrate the FM's effectiveness in predicting emotional states. Our results show that the discrete latent structure of the VQ-VAE outperforms a state-of-the-art Informer architecture in unsupervised suicide detection, while matching its performance in supervised emotion prediction when the latent dimensionality is increased, though at the cost of reduced unsupervised accuracy. This trade-off highlights the need for future FMs to integrate hybrid discrete-continuous structures for balanced performance across tasks.
A General Adaptive Dual-level Weighting Mechanism for Remote Sensing Pansharpening
Authors: Jie Huang, Haorui Chen, Jiaxuan Ren, Siran Peng, LiangJian Deng
Date: 2025-03-17 00:00:00
Proceeding: CVPR 2025 1
Tasks: Pansharpening

Currently, deep learning-based methods for remote sensing pansharpening have advanced rapidly. However, many existing methods struggle to fully leverage feature heterogeneity and redundancy, thereby limiting their effectiveness. We use the covariance matrix to model the feature heterogeneity and redundancy and propose Correlation-Aware Covariance Weighting (CACW) to adjust them. CACW captures these correlations through the covariance matrix, which is then processed by a nonlinear function to generate weights for adjustment. Building upon CACW, we introduce a general adaptive dual-level weighting mechanism (ADWM) to address these challenges from two key perspectives, enhancing a wide range of existing deep-learning methods. First, Intra-Feature Weighting (IFW) evaluates correlations among channels within each feature to reduce redundancy and enhance unique information. Second, Cross-Feature Weighting (CFW) adjusts contributions across layers based on inter-layer correlations, refining the final output. Extensive experiments demonstrate the superior performance of ADWM compared to recent state-of-the-art (SOTA) methods. Furthermore, we validate the effectiveness of our approach through generality experiments, redundancy visualization, comparison experiments, key variables and complexity analysis, and ablation studies. Our code is available at https://github.com/Jie-1203/ADWM.