pwc-archive/papers-with-abstracts huggingface dataset:
Numerical simulation of transient heat conduction with moving heat source using Physics Informed Neural Networks
Authors: Anirudh Kalyan, Sundararajan Natarajan
Date: 2025-06-21 00:00:00
Tasks: Transfer Learning

In this paper, the physics informed neural networks (PINNs) is employed for the numerical simulation of heat transfer involving a moving source. To reduce the computational effort, a new training method is proposed that uses a continuous time-stepping through transfer learning. Within this, the time interval is divided into smaller intervals and a single network is initialized. On this single network each time interval is trained with the initial condition for (n+1)th as the solution obtained at nth time increment. Thus, this framework enables the computation of large temporal intervals without increasing the complexity of the network itself. The proposed framework is used to estimate the temperature distribution in a homogeneous medium with a moving heat source. The results from the proposed framework is compared with traditional finite element method and a good agreement is seen.
Regular Tree Search for Simulation Optimization
Authors: Du-Yi Wang, Guo Liang, Guangwu Liu, Kun Zhang
Date: 2025-06-21 00:00:00

Tackling simulation optimization problems with non-convex objective functions remains a fundamental challenge in operations research. In this paper, we propose a class of random search algorithms, called Regular Tree Search, which integrates adaptive sampling with recursive partitioning of the search space. The algorithm concentrates simulations on increasingly promising regions by iteratively refining a tree structure. A tree search strategy guides sampling decisions, while partitioning is triggered when the number of samples in a leaf node exceeds a threshold that depends on its depth. Furthermore, a specific tree search strategy, Upper Confidence Bounds applied to Trees (UCT), is employed in the Regular Tree Search. We prove global convergence under sub-Gaussian noise, based on assumptions involving the optimality gap, without requiring continuity of the objective function. Numerical experiments confirm that the algorithm reliably identifies the global optimum and provides accurate estimates of its objective value.
LieDetect: Detection of representation orbits of compact Lie groups from point clouds
Authors: Henrique Ennes, Raphaël Tinarrage
Date: 2023-07-26 00:00:00
Tasks: Density Estimation

We suggest a new algorithm to estimate representations of compact Lie groups from finite samples of their orbits. Different from other reported techniques, our method allows the retrieval of the precise representation type as a direct sum of irreducible representations. Moreover, the knowledge of the representation type permits the reconstruction of its orbit, which is useful for identifying the Lie group that generates the action, from a finite list of candidates. Our algorithm is general for any compact Lie group, but only instantiations for SO(2), T^d, SU(2), and SO(3) are considered. Theoretical guarantees of robustness in terms of Hausdorff and Wasserstein distances are derived. Our tools are drawn from geometric measure theory, computational geometry, and optimization on matrix manifolds. The algorithm is tested for synthetic data up to dimension 32, as well as real-life applications in image analysis, harmonic analysis, density estimation, equivariant neural networks, chemical conformational spaces, and classical mechanics systems, achieving very accurate results.
Gaussian Processes and Reproducing Kernels: Connections and Equivalences
Authors: Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic, Bharath K. Sriperumbudur
Date: 2025-06-20 00:00:00
Tasks: Gaussian Processes, Numerical Integration

This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes, and non-probabilistic methods using reproducing kernel Hilbert spaces (RKHS). They are widely studied and used in machine learning, statistics, and numerical analysis. Connections and equivalences between them are reviewed for fundamental topics such as regression, interpolation, numerical integration, distributional discrepancies, and statistical dependence, as well as for sample path properties of Gaussian processes. A unifying perspective for these equivalences is established, based on the equivalence between the Gaussian Hilbert space and the RKHS. The monograph serves as a basis to bridge many other methods based on Gaussian processes and reproducing kernels, which are developed in parallel by the two research communities.
Convergent Methods for Koopman Operators on Reproducing Kernel Hilbert Spaces
Authors: Nicolas Boullé, Matthew J. Colbrook, Gustav Conradie
Date: 2025-06-18 00:00:00

Data-driven spectral analysis of Koopman operators is a powerful tool for understanding numerous real-world dynamical systems, from neuronal activity to variations in sea surface temperature. The Koopman operator acts on a function space and is most commonly studied on the space of square-integrable functions. However, defining it on a suitable reproducing kernel Hilbert space (RKHS) offers numerous practical advantages, including pointwise predictions with error bounds, improved spectral properties that facilitate computations, and more efficient algorithms, particularly in high dimensions. We introduce the first general, provably convergent, data-driven algorithms for computing spectral properties of Koopman and Perron--Frobenius operators on RKHSs. These methods efficiently compute spectra and pseudospectra with error control and spectral measures while exploiting the RKHS structure to avoid the large-data limits required in the $L^2$ settings. The function space is determined by a user-specified kernel, eliminating the need for quadrature-based sampling as in $L^2$ and enabling greater flexibility with finite, externally provided datasets. Using the Solvability Complexity Index hierarchy, we construct adversarial dynamical systems for these problems to show that no algorithm can succeed in fewer limits, thereby proving the optimality of our algorithms. Notably, this impossibility extends to randomized algorithms and datasets. We demonstrate the effectiveness of our algorithms on challenging, high-dimensional datasets arising from real-world measurements and high-fidelity numerical simulations, including turbulent channel flow, molecular dynamics of a binding protein, Antarctic sea ice concentration, and Northern Hemisphere sea surface height. The algorithms are publicly available in the software package $\texttt{SpecRKHS}$.
On the Upper Bounds for the Matrix Spectral Norm
Authors: Alexey Naumov, Maxim Rakhuba, Denis Ryapolov, Sergey Samsonov
Date: 2025-06-18 00:00:00

We consider the problem of estimating the spectral norm of a matrix using only matrix-vector products. We propose a new Counterbalance estimator that provides upper bounds on the norm and derive probabilistic guarantees on its underestimation. Compared to standard approaches such as the power method, the proposed estimator produces significantly tighter upper bounds in both synthetic and real-world settings. Our method is especially effective for matrices with fast-decaying spectra, such as those arising in deep learning and inverse problems.
Intrinsic and Extrinsic Organized Attention: Softmax Invariance and Network Sparsity
Authors: Oluwadamilola Fasina, Ruben V. C. Pohle, Pei-Chun Su, Ronald R. Coifman
Date: 2025-06-18 00:00:00

We examine the intrinsic (within the attention head) and extrinsic (amongst the attention heads) structure of the self-attention mechanism in transformers. Theoretical evidence for invariance of the self-attention mechanism to softmax activation is obtained by appealing to paradifferential calculus, (and is supported by computational examples), which relies on the intrinsic organization of the attention heads. Furthermore, we use an existing methodology for hierarchical organization of tensors to examine network structure by constructing hierarchal partition trees with respect to the query, key, and head axes of network 3-tensors. Such an organization is consequential since it allows one to profitably execute common signal processing tasks on a geometry where the organized network 3-tensors exhibit regularity. We exemplify this qualitatively, by visualizing the hierarchical organization of the tree comprised of attention heads and the diffusion map embeddings, and quantitatively by investigating network sparsity with the expansion coefficients of individual attention heads and the entire network with respect to the bi and tri-haar bases (respectively) on the space of queries, keys, and heads of the network. To showcase the utility of our theoretical and methodological findings, we provide computational examples using vision and language transformers. The ramifications of these findings are two-fold: (1) a subsequent step in interpretability analysis is theoretically admitted, and can be exploited empirically for downstream interpretability tasks (2) one can use the network 3-tensor organization for empirical network applications such as model pruning (by virtue of network sparsity) and network architecture comparison.
HiPreNets: High-Precision Neural Networks through Progressive Training
Authors: Ethan Mulle, Wei Kang, Qi Gong
Date: 2025-06-18 00:00:00

Deep neural networks are powerful tools for solving nonlinear problems in science and engineering, but training highly accurate models becomes challenging as problem complexity increases. Non-convex optimization and numerous hyperparameters to tune make performance improvement difficult, and traditional approaches often prioritize minimizing mean squared error (MSE) while overlooking $L^{\infty}$ error, which is the critical focus in many applications. To address these challenges, we present a progressive framework for training and tuning high-precision neural networks (HiPreNets). Our approach refines a previously explored staged training technique for neural networks that improves an existing fully connected neural network by sequentially learning its prediction residuals using additional networks, leading to improved overall accuracy. We discuss how to take advantage of the structure of the residuals to guide the choice of loss function, number of parameters to use, and ways to introduce adaptive data sampling techniques. We validate our framework's effectiveness through several benchmark problems.
Experimental Assessment of A Framework for In-body RF-backscattering Localization
Authors: Noa Jie Vives Zaguirre, Oscar Lasierra, Filip Lemic, Gerard Calvo Bartra, Pablo José Galván Calderón, Gines Garcia-Aviles, Sergi Abadal, Xavier Costa-Pérez
Date: 2025-06-24 00:00:00

Localization of in-body devices is beneficial for Gastrointestinal (GI) diagnosis and targeted treatment. Traditional methods such as imaging and endoscopy are invasive and limited in resolution, highlighting the need for innovative alternatives. This study presents an experimental framework for Radio Frequency (RF)-backscatter-based in-body localization, inspired by the ReMix approach, and evaluates its performance in real-world conditions. The experimental setup includes an in-body backscatter device and various off-body antenna configurations to investigate harmonic generation and reception in air, chicken and pork tissues. The results indicate that optimal backscatter device positioning, antenna selection, and gain settings significantly impact performance, with denser biological tissues leading to greater attenuation. The study also highlights challenges such as external interference and plastic enclosures affecting propagation. The findings emphasize the importance of interference mitigation and refined propagation models to enhance performance.
Client Selection Strategies for Federated Semantic Communications in Heterogeneous IoT Networks
Authors: Samer Lahoud, Kinda Khawam
Date: 2025-06-20 00:00:00
Tasks: Computational Efficiency, Fairness, Federated Learning, Image Reconstruction, Privacy Preserving, Semantic Communication

The exponential growth of IoT devices presents critical challenges in bandwidth-constrained wireless networks, particularly regarding efficient data transmission and privacy preservation. This paper presents a novel federated semantic communication (SC) framework that enables collaborative training of bandwidth-efficient models for image reconstruction across heterogeneous IoT devices. By leveraging SC principles to transmit only semantic features, our approach dramatically reduces communication overhead while preserving reconstruction quality. We address the fundamental challenge of client selection in federated learning environments where devices exhibit significant disparities in dataset sizes and data distributions. Our framework implements three distinct client selection strategies that explore different trade-offs between system performance and fairness in resource allocation. The system employs an end-to-end SC architecture with semantic bottlenecks, coupled with a loss-based aggregation mechanism that naturally adapts to client heterogeneity. Experimental evaluation on image data demonstrates that while Utilitarian selection achieves the highest reconstruction quality, Proportional Fairness maintains competitive performance while significantly reducing participation inequality and improving computational efficiency. These results establish that federated SC can successfully balance reconstruction quality, resource efficiency, and fairness in heterogeneous IoT deployments, paving the way for sustainable and privacy-preserving edge intelligence applications.
GCN-Driven Reinforcement Learning for Probabilistic Real-Time Guarantees in Industrial URLLC
Authors: Eman Alqudah, Ashfaq Khokhar
Date: 2025-06-17 00:00:00
Tasks: Scheduling

Ensuring packet-level communication quality is vital for ultra-reliable, low-latency communications (URLLC) in large-scale industrial wireless networks. We enhance the Local Deadline Partition (LDP) algorithm by introducing a Graph Convolutional Network (GCN) integrated with a Deep Q-Network (DQN) reinforcement learning framework for improved interference coordination in multi-cell, multi-channel networks. Unlike LDP's static priorities, our approach dynamically learns link priorities based on real-time traffic demand, network topology, remaining transmission opportunities, and interference patterns. The GCN captures spatial dependencies, while the DQN enables adaptive scheduling decisions through reward-guided exploration. Simulation results show that our GCN-DQN model achieves mean SINR improvements of 179.6\%, 197.4\%, and 175.2\% over LDP across three network configurations. Additionally, the GCN-DQN model demonstrates mean SINR improvements of 31.5\%, 53.0\%, and 84.7\% over our previous CNN-based approach across the same configurations. These results underscore the effectiveness of our GCN-DQN model in addressing complex URLLC requirements with minimal overhead and superior network performance.
CNN-Enabled Scheduling for Probabilistic Real-Time Guarantees in Industrial URLLC
Authors: Eman Alqudah, Ashfaq Khokhar
Date: 2025-06-17 00:00:00
Tasks: Scheduling

Ensuring packet-level communication quality is vital for ultra-reliable, low-latency communications (URLLC) in large-scale industrial wireless networks. We enhance the Local Deadline Partition (LDP) algorithm by introducing a CNN-based dynamic priority prediction mechanism for improved interference coordination in multi-cell, multi-channel networks. Unlike LDP's static priorities, our approach uses a Convolutional Neural Network and graph coloring to adaptively assign link priorities based on real-time traffic, transmission opportunities, and network conditions. Assuming that first training phase is performed offline, our approach introduced minimal overhead, while enabling more efficient resource allocation, boosting network capacity, SINR, and schedulability. Simulation results show SINR gains of up to 113\%, 94\%, and 49\% over LDP across three network configurations, highlighting its effectiveness for complex URLLC scenarios.
VeriLocc: End-to-End Cross-Architecture Register Allocation via LLM
Authors: Lesheng Jin, Zhenyuan Ruan, Haohui Mai, Jingbo Shang
Date: 2025-06-20 00:00:00
Tasks: GPU

Modern GPUs evolve rapidly, yet production compilers still rely on hand-crafted register allocation heuristics that require substantial re-tuning for each hardware generation. We introduce VeriLocc, a framework that combines large language models (LLMs) with formal compiler techniques to enable generalizable and verifiable register allocation across GPU architectures. VeriLocc fine-tunes an LLM to translate intermediate representations (MIRs) into target-specific register assignments, aided by static analysis for cross-architecture normalization and generalization and a verifier-guided regeneration loop to ensure correctness. Evaluated on matrix multiplication (GEMM) and multi-head attention (MHA), VeriLocc achieves 85-99% single-shot accuracy and near-100% pass@100. Case study shows that VeriLocc discovers more performant assignments than expert-tuned libraries, outperforming rocBLAS by over 10% in runtime.
OSWorld-Human: Benchmarking the Efficiency of Computer-Use Agents
Authors: Reyna Abhyankar, Qi Qi, Yiying Zhang
Date: 2025-06-19 00:00:00
Tasks: Benchmarking

Generative AI is being leveraged to solve a variety of computer-use tasks involving desktop applications. State-of-the-art systems have focused solely on improving accuracy on leading benchmarks. However, these systems are practically unusable due to extremely high end-to-end latency (e.g., tens of minutes) for tasks that typically take humans just a few minutes to complete. To understand the cause behind this and to guide future developments of computer agents, we conduct the first study on the temporal performance of computer-use agents on OSWorld, the flagship benchmark in computer-use AI. We find that large model calls for planning and reflection account for the majority of the overall latency, and as an agent uses more steps to complete a task, each successive step can take 3x longer than steps at the beginning of a task. We then construct OSWorld-Human, a manually annotated version of the original OSWorld dataset that contains a human-determined trajectory for each task. We evaluate 16 agents on their efficiency using OSWorld-Human and found that even the highest-scoring agents on OSWorld take 1.4-2.7x more steps than necessary.
GPU Kernel Scientist: An LLM-Driven Framework for Iterative Kernel Optimization
Authors: Martin Andrews, Sam Witteveen
Date: 2025-06-25 00:00:00
Tasks: GPU

Optimizing GPU kernels for high performance is a complex task, often demanding deep architectural knowledge, extensive profiling, and iterative experimentation. This challenge is amplified when targeting newer or less-documented GPU architectures where traditional development aids are scarce. This paper introduces an LLM-powered "GPU Kernel Scientist," an automated methodology for iteratively refining accelerator kernels. Our methodology employs LLMs in a multi-stage, evolutionary process: (a) strategically selecting promising prior code versions as a basis for new iterations; (b) generating hypotheses for optimization experiments, based on existing code and assimilated knowledge from general GPU literature; and (c) autonomously implementing these experiments through code modification and subsequent submission to an external evaluation system, using only observed timing data as performance feedback. We detail how this approach navigates the challenges of the AMD MI300 target architecture and leverages LLMs to compensate for limited domain-specific human expertise. Since quantitative results from an ongoing performance competition were embargoed on paper submission date, we present the architectural design, operational workflow, and qualitative insights, highlighting the potential of LLM-driven agents to democratise and accelerate GPU kernel optimization, especially in resource-constrained or rapidly evolving hardware environments.
PEVLM: Parallel Encoding for Vision-Language Models
Authors: Letian Kang, Shixian Luo, Yiqiang Li, Xiaoyang Yu, Shenxuan Zhou, Yong Wu
Date: 2025-06-24 00:00:00
Tasks: Autonomous Driving, Video Understanding

Vision-Language Models (VLMs) have demonstrated strong performance in video-language tasks, yet their application to long video understanding remains constrained by the quadratic complexity of standard attention mechanisms. In this paper, we propose \textbf{PEVLM}, a parallel encoding strategy specifically designed to improve the prefill efficiency of VLMs without requiring model finetuning. PEVLM partitions the input into block-wise segments with a shared sink, preserves full-attention positional embeddings, and aligns attention weights to mimic full-attention distributions. This design reduces attention computation from $O((T \times N)^2)$ to $O(T \times N)$ while maintaining high accuracy. Extensive experiments on the LongVideoBench benchmark show that PEVLM achieves up to 8.37\% accuracy improvement over existing inference-efficient methods and delivers up to 7.47x speedup in attention computation and 40\% reduction in end-to-end latency. Under strict latency constraints, PEVLM significantly outperforms baselines, raising accuracy from 23.26\% to 61.03\%. These results highlight PEVLM's effectiveness for low-latency, long-context video understanding, making it well-suited for real-world applications such as autonomous driving.
Spatially-Aware Evaluation of Segmentation Uncertainty
Authors: Tal Zeevi, Eléonore V. Lieffrig, Lawrence H. Staib, John A. Onofrey
Date: 2025-06-19 00:00:00
Tasks: Segmentation

Uncertainty maps highlight unreliable regions in segmentation predictions. However, most uncertainty evaluation metrics treat voxels independently, ignoring spatial context and anatomical structure. As a result, they may assign identical scores to qualitatively distinct patterns (e.g., scattered vs. boundary-aligned uncertainty). We propose three spatially aware metrics that incorporate structural and boundary information and conduct a thorough validation on medical imaging data from the prostate zonal segmentation challenge within the Medical Segmentation Decathlon. Our results demonstrate improved alignment with clinically important factors and better discrimination between meaningful and spurious uncertainty patterns.
SAM4D: Segment Anything in Camera and LiDAR Streams
Authors: Jianyun Xu, Song Wang, Ziqian Ni, Chunyong Hu, Sheng Yang, Jianke Zhu, Qiang Li
Date: 2025-06-26 00:00:00
Tasks: 4D reconstruction, Autonomous Driving, Motion Compensation, Segmentation

We present SAM4D, a multi-modal and temporal foundation model designed for promptable segmentation across camera and LiDAR streams. Unified Multi-modal Positional Encoding (UMPE) is introduced to align camera and LiDAR features in a shared 3D space, enabling seamless cross-modal prompting and interaction. Additionally, we propose Motion-aware Cross-modal Memory Attention (MCMA), which leverages ego-motion compensation to enhance temporal consistency and long-horizon feature retrieval, ensuring robust segmentation across dynamically changing autonomous driving scenes. To avoid annotation bottlenecks, we develop a multi-modal automated data engine that synergizes VFM-driven video masklets, spatiotemporal 4D reconstruction, and cross-modal masklet fusion. This framework generates camera-LiDAR aligned pseudo-labels at a speed orders of magnitude faster than human annotation while preserving VFM-derived semantic fidelity in point cloud representations. We conduct extensive experiments on the constructed Waymo-4DSeg, which demonstrate the powerful cross-modal segmentation ability and great potential in data annotation of proposed SAM4D.