pwc-archive/papers-with-abstracts huggingface dataset:
Multi-agent Multi-armed Bandits with Minimum Reward Guarantee Fairness
Authors: Piyushi Manupriya, Himanshu, SakethaNath Jagarlapudi, Ganesh Ghalme
Date: 2025-02-21 00:00:00
Tasks: Fairness, Multi-Armed Bandits

We investigate the problem of maximizing social welfare while ensuring fairness in a multi-agent multi-armed bandit (MA-MAB) setting. In this problem, a centralized decision-maker takes actions over time, generating random rewards for various agents. Our goal is to maximize the sum of expected cumulative rewards, a.k.a. social welfare, while ensuring that each agent receives an expected reward that is at least a constant fraction of the maximum possible expected reward. Our proposed algorithm, RewardFairUCB, leverages the Upper Confidence Bound (UCB) technique to achieve sublinear regret bounds for both fairness and social welfare. The fairness regret measures the positive difference between the minimum reward guarantee and the expected reward of a given policy, whereas the social welfare regret measures the difference between the social welfare of the optimal fair policy and that of the given policy. We show that RewardFairUCB algorithm achieves instance-independent social welfare regret guarantees of $\tilde{O}(T^{1/2})$ and a fairness regret upper bound of $\tilde{O}(T^{3/4})$. We also give the lower bound of $\Omega(\sqrt{T})$ for both social welfare and fairness regret. We evaluate RewardFairUCB's performance against various baseline and heuristic algorithms using simulated data and real world data, highlighting trade-offs between fairness and social welfare regrets.
Discrete implementations of sliding-mode controllers with barrier-function adaptations require a revised framework
Authors: Luis Ovalle, Andrés González, Leonid Fridman, Hernan Haimovich
Date: 2025-02-21 00:00:00
Tasks: Relation

Challenges in the discrete implementation of sliding-mode controllers (SMC) with barrier-function-based adaptations are analyzed, revealing fundamental limitations in conventional design frameworks. It is shown that under uniform sampling, the original continuous-time problem motivating these controllers becomes theoretically unsolvable under standard assumptions. To address this incompatibility, a revised control framework is proposed, explicitly incorporating actuator capacity constraints and sampled-data dynamics. Within this structure, the behavior of barrier function-based adaptive controllers (BFASMC) is rigorously examined, explaining their empirical success in digital implementations. A key theoretical result establishes an explicit relation between the actuator capacity, the sampling rate, and the width of the barrier function, providing a principled means to tune these controllers for different application requirements. This relation enables the resolution of various design problems with direct practical implications. A modified BFASMC is then introduced, systematically leveraging sampling effects to ensure finite-time convergence to a positively invariant predefined set, a key advancement for guaranteeing predictable safety margins.
Reducing false positives in strong lens detection through effective augmentation and ensemble learning
Authors: Samira Rezaei, Amirmohammad Chegeni, Bharath Chowdhary Nagam, J. P. McKean, Mitra Baratchi, Koen Kuijken, Léon V. E. Koopmans
Date: 2025-02-20 00:00:00
Tasks: Data Augmentation, Diversity, Ensemble Learning

This research studies the impact of high-quality training datasets on the performance of Convolutional Neural Networks (CNNs) in detecting strong gravitational lenses. We stress the importance of data diversity and representativeness, demonstrating how variations in sample populations influence CNN performance. In addition to the quality of training data, our results highlight the effectiveness of various techniques, such as data augmentation and ensemble learning, in reducing false positives while maintaining model completeness at an acceptable level. This enhances the robustness of gravitational lens detection models and advancing capabilities in this field. Our experiments, employing variations of DenseNet and EfficientNet, achieved a best false positive rate (FP rate) of $10^{-4}$, while successfully identifying over 88 per cent of genuine gravitational lenses in the test dataset. This represents an 11-fold reduction in the FP rate compared to the original training dataset. Notably, this substantial enhancement in the FP rate is accompanied by only a 2.3 per cent decrease in the number of true positive samples. Validated on the KiDS dataset, our findings offer insights applicable to ongoing missions, like Euclid.
Slamming: Training a Speech Language Model on One GPU in a Day
Authors: Gallil Maimon, Avishai Elmakies, Yossi Adi
Date: 2025-02-19 00:00:00
Tasks: GPU, Language Modeling, Language Modelling

We introduce Slam, a recipe for training high-quality Speech Language Models (SLMs) on a single academic GPU in 24 hours. We do so through empirical analysis of model initialisation and architecture, synthetic training data, preference optimisation with synthetic data and tweaking all other components. We empirically demonstrate that this training recipe also scales well with more compute getting results on par with leading SLMs in a fraction of the compute cost. We hope these insights will make SLM training and research more accessible. In the context of SLM scaling laws, our results far outperform predicted compute optimal performance, giving an optimistic view to SLM feasibility. See code, data, models, samples at - https://pages.cs.huji.ac.il/adiyoss-lab/slamming .
Comparative Analysis of Black Hole Mass Estimation in Type-2 AGNs: Classical vs. Quantum Machine Learning and Deep Learning Approaches
Authors: Sathwik Narkedimilli, Venkata Sriram Amballa, N V Saran Kumar, R Arun Kumar, R Praneeth Reddy, Satvik Raghav, Manish M, Aswath Babu H
Date: 2025-02-21 00:00:00
Tasks: Quantum Machine Learning, regression

In the case of Type-2 AGNs, estimating the mass of the black hole is challenging. Understanding how galaxies form and evolve requires considerable insight into the mass of black holes. This work compared different classical and quantum machine learning (QML) algorithms for black hole mass estimation, wherein the classical algorithms are Linear Regression, XGBoost Regression, Random Forest Regressor, Support Vector Regressor (SVR), Lasso Regression, Ridge Regression, Elastic Net Regression, Bayesian Regression, Decision Tree Regressor, Gradient Booster Regressor, Classical Neural Networks, Gated Recurrent Unit (GRU), LSTM, Deep Residual Networks (ResNets) and Transformer-Based Regression. On the other hand, quantum algorithms including Hybrid Quantum Neural Networks (QNN), Quantum Long Short-Term Memory (Q-LSTM), Sampler-QNN, Estimator-QNN, Variational Quantum Regressor (VQR), Quantum Linear Regression(Q-LR), QML with JAX optimization were also tested. The results revealed that classical algorithms gave better R^2, MAE, MSE, and RMSE results than the quantum models. Among the classical models, LSTM has the best result with an accuracy of 99.77%. Estimator-QNN has the highest accuracy for quantum algorithms with an MSE of 0.0124 and an accuracy of 99.75%. This study ascertains both the strengths and weaknesses of the classical and the quantum approaches. As far as our knowledge goes, this work could pave the way for the future application of quantum algorithms in astrophysical data analysis.
An Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions
Authors: Huseyin Cavus, Jason T. L. Wang, Teja P. S. Singampalli, Gani Caglar Coban, Hongyang Zhang, Abd-ur Raheem, Haimin Wang
Date: 2025-02-20 00:00:00
Tasks: Binary Classification, Decision Making, Interpretable Machine Learning

Solar flares are defined as outbursts on the surface of the Sun. They occur when energy accumulated in magnetic fields enclosing solar active regions (ARs) is abruptly expelled. Solar flares and associated coronal mass ejections are sources of space weather that adversely impact devices at or near Earth, including the obstruction of high-frequency radio waves utilized for communication and the deterioration of power grid operations. Tracking and delivering early and precise predictions of solar flares is essential for readiness and catastrophe risk mitigation. This paper employs the random forest (RF) model to address the binary classification task, analyzing the links between solar flares and their originating ARs with observational data gathered from 2011 to 2021 by SolarMonitor.org and the XRT flare database. We seek to identify the physical features of a source AR that significantly influence its potential to trigger >=C-class flares. We found that the features of AR_Type_Today, Hale_Class_Yesterday are the most and the least prepotent features, respectively. NoS_Difference has a remarkable effect in decision-making in both global and local interpretations.
State-space kinetic Ising model reveals task-dependent entropy flow in sparsely active nonequilibrium neuronal dynamics
Authors: Ken Ishihara, Hideaki Shimazaki
Date: 2025-02-21 00:00:00

Neuronal ensemble activity, including coordinated and oscillatory patterns, exhibits hallmarks of nonequilibrium systems with time-asymmetric trajectories to maintain their organization. However, assessing time asymmetry from neuronal spiking activity remains challenging. The kinetic Ising model provides a framework for studying the causal, nonequilibrium dynamics in spiking recurrent neural networks. Recent theoretical advances in this model have enabled time-asymmetry estimation from large-scale steady-state data. Yet, neuronal activity often exhibits time-varying firing rates and coupling strengths, violating steady-state assumption. To overcome these limitations, we developed a state-space kinetic Ising model that accounts for non-stationary and nonequilibrium properties of neural systems. This approach incorporates a mean-field method for estimating time-varying entropy flow, a key measure for maintaining the system's organization by dissipation. Applying this method to mouse visual cortex data revealed greater variability in causal couplings during task engagement despite the reduced neuronal activity with increased sparsity. Moreover, higher-performing mice exhibited increased entropy flow in higher-firing neurons during task engagement, suggesting that stronger directed activity emerged in a fewer neurons within sparsely active populations. These findings underscore the model's utility in uncovering intricate asymmetric causal dynamics in neuronal ensembles and linking them to behavior through the thermodynamic underpinnings of neural computation.
Learning Chern Numbers of Topological Insulators with Gauge Equivariant Neural Networks
Authors: Longde Huang, Oleksandr Balabanov, Hampus Linander, Mats Granath, Daniel Persson, Jan E. Gerken
Date: 2025-02-21 00:00:00

Equivariant network architectures are a well-established tool for predicting invariant or equivariant quantities. However, almost all learning problems considered in this context feature a global symmetry, i.e. each point of the underlying space is transformed with the same group element, as opposed to a local ``gauge'' symmetry, where each point is transformed with a different group element, exponentially enlarging the size of the symmetry group. Gauge equivariant networks have so far mainly been applied to problems in quantum chromodynamics. Here, we introduce a novel application domain for gauge-equivariant networks in the theory of topological condensed matter physics. We use gauge equivariant networks to predict topological invariants (Chern numbers) of multiband topological insulators. The gauge symmetry of the network guarantees that the predicted quantity is a topological invariant. We introduce a novel gauge equivariant normalization layer to stabilize the training and prove a universal approximation theorem for our setup. We train on samples with trivial Chern number only but show that our models generalize to samples with non-trivial Chern number. We provide various ablations of our setup. Our code is available at https://github.com/sitronsea/GENet/tree/main.
MoMa: A Modular Deep Learning Framework for Material Property Prediction
Authors: Botian Wang, Yawen Ouyang, Yaohui Li, Yiqun Wang, Haorui Cui, Jianbing Zhang, Xiaonan Wang, Wei-Ying Ma, Hao Zhou
Date: 2025-02-21 00:00:00
Tasks: Continual Learning, Deep Learning, Diversity, Property Prediction

Deep learning methods for material property prediction have been widely explored to advance materials discovery. However, the prevailing pre-train then fine-tune paradigm often fails to address the inherent diversity and disparity of material tasks. To overcome these challenges, we introduce MoMa, a Modular framework for Materials that first trains specialized modules across a wide range of tasks and then adaptively composes synergistic modules tailored to each downstream scenario. Evaluation across 17 datasets demonstrates the superiority of MoMa, with a substantial 14% average improvement over the strongest baseline. Few-shot and continual learning experiments further highlight MoMa's potential for real-world applications. Pioneering a new paradigm of modular material learning, MoMa will be open-sourced to foster broader community collaboration.
Universal Semantic Embeddings of Chemical Elements for Enhanced Materials Inference and Discovery
Authors: Yunze Jia, Yuehui Xian, Yangyang Xu, Pengfei Dang, Xiangdong Ding, Jun Sun, Yumei Zhou, Dezhen Xue
Date: 2025-02-19 00:00:00
Tasks: Bayesian Optimization

We present a framework for generating universal semantic embeddings of chemical elements to advance materials inference and discovery. This framework leverages ElementBERT, a domain-specific BERT-based natural language processing model trained on 1.29 million abstracts of alloy-related scientific papers, to capture latent knowledge and contextual relationships specific to alloys. These semantic embeddings serve as robust elemental descriptors, consistently outperforming traditional empirical descriptors with significant improvements across multiple downstream tasks. These include predicting mechanical and transformation properties, classifying phase structures, and optimizing materials properties via Bayesian optimization. Applications to titanium alloys, high-entropy alloys, and shape memory alloys demonstrate up to 23% gains in prediction accuracy. Our results show that ElementBERT surpasses general-purpose BERT variants by encoding specialized alloy knowledge. By bridging contextual insights from scientific literature with quantitative inference, our framework accelerates the discovery and optimization of advanced materials, with potential applications extending beyond alloys to other material classes.
Towards an automated workflow in materials science for combining multi-modal simulative and experimental information using data mining and large language models
Authors: Balduin Katzer, Steffen Klinder, Katrin Schulz
Date: 2025-02-18 00:00:00
Tasks: Information Retrieval, Large Language Model, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

To retrieve and compare scientific data of simulations and experiments in materials science, data needs to be easily accessible and machine readable to qualify and quantify various materials science phenomena. The recent progress in open science leverages the accessibility to data. However, a majority of information is encoded within scientific documents limiting the capability of finding suitable literature as well as material properties. This manuscript showcases an automated workflow, which unravels the encoded information from scientific literature to a machine readable data structure of texts, figures, tables, equations and meta-data, using natural language processing and language as well as vision transformer models to generate a machine-readable database. The machine-readable database can be enriched with local data, as e.g. unpublished or private material data, leading to knowledge synthesis. The study shows that such an automated workflow accelerates information retrieval, proximate context detection and material property extraction from multi-modal input data exemplarily shown for the research field of microstructural analyses of face-centered cubic single crystals. Ultimately, a Retrieval-Augmented Generation (RAG) based Large Language Model (LLM) enables a fast and efficient question answering chat bot.
Continual Person Identification using Footstep-Induced Floor Vibrations on Heterogeneous Floor Structures
Authors: Yiwen Dong, Hae Young Noh
Date: 2025-02-21 00:00:00
Tasks: Person Identification

Person identification is important for smart buildings to provide personalized services such as health monitoring, activity tracking, and personnel management. However, previous person identification relies on pre-collected data from everyone, which is impractical in many buildings and public facilities in which visitors are typically expected. This calls for a continual person identification system that gradually learns people's identities on the fly. Existing studies use cameras to achieve this goal, but they require direct line-of-sight and also have raised privacy concerns in public. Other modalities such as wearables and pressure mats are limited by the requirement of device-carrying or dense deployment. Thus, prior studies introduced footstep-induced structural vibration sensing, which is non-intrusive and perceived as more privacy-friendly. However, this approach has a significant challenge: the high variability of vibration data due to structural heterogeneity and human gait variations, which makes online person identification algorithms perform poorly. In this paper, we characterize the variability in footstep-induced structural vibration data for accurate online person identification. To achieve this, we quantify and decompose different sources of variability and then design a feature transformation function to reduce the variability within each person's data to make different people's data more separable. We evaluate our approach through field experiments with 20 people. The results show a 70% variability reduction and a 90% accuracy for online person identification.
Key Body Posture Characteristics of Short-distance Speed Skaters at the Start Based on Artificial Intelligence
Authors: Zhang Xueliana, Fang Yingjieb, Liu Hang
Date: 2025-02-21 00:00:00

Objective To conduct biomechanical analysis on the starting technique of male short-distance speed skating athletes in China and determine the key factors affecting the effectiveness of the starting movement. Methods 13 high-level male short-distance speed skating athletes were selected as the test subjects, and kinematic data were collected using an artificial intelligence video capture and analysis system. The body posture features and their effects on the starting movement performance were analyzed in the three stages of starting preparation, starting, and sprinting. Results The post-stability angle, anterior knee angle of the front leg, posterior knee angle of the rear leg, and stride length showed moderate to high positive correlations with the starting speed during the starting preparation stage. The trunk angle showed a high negative correlation with the starting speed. The trunk angle (TO4, TD4, TO6, TD6), hip angle (TO1, TO4, TO6), and knee angle (TD1) showed moderate to high negative correlations with the effectiveness of the starting movement during the starting and sprinting stages. The knee angle (TD2), ice-contact angle (TD2, TD4, TD5, TD6), and propulsion angle (TO1, TO4, TO7) showed moderate positive correlations with the effectiveness of the starting movement. Conclusion Stride length, left knee angle, and post-stability angle are the key factors affecting the starting speed. The larger the post-stability angle and left knee angle and the longer the stride length, the faster the starting speed. During the starting and sprinting stages, the smaller the ice-contact angle and propulsion angle, the greater the trunk angle and hip angle changes, the more effective the starting movement.
Super-Resolution for Interferometric Imaging: Model Comparisons and Performance Analysis
Authors: Hasan Berkay Abdioglu, Rana Gursoy, Yagmur Isik, Ibrahim Cem Balci, Taha Unal, Kerem Bayer, Mustafa Ismail Inal, Nehir Serin, Muhammed Furkan Kosar, Gokhan Bora Esmer, Huseyin Uvet
Date: 2025-02-21 00:00:00
Tasks: Super-Resolution

This study investigates the application of Super-Resolution techniques in holographic microscopy to enhance quantitative phase imaging. An off-axis Mach-Zehnder interferometric setup was employed to capture interferograms. The study evaluates two Super-Resolution models, RCAN and Real-ESRGAN, for their effectiveness in reconstructing high-resolution interferograms from a microparticle-based dataset. The models were assessed using two primary approaches: image-based analysis for structural detail enhancement and morphological evaluation for maintaining sample integrity and phase map accuracy. The results demonstrate that RCAN achieves superior numerical precision, making it ideal for applications requiring highly accurate phase map reconstruction, while Real-ESRGAN enhances visual quality and structural coherence, making it suitable for visualization-focused applications. This study highlights the potential of Super-Resolution models in overcoming diffraction-imposed resolution limitations in holographic microscopy, opening the way for improved imaging techniques in biomedical diagnostics, materials science, and other high-precision fields.
Forecasting Local Ionospheric Parameters Using Transformers
Authors: Daniel J. Alford-Lago, Christopher W. Curtis, Alexander T. Ihler, Katherine A. Zawdie, Douglas P. Drob
Date: 2025-02-20 00:00:00
Tasks: Uncertainty Quantification

We present a novel method for forecasting key ionospheric parameters using transformer-based neural networks. The model provides accurate forecasts and uncertainty quantification of the F2-layer peak plasma frequency (foF2), the F2-layer peak density height (hmF2), and total electron content (TEC) for a given geographic location. It supports a number of exogenous variables, including F10.7cm solar flux and disturbance storm time (Dst). We demonstrate how transformers can be trained in a data assimilation-like fashion that use these exogenous variables along with na\"ive predictions from climatology to generate 24-hour forecasts with non-parametric uncertainty bounds. We call this method the Local Ionospheric Forecast Transformer (LIFT). We demonstrate that the trained model can generalize to new geographic locations and time periods not seen during training, and we compare its performance to that of the International Reference Ionosphere (IRI).