Tootfinder

This https://arxiv.org/abs/2505.13182 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…

Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection
Gabriele Digregorio, Elisabetta Cainazzo, Stefano Longari, Michele Carminati, Stefano Zanero
https://arxiv.org/abs/2506.04978

Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection
The challenges derived from the data-intensive nature of machine learning in conjunction with technologies that enable novel paradigms such as V2X and the potential offered by 5G communication, allow and justify the deployment of Federated Learning (FL) solutions in the vehicular intrusion detection domain. In this paper, we investigate the effects of integrating FL strategies into the machine learning-based intrusion detection process for on-board vehicular networks. Accordingly, we propose a …

This https://arxiv.org/abs/2505.24765 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
Supervised Quantum Machine Learning (QML) represents an intersection of quantum computing and classical machine learning, aiming to use quantum resources to support model training and inference. This paper reviews recent developments in supervised QML, focusing on methods such as variational quantum circuits, quantum neural networks, and quantum kernel methods, along with hybrid quantum-classical workflows. We examine recent experimental studies that show partial indications of quantum advantag…

Frugal Machine Learning for Energy-efficient, and Resource-aware Artificial Intelligence
John Violos, Konstantina-Christina Diamanti, Ioannis Kompatsiaris, Symeon Papadopoulos
https://arxiv.org/abs/2506.01869

Frugal Machine Learning for Energy-efficient, and Resource-aware Artificial Intelligence
Frugal Machine Learning (FML) refers to the practice of designing Machine Learning (ML) models that are efficient, cost-effective, and mindful of resource constraints. This field aims to achieve acceptable performance while minimizing the use of computational resources, time, energy, and data for both training and inference. FML strategies can be broadly categorized into input frugality, learning process frugality, and model frugality, each focusing on reducing resource consumption at different…

The program of the 25th Swiss Transport Research Conference (#STRC25) is now online. There will be 3 papers/presentations by employees of the Swiss Federal Railways (SBB):
👉 Quantifying Future Mobility: Scenario-Based Analysis with Agent-Based Modeling
👉 Integrating Machine Learning and MATSim for High-Granularity Passenger Load Predictions at SBB
👉 Differentiation of Modal Preferen…

This https://arxiv.org/abs/2411.14608 has been replaced.
initial toot: https://mastoxiv.page/@a…

Toward machine learning interatomic potentials for modeling uranium mononitride
Uranium mononitride (UN) is a promising accident-tolerant fuel because of its high fissile density and high thermal conductivity. In this study, we developed the first machine learning interatomic potentials for reliable atomic-scale modeling of UN at finite temperatures. We constructed a training set using density functional theory (DFT) calculations that was enriched through an active learning procedure, and two neural network potentials were generated. Both potentials successfully reproduce …

This https://arxiv.org/abs/2505.04947 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

DFPL: Decentralized Federated Prototype Learning Across Heterogeneous Data Distributions
Federated learning is a distributed machine learning paradigm through centralized model aggregation. However, standard federated learning relies on a centralized server, making it vulnerable to server failures. While existing solutions utilize blockchain technology to implement Decentralized Federated Learning (DFL), the statistical heterogeneity of data distributions among clients severely degrades the performance of DFL. Driven by this issue, this paper proposes a decentralized federated prot…

This https://arxiv.org/abs/2312.09454 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review
Uncertainty Quantification (UQ) has gained traction in an attempt to improve the interpretability and robustness of machine learning predictions. Specifically (medical) biosignals such as electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), and electromyography (EMG) could benefit from good UQ, since these suffer from a poor signal-to-noise ratio, and good human interpretability is pivotal for medical applications. In this paper, we review the state of the art of a…

This https://arxiv.org/abs/2407.13213 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Leveraging Machine Learning for High-Dimensional Option Pricing within the Uncertain Volatility Model
This paper explores the application of Machine Learning techniques for pricing high-dimensional options within the framework of the Uncertain Volatility Model (UVM). The UVM is a robust framework that accounts for the inherent unpredictability of market volatility by setting upper and lower bounds on volatility and the correlation among underlying assets. By integrating advanced Machine Learning algorithms, we aim to enhance the accuracy and efficiency of option pricing under the UVM, especiall…

The Impact of Software Testing with Quantum Optimization Meets Machine Learning
Gopichand Bandarupalli
https://arxiv.org/abs/2506.02090 https://

The Impact of Software Testing with Quantum Optimization Meets Machine Learning
Modern software systems complexity challenges efficient testing, as traditional machine learning (ML) struggles with large test suites. This research presents a hybrid framework integrating Quantum Annealing with ML to optimize test case prioritization in CI/CD pipelines. Leveraging quantum optimization, it achieves a 25 percent increase in defect detection efficiency and a 30 percent reduction in test execution time versus classical ML, validated on the Defects4J dataset. A simulated CI/CD env…

Trustworthiness Preservation by Copies of Machine Learning Systems
Leonardo Ceragioli, Giuseppe Primiero
https://arxiv.org/abs/2506.05203 https://

Trustworthiness Preservation by Copies of Machine Learning Systems
A common practice of ML systems development concerns the training of the same model under different data sets, and the use of the same (training and test) sets for different learning models. The first case is a desirable practice for identifying high quality and unbiased training conditions. The latter case coincides with the search for optimal models under a common dataset for training. These differently obtained systems have been considered akin to copies. In the quest for responsible AI, a l…

This https://arxiv.org/abs/2505.21427 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Policy Induction: Predicting Startup Success via Explainable Memory-Augmented In-Context Learning
Early-stage startup investment is a high-risk endeavor characterized by scarce data and uncertain outcomes. Traditional machine learning approaches often require large, labeled datasets and extensive fine-tuning, yet remain opaque and difficult for domain experts to interpret or improve. In this paper, we propose a transparent and data-efficient investment decision framework powered by memory-augmented large language models (LLMs) using in-context learning (ICL). Central to our method is a natu…

Quantum Cognition Machine Learning for Forecasting Chromosomal Instability
Giuseppe Di Caro, Vahagn Kirakosyan, Alexander G. Abanov, Luca Candelori, Nadine Hartmann, Ernest T. Lam, Kharen Musaelian, Ryan Samson, Dario Villani, Martin T. Wells, Richard J. Wenstrup, Mengjia Xu
https://arxiv.org/abs/2506.03199

Quantum Cognition Machine Learning for Forecasting Chromosomal Instability
The accurate prediction of chromosomal instability from the morphology of circulating tumor cells (CTCs) enables real-time detection of CTCs with high metastatic potential in the context of liquid biopsy diagnostics. However, it presents a significant challenge due to the high dimensionality and complexity of single-cell digital pathology data. Here, we introduce the application of Quantum Cognition Machine Learning (QCML), a quantum-inspired computational framework, to estimate morphology-pred…

Estimating properties of a homogeneous bounded soil using machine learning models
Konstantinos Kalimeris, Leonidas Mindrinos, Nikolaos Pallikarakis
https://arxiv.org/abs/2506.04256

Estimating properties of a homogeneous bounded soil using machine learning models
This work focuses on estimating soil properties from water moisture measurements. We consider simulated data generated by solving the initial-boundary value problem governing vertical infiltration in a homogeneous, bounded soil profile, with the usage of the Fokas method. To address the parameter identification problem, which is formulated as a two-output regression task, we explore various machine learning models. The performance of each model is assessed under different data conditions: full,…

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning
Hasin Us Sami, Swapneel Sen, Amit K. Roy-Chowdhury, Srikanth V. Krishnamurthy, Basak Guler
https://arxiv.org/abs/2506.04453

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning
Federated learning (FL) allows multiple data-owners to collaboratively train machine learning models by exchanging local gradients, while keeping their private data on-device. To simultaneously enhance privacy and training efficiency, recently parameter-efficient fine-tuning (PEFT) of large-scale pretrained models has gained substantial attention in FL. While keeping a pretrained (backbone) model frozen, each user fine-tunes only a few lightweight modules to be used in conjunction, to fit speci…

I’m concerned the Overlay Community Group has found a way to normalize overlays by influencing the W3C draft “Accessibility of machine learning and generative AI”:
https://adrianroselli.com/2022/09/accessibility-at-the-edg…

‘Accessibility at the Edge’ W3C CG Is an Overlay Smoke Screen
Another post where I lay it all out in the title. What follows is why I am making this assertion (with a handy table of contents). Timeline 26 May 2022 at 10:58am ET 26 May 2022 at 7:56pm ET 27 May 2022 at 7:38pm ET 28 May 2022 at 5:04pm…

BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
Elmira Mirzabeigi, Rezvan Salehi, Kourosh Parand
https://arxiv.org/abs/2506.04354

BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
BridgeNet is a novel hybrid framework that integrates convolutional neural networks with physics-informed neural networks to efficiently solve non-linear, high-dimensional Fokker-Planck equations (FPEs). Traditional PINNs, which typically rely on fully connected architectures, often struggle to capture complex spatial hierarchies and enforce intricate boundary conditions. In contrast, BridgeNet leverages adaptive CNN layers for effective local feature extraction and incorporates a dynamically w…

This https://arxiv.org/abs/2407.20384 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Unified Deep Learning Framework for Many-Body Quantum Chemistry via Green's Functions
Quantum many-body methods provide a systematic route to computing electronic properties of molecules and materials, but high computational costs restrict their use in large-scale applications. Due to the complexity in many-electron wavefunctions, machine learning models capable of capturing fundamental many-body physics remain limited. Here, we present a deep learning framework targeting the many-body Green's function, which unifies predictions of electronic properties in ground and excited sta…

[2025-06-06 Fri (UTC), 7 new articles found for stat.ML Machine Learning]
#toXiv_bot_toot

AGNBoost: A Machine Learning Approach to AGN Identification with JWST/NIRCam MIRI Colors and Photometry
Kurt Hamblin, Allison Kirkpatrick, Bren E. Backhaus, Gregory Troiani, Fabio Pacucci, Jonathan R. Trump, Alexander de la Vega, L. Y. Aaron Yung, Jeyhan S. Kartaltepe, Dale D. Kocevski, Anton M. Koekemoer, Erini Lambrides, Casey Papovich, Kaila Ronayne, Guang Yang, Pablo Arrabal Haro, Micaela B. Bagley, Mark Dickinson, Steven L. Finkelstein, Nor Pirzkal

AGNBoost: A Machine Learning Approach to AGN Identification with JWST/NIRCam+MIRI Colors and Photometry
We present AGNBoost, a machine learning framework utilizing XGBoostLSS to identify AGN and estimate redshifts from JWST NIRCam and MIRI photometry. AGNBoost constructs 121 input features from 7 NIRCam and 4 MIRI bands-including magnitudes, colors, and squared color terms-to simultaneously predict the fraction of mid-IR $3-30\,μ$m emission attributable to an AGN power law (frac$_\text{AGN}$) and photometric redshift. Each model is trained on a sample of $10^6$ simulated galaxies from CIGALE pro…

This https://arxiv.org/abs/2409.17800 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey
Machine Learning (ML) models have gained popularity in medical imaging analysis given their expert level performance in many medical domains. To enhance the trustworthiness, acceptance, and regulatory compliance of medical imaging models and to facilitate their integration into clinical settings, we review and categorise methods for ensuring ML reliability, both during development and throughout the model's lifespan. Specifically, we provide an overview of methods assessing models' inner-workin…

This https://arxiv.org/abs/2501.12911 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning
Federated learning (FL) has come forward as a critical approach for privacy-preserving machine learning in healthcare, allowing collaborative model training across decentralized medical datasets without exchanging clients' data. However, current security implementations for these systems face a fundamental trade-off: rigorous cryptographic protections like fully homomorphic encryption (FHE) impose prohibitive computational overhead, while lightweight alternatives risk vulnerable data leakage th…

Nach Rücksprache mit den Buch-Herausgebern und dem Verlag darf ich meine jüngste (und ausführlichste) Publikation zu generativen Machine-Learning-Systemen (GMLS) vom Januar 2025 bereits jetzt online zur Verfügung stellen: https://zenodo.org/records/15042499

From Average-Iterate to Last-Iterate Convergence in Games: A Reduction and Its Applications
Yang Cai, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng
https://arxiv.org/abs/2506.03464

From Average-Iterate to Last-Iterate Convergence in Games: A Reduction and Its Applications
The convergence of online learning algorithms in games under self-play is a fundamental question in game theory and machine learning. Among various notions of convergence, last-iterate convergence is particularly desirable, as it reflects the actual decisions made by the learners and captures the day-to-day behavior of the learning dynamics. While many algorithms are known to converge in the average-iterate, achieving last-iterate convergence typically requires considerably more effort in both …

Enduring Disparities in the Workplace: A Pilot Study in the AI Community
Yunusa Simipa Abdulsalam, Siobhan Mackenzie Hall, Ana Quintero-Ossa, William Agnew, Carla Muntean, Sarah Tan, Ashley Heady, Savannah Thais, Jessica Schrouff
https://arxiv.org/abs/2506.04305

Enduring Disparities in the Workplace: A Pilot Study in the AI Community
In efforts toward achieving responsible artificial intelligence (AI), fostering a culture of workplace transparency, diversity, and inclusion can breed innovation, trust, and employee contentment. In AI and Machine Learning (ML), such environments correlate with higher standards of responsible development. Without transparency, disparities, microaggressions and misconduct will remain unaddressed, undermining the very structural inequities responsible AI aims to mitigate. While prior work invest…

This https://arxiv.org/abs/2312.16139 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Abnormal component analysis
At the crossway of machine learning and data analysis, anomaly detection aims at identifying observations that exhibit abnormal behaviour. Be it measurement errors, disease development, severe weather, production quality default(s) (items) or failed equipment, financial frauds or crisis events, their on-time identification and isolation constitute an important task in almost any area of industry and science. While a substantial body of literature is devoted to detection of anomalies, little att…

Unregularized limit of stochastic gradient method for Wasserstein distributionally robust optimization
Tam Le (LPSM, UPCit\'e)
https://arxiv.org/abs/2506.04948

Unregularized limit of stochastic gradient method for Wasserstein distributionally robust optimization
Distributionally robust optimization offers a compelling framework for model fitting in machine learning, as it systematically accounts for data uncertainty. Focusing on Wasserstein distributionally robust optimization, we investigate the regularized problem where entropic smoothing yields a sampling-based approximation of the original objective. We establish the convergence of the approximate gradient over a compact set, leading to the concentration of the regularized problem critical points o…

This https://arxiv.org/abs/2503.09977 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Quadratic Transform for Fractional Programming in Signal Processing and Machine Learning
Fractional programming (FP) is a branch of mathematical optimization that deals with the optimization of ratios. It is an invaluable tool for signal processing and machine learning, because many key metrics in these fields are fractionally structured, e.g., the signal-to-interference-plus-noise ratio (SINR) in wireless communications, the Cramér-Rao bound (CRB) in radar sensing, the normalized cut in graph clustering, and the margin in support vector machine (SVM). This article provides a comp…

Line shapes in time- and angle-resolved photoemission spectroscopy explored by machine learning
Tami C. Meyer, Gesa-R. Siemann, Paulina Majchrzak, Thomas Seyller, Jennifer Rigden, Yu Zhang, Emma Springate, Charlotte Sanders, Philip Hofmann
https://arxiv.org/abs/2506.02137

Line shapes in time- and angle-resolved photoemission spectroscopy explored by machine learning
Time- and angle-resolved photoemission spectroscopy is a powerful technique for investigating the dynamics of excited carriers in quantum materials. Typically, data analysis proceeds via the inspection of time distribution curves (TDCs), which represent the time-dependent photoemission intensity in a region of interest -- often chosen somewhat arbitrarily -- in energy-momentum space. Here, we employ $k$-means, an unsupervised machine learning technique, to systematically investigate trends in T…

This https://arxiv.org/abs/2505.17226 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation
Federated Learning (FL) enables collaborative machine learning across decentralized data sources without sharing raw data. It offers a promising approach to privacy-preserving AI. However, FL remains vulnerable to adversarial threats from malicious participants, referred to as Byzantine clients, who can send misleading updates to corrupt the global model. Traditional aggregation methods, such as simple averaging, are not robust to such attacks. More resilient approaches, like the Krum algorithm…

BVLSM: Write-Efficient LSM-Tree Storage via WAL-Time Key-Value Separation
Ming Li, Wendi Cheng, Jiahe Wei, Xueqiang Shan, Liu Weikai, Xiaonan Zhao, Xiao Zhang
https://arxiv.org/abs/2506.04678

BVLSM: Write-Efficient LSM-Tree Storage via WAL-Time Key-Value Separation
Modern data-intensive applications increasingly store and process big-value items, such as multimedia objects and machine learning embeddings, which exacerbate storage inefficiencies in Log-Structured Merge-Tree (LSM)-based key-value stores. This paper presents BVLSM, a Write-Ahead Log (WAL)-time key-value separation mechanism designed to address three key challenges in LSM-Tree storage systems: write amplification, poor memory utilization, and I/O jitter under big-value workloads. Unlike state…

AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software
Satoshi Tanaka, Samrat Thapa, Kok Seang Tan, Amadeusz Szymko, Lobos Kenzo, Koji Minoda, Shintaro Tomie, Kotaro Uetake, Guolong Zhang, Isamu Yamashita, Takamasa Horibe
https://arxiv.org/abs/2506.00645

AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software
In recent years, machine learning technologies have played an important role in robotics, particularly in the development of autonomous robots and self-driving vehicles. As the industry matures, robotics frameworks like ROS 2 have been developed and provides a broad range of applications from research to production. In this work, we introduce AWML, a framework designed to support MLOps for robotics. AWML provides a machine learning infrastructure for autonomous driving, supporting not only the …

Machine Learning for Consistency Violation Faults Analysis
Kamal Giri, Amit Garu
https://arxiv.org/abs/2506.02002 https://arxiv.org/p…

Machine Learning for Consistency Violation Faults Analysis
Distributed systems frequently encounter consistency violation faults (cvfs), where nodes operate on outdated or inaccurate data, adversely affecting convergence and overall system performance. This study presents a machine learning-based approach for analyzing the impact of CVFs, using Dijkstra's Token Ring problem as a case study. By computing program transition ranks and their corresponding effects, the proposed method quantifies the influence of cvfs on system behavior. To address the state…

Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
Srikanth Thudumu, Jason Fisher, Hung Du
https://arxiv.org/abs/2505.24765

Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
Supervised Quantum Machine Learning (QML) represents an intersection of quantum computing and classical machine learning, aiming to use quantum resources to support model training and inference. This paper reviews recent developments in supervised QML, focusing on methods such as variational quantum circuits, quantum neural networks, and quantum kernel methods, along with hybrid quantum-classical workflows. We examine recent experimental studies that show partial indications of quantum advantag…

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Tinghuan Li, Shuheng Chen, Junyi Fan, Elham Pishgar, Kamiar Alaei, Greg Placencia, Maryam Pishgar
https://arxiv.org/abs/2506.03209

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalization, elevated healthcare costs, and increased mortality. Accurate early risk stratification is essential to enable timely intervention and improve clinical outcomes. We constructed a combined cohort of 19,085 elderly SICU admissions from the MIMIC-III and MIMIC-IV databases and developed an interpretable machine learning (ML) framework to predict in…

Detailed Time Resolved Spectral and Temporal Investigations of SGR J1550-5418 Bursts Detected with Fermi/Gamma-ray Burst Monitor
Mustafa Demirer, Ersin G\"o\u{g}\"u\c{s}, Yuki Kaneko, \"Ozge Keskin, Sinem \c{S}a\c{s}maz, Shotaro Yamasaki
https://arxiv.org/abs/2506.04414

Detailed Time Resolved Spectral and Temporal Investigations of SGR J1550-5418 Bursts Detected with Fermi/Gamma-ray Burst Monitor
We have conducted a time-resolved spectral analysis of magnetar bursts originating from SGR J1550-5418. Our analysis utilizes a two-step methodology for temporal segmentation of the data. We first generated and fitted overlapping time segments. Subsequently, we obtained non-overlapping time segments with varying lengths based on their spectral evolution patterns, employing a machine learning algorithm called k-means clustering. For the fitting process, we employed three distinct models, namely …

High-throughput viscometry via machine-learning from videos of inverted vials
Ignacio Arretche, Mohammad Tanver Hossain, Ramdas Tiwari, Abbie Kim, Mya G. Mills, Connor D. Armstrong, Jacob J. Lessard, Sameh H. Tawfick, Randy H. Ewoldt
https://arxiv.org/abs/2506.02034

High-throughput viscometry via machine-learning from videos of inverted vials
Although the inverted vial test has been widely used as a qualitative method for estimating fluid viscosity, quantitative rheological characterization has remained limited due to its complex, uncontrolled flow - driven by gravity, surface tension, inertia, and initial conditions. Here, we present a computer vision (CV) viscometer that automates the inverted vial test and enables quantitative viscosity inference across nearly five orders of magnitude (0.01-1000 Pas), without requiring direct vel…

Machine-learning Growth at Risk
Tobias Adrian, Hongqi Chen, Max-Sebastian Dov\`i, Ji Hyung Lee
https://arxiv.org/abs/2506.00572 https://

Machine-learning Growth at Risk
We analyse growth vulnerabilities in the US using quantile partial correlation regression, a selection-based machine-learning method that achieves model selection consistency under time series. We find that downside risk is primarily driven by financial, labour-market, and housing variables, with their importance changing over time. Decomposing downside risk into its individual components, we construct sector-specific indices that predict it, while controlling for information from other sectors…

A combined Machine Learning and Finite Element Modelling tool for the surgical planning of craniosynostosis correction
Itxasne Ant\'unez S\'aenz, Ane Alberdi Aramendi, David Dunaway, Juling Ong, Lara Deli\`ege, Amparo S\'aenz, Anita Ahmadi Birjandi, Noor UI Owase Jeelani, Silvia Schievano, Alessandro Borghi
https://a…

A combined Machine Learning and Finite Element Modelling tool for the surgical planning of craniosynostosis correction
Craniosynostosis is a medical condition that affects the growth of babies' heads, caused by an early fusion of cranial sutures. In recent decades, surgical treatments for craniosynostosis have significantly improved, leading to reduced invasiveness, faster recovery, and less blood loss. At Great Ormond Street Hospital (GOSH), the main surgical treatment for patients diagnosed with sagittal craniosynostosis (SC) is spring assisted cranioplasty (SAC). This procedure involves a 15x15 mm2 osteotomy…

BEAR: BGP Event Analysis and Reporting
Hanqing Li, Melania Fedeli, Vinay Kolar, Diego Klabjan
https://arxiv.org/abs/2506.04514 https://

BEAR: BGP Event Analysis and Reporting
The Internet comprises of interconnected, independently managed Autonomous Systems (AS) that rely on the Border Gateway Protocol (BGP) for inter-domain routing. BGP anomalies--such as route leaks and hijacks--can divert traffic through unauthorized or inefficient paths, jeopardizing network reliability and security. Although existing rule-based and machine learning methods can detect these anomalies using structured metrics, they still require experts with in-depth BGP knowledge of, for example…

Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence
Xintong Zou, Zhijie Li, Yunpeng Wang, Huiyu Yang, Jianchun Wang
https://arxiv.org/abs/2506.04898

Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence
Turbulence poses challenges for numerical simulation due to its chaotic, multiscale nature and high computational cost. Traditional turbulence modeling often struggles with accuracy and long-term stability. Recent scientific machine learning (SciML) models, such as Fourier Neural Operators (FNO), show promise in solving PDEs, but are typically limited to one-step-ahead predictions and often fail over long time horizons, especially in 3D turbulence. This study proposes a framework to assess the …

This https://arxiv.org/abs/2411.00093 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Machine Learning Electroweakino Production
The system of light electroweakinos and heavy squarks gives rise to one of the most challenging signatures to detect at the LHC. It consists of missing transverse energy recoiled against a few hadronic jets originating either from QCD radiation or squark decays. The analysis generally suffers from the large irreducible Z + jets $(Z \to ν\bar ν)$ background. In this study, we explore Machine Learning (ML) methods for efficient signal/background discrimination. Our best attempt uses both recons…

Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient
Conor Rowan, John Evans, Kurt Maute, Alireza Doostan
https://arxiv.org/abs/2506.04375

Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient
From characterizing the speed of a thermal system's response to computing natural modes of vibration, eigenvalue analysis is ubiquitous in engineering. In spite of this, eigenvalue problems have received relatively little treatment compared to standard forward and inverse problems in the physics-informed machine learning literature. In particular, neural network discretizations of solutions to eigenvalue problems have seen only a handful of studies. Owing to their nonlinearity, neural network d…

Beyond Diamond: Interpretable Machine Learning Discovery of Coherent Quantum Defect Hosts in Semiconductors
Mohammed Mahshook, Rudra Banerjee
https://arxiv.org/abs/2506.03844

Beyond Diamond: Interpretable Machine Learning Discovery of Coherent Quantum Defect Hosts in Semiconductors
Quantum point defects in wide bandgap semiconductors-such as the nitrogen-vacancy (NV) center in diamond are leading candidates for solid-state spin qubits due to their long coherence times and optically addressable spin states. Yet, identifying host materials capable of supporting such deep-level defects remains a significant challenge, owing to the complex interplay between chemical composition, crystal structure, and electronic environment. Here, we present a scalable, interpretable machine …

It's about time: a thermodynamic information criterion (TIC)
Brendan Lucas, Google Gemini 2. 5 Pro Preview 05-06
https://arxiv.org/abs/2506.04519 https…

It's about time: a thermodynamic information criterion (TIC)
Useful chemical processes often involve a desired steady state probability distribution, equilibrium or not, from which product is extracted. Given many different ways to attain the same steady state, which candidate "loses" the least in terms of time and energy? A scalar thermodynamic information criterion (TIC), inspired by AIC, assigns lower values to chemical processes with less estimated "loss" to generate the same desired steady state. As an element of thermodynamic machine learning, TIC …

This https://arxiv.org/abs/2505.06146 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Learning-Augmented Algorithms for Boolean Satisfiability
Learning-augmented algorithms are a prominent recent development in beyond worst-case analysis. In this framework, a problem instance is provided with a prediction (``advice'') from a machine-learning oracle, which provides partial information about an optimal solution, and the goal is to design algorithms that leverage this advice to improve worst-case performance. We study the classic Boolean satisfiability (SAT) decision and optimization problems within this framework using two forms of advi…

This https://arxiv.org/abs/2505.13182 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…

This https://arxiv.org/abs/2503.04672 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Finding White Dwarfs' Hidden Companions using an Unsupervised Machine Learning Technique
White dwarfs (WD) with main-sequence (MS) companions are crucial probes of stellar evolution. However, due to the significant difference in their luminosities, the WD is often outshined by the MS star. The aim of this work is to find hidden companions in Gaia's sample of WD candidates. Our methodology involves applying an unsupervised machine learning algorithm for dimensionality reduction and clustering, known as Self-Organizing Map (SOM), to Gaia BP/RP (XP) spectra. This strategy allows us to…

Cluster Reconstruction in Electromagnetic Calorimeters Using Machine Learning Methods
Kalina Dimitrova, Venelin Kozhuharov, Ruslan Nastaev, Peicho Petkov
https://arxiv.org/abs/2505.24740

Cluster Reconstruction in Electromagnetic Calorimeters Using Machine Learning Methods
Machine-learning-based methods can be developed for the reconstruction of clusters in segmented detectors for high energy physics experiments. Convolutional neural networks with autoencoder architecture trained on labeled data from a simulated dataset reconstruct events by providing information about the hit point and energy of each particle that has entered the detector. The correct reconstruction of the positionand the energy of the incident particles is crucial for the accurate events recons…

This https://arxiv.org/abs/2502.06765 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Are all models wrong? Fundamental limits in distribution-free empirical model falsification
In statistics and machine learning, when we train a fitted model on available data, we typically want to ensure that we are searching within a model class that contains at least one accurate model -- that is, we would like to ensure an upper bound on the model class risk (the lowest possible risk that can be attained by any model in the class). However, it is also of interest to establish lower bounds on the model class risk, for instance so that we can determine whether our fitted model is at …

This https://arxiv.org/abs/2502.11657 has been replaced.
initial toot: https://mastoxiv.page/@a…

How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning
Magnetic geometry has a significant effect on the level of turbulent transport in fusion plasmas. Here, we model and analyze this dependence using multiple machine learning methods and a dataset of > 200,000 nonlinear simulations of ion-temperature-gradient turbulence in diverse non-axisymmetric geometries. The dataset is generated using a large collection of both optimized and randomly generated stellarator equilibria. At fixed gradients, the turbulent heat flux varies between geometries by se…

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations
Paul Fuchs, Weilong Chen, Stephan Thaler, Julija Zavadlav
https://arxiv.org/abs/2506.04055

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations
Machine learning potentials (MLPs) have advanced rapidly and show great promise to transform molecular dynamics (MD) simulations. However, most existing software tools are tied to specific MLP architectures, lack integration with standard MD packages, or are not parallelizable across GPUs. To address these challenges, we present chemtrain-deploy, a framework that enables model-agnostic deployment of MLPs in LAMMPS. chemtrain-deploy supports any JAX-defined semi-local potential, allowing users t…

Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models
Dominik Stempie\'n, Robert \'Slepaczuk
https://arxiv.org/abs/2505.19617

Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models
This research systematically develops and evaluates various hybrid modeling approaches by combining traditional econometric models (ARIMA and ARFIMA models) with machine learning and deep learning techniques (SVM, XGBoost, and LSTM models) to forecast financial time series. The empirical analysis is based on two distinct financial assets: the S&P 500 index and Bitcoin. By incorporating over two decades of daily data for the S&P 500 and almost ten years of Bitcoin data, the study provides a comp…

Benchmarking Large Language Models for Polymer Property Predictions
Sonakshi Gupta, Akhlak Mahmood, Shivank Shukla, Rampi Ramprasad
https://arxiv.org/abs/2506.02129

Benchmarking Large Language Models for Polymer Property Predictions
Machine learning has revolutionized polymer science by enabling rapid property prediction and generative design. Large language models (LLMs) offer further opportunities in polymer informatics by simplifying workflows that traditionally rely on large labeled datasets, handcrafted representations, and complex feature engineering. LLMs leverage natural language inputs through transfer learning, eliminating the need for explicit fingerprinting and streamlining training. In this study, we finetune …

Quake: Adaptive Indexing for Vector Search
Jason Mohoney, Devesh Sarda, Mengze Tang, Shihabur Rahman Chowdhury, Anil Pacaci, Ihab F. Ilyas, Theodoros Rekatsinas, Shivaram Venkataraman
https://arxiv.org/abs/2506.03437

Quake: Adaptive Indexing for Vector Search
Vector search, the task of finding the k-nearest neighbors of high-dimensional vectors, underpins many machine learning applications, including recommendation systems and information retrieval. However, existing approximate nearest neighbor (ANN) methods perform poorly under dynamic, skewed workloads where data distributions evolve. We introduce Quake, an adaptive indexing system that maintains low latency and high recall in such environments. Quake employs a hierarchical partitioning scheme th…

Estimating the Euclidean distortion of an orbit space
Ben Blum-Smith, Harm Derksen, Dustin G. Mixon, Yousef Qaddura, Brantley Vose
https://arxiv.org/abs/2506.04425

Estimating the Euclidean distortion of an orbit space
Given a finite-dimensional inner product space $V$ and a group $G$ of isometries, we consider the problem of embedding the orbit space $V/G$ into a Hilbert space in a way that preserves the quotient metric as well as possible. This inquiry is motivated by applications to invariant machine learning. We introduce several new theoretical tools before using them to tackle various fundamental instances of this problem.

Model Splitting Enhanced Communication-Efficient Federated Learning for CSI Feedback
Yanjie Dong, Haijun Zhang, Gaojie Chen, Xiaoyi Fan, Victor C. M. Leung, Xiping Hu
https://arxiv.org/abs/2506.04113

Model Splitting Enhanced Communication-Efficient Federated Learning for CSI Feedback
Recent advancements have introduced federated machine learning-based channel state information (CSI) compression before the user equipments (UEs) upload the downlink CSI to the base transceiver station (BTS). However, most existing algorithms impose a high communication overhead due to frequent parameter exchanges between UEs and BTS. In this work, we propose a model splitting approach with a shared model at the BTS and multiple local models at the UEs to reduce communication overhead. Moreover…

This https://arxiv.org/abs/2503.09128 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

FlexiReg: Flexible Urban Region Representation Learning
The increasing availability of urban data offers new opportunities for learning region representations, which can be used as input to machine learning models for downstream tasks such as check-in or crime prediction. While existing solutions have produced promising results, an issue is their fixed formation of regions and fixed input region features, which may not suit the needs of different downstream tasks. To address this limitation, we propose a model named FlexiReg for urban region represe…

Big Data-Driven Fraud Detection Using Machine Learning and Real-Time Stream Processing
Chen Liu, Hengyu Tang, Zhixiao Yang, Ke Zhou, Sangwhan Cha
https://arxiv.org/abs/2506.02008 …

Big Data-Driven Fraud Detection Using Machine Learning and Real-Time Stream Processing
In the age of digital finance, detecting fraudulent transactions and money laundering is critical for financial institutions. This paper presents a scalable and efficient solution using Big Data tools and machine learning models. We utilize realtime data streaming platforms like Apache Kafka and Flink, distributed processing frameworks such as Apache Spark, and cloud storage services AWS S3 and RDS. A synthetic dataset representing real-world Anti-Money Laundering (AML) challenges is employed t…

Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning
Sayyed Farid Ahamed, Sandip Roy, Soumya Banerjee, Marc Vucovich, Kevin Choi, Abdul Rahman, Alison Hu, Edward Bowen, Sachin Shetty
https://arxiv.org/abs/2505.23791

Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning
Federated Learning (FL) is a collaborative learning framework designed to protect client data, yet it remains highly vulnerable to Intellectual Property (IP) threats. Model extraction (ME) attacks pose a significant risk to Machine Learning as a Service (MLaaS) platforms, enabling attackers to replicate confidential models by querying black-box (without internal insight) APIs. Despite FL's privacy-preserving goals, its distributed nature makes it particularly susceptible to such attacks. This p…

This https://arxiv.org/abs/2412.16387 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Information Limits of Joint Community Detection and Finite Group Synchronization
The emerging problem of joint community detection and group synchronization, with applications in signal processing and machine learning, has been extensively studied in recent years. Previous research has predominantly focused on a statistical model that extends the stochastic block model~(SBM) by incorporating additional group transformations. In its simplest form, the model randomly generates a network of size $n$ that consists of two equal-sized communities, where each node $i$ is associate…

Looked at latest W3C Draft of “Accessibility of machine learning and generative AI,” specifically the overlays section:
https://w3c.github.io/ai-accessibility/#accessibility-overlays
I still think it needs to be stricken:

First-Principles and Machine Learning Investigation of the Structural and Optoelectronic Properties of Dodecaphenylyne: A Novel Carbon Allotrope
Kleuton A. L. Lima, Jose A. S. Laranjeira, Nicolas F. Martins, Julio R. Sambrano, Alexandre C. Dias, Luiz A. Ribeiro Junior, Douglas S. Galvao
https://arxiv.org/abs/2506.02218

First-Principles and Machine Learning Investigation of the Structural and Optoelectronic Properties of Dodecaphenylyne: A Novel Carbon Allotrope
We report the computational discovery and characterization of Dodecaphenylyne (DP), a novel carbon allotrope with a distinctive geometric arrangement. DP structural, thermodynamic, mechanical, electronic, and optical properties were evaluated using density functional theory and a machine learning interatomic potential trained explicitly for this material. The formation energy of -7.98 eV/atom indicates high thermodynamic stability, further supported by the absence of imaginary phonon modes and …

Path Signatures for Feature Extraction. An Introduction to the Mathematics Underpinning an Efficient Machine Learning Technique
Stephan Sturm
https://arxiv.org/abs/2506.01815

Path Signatures for Feature Extraction. An Introduction to the Mathematics Underpinning an Efficient Machine Learning Technique
We provide an introduction to the topic of path signatures as means of feature extraction for machine learning from data streams. The article stresses the mathematical theory underlying the signature methodology, highlighting the conceptual character without plunging into the technical details of rigorous proofs. These notes are based on an introductory presentation given to students of the Research Experience for Undergraduates in Industrial Mathematics and Statistics at Worcester Polytechnic …

This https://arxiv.org/abs/2502.17821 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

CAML: Collaborative Auxiliary Modality Learning for Multi-Agent Systems
Multi-modal learning has become a crucial technique for improving the performance of machine learning applications across domains such as autonomous driving, robotics, and perception systems. However, in certain scenarios, particularly in resource-constrained environments, some modalities available during training may be absent during inference. While existing frameworks effectively utilize multiple data sources during training and enable inference with reduced modalities, they are primarily de…

DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Kevin Han, Bowen Deng, Amir Barati Farimani, Gerbrand Ceder
https://arxiv.org/abs/2506.02023

DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Large-scale atomistic simulations are essential to bridge computational materials and chemistry to realistic materials and drug discovery applications. In the past few years, rapid developments of machine learning interatomic potentials (MLIPs) have offered a solution to scale up quantum mechanical calculations. Parallelizing these interatomic potentials across multiple devices poses a challenging, but promising approach to further extending simulation scales to real-world applications. In this…

This https://arxiv.org/abs/2505.13182 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…

This https://arxiv.org/abs/2502.03701 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

First-ish Order Methods: Hessian-aware Scalings of Gradient Descent
Gradient descent is the primary workhorse for optimizing large-scale problems in machine learning. However, its performance is highly sensitive to the choice of the learning rate. A key limitation of gradient descent is its lack of natural scaling, which often necessitates expensive line searches or heuristic tuning to determine an appropriate step size. In this paper, we address this limitation by incorporating Hessian information to scale the gradient direction. By accounting for the curvatur…

This https://arxiv.org/abs/2505.13469 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Algorithmic Tradeoffs in Fair Lending: Profitability, Compliance, and Long-Term Impact
As financial institutions increasingly rely on machine learning models to automate lending decisions, concerns about algorithmic fairness have risen. This paper explores the tradeoff between enforcing fairness constraints (such as demographic parity or equal opportunity) and maximizing lender profitability. Through simulations on synthetic data that reflects real-world lending patterns, we quantify how different fairness interventions impact profit margins and default rates. Our results demonst…

Leveraging machine learning features for linear optical interferometer control
Sergei S. Kuzmin, Ivan V. Dyakonov, Stanislav S. Straupe
https://arxiv.org/abs/2505.24032

Leveraging machine learning features for linear optical interferometer control
We have developed an algorithm that constructs a model of a reconfigurable optical interferometer, independent of specific architectural constraints. The programming of unitary transformations on the interferometer's optical modes relies on either an analytical method for deriving the unitary matrix from a set of phase shifts or an optimization routine when such decomposition is not available. Our algorithm employs a supervised learning approach, aligning the interferometer model with a trainin…

This https://arxiv.org/abs/2401.00510 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Smoothness Estimation for Whittle-Matérn Processes on Closed Riemannian Manifolds
The family of Matérn kernels are often used in spatial statistics, function approximation and Gaussian process methods in machine learning. One reason for their popularity is the presence of a smoothness parameter that controls, for example, optimal error bounds for kriging and posterior contraction rates in Gaussian process regression. On closed Riemannian manifolds, we show that the smoothness parameter can be consistently estimated from the maximizer(s) of the Gaussian likelihood when the u…

Stochastically Dominant Peer Prediction
Yichi Zhang, Shengwei Xu, David Pennock, Grant Schoenebeck
https://arxiv.org/abs/2506.02259 https://

Stochastically Dominant Peer Prediction
Eliciting reliable human feedback is essential for many machine learning tasks, such as learning from noisy labels and aligning AI systems with human preferences. Peer prediction mechanisms incentivize truthful reporting without ground truth verification by scoring agents based on correlations with peers. Traditional mechanisms, which ensure that truth-telling maximizes the expected scores in equilibrium, can elicit honest information while assuming agents' utilities are linear functions of the…

Generator Based Inference (GBI)
Chi Lung Cheng, Ranit Das, Runze Li, Radha Mastandrea, Vinicius Mikuni, Benjamin Nachman, David Shih, Gup Singh
https://arxiv.org/abs/2506.00119

Generator Based Inference (GBI)
Statistical inference in physics is often based on samples from a generator (sometimes referred to as a ``forward model") that emulate experimental data and depend on parameters of the underlying theory. Modern machine learning has supercharged this workflow to enable high-dimensional and unbinned analyses to utilize much more information than ever before. We propose a general framework for describing the integration of machine learning with generators called Generator Based Inference (GBI). A …

ViT-based Local Volume dwarf galaxy Identificationin (VIDA) in the CSST survey
Han Qu, Zhen Yuan, Chengliang Wei, Chao Liu, Jiang Chang, Guoliang Li, Nicolas F. Martin, Chaowei Tsai, Shi Shao, Yu Luo, Ran Li, Xi Kang, Xiangxiang Xue, Zhou Fan
https://arxiv.org/abs/2506.04361

ViT-based Local Volume dwarf galaxy Identificationin (VIDA) in the CSST survey
Identifying dwarf galaxies within the Local Volume is crucial for constraining the luminosity function of satellite galaxies in the nearby universe. We report the detection capabilities of dwarf galaxies within the Local Volume using the Chinese Space Station Telescope (CSST). Based on the simulated imaging data of CSST, we develop a detection and classification pipeline that combines traditional image-based search techniques with advanced machine learning classification models. The simulated L…

This https://arxiv.org/abs/2205.10538 has been replaced.
link: https://scholar.google.com/scholar?q=a

Automated machine learning: AI-driven decision making in business analytics
The realization that AI-driven decision-making is indispensable in today's fast-paced and ultra-competitive marketplace has raised interest in industrial machine learning (ML) applications significantly. The current demand for analytics experts vastly exceeds the supply. One solution to this problem is to increase the user-friendliness of ML frameworks to make them more accessible for the non-expert. Automated machine learning (AutoML) is an attempt to solve the problem of expertise by providin…

A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges
Sudhanshu Sekhar Tripathy, Bichitrananda Behera
https://arxiv.org/abs/2506.02438

A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges
IDS aims to protect computer networks from security threats by detecting, notifying, and taking appropriate action to prevent illegal access and protect confidential information. As the globe becomes increasingly dependent on technology and automated processes, ensuring secured systems, applications, and networks has become one of the most significant problems of this era. The global web and digital technology have significantly accelerated the evolution of the modern world, necessitating the u…

Joint Beamforming and Integer User Association using a GNN with Gumbel-Softmax Reparameterizations
Qing Lyu, Mai Vu
https://arxiv.org/abs/2506.05241 https:…

Joint Beamforming and Integer User Association using a GNN with Gumbel-Softmax Reparameterizations
Machine learning (ML) models can effectively optimize a multi-cell wireless network by designing the beamforming vectors and association decisions. Existing ML designs, however, often needs to approximate the integer association variables with a probability distribution output. We propose a novel graph neural network (GNN) structure that jointly optimize beamforming vectors and user association while guaranteeing association output as integers. The integer association constraints are satisfied …

Machine-learning-driven modelling of amorphous and polycrystalline BaZrS$_{3}$
Laura-Bianca Pa\c{s}ca, Yuanbin Liu, Andy S. Anker, Ludmilla Steier, Volker L. Deringer
https://arxiv.org/abs/2506.01517

Machine-learning-driven modelling of amorphous and polycrystalline BaZrS$_{3}$
The chalcogenide perovskite material BaZrS$_{3}$ is of growing interest for emerging thin-film photovoltaics. Here we show how machine-learning-driven modelling can be used to describe the material's amorphous precursor as well as polycrystalline structures with complex grain boundaries. Using a bespoke machine-learned interatomic potential (MLIP) model for BaZrS$_{3}$, we study the atomic-scale structure of the amorphous phase, quantify grain-boundary formation energies, and create realistic-s…

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data
Bowen Han, Yongqiang Cheng
https://arxiv.org/abs/2506.01860

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data
The accurate calculation of phonons and vibrational spectra remains a significant challenge, requiring highly precise evaluations of interatomic forces. Traditional methods based on the quantum description of the electronic structure, while widely used, are computationally expensive and demand substantial expertise. Emerging universal machine learning interatomic potentials (uMLIPs) offer a transformative alternative by employing pre-trained neural network surrogates to predict interatomic forc…

Learning thermodynamic master equations for open quantum systems
Peter Sentz, Stanley Nicholson, Yujin Cho, Sohail Reddy, Brendan Keith, Stefanie G\"unther
https://arxiv.org/abs/2506.01882

Learning thermodynamic master equations for open quantum systems
The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and other applications. Scientific machine learning techniques have been applied to this problem in a variety of ways, including by modeling with deep neural networks. However, the majority of mathematical models describing open quantum systems are linear, and the natural nonlinearities in learnable models have not been incorporated using physical principles. We …

CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
Kaveen Hiniduma, Zilinghan Li, Aditya Sinha, Ravi Madduri, Suren Byna
https://arxiv.org/abs/2505.23849

CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
Privacy-Preserving Federated Learning (PPFL) is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves privacy and security of the client's data by not exchanging it. However, ensuring that data at each client is of high quality and ready for federated learning (FL) is a challenge due to restricted data access. In this paper, we introduce CADRE (Customizable Assurance of Data REadiness) for FL, a novel framework that allows users to define…

This https://arxiv.org/abs/2205.09337 has been replaced.
link: https://scholar.google.com/scholar?q=a

Deep Learning in Business Analytics: A Clash of Expectations and Reality
Our fast-paced digital economy shaped by global competition requires increased data-driven decision-making based on artificial intelligence (AI) and machine learning (ML). The benefits of deep learning (DL) are manifold, but it comes with limitations that have, so far, interfered with widespread industry adoption. This paper explains why DL, despite its popularity, has difficulties speeding up its adoption within business analytics. It is shown that the adoption of deep learning is not only aff…

Improving statistical learning methods via features selection without replacement sampling and random projection
Sulaiman khan, Muhammad Ahmad, Fida Ullah, Carlos Aguilar Iba\~nez, Jos\'e Eduardo Valdez Rodriguez
https://arxiv.org/abs/2506.00053

Improving statistical learning methods via features selection without replacement sampling and random projection
Cancer is fundamentally a genetic disease characterized by genetic and epigenetic alterations that disrupt normal gene expression, leading to uncontrolled cell growth and metastasis. High-dimensional microarray datasets pose challenges for classification models due to the "small n, large p" problem, resulting in overfitting. This study makes three different key contributions: 1) we propose a machine learning-based approach integrating the Feature Selection Without Re-placement (FSWOR) technique…

eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems
Ruilin Xu, Zongxuan Xie, Pengfei Chen
https://arxiv.org/abs/2506.02007

eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems
We present eACGM, a full-stack AI/ML system monitoring framework based on eBPF. eACGM collects real-time performance data from key hardware components, including the GPU and network communication layer, as well as from key software stacks such as CUDA, Python, and PyTorch, all without requiring any code instrumentation or modifications. Additionally, it leverages libnvml to gather process-level GPU resource usage information. By applying a Gaussian Mixture Model (GMM) to the collected multidime…

Machine Learning-Guided Discovery of Temperature-Induced Solid-Solid Phase Transitions in Inorganic Materials
Cibr\'an L\'opez, Joshua Ojih, Ming Hu, Josep Lluis Tamarit, Edgardo Saucedo, Claudio Cazorla
https://arxiv.org/abs/2506.01449

Machine Learning-Guided Discovery of Temperature-Induced Solid-Solid Phase Transitions in Inorganic Materials
Predicting solid-solid phase transitions remains a long-standing challenge in materials science. Solid-solid transformations underpin a wide range of functional properties critical to energy conversion, information storage, and thermal management technologies. However, their prediction is computationally intensive due to the need to account for finite-temperature effects. Here, we present an uncertainty-aware machine-learning-guided framework for high-throughput prediction of temperature-induce…

Noise-Driven AI Sensors: Secure Healthcare Monitoring with PUFs
Christiana Chamon, Abhijit Sarkar, A. Lynn Abbott
https://arxiv.org/abs/2506.05135 https://…

Noise-Driven AI Sensors: Secure Healthcare Monitoring with PUFs
Wearable and implantable healthcare sensors are pivotal for real-time patient monitoring but face critical challenges in power efficiency, data security, and signal noise. This paper introduces a novel platform that leverages hardware noise as a dual-purpose resource to enhance machine learning (ML) robustness and secure data via Physical Unclonable Functions (PUFs). By integrating noise-driven signal processing, PUFbased authentication, and ML-based anomaly detection, our system achieves secur…

Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges
Raj Patel, Himanshu Tripathi, Jasper Stone, Noorbakhsh Amiri Golilarz, Sudip Mittal, Shahram Rahimi, Vini Chaudhary
https://arxiv.org/abs/2506.02032

Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges
The rapid adoption of machine learning (ML) technologies has driven organizations across diverse sectors to seek efficient and reliable methods to accelerate model development-to-deployment. Machine Learning Operations (MLOps) has emerged as an integrative approach addressing these requirements by unifying relevant roles and streamlining ML workflows. As the MLOps market continues to grow, securing these pipelines has become increasingly critical. However, the unified nature of MLOps ecosystem …

This https://arxiv.org/abs/2505.24603 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches
Gaussian sketching, which consists of pre-multiplying the data with a random Gaussian matrix, is a widely used technique for multiple problems in data science and machine learning, with applications spanning computationally efficient optimization, coded computing, and federated learning. This operation also provides differential privacy guarantees due to its inherent randomness. In this work, we revisit this operation through the lens of Renyi Differential Privacy (RDP), providing a refined pri…

This https://arxiv.org/abs/2505.13182 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…

The transformative capability of quantum-accurate machine learning interatomic potentials
Alfredo A. Correa, Sebastien Hamel
https://arxiv.org/abs/2506.02328

The transformative capability of quantum-accurate machine learning interatomic potentials
Many materials's properties and phase boundaries are generally not well known under extreme pressure and temperature conditions. This is a consequence of the scarcity of experimental information and the difficulty of extrapolating approximations to the atomic interactions in such conditions. Nguyen-Cong and colleagues, in their publication (J.Phys.Chem.Lett. 15, 1152 (2024)), achieved an impressive result using a SNAP (Spectral Neighbor Analysis Potential), an interatomic potential for carbon o…

This https://arxiv.org/abs/2506.00377 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Systematic Review of Metaheuristics-Based and Machine Learning-Driven Intrusion Detection Systems in IoT
The widespread adoption of the Internet of Things (IoT) has raised a new challenge for developers since it is prone to known and unknown cyberattacks due to its heterogeneity, flexibility, and close connectivity. To defend against such security breaches, researchers have focused on building sophisticated intrusion detection systems (IDSs) using machine learning (ML) techniques. Although these algorithms notably improve detection performance, they require excessive computing power and resources,…

Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches
A. Hippert-Ferrer, A. Sportisse, A. Javaheri, M. N. El Korso, D. P. Palomar
https://arxiv.org/abs/2506.01696

Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches
The goal of this tutorial is to provide an overview of recent methods for handling missing data in signal processing methods, from their origins to the challenges ahead. Missing data approaches are grouped by three main categories: i) missing-data imputation, ii) estimation with missing values and iii) prediction with missing values. We focus on methodological and experimental results through specific case studies on real-world applications. Promising and future research directions, including a…

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG
Amit K Chattopadhyay, Aimee Pascaline N Unkundiye, Gillian Pearce
https://arxiv.org/abs/2505.21094

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG
This is a Machine Learning guided study towards zone-specific ray therapy. Combining Machine Learning (Extreme Gradient Boosting) with continuum modeling (exponential and logistic growth), we find that while fluorodeoxyglucose-coated (mNP-FDG) can control cancerous tumor progression within 2 days compared to 18 days by Superparamagnetic Iron Oxide Nanoparticles (SPIONs), for complete termination of the tumor, SPIONS (20 days) are superior compared to mNP-FDG (more than 40 days). We also provide…

This https://arxiv.org/abs/2503.09969 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework
Artificial Intelligence (AI) is now firmly at the center of evidence-based medicine. Despite many success stories that edge the path of AI's rise in healthcare, there are comparably many reports of significant shortcomings and unexpected behavior of AI in deployment. A major reason for these limitations is AI's reliance on association-based learning, where non-representative machine learning datasets can amplify latent bias during training and/or hide it during testing. To unlock new tools capa…

This https://arxiv.org/abs/2303.17475 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Learning distributed representations with efficient SoftMax normalization
Learning distributed representations, or embeddings, that encode the relational similarity patterns among objects is a relevant task in machine learning. A popular method to learn the embedding matrices $X, Y$ is optimizing a loss function of the term ${\rm SoftMax}(XY^T)$. The complexity required to calculate this term, however, runs quadratically with the problem size, making it a computationally heavy solution. In this article, we propose a linear-time heuristic approximation to compute the …

Profiling and optimization of multi-card GPU machine learning jobs
Marcin Lawenda, Kyrylo Khloponin, Krzesimir Samborski, {\L}ukasz Szustak
https://arxiv.org/abs/2505.22905

Profiling and optimization of multi-card GPU machine learning jobs
The effectiveness and efficiency of machine learning methodologies are crucial, especially with respect to the quality of results and computational cost. This paper discusses different model optimization techniques, providing a comprehensive analysis of key performance indicators. Several parallelization strategies for image recognition, adapted to different hardware and software configurations, including distributed data parallelism and distributed hardware processing, are analyzed. Selected o…

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries
Abhijit Talluri
https://arxiv.org/abs/2505.23813

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries
Federated Learning (FL) has emerged as a critical paradigm for enabling privacy-preserving machine learning, particularly in regulated sectors such as finance and healthcare. However, standard FL strategies often encounter significant operational challenges related to fault tolerance, system resilience against concurrent client and server failures, and the provision of robust, verifiable privacy guarantees essential for handling sensitive data. These deficiencies can lead to training disruption…

This https://arxiv.org/abs/2505.22813 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

X-Factor: Quality Is a Dataset-Intrinsic Property
In the universal quest to optimize machine-learning classifiers, three factors -- model architecture, dataset size, and class balance -- have been shown to influence test-time performance but do not fully account for it. Previously, evidence was presented for an additional factor that can be referred to as dataset quality, but it was unclear whether this was actually a joint property of the dataset and the model architecture, or an intrinsic property of the dataset itself. If quality is truly d…

This https://arxiv.org/abs/2503.00140 has been replaced.
link: https://scholar.google.com/scholar?q=a

Approaching the Harm of Gradient Attacks While Only Flipping Labels
Machine learning systems deployed in distributed or federated environments are highly susceptible to adversarial manipulations, particularly availability attacks -adding imperceptible perturbations to training data, thereby rendering the trained model unavailable. Prior research in distributed machine learning has demonstrated such adversarial effects through the injection of gradients or data poisoning. In this study, we aim to enhance comprehension of the potential of weaker (and more probabl…

NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials
Chengbing Chen, Yutong Li, Rui Zhao, Zhoulin Liu, Zheyong Fan, Gang Tang, Zhiyong Wang
https://arxiv.org/abs/2506.01868

NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials
As a machine-learned potential, the neuroevolution potential (NEP) method features exceptional computational efficiency and has been successfully applied in materials science. Constructing high-quality training datasets is crucial for developing accurate NEP models. However, the preparation and screening of NEP training datasets remain a bottleneck for broader applications due to their time-consuming, labor-intensive, and resource-intensive nature. In this work, we have developed NepTrain and N…