pwc-archive/papers-with-abstracts huggingface dataset:
Trade-offs in Privacy-Preserving Eye Tracking through Iris Obfuscation: A Benchmarking Study
Authors: Mengdi Wang, Efe Bozkir, Enkelejda Kasneci
Date: 2025-04-14 00:00:00
Tasks: Benchmarking, Gaze Estimation, Iris Recognition, Privacy Preserving, Style Transfer

Recent developments in hardware, computer graphics, and AI may soon enable AR/VR head-mounted displays (HMDs) to become everyday devices like smartphones and tablets. Eye trackers within HMDs provide a special opportunity for such setups as it is possible to facilitate gaze-based research and interaction. However, estimating users' gaze information often requires raw eye images and videos that contain iris textures, which are considered a gold standard biometric for user authentication, and this raises privacy concerns. Previous research in the eye-tracking community focused on obfuscating iris textures while keeping utility tasks such as gaze estimation accurate. Despite these attempts, there is no comprehensive benchmark that evaluates state-of-the-art approaches. Considering all, in this paper, we benchmark blurring, noising, downsampling, rubber sheet model, and iris style transfer to obfuscate user identity, and compare their impact on image quality, privacy, utility, and risk of imposter attack on two datasets. We use eye segmentation and gaze estimation as utility tasks, and reduction in iris recognition accuracy as a measure of privacy protection, and false acceptance rate to estimate risk of attack. Our experiments show that canonical image processing methods like blurring and noising cause a marginal impact on deep learning-based tasks. While downsampling, rubber sheet model, and iris style transfer are effective in hiding user identifiers, iris style transfer, with higher computation cost, outperforms others in both utility tasks, and is more resilient against spoof attacks. Our analyses indicate that there is no universal optimal approach to balance privacy, utility, and computation burden. Therefore, we recommend practitioners consider the strengths and weaknesses of each approach, and possible combinations of those to reach an optimal privacy-utility trade-off.
Fluid Antenna Enabled Over-the-Air Federated Learning: Joint Optimization of Positioning, Beamforming, and User Selection
Authors: Yang Zhao, Minrui Xu, Ping Wang, Dusit Niyato
Date: 2025-02-17 00:00:00
Tasks: Federated Learning, Stochastic Optimization

Over-the-air (OTA) federated learning (FL) effectively utilizes communication bandwidth, yet it is vulnerable to errors during analog aggregation. While removing users with unfavorable channel conditions can mitigate these errors, it also reduces the available local training data for FL, which in turn hinders the convergence rate of the training process. To tackle this issue, we propose using fluid antenna (FA) techniques to enhance the degrees of freedom within the channel space, ultimately boosting the convergence speed of FL training. Moreover, we develop a novel approach that effectively coordinates uplink receiver beamforming, user selection, and FA positioning to optimize the convergence rate of OTA FL training in dynamic wireless environments. We address this challenging stochastic optimization by reformulating it as a mixed-integer programming problem by utilizing the training loss upper bound. We then introduce a penalty dual decomposition (PDD) method to solve the mixed-integer mixed programming problem. Experimental results indicate that incorporating FA techniques significantly accelerates the training convergence of FL and greatly surpasses conventional methods.
WildLive: Near Real-time Visual Wildlife Tracking onboard UAVs
Authors: Nguyen Ngoc Dat, Tom Richardson, Matthew Watson, Kilian Meier, Jenna Kline, Sid Reid, Guy Maalouf, Duncan Hine, Majid Mirmehdi, Tilo Burghardt
Date: 2025-04-14 00:00:00
Tasks: 4k, Object Tracking, Optical Flow Estimation

Live tracking of wildlife via high-resolution video processing directly onboard drones is widely unexplored and most existing solutions rely on streaming video to ground stations to support navigation. Yet, both autonomous animal-reactive flight control beyond visual line of sight and/or mission-specific individual and behaviour recognition tasks rely to some degree on this capability. In response, we introduce WildLive -- a near real-time animal detection and tracking framework for high-resolution imagery running directly onboard uncrewed aerial vehicles (UAVs). The system performs multi-animal detection and tracking at 17fps+ for HD and 7fps+ on 4K video streams suitable for operation during higher altitude flights to minimise animal disturbance. Our system is optimised for Jetson Orin AGX onboard hardware. It integrates the efficiency of sparse optical flow tracking and mission-specific sampling with device-optimised and proven YOLO-driven object detection and segmentation techniques. Essentially, computational resource is focused onto spatio-temporal regions of high uncertainty to significantly improve UAV processing speeds without domain-specific loss of accuracy. Alongside, we introduce our WildLive dataset, which comprises 200k+ annotated animal instances across 19k+ frames from 4K UAV videos collected at the Ol Pejeta Conservancy in Kenya. All frames contain ground truth bounding boxes, segmentation masks, as well as individual tracklets and tracking point trajectories. We compare our system against current object tracking approaches including OC-SORT, ByteTrack, and SORT. Our multi-animal tracking experiments with onboard hardware confirm that near real-time high-resolution wildlife tracking is possible on UAVs whilst maintaining high accuracy levels as needed for future navigational and mission-specific animal-centric operational autonomy.
An Ensemble Framework for Probabilistic Short-Term Load Forecasting Based on BiTCN and Deep Attention Networks
Authors: Bilel Benziane, Benoit Lardeux, Maher Jridi, Ayoub Mcharek
Date: 2025-02-25 00:00:00
Proceeding: Research gate 2025 2
Tasks: Decision Making, Deep Attention, Load Forecasting, Uncertainty Quantification

Probabilistic load forecasting is crucial for modern energy systems, providing point forecasts and uncertainty quantification to enhance decision-making and grid reliability. Achieving accurate forecasts often requires deeper hybrid ensemble models to capture power load complexities. However, increasing model depth can introduce challenges like vanishing gradients, hindering learning efficiency. To address this, we propose an ensemble framework integrating Bidirectional Temporal Convolutional Networks (BiTCN) to capture forward and backward temporal dependencies. An attention mechanism highlights key temporal features, improving accuracy, while skip connections mitigate vanishing gradients by preserving early outputs. A classical neural network models forecast distributions, ensuring a comprehensive uncertainty representation. Compared to state-of-the-art models like WaveNet and Transformers models, our framework achieves a 9.3\% improvement in mean standardized error on a public demand dataset of 500,000 data points. These results demonstrate the effectiveness and robustness of the proposed approach, offering a promising solution for probabilistic load forecasting in power systems.
Adaptive Insurance Reserving with CVaR-Constrained Reinforcement Learning under Macroeconomic Regimes
Authors: Stella C. Dong, James R. Finlay
Date: 2025-04-13 00:00:00
Tasks: Reinforcement Learning (RL)

This paper proposes a reinforcement learning (RL) framework for insurance reserving that integrates tail-risk sensitivity, macroeconomic regime modeling, and regulatory compliance. The reserving problem is formulated as a finite-horizon Markov Decision Process (MDP), in which reserve adjustments are optimized using Proximal Policy Optimization (PPO) subject to Conditional Value-at-Risk (CVaR) constraints. To enhance policy robustness across varying economic conditions, the agent is trained using a regime-aware curriculum that progressively increases volatility exposure. The reward structure penalizes reserve shortfall, capital inefficiency, and solvency floor violations, with design elements informed by Solvency II and Own Risk and Solvency Assessment (ORSA) frameworks. Empirical evaluations on two industry datasets--Workers' Compensation, and Other Liability--demonstrate that the RL-CVaR agent achieves superior performance relative to classical reserving methods across multiple criteria, including tail-risk control (CVaR$_{0.95}$), capital efficiency, and regulatory violation rate. The framework also accommodates fixed-shock stress testing and regime-stratified analysis, providing a principled and extensible approach to reserving under uncertainty.
SCORCH2: a generalised heterogeneous consensus model for high-enrichment interaction-based virtual screening
Authors: Lin Chen, Vincent Blay, Pedro J. Ballester, Douglas R. Houston
Date: 2025-04-05 00:00:00
Proceeding: bioRxiv 2025 4
Tasks: Drug Discovery

The discovery of effective therapeutics remains a complex, costly, and time-consuming endeavor, characterized
by high failure rates and significant resource investments. A central bottleneck in early-stage drug discovery is
identifying suitable hit compounds with moderate affinity for known biological targets. Although advancements have
been made, current in silico virtual screening methods are subject to limitations, including model overfitting, data
bias, and constrained interpretability in their predictive processes. In this study, we present SCORCH2, a machine
learning-based framework designed to enhance both the performance and interpretability of virtual screening by
leveraging interaction features. Compared with its predecessor SCORCH, SCORCH2 exhibits superior predictive
accuracy and generalizability across a wide range of biological targets. Importantly, SCORCH2 demonstrates robust
hit identification capabilities on previously unseen targets, indicating strong transferability. These results highlight
the potential of SCORCH2 as a valuable tool in accelerating drug discovery, offering reliable predictive capabilities
while improving the interpretability of virtual screening models.
3D CoCa: Contrastive Learners are 3D Captioners
Authors: Ting Huang, Zeyu Zhang, Yemin Wang, Hao Tang
Date: 2025-04-13 00:00:00
Tasks: 3D dense captioning, Caption Generation, cross-modal alignment, Decoder, Descriptive, Spatial Reasoning

3D captioning, which aims to describe the content of 3D scenes in natural language, remains highly challenging due to the inherent sparsity of point clouds and weak cross-modal alignment in existing methods. To address these challenges, we propose 3D CoCa, a novel unified framework that seamlessly combines contrastive vision-language learning with 3D caption generation in a single architecture. Our approach leverages a frozen CLIP vision-language backbone to provide rich semantic priors, a spatially-aware 3D scene encoder to capture geometric context, and a multi-modal decoder to generate descriptive captions. Unlike prior two-stage methods that rely on explicit object proposals, 3D CoCa jointly optimizes contrastive and captioning objectives in a shared feature space, eliminating the need for external detectors or handcrafted proposals. This joint training paradigm yields stronger spatial reasoning and richer semantic grounding by aligning 3D and textual representations. Extensive experiments on the ScanRefer and Nr3D benchmarks demonstrate that 3D CoCa significantly outperforms current state-of-the-arts by 10.2% and 5.76% in CIDEr at 0.5IoU, respectively. Code will be available at https://github.com/AIGeeksGroup/3DCoCa.
Foundation models for electronic health records: representation dynamics and transferability
Authors: Michael C. Burkhart, Bashar Ramadan, Zewei Liao, Kaveri Chhikara, Juan C. Rojas, William F. Parker, Brett K. Beaulieu-Jones
Date: 2025-04-14 00:00:00

Foundation models (FMs) trained on electronic health records (EHRs) have shown strong performance on a range of clinical prediction tasks. However, adapting these models to local health systems remains challenging due to limited data availability and resource constraints. In this study, we investigated what these models learn and evaluated the transferability of an FM trained on MIMIC-IV to an institutional EHR dataset at the University of Chicago Medical Center. We assessed their ability to identify outlier patients and examined representation-space patient trajectories in relation to future clinical outcomes. We also evaluated the performance of supervised fine-tuned classifiers on both source and target datasets. Our findings offer insights into the adaptability of FMs across different healthcare systems, highlight considerations for their effective implementation, and provide an empirical analysis of the underlying factors that contribute to their predictive performance.
Can LLMs Assist Expert Elicitation for Probabilistic Causal Modeling?
Authors: Olha Shaposhnyk, Daria Zahorska, Svetlana Yanushkevich
Date: 2025-04-14 00:00:00
Tasks: Decision Making

Objective: This study investigates the potential of Large Language Models (LLMs) as an alternative to human expert elicitation for extracting structured causal knowledge and facilitating causal modeling in biometric and healthcare applications. Material and Methods: LLM-generated causal structures, specifically Bayesian networks (BNs), were benchmarked against traditional statistical methods (e.g., Bayesian Information Criterion) using healthcare datasets. Validation techniques included structural equation modeling (SEM) to verifying relationships, and measures such as entropy, predictive accuracy, and robustness to compare network structures. Results and Discussion: LLM-generated BNs demonstrated lower entropy than expert-elicited and statistically generated BNs, suggesting higher confidence and precision in predictions. However, limitations such as contextual constraints, hallucinated dependencies, and potential biases inherited from training data require further investigation. Conclusion: LLMs represent a novel frontier in expert elicitation for probabilistic causal modeling, promising to improve transparency and reduce uncertainty in the decision-making using such models.
Time-adaptive Video Frame Interpolation based on Residual Diffusion
Authors: Victor Fonte Chavez, Claudia Esteves, Jean-Bernard Hayet
Date: 2025-04-07 00:00:00
Tasks: Video Frame Interpolation

In this work, we propose a new diffusion-based method for video frame interpolation (VFI), in the context of traditional hand-made animation. We introduce three main contributions: The first is that we explicitly handle the interpolation time in our model, which we also re-estimate during the training process, to cope with the particularly large variations observed in the animation domain, compared to natural videos; The second is that we adapt and generalize a diffusion scheme called ResShift recently proposed in the super-resolution community to VFI, which allows us to perform a very low number of diffusion steps (in the order of 10) to produce our estimates; The third is that we leverage the stochastic nature of the diffusion process to provide a pixel-wise estimate of the uncertainty on the interpolated frame, which could be useful to anticipate where the model may be wrong. We provide extensive comparisons with respect to state-of-the-art models and show that our model outperforms these models on animation videos. Our code is available at https://github.com/VicFonch/Multi-Input-Resshift-Diffusion-VFI.
Towards scientific machine learning for granular material simulations -- challenges and opportunities
Authors: Marc Fransen, Andreas Fürst, Deepak Tunuguntla, Daniel N. Wilke, Benedikt Alkin, Daniel Barreto, Johannes Brandstetter, Miguel Angel Cabrera, Xinyan Fan, Mengwu Guo, Bram Kieskamp, Krishna Kumar, John Morrissey, Jonathan Nuttall, Jin Ooi, Luisa Orozco, Stefanos-Aldo Papanicolopulos, Tongming Qu, Dingena Schott, Takayuki Shuku, WaiChing Sun, Thomas Weinhart, Dongwei Ye, Hongyang Cheng
Date: 2025-04-01 00:00:00
Tasks: Operator learning

Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights into these interactions, their computational cost is often prohibitive. Attended by researchers from both the granular materials (GM) and machine learning (ML) communities, a recent Lorentz Center Workshop on "Machine Learning for Discrete Granular Media" brought the ML community up to date with GM challenges. This position paper emerged from the workshop discussions. We define granular materials and identify seven key challenges that characterise their distinctive behaviour across various scales and regimes, ranging from gas-like to fluid-like and solid-like. Addressing these challenges is essential for developing robust and efficient digital twins for granular systems in various industrial applications. To showcase the potential of ML to the GM community, we present classical and emerging machine/deep learning techniques that have been, or could be, applied to granular materials. We reviewed sequence-based learning models for path-dependent constitutive behaviour, followed by encoder-decoder type models for representing high-dimensional data. We then explore graph neural networks and recent advances in neural operator learning. Lastly, we discuss model-order reduction and probabilistic learning techniques for high-dimensional parameterised systems, which are crucial for quantifying uncertainties arising from physics-based and data-driven models. We present a workflow aimed at unifying data structures and modelling pipelines and guiding readers through the selection, training, and deployment of ML surrogates for granular material simulations. Finally, we illustrate the workflow's practical use with two representative examples, focusing on granular materials in solid-like and fluid-like regimes.
VEXP: A Low-Cost RISC-V ISA Extension for Accelerated Softmax Computation in Transformers
Authors: Run Wang, Gamze Islamoglu, Andrea Belano, Viviane Potocnik, Francesco Conti, Angelo Garofalo, Luca Benini
Date: 2025-04-15 00:00:00

While Transformers are dominated by Floating-Point (FP) Matrix-Multiplications, their aggressive acceleration through dedicated hardware or many-core programmable systems has shifted the performance bottleneck to non-linear functions like Softmax. Accelerating Softmax is challenging due to its non-pointwise, non-linear nature, with exponentiation as the most demanding step. To address this, we design a custom arithmetic block for Bfloat16 exponentiation leveraging a novel approximation algorithm based on Schraudolph's method, and we integrate it into the Floating-Point Unit (FPU) of the RISC-V cores of a compute cluster, through custom Instruction Set Architecture (ISA) extensions, with a negligible area overhead of 1\%. By optimizing the software kernels to leverage the extension, we execute Softmax with 162.7$\times$ less latency and 74.3$\times$ less energy compared to the baseline cluster, achieving an 8.2$\times$ performance improvement and 4.1$\times$ higher energy efficiency for the FlashAttention-2 kernel in GPT-2 configuration. Moreover, the proposed approach enables a multi-cluster system to efficiently execute end-to-end inference of pre-trained Transformer models, such as GPT-2, GPT-3 and ViT, achieving up to 5.8$\times$ and 3.6$\times$ reduction in latency and energy consumption, respectively, without requiring re-training and with negligible accuracy loss.
SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoning
Authors: Yiting Wang, Wanghao Ye, Ping Guo, Ziyao Wang, Yexiao He, Bowei Tian, Shwai He, Guoheng Sun, Zheyu Shen, Sihan Chen, Ankur Srivastava, Qingfu Zhang, Gang Qu, Ang Li
Date: 2025-04-14 00:00:00
Tasks: Large Language Model, RAG, Retrieval-augmented Generation

Optimizing Register Transfer Level (RTL) code is crucial for improving the power, performance, and area (PPA) of digital circuits in the early stages of synthesis. Manual rewriting, guided by synthesis feedback, can yield high-quality results but is time-consuming and error-prone. Most existing compiler-based approaches have difficulty handling complex design constraints. Large Language Model (LLM)-based methods have emerged as a promising alternative to address these challenges. However, LLM-based approaches often face difficulties in ensuring alignment between the generated code and the provided prompts. This paper presents SymRTLO, a novel neuron-symbolic RTL optimization framework that seamlessly integrates LLM-based code rewriting with symbolic reasoning techniques. Our method incorporates a retrieval-augmented generation (RAG) system of optimization rules and Abstract Syntax Tree (AST)-based templates, enabling LLM-based rewriting that maintains syntactic correctness while minimizing undesired circuit behaviors. A symbolic module is proposed for analyzing and optimizing finite state machine (FSM) logic, allowing fine-grained state merging and partial specification handling beyond the scope of pattern-based compilers. Furthermore, a fast verification pipeline, combining formal equivalence checks with test-driven validation, further reduces the complexity of verification. Experiments on the RTL-Rewriter benchmark with Synopsys Design Compiler and Yosys show that SymRTLO improves power, performance, and area (PPA) by up to 43.9%, 62.5%, and 51.1%, respectively, compared to the state-of-the-art methods.
GNN-ACLP: Graph Neural Networks based Analog Circuit Link Prediction
Authors: Guanyuan Pan, Tiansheng Zhou, Bingtao Ma, Yaqi Wang, Jianxiang Zhao, Zhi Li, Yugui Lin, Pietro Lio, Shuai Wang
Date: 2025-04-14 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Link Prediction, Prediction, RAG, Retrieval-augmented Generation

Circuit link prediction identifying missing component connections from incomplete netlists is crucial in automating analog circuit design. However, existing methods face three main challenges: 1) Insufficient use of topological patterns in circuit graphs reduces prediction accuracy; 2) Data scarcity due to the complexity of annotations hinders model generalization; 3) Limited adaptability to various netlist formats. We propose GNN-ACLP, a Graph Neural Networks (GNNs) based framework featuring three innovations to tackle these challenges. First, we introduce the SEAL (Subgraphs, Embeddings, and Attributes for Link Prediction) framework and achieve port-level accuracy in circuit link prediction. Second, we propose Netlist Babel Fish, a netlist format conversion tool leveraging retrieval-augmented generation (RAG) with a large language model (LLM) to enhance the compatibility of netlist formats. Finally, we construct SpiceNetlist, a comprehensive dataset that contains 775 annotated circuits across 10 different component classes. Experimental results achieve accuracy improvements of 16.08% on SpiceNetlist, 11.38% on Image2Net, and 16.01% on Masala-CHAI in intra-dataset evaluation, while maintaining accuracy from 92.05% to 99.07% in cross-dataset evaluation, exhibiting robust feature transfer capabilities.