pwc-archive/papers-with-abstracts huggingface dataset:
Mixed Bernstein-Fourier Approximants for Optimal Trajectory Generation with Periodic Behavior
Authors: Liraz Mudrik, Sean Kragelund, Isaac Kaminer
Date: 2025-04-24 00:00:00
Tasks: Computational Efficiency, Motion Planning, Optimal Motion Planning, Trajectory Planning

Efficient trajectory generation is critical for autonomous systems, yet current numerical methods often struggle to handle periodic behaviors effectively, especially when equidistant time nodes are required. This paper introduces a novel mixed Bernstein-Fourier approximation framework tailored explicitly for optimal motion planning. Our proposed methodology leverages the uniform convergence properties of Bernstein polynomials for nonperiodic behaviors while effectively capturing periodic dynamics through Fourier series. Theoretical results are established, including uniform convergence proofs for approximations of functions, derivatives, and integrals, as well as detailed error bound analyses. We further introduce a regulated least squares approach for determining approximation coefficients, enhancing numerical stability and practical applicability. Within an optimal control context, we establish feasibility and consistency of approximated solutions to their continuous counterparts. We also extend the covector mapping theorem, providing theoretical guarantees for approximating dual variables crucial in verifying the necessary optimality conditions from Pontryagin's Maximum Principle. Comprehensive numerical examples illustrate the method's superior performance, demonstrating substantial improvements in computational efficiency and precision in scenarios with complex periodic constraints and dynamics. Our mixed Bernstein-Fourier methodology thus presents a robust, theoretically grounded, and computationally efficient approach for advanced optimal trajectory planning in autonomous systems.
Long-term consequences of COVID-19 on sleep, mental health, fatigue, and cognition: a preliminary study
Authors: Armin Adibi, Ali Motahharynia, Iman Adibi, Mehdi Sanayei
Date: 2025-05-01 00:00:00
Proceeding: Discover Mental Health 2025 5
Tasks: Sleep Quality

Introduction
Post-COVID-19 Syndrome (PCS) is defined as symptoms persisting beyond 12 weeks from the onset of symptoms. Notably, COVID-19 has been associated with long-term effects on the brain and mental health. This cross-sectional study aims to investigate depression, fatigue, sleep quality, and cognitive dysfunction, particularly working memory, in individuals with PCS compared to a healthy control group.

Material and methods
Between April and December 2021, 45 COVID-19 individuals and 60 healthy individuals met the eligibility criteria. Demographic information and the Montreal Cognitive Assessment were collected. Two visual working memory tasks, Delayed Match-to-Sample (DMS) and n-back, were performed, along with self-report questionnaires: Beck Depression Inventory, Modified Fatigue Impact Scale, and Pittsburgh Sleep Quality Index.

Results
A total of 105 participants were enrolled. Findings reveal that the PCS group exhibited notably higher levels of cognitive impairment (13.3% vs. 1.6%, p = 0.04), depression (53.9% vs. 25.9%, p = 0.03), and sleep disturbances (53.9% vs. 18.6%, p = 0.01) compared to the healthy control group. Sleep latency and sleep duration were particularly affected. No significant differences in working memory function were observed between the two groups (p = 0.90 for DMS and p = 0.98 for n-back).

Conclusion
The study highlights the higher prevalence of sleep disturbance, depression, and cognitive impairment in the PCS phase, with inflammation likely playing a significant role. Moreover, the study suggests that untreated depression and sleep disturbances may pose long-term risks for dementia. Understanding the underlying mechanisms is crucial for developing effective interventions and support for individuals recovering from the infection. Prospective longitudinal studies with larger and more diverse samples are warranted to confirm and expand upon these findings.
Bridging the Generalisation Gap: Synthetic Data Generation for Multi-Site Clinical Model Validation
Authors: Bradley Segal, Joshua Fieggen, David Clifton, Lei Clifton
Date: 2025-04-29 00:00:00
Tasks: Benchmarking, Fairness, Synthetic Data Generation

Ensuring the generalisability of clinical machine learning (ML) models across diverse healthcare settings remains a significant challenge due to variability in patient demographics, disease prevalence, and institutional practices. Existing model evaluation approaches often rely on real-world datasets, which are limited in availability, embed confounding biases, and lack the flexibility needed for systematic experimentation. Furthermore, while generative models aim for statistical realism, they often lack transparency and explicit control over factors driving distributional shifts. In this work, we propose a novel structured synthetic data framework designed for the controlled benchmarking of model robustness, fairness, and generalisability. Unlike approaches focused solely on mimicking observed data, our framework provides explicit control over the data generating process, including site-specific prevalence variations, hierarchical subgroup effects, and structured feature interactions. This enables targeted investigation into how models respond to specific distributional shifts and potential biases. Through controlled experiments, we demonstrate the framework's ability to isolate the impact of site variations, support fairness-aware audits, and reveal generalisation failures, particularly highlighting how model complexity interacts with site-specific effects. This work contributes a reproducible, interpretable, and configurable tool designed to advance the reliable deployment of ML in clinical settings.
HPU: High-Bandwidth Processing Unit for Scalable, Cost-effective LLM Inference via GPU Co-processing
Authors: Myunghyun Rhee, Joonseop Sim, Taeyoung Ahn, Seungyong Lee, Daegun Yoon, Euiseok Kim, Kyoung Park, Youngpyo Joo, Hosik Kim
Date: 2025-04-18 00:00:00
Tasks: GPU

The attention layer, a core component of Transformer-based LLMs, brings out inefficiencies in current GPU systems due to its low operational intensity and the substantial memory requirements of KV caches. We propose a High-bandwidth Processing Unit (HPU), a memoryintensive co-processor that enhances GPU resource utilization during large-batched LLM inference. By offloading memory-bound operations, the HPU allows the GPU to focus on compute-intensive tasks, increasing overall efficiency. Also, the HPU, as an add-on card, scales out to accommodate surging memory demands driven by large batch sizes and extended sequence lengths. In this paper, we show the HPU prototype implemented with PCIe-based FPGA cards mounted on a GPU system. Our novel GPU-HPU heterogeneous system demonstrates up to 4.1x performance gains and 4.6x energy efficiency improvements over a GPUonly system, providing scalability without increasing the number of GPUs.
Temporal Attention Evolutional Graph Convolutional Network for Multivariate Time Series Forecasting
Authors: Xinlong Zhao, Liying Zhang, Tianbo Zou, Yan Zhang
Date: 2025-05-01 00:00:00
Tasks: Multivariate Time Series Forecasting, Prediction, Time Series, Time Series Forecasting

Multivariate time series forecasting enables the prediction of future states by leveraging historical data, thereby facilitating decision-making processes. Each data node in a multivariate time series encompasses a sequence of multiple dimensions. These nodes exhibit interdependent relationships, forming a graph structure. While existing prediction methods often assume a fixed graph structure, many real-world scenarios involve dynamic graph structures. Moreover, interactions among time series observed at different time scales vary significantly. To enhance prediction accuracy by capturing precise temporal and spatial features, this paper introduces the Temporal Attention Evolutional Graph Convolutional Network (TAEGCN). This novel method not only integrates causal temporal convolution and a multi-head self-attention mechanism to learn temporal features of nodes, but also construct the dynamic graph structure based on these temporal features to keep the consistency of the changing in spatial feature with temporal series. TAEGCN adeptly captures temporal causal relationships and hidden spatial dependencies within the data. Furthermore, TAEGCN incorporates a unified neural network that seamlessly integrates these components to generate final predictions. Experimental results conducted on two public transportation network datasets, METR-LA and PEMS-BAY, demonstrate the superior performance of the proposed model.
Visibility-Uncertainty-guided 3D Gaussian Inpainting via Scene Conceptional Learning
Authors: Mingxuan Cui, Qing Guo, Yuyi Wang, Hongkai Yu, Di Lin, Qin Zou, Ming-Ming Cheng, Xi Li
Date: 2025-04-23 00:00:00
Tasks: 3DGS, 3D Inpainting, Image Inpainting, NeRF, Novel View Synthesis

3D Gaussian Splatting (3DGS) has emerged as a powerful and efficient 3D representation for novel view synthesis. This paper extends 3DGS capabilities to inpainting, where masked objects in a scene are replaced with new contents that blend seamlessly with the surroundings. Unlike 2D image inpainting, 3D Gaussian inpainting (3DGI) is challenging in effectively leveraging complementary visual and semantic cues from multiple input views, as occluded areas in one view may be visible in others. To address this, we propose a method that measures the visibility uncertainties of 3D points across different input views and uses them to guide 3DGI in utilizing complementary visual cues. We also employ uncertainties to learn a semantic concept of scene without the masked object and use a diffusion model to fill masked objects in input images based on the learned concept. Finally, we build a novel 3DGI framework, VISTA, by integrating VISibility-uncerTainty-guided 3DGI with scene conceptuAl learning. VISTA generates high-quality 3DGS models capable of synthesizing artifact-free and naturally inpainted novel views. Furthermore, our approach extends to handling dynamic distractors arising from temporal object changes, enhancing its versatility in diverse scene reconstruction scenarios. We demonstrate the superior performance of our method over state-of-the-art techniques using two challenging datasets: the SPIn-NeRF dataset, featuring 10 diverse static 3D inpainting scenes, and an underwater 3D inpainting dataset derived from UTB180, including fast-moving fish as inpainting targets.
SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction
Authors: Junchi Liu, Ying Tang, Sergei Tretiak, Wenhui Duan, Liujiang Zhou
Date: 2025-05-01 00:00:00
Tasks: Graph Attention, Property Prediction, Symbolic Regression

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm, Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR), that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 times acceleration compared to conventional SR implementations that heavily rely on first principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.
FineScope : Precision Pruning for Domain-Specialized Large Language Models Using SAE-Guided Self-Data Cultivation
Authors: Chaitali Bhattacharyya, Yeseong Kim
Date: 2025-05-01 00:00:00

Training large language models (LLMs) from scratch requires significant computational resources, driving interest in developing smaller, domain-specific LLMs that maintain both efficiency and strong task performance. Medium-sized models such as LLaMA, llama} have served as starting points for domain-specific adaptation, but they often suffer from accuracy degradation when tested on specialized datasets. We introduce FineScope, a framework for deriving compact, domain-optimized LLMs from larger pretrained models. FineScope leverages the Sparse Autoencoder (SAE) framework, inspired by its ability to produce interpretable feature representations, to extract domain-specific subsets from large datasets. We apply structured pruning with domain-specific constraints, ensuring that the resulting pruned models retain essential knowledge for the target domain. To further enhance performance, these pruned models undergo self-data distillation, leveraging SAE-curated datasets to restore key domain-specific information lost during pruning. Extensive experiments and ablation studies demonstrate that FineScope achieves highly competitive performance, outperforming several large-scale state-of-the-art LLMs in domain-specific tasks. Additionally, our results show that FineScope enables pruned models to regain a substantial portion of their original performance when fine-tuned with SAE-curated datasets. Furthermore, applying these datasets to fine-tune pretrained LLMs without pruning also improves their domain-specific accuracy, highlighting the robustness of our approach. The code will be released.
Towards Scalable Human-aligned Benchmark for Text-guided Image Editing
Authors: Suho Ryu, Kihyun Kim, Eugene Baek, Dongsoo Shin, Joonseok Lee
Date: 2025-05-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: text-guided-image-editing

A variety of text-guided image editing models have been proposed recently. However, there is no widely-accepted standard evaluation method mainly due to the subjective nature of the task, letting researchers rely on manual user study. To address this, we introduce a novel Human-Aligned benchmark for Text-guided Image Editing (HATIE). Providing a large-scale benchmark set covering a wide range of editing tasks, it allows reliable evaluation, not limited to specific easy-to-evaluate cases. Also, HATIE provides a fully-automated and omnidirectional evaluation pipeline. Particularly, we combine multiple scores measuring various aspects of editing so as to align with human perception. We empirically verify that the evaluation of HATIE is indeed human-aligned in various aspects, and provide benchmark results on several state-of-the-art models to provide deeper insights on their performance.
AWARE-NET: Adaptive Weighted Averaging for Robust Ensemble Network in Deepfake Detection
Authors: Muhammad Salman, Iqra Tariq, Mishal Zulfiqar, Muqadas Jalal, Sami Aujla, Sumbal Fatima
Date: 2025-05-01 00:00:00
Tasks: DeepFake Detection, Face Swapping

Deepfake detection has become increasingly important due to the rise of synthetic media, which poses significant risks to digital identity and cyber presence for security and trust. While multiple approaches have improved detection accuracy, challenges remain in achieving consistent performance across diverse datasets and manipulation types. In response, we propose a novel two-tier ensemble framework for deepfake detection based on deep learning that hierarchically combines multiple instances of three state-of-the-art architectures: Xception, Res2Net101, and EfficientNet-B7. Our framework employs a unique approach where each architecture is instantiated three times with different initializations to enhance model diversity, followed by a learnable weighting mechanism that dynamically combines their predictions. Unlike traditional fixed-weight ensembles, our first-tier averages predictions within each architecture family to reduce model variance, while the second tier learns optimal contribution weights through backpropagation, automatically adjusting each architecture's influence based on their detection reliability. Our experiments achieved state-of-the-art intra-dataset performance with AUC scores of 99.22% (FF++) and 100.00% (CelebDF-v2), and F1 scores of 98.06% (FF++) and 99.94% (CelebDF-v2) without augmentation. With augmentation, we achieve AUC scores of 99.47% (FF++) and 100.00% (CelebDF-v2), and F1 scores of 98.43% (FF++) and 99.95% (CelebDF-v2). The framework demonstrates robust cross-dataset generalization, achieving AUC scores of 88.20% and 72.52%, and F1 scores of 93.16% and 80.62% in cross-dataset evaluations.
Clustering Internet Memes Through Template Matching and Multi-Dimensional Similarity
Authors: Tygo Bloem, Filip Ilievski
Date: 2025-04-30 00:00:00
Tasks: Clustering, Template Matching

Meme clustering is critical for toxicity detection, virality modeling, and typing, but it has received little attention in previous research. Clustering similar Internet memes is challenging due to their multimodality, cultural context, and adaptability. Existing approaches rely on databases, overlook semantics, and struggle to handle diverse dimensions of similarity. This paper introduces a novel method that uses template-based matching with multi-dimensional similarity features, thus eliminating the need for predefined databases and supporting adaptive matching. Memes are clustered using local and global features across similarity categories such as form, visual content, text, and identity. Our combined approach outperforms existing clustering methods, producing more consistent and coherent clusters, while similarity-based feature sets enable adaptability and align with human intuition. We make all supporting code publicly available to support subsequent research.
BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition
Authors: Paige Tuttösí, Mantaj Dhillon, Luna Sang, Shane Eastwood, Poorvi Bhatia, Quang Minh Dinh, Avni Kapoor, Yewon Jin, Angelica Lim
Date: 2025-04-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Emotion Recognition, Speech Emotion Recognition, speech-recognition, Speech Recognition

Some speech recognition tasks, such as automatic speech recognition (ASR), are approaching or have reached human performance in many reported metrics. Yet, they continue to struggle in complex, real-world, situations, such as with distanced speech. Previous challenges have released datasets to address the issue of distanced ASR, however, the focus remains primarily on distance, specifically relying on multi-microphone array systems. Here we present the B(asic) E(motion) R(andom phrase) S(hou)t(s) (BERSt) dataset. The dataset contains almost 4 hours of English speech from 98 actors with varying regional and non-native accents. The data was collected on smartphones in the actors homes and therefore includes at least 98 different acoustic environments. The data also includes 7 different emotion prompts and both shouted and spoken utterances. The smartphones were places in 19 different positions, including obstructions and being in a different room than the actor. This data is publicly available for use and can be used to evaluate a variety of speech recognition tasks, including: ASR, shout detection, and speech emotion recognition (SER). We provide initial benchmarks for ASR and SER tasks, and find that ASR degrades both with an increase in distance and shout level and shows varied performance depending on the intended emotion. Our results show that the BERSt dataset is challenging for both ASR and SER tasks and continued work is needed to improve the robustness of such systems for more accurate real-world use.
Fast and Low-Cost Genomic Foundation Models via Outlier Removal
Authors: Haozheng Luo, Chenghao Qiu, Maojiang Su, Zhihan Zhou, Zoe Mehta, Guo Ye, Jerry Yao-Chieh Hu, Han Liu
Date: 2025-05-01 00:00:00
Tasks: Adversarial Attack, Adversarial Robustness, Computational Efficiency, DNA analysis, Genome Understanding, Quantization, Splice Site Prediction, Transcription Factor Binding Site Prediction (Human), Transcription Factor Binding Site Prediction (Mouse)

We propose the first unified adversarial attack benchmark for Genomic Foundation Models (GFMs), named GERM. Unlike existing GFM benchmarks, GERM offers the first comprehensive evaluation framework to systematically assess the vulnerability of GFMs to adversarial attacks. Methodologically, we evaluate the adversarial robustness of five state-of-the-art GFMs using four widely adopted attack algorithms and three defense strategies. Importantly, our benchmark provides an accessible and comprehensive framework to analyze GFM vulnerabilities with respect to model architecture, quantization schemes, and training datasets. Empirically, transformer-based models exhibit greater robustness to adversarial perturbations compared to HyenaDNA, highlighting the impact of architectural design on vulnerability. Moreover, adversarial attacks frequently target biologically significant genomic regions, suggesting that these models effectively capture meaningful sequence features.
Expert-guided protein language models enable accurate and blazingly fast fitness prediction
Authors: Céline Marquet, Julius Schlensok, Marina Abakarova, Burkhard Rost, Elodie Laine
Date: 2024-11-22 00:00:00
Proceeding: Bioinformatics 2024 11
Tasks: CPU, Multiple Sequence Alignment, Protein Language Model

Motivation
Exhaustive experimental annotation of the effect of all known protein variants remains daunting and expensive, stressing the need for scalable effect predictions. We introduce VespaG, a blazingly fast missense amino acid variant effect predictor, leveraging protein language model (pLM) embeddings as input to a minimal deep learning model.

Results
To overcome the sparsity of experimental training data, we created a dataset of 39 million single amino acid variants from the human proteome applying the multiple sequence alignment-based effect predictor GEMME as a pseudo standard-of-truth. This setup increases interpretability compared to the baseline pLM and is easily retrainable with novel or updated pLMs. Assessed against the ProteinGym benchmark (217 multiplex assays of variant effect—MAVE—with 2.5 million variants), VespaG achieved a mean Spearman correlation of 0.48 ± 0.02, matching top-performing methods evaluated on the same data. VespaG has the advantage of being orders of magnitude faster, predicting all mutational landscapes of all proteins in proteomes such as Homo sapiens or Drosophila melanogaster in under 30 min on a consumer laptop (12-core CPU, 16 GB RAM).

Availability and implementation
VespaG is available freely at https://github.com/jschlensok/vespag. The associated training data and predictions are available at https://doi.org/10.5281/zenodo.11085958.
A Finite-State Controller Based Offline Solver for Deterministic POMDPs
Authors: Alex Schutz, Yang You, Matias Mattamala, Ipek Caliskanelli, Bruno Lacerda, Nick Hawes
Date: 2025-05-01 00:00:00

Deterministic partially observable Markov decision processes (DetPOMDPs) often arise in planning problems where the agent is uncertain about its environmental state but can act and observe deterministically. In this paper, we propose DetMCVI, an adaptation of the Monte Carlo Value Iteration (MCVI) algorithm for DetPOMDPs, which builds policies in the form of finite-state controllers (FSCs). DetMCVI solves large problems with a high success rate, outperforming existing baselines for DetPOMDPs. We also verify the performance of the algorithm in a real-world mobile robot forest mapping scenario.