pwc-archive/papers-with-abstracts huggingface dataset:
HACTS: a Human-As-Copilot Teleoperation System for Robot Learning
Authors: Zhiyuan Xu, Yinuo Zhao, Kun Wu, Ning Liu, Junjie Ji, Zhengping Che, Chi Harold Liu, Jian Tang
Date: 2025-03-31 00:00:00
Tasks: Autonomous Vehicles, Imitation Learning, Reinforcement Learning (RL), Robot Manipulation

Teleoperation is essential for autonomous robot learning, especially in manipulation tasks that require human demonstrations or corrections. However, most existing systems only offer unilateral robot control and lack the ability to synchronize the robot's status with the teleoperation hardware, preventing real-time, flexible intervention. In this work, we introduce HACTS (Human-As-Copilot Teleoperation System), a novel system that establishes bilateral, real-time joint synchronization between a robot arm and teleoperation hardware. This simple yet effective feedback mechanism, akin to a steering wheel in autonomous vehicles, enables the human copilot to intervene seamlessly while collecting action-correction data for future learning. Implemented using 3D-printed components and low-cost, off-the-shelf motors, HACTS is both accessible and scalable. Our experiments show that HACTS significantly enhances performance in imitation learning (IL) and reinforcement learning (RL) tasks, boosting IL recovery capabilities and data efficiency, and facilitating human-in-the-loop RL. HACTS paves the way for more effective and interactive human-robot collaboration and data-collection, advancing the capabilities of robot manipulation.
TransMamba: Flexibly Switching between Transformer and Mamba
Authors: Yixing Li, Ruobing Xie, Zhen Yang, Xingwu Sun, Shuaipeng Li, Weidong Han, Zhanhui Kang, Yu Cheng, Chengzhong Xu, Di Wang, Jie Jiang
Date: 2025-03-31 00:00:00
Tasks: Mamba, Scheduling

Transformers are the cornerstone of modern large language models, but their quadratic computational complexity limits efficiency in long-sequence processing. Recent advancements in Mamba, a state space model (SSM) with linear complexity, offer promising efficiency gains but suffer from unstable contextual learning and multitask generalization. This paper proposes TransMamba, a novel framework that unifies Transformer and Mamba through shared parameter matrices (e.g., QKV and CBx), and thus could dynamically switch between attention and SSM mechanisms at different token lengths and layers. We design the Memory converter to bridge Transformer and Mamba by converting attention outputs into SSM-compatible states, ensuring seamless information flow at TransPoints where the transformation happens. The TransPoint scheduling is also thoroughly explored for further improvements. We conducted extensive experiments demonstrating that TransMamba achieves superior training efficiency and performance compared to baselines, and validated the deeper consistency between Transformer and Mamba paradigms, offering a scalable solution for next-generation sequence modeling.
Accelerated Airfoil Design Using Neural Network Approaches
Authors: Anantram Patel, Nikhil Mogre, Mandar Mane, Jayavardhan Reddy Enumula, Vijay Kumar Sutrakar
Date: 2025-03-31 00:00:00

In this paper, prediction of airfoil shape from targeted pressure distribution (suction and pressure sides) and vice versa is demonstrated using both Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) techniques. The dataset is generated for 1600 airfoil shapes, with simulations carried out at Reynolds numbers (Re) ranging from 10,000 and 90,00,000 and angles of attack (AoA) ranging from 0 to 15 degrees, ensuring the dataset captured diverse aerodynamic conditions. Five different CNN and DNN models are developed depending on the input/output parameters. Results demonstrate that the refined models exhibit improved efficiency, with the DNN model achieving a multi-fold reduction in training time compared to the CNN model for complex datasets consisting of varying airfoil, Re, and AoA. The predicted airfoil shapes/pressure distribution closely match the targeted values, validating the effectiveness of deep learning frameworks. However, the performance of CNN models is found to be better compared to DNN models. Lastly, a flying wing aircraft model of wingspan >10 m is considered for the prediction of pressure distribution along the chordwise. The proposed CNN and DNN models show promising results. This research underscores the potential of deep learning models accelerating aerodynamic optimization and advancing the design of high-performance airfoils.
Frequency-Aware Attention-LSTM for PM$_{2.5}$ Time Series Forecasting
Authors: Jiahui Lu, Shuang Wu, Zhenkai Qin, Dongze Wu, Guifang Yang
Date: 2025-03-31 00:00:00
Tasks: Air Pollution Prediction, Decision Making, Time Series, Time Series Forecasting

To enhance the accuracy and robustness of PM$_{2.5}$ concentration forecasting, this paper introduces FALNet, a Frequency-Aware LSTM Network that integrates frequency-domain decomposition, temporal modeling, and attention-based refinement. The model first applies STL and FFT to extract trend, seasonal, and denoised residual components, effectively filtering out high-frequency noise. The filtered residuals are then fed into a stacked LSTM to capture long-term dependencies, followed by a multi-head attention mechanism that dynamically focuses on key time steps. Experiments conducted on real-world urban air quality datasets demonstrate that FALNet consistently outperforms conventional models across standard metrics such as MAE, RMSE, and $R^2$. The model shows strong adaptability in capturing sharp fluctuations during pollution peaks and non-stationary conditions. These results validate the effectiveness and generalizability of FALNet for real-time air pollution prediction, environmental risk assessment, and decision-making support.
AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting
Authors: Keshav Das, Julie Keisler, Margaux Brégère, Amaury Durand
Date: 2025-03-31 00:00:00
Tasks: Additive models, AutoML, Demand Forecasting, Model Selection, Neural Architecture Search

Electricity demand forecasting is key to ensuring that supply meets demand lest the grid would blackout. Reliable short-term forecasts may be obtained by combining a Generalized Additive Models (GAM) with a State-Space model (Obst et al., 2021), leading to an adaptive (or online) model. A GAM is an over-parameterized linear model defined by a formula and a state-space model involves hyperparameters. Both the formula and adaptation parameters have to be fixed before model training and have a huge impact on the model's predictive performance. We propose optimizing them using the DRAGON package of Keisler (2025), originally designed for neural architecture search. This work generalizes it for automated online generalized additive model selection by defining an efficient modeling of the search space (namely, the space of the GAM formulae and adaptation parameters). Its application to short-term French electricity demand forecasting demonstrates the relevance of the approach
Tree-Guided $L_1$-Convex Clustering
Authors: Bingyuan Zhang, Yoshikazu Terada
Date: 2025-03-31 00:00:00
Tasks: Clustering, Computational Efficiency, Distributed Computing

Convex clustering is a modern clustering framework that guarantees globally optimal solutions and performs comparably to other advanced clustering methods. However, obtaining a complete dendrogram (clusterpath) for large-scale datasets remains computationally challenging due to the extensive costs associated with iterative optimization approaches. To address this limitation, we develop a novel convex clustering algorithm called Tree-Guided $L_1$-Convex Clustering (TGCC). We first focus on the fact that the loss function of $L_1$-convex clustering with tree-structured weights can be efficiently optimized using a dynamic programming approach. We then develop an efficient cluster fusion algorithm that utilizes the tree structure of the weights to accelerate the optimization process and eliminate the issue of cluster splits commonly observed in convex clustering. By combining the dynamic programming approach with the cluster fusion algorithm, the TGCC algorithm achieves superior computational efficiency without sacrificing clustering performance. Remarkably, our TGCC algorithm can construct a complete clusterpath for $10^6$ points in $\mathbb{R}^2$ within 15 seconds on a standard laptop without the need for parallel or distributed computing frameworks. Moreover, we extend the TGCC algorithm to develop biclustering and sparse convex clustering algorithms.
Federated Structured Sparse PCA for Anomaly Detection in IoT Networks
Authors: Chenyi Huang, Xinrong Li, Xianchao Xiu
Date: 2025-03-31 00:00:00
Tasks: Anomaly Detection, Federated Learning, Privacy Preserving

Although federated learning has gained prominence as a privacy-preserving framework tailored for distributed Internet of Things (IoT) environments, current federated principal component analysis (PCA) methods lack integration of sparsity, a critical feature for robust anomaly detection. To address this limitation, we propose a novel federated structured sparse PCA (FedSSP) approach for anomaly detection in IoT networks. The proposed model uniquely integrates double sparsity regularization: (1) row-wise sparsity governed by $\ell_{2,p}$-norm with $p\in[0,1)$ to eliminate redundant feature dimensions, and (2) element-wise sparsity via $\ell_{q}$-norm with $q\in[0,1)$ to suppress noise-sensitive components. To efficiently solve this non-convex optimization problem in a distributed setting, we devise a proximal alternating minimization (PAM) algorithm with rigorous theoretical proofs establishing its convergence guarantees. Experiments on real datasets validate that incorporating structured sparsity enhances both model interpretability and detection accuracy.
The more the merrier: logical and multistage processors in credit scoring
Authors: Arturo Pérez-Peralta, Sandra Benítez-Peña, Rosa E. Lillo
Date: 2025-03-31 00:00:00
Tasks: Decision Making, Fairness

Machine Learning algorithms are ubiquitous in key decision-making contexts such as organizational justice or healthcare, which has spawned a great demand for fairness in these procedures. In this paper we focus on the application of fair ML in finance, more concretely on the use of fairness techniques on credit scoring. This paper makes two contributions. On the one hand, it addresses the existent gap concerning the application of established methods in the literature to the case of multiple sensitive variables through the use of a new technique called logical processors (LP). On the other hand, it also explores the novel method of multistage processors (MP) to investigate whether the combination of fairness methods can work synergistically to produce solutions with improved fairness or accuracy. Furthermore, we examine the intersection of these two lines of research by exploring the integration of fairness methods in the multivariate case. The results are very promising and suggest that logical processors are an appropriate way of handling multiple sensitive variables. Furthermore, multistage processors are capable of improving the performance of existing methods.
Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems
Authors: Yohei Hamakawa, Tomoya Kashimata, Masaya Yamasaki, Kosuke Tatsumura
Date: 2025-03-31 00:00:00
Tasks: Combinatorial Optimization, Scheduling

Quantum or quantum-inspired Ising machines have recently shown promise in solving combinatorial optimization problems in a short time. Real-world applications, such as time division multiple access (TDMA) scheduling for wireless multi-hop networks and financial trading, require solving those problems sequentially where the size and characteristics change dynamically. However, using Ising machines involves challenges to shorten system-wide latency due to the transfer of large Ising model or the cloud access and to determine the parameters for each problem. Here we show a combinatorial optimization method using embedded Ising machines, which enables solving diverse problems at high speed without runtime parameter tuning. We customize the algorithm and circuit architecture of the simulated bifurcation-based Ising machine to compress the Ising model and accelerate computation and then built a machine learning model to estimate appropriate parameters using extensive training data. In TDMA scheduling for wireless multi-hop networks, our demonstration has shown that the sophisticated system can adapt to changes in the problem and showed that it has a speed advantage over conventional methods.
PharMolixFM: All-Atom Foundation Models for Molecular Modeling and Generation
Authors: Yizhen Luo, Jiashuo Wang, Siqi Fan, Zaiqing Nie
Date: 2025-03-12 00:00:00
Tasks: All, Denoising

Structural biology relies on accurate three-dimensional biomolecular structures to advance our understanding of biological functions, disease mechanisms, and therapeutics. While recent advances in deep learning have enabled the development of all-atom foundation models for molecular modeling and generation, existing approaches face challenges in generalization due to the multi-modal nature of atomic data and the lack of comprehensive analysis of training and sampling strategies. To address these limitations, we propose PharMolixFM, a unified framework for constructing all-atom foundation models based on multi-modal generative techniques. Our framework includes three variants using state-of-the-art multi-modal generative models. By formulating molecular tasks as a generalized denoising process with task-specific priors, PharMolixFM achieves robust performance across various structural biology applications. Experimental results demonstrate that PharMolixFM-Diff achieves competitive prediction accuracy in protein-small-molecule docking (83.9% vs. 90.2% RMSD < 2{\AA}, given pocket) with significantly improved inference speed. Moreover, we explore the empirical inference scaling law by introducing more sampling repeats or steps. Our code and model are available at https://github.com/PharMolix/OpenBioMed.
Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip Statistics
Authors: Sebastian Springer, Andre Scaffidi, Maximilian Autenrieth, Gabriella Contardo, Alessandro Laio, Roberto Trotta, Heikki Haario
Date: 2025-03-31 00:00:00
Tasks: Anomaly Detection, Computational Efficiency, scientific discovery

Detecting localized density differences in multivariate data is a crucial task in computational science. Such anomalies can indicate a critical system failure, lead to a groundbreaking scientific discovery, or reveal unexpected changes in data distribution. We introduce EagleEye, an anomaly detection method to compare two multivariate datasets with the aim of identifying local density anomalies, namely over- or under-densities affecting only localised regions of the feature space. Anomalies are detected by modelling, for each point, the ordered sequence of its neighbours' membership label as a coin-flipping process and monitoring deviations from the expected behaviour of such process. A unique advantage of our method is its ability to provide an accurate, entirely unsupervised estimate of the local signal purity. We demonstrate its effectiveness through experiments on both synthetic and real-world datasets. In synthetic data, EagleEye accurately detects anomalies in multiple dimensions even when they affect a tiny fraction of the data. When applied to a challenging resonant anomaly detection benchmark task in simulated Large Hadron Collider data, EagleEye successfully identifies particle decay events present in just 0.3% of the dataset. In global temperature data, EagleEye uncovers previously unidentified, geographically localised changes in temperature fields that occurred in the most recent years. Thanks to its key advantages of conceptual simplicity, computational efficiency, trivial parallelisation, and scalability, EagleEye is widely applicable across many fields.
Model Hemorrhage and the Robustness Limits of Large Language Models
Authors: Ziyang Ma, Zuchao Li, Lefei Zhang, Gui-Song Xia, Bo Du, Liangpei Zhang, DaCheng Tao
Date: 2025-03-31 00:00:00
Tasks: Quantization

Large language models (LLMs) demonstrate strong performance across natural language processing tasks, yet undergo significant performance degradation when modified for deployment through quantization, pruning, or decoding strategy adjustments. We define this phenomenon as model hemorrhage - performance decline caused by parameter alterations and architectural changes. Through systematic analysis of various LLM frameworks, we identify key vulnerability patterns: layer expansion frequently disrupts attention mechanisms, compression techniques induce information loss cascades, and decoding adjustments amplify prediction divergences. Our investigation reveals transformer architectures exhibit inherent robustness thresholds that determine hemorrhage severity across modification types. We propose three mitigation strategies: gradient-aware pruning preserves critical weight pathways, dynamic quantization scaling maintains activation integrity, and decoding calibration aligns generation trajectories with original model distributions. This work establishes foundational metrics for evaluating model stability during adaptation, providing practical guidelines for maintaining performance while enabling efficient LLM deployment. Our findings advance understanding of neural network resilience under architectural transformations, particularly for large-scale language models.
Certified Approximate Reachability (CARe): Formal Error Bounds on Deep Learning of Reachable Sets
Authors: Prashant Solanki, Nikolaus Vertovec, Yannik Schnitzer, Jasper van Beers, Coen de Visser, Alessandro Abate
Date: 2025-03-31 00:00:00

Recent approaches to leveraging deep learning for computing reachable sets of continuous-time dynamical systems have gained popularity over traditional level-set methods, as they overcome the curse of dimensionality. However, as with level-set methods, considerable care needs to be taken in limiting approximation errors, particularly since no guarantees are provided during training on the accuracy of the learned reachable set. To address this limitation, we introduce an epsilon-approximate Hamilton-Jacobi Partial Differential Equation (HJ-PDE), which establishes a relationship between training loss and accuracy of the true reachable set. To formally certify this approximation, we leverage Satisfiability Modulo Theories (SMT) solvers to bound the residual error of the HJ-based loss function across the domain of interest. Leveraging Counter Example Guided Inductive Synthesis (CEGIS), we close the loop around learning and verification, by fine-tuning the neural network on counterexamples found by the SMT solver, thus improving the accuracy of the learned reachable set. To the best of our knowledge, Certified Approximate Reachability (CARe) is the first approach to provide soundness guarantees on learned reachable sets of continuous dynamical systems.
RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection
Authors: Tomomasa Yamasaki, Zhehui Wang, Tao Luo, Niangjun Chen, Bo wang
Date: 2025-03-26 00:00:00
Tasks: Image Classification, Neural Architecture Search, Semantic Segmentation

Neural Architecture Search (NAS) is an automated technique to design optimal neural network architectures for a specific workload. Conventionally, evaluating candidate networks in NAS involves extensive training, which requires significant time and computational resources. To address this, training-free NAS has been proposed to expedite network evaluation with minimal search time. However, state-of-the-art training-free NAS algorithms struggle to precisely distinguish well-performing networks from poorly-performing networks, resulting in inaccurate performance predictions and consequently sub-optimal top-1 network accuracy. Moreover, they are less effective in activation function exploration. To tackle the challenges, this paper proposes RBFleX-NAS, a novel training-free NAS framework that accounts for both activation outputs and input features of the last layer with a Radial Basis Function (RBF) kernel. We also present a detection algorithm to identify optimal hyperparameters using the obtained activation outputs and input feature maps. We verify the efficacy of RBFleX-NAS over a variety of NAS benchmarks. RBFleX-NAS significantly outperforms state-of-the-art training-free NAS methods in terms of top-1 accuracy, achieving this with short search time in NAS-Bench-201 and NAS-Bench-SSS. In addition, it demonstrates higher Kendall correlation compared to layer-based training-free NAS algorithms. Furthermore, we propose NAFBee, a new activation design space that extends the activation type to encompass various commonly used functions. In this extended design space, RBFleX-NAS demonstrates its superiority by accurately identifying the best-performing network during activation function search, providing a significant advantage over other NAS algorithms.
Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions
Authors: Fabiola Ricci, Lorenzo Bardone, Sebastian Goldt
Date: 2025-03-31 00:00:00

Deep neural networks learn structured features from complex, non-Gaussian inputs, but the mechanisms behind this process remain poorly understood. Our work is motivated by the observation that the first-layer filters learnt by deep convolutional neural networks from natural images resemble those learnt by independent component analysis (ICA), a simple unsupervised method that seeks the most non-Gaussian projections of its inputs. This similarity suggests that ICA provides a simple, yet principled model for studying feature learning. Here, we leverage this connection to investigate the interplay between data structure and optimisation in feature learning for the most popular ICA algorithm, FastICA, and stochastic gradient descent (SGD), which is used to train deep networks. We rigorously establish that FastICA requires at least $n\gtrsim d^4$ samples to recover a single non-Gaussian direction from $d$-dimensional inputs on a simple synthetic data model. We show that vanilla online SGD outperforms FastICA, and prove that the optimal sample complexity $n \gtrsim d^2$ can be reached by smoothing the loss, albeit in a data-dependent way. We finally demonstrate the existence of a search phase for FastICA on ImageNet, and discuss how the strong non-Gaussianity of said images compensates for the poor sample complexity of FastICA.