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Regression transients modeling of solid rocket motor burning surfaces with physics-guided neural network Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-26 XueQin Sun, Yu Li, YiHong Li, SuKai Wang, Xuan Li, Ming Lu, Ping Chen
Monitoring the burning surface regression in ground static ignition tests is crucial for predicting the internal ballistic performance of solid rocket motors (SRMs). A previously proposed ultra-sparse computed tomography imaging method provides a possibility for real-time monitoring. However, sample shortages of SRMs highlights the need for monitoring accuracy, especially given the high cost associated
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Supervised and unsupervised learning of (1+1) -dimensional even-offspring branching annihilating random walks Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-22 Yanyang Wang, Wei Li, Feiyi Liu, Jianmin Shen
Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and nonequilibrium systems. Unlike equilibrium systems, non-equilibrium systems display more complicated and diverse features because of the extra dimension of time, which is not readily tractable, both theoretically and numerically
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Physics informed token transformer for solving partial differential equations Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-21 Cooper Lorsung, Zijie Li, Amir Barati Farimani
Solving partial differential equations (PDEs) is the core of many fields of science and engineering. While classical approaches are often prohibitively slow, machine learning models often fail to incorporate complete system information. Over the past few years, transformers have had a significant impact on the field of Artificial Intelligence and have seen increased usage in PDE applications. However
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Deep energy-pressure regression for a thermodynamically consistent EOS model Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-20 Dayou Yu, Deep Shankar Pandey, Joshua Hinz, Deyan Mihaylov, Valentin V Karasiev, S X Hu, Qi Yu
In this paper, we aim to explore novel machine learning (ML) techniques to facilitate and accelerate the construction of universal equation-Of-State (EOS) models with a high accuracy while ensuring important thermodynamic consistency. When applying ML to fit a universal EOS model, there are two key requirements: (1) a high prediction accuracy to ensure precise estimation of relevant physics properties
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Functional data learning using convolutional neural networks Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-19 J Galarza, T Oraby
In this paper, we show how convolutional neural networks (CNNs) can be used in regression and classification learning problems for noisy and non-noisy functional data (FD). The main idea is to transform the FD into a 28 by 28 image. We use a specific but typical architecture of a CNN to perform all the regression exercises of parameter estimation and functional form classification. First, we use some
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Deep quantum graph dreaming: deciphering neural network insights into quantum experiments Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-15 Tareq Jaouni, Sören Arlt, Carlos Ruiz-Gonzalez, Ebrahim Karimi, Xuemei Gu, Mario Krenn
Despite their promise to facilitate new scientific discoveries, the opaqueness of neural networks presents a challenge in interpreting the logic behind their findings. Here, we use a eXplainable-AI technique called inception or deep dreaming, which has been invented in machine learning for computer vision. We use this technique to explore what neural networks learn about quantum optics experiments
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Deep learning cosmic ray transport from density maps of simulated, turbulent gas Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-14 Chad Bustard, John Wu
The coarse-grained propagation of galactic cosmic rays (CRs) is traditionally constrained by phenomenological models of Milky Way CR propagation fit to a variety of direct and indirect observables; however, constraining the fine-grained transport of CRs along individual magnetic field lines—for instance, diffusive vs streaming transport models—is an unsolved challenge. Leveraging a recent training
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An open-source robust machine learning platform for real-time detection and classification of 2D material flakes Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-13 Jan-Lucas Uslu, Taoufiq Ouaj, David Tebbe, Alexey Nekrasov, Jo Henri Bertram, Marc Schütte, Kenji Watanabe, Takashi Taniguchi, Bernd Beschoten, Lutz Waldecker, Christoph Stampfer
The most widely used method for obtaining high-quality two-dimensional (2D) materials is through mechanical exfoliation of bulk crystals. Manual identification of suitable flakes from the resulting random distribution of crystal thicknesses and sizes on a substrate is a time-consuming, tedious task. Here, we present a platform for fully automated scanning, detection, and classification of 2D materials
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Qualitative and quantitative enhancement of parameter estimation for model-based diagnostics using automatic differentiation with an application to inertial fusion Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-13 A L Milder, A S Joglekar, W Rozmus, D H Froula
Parameter estimation using observables is a fundamental concept in the experimental sciences. Mathematical models that represent the physical processes can enable reconstructions of the experimental observables and greatly assist in parameter estimation by turning it into an optimization problem which can be solved by gradient-free or gradient-based methods. In this work, the recent rise in flexible
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MS2OD: outlier detection using minimum spanning tree and medoid selection Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-12 Jia Li, Jiangwei Li, Chenxu Wang, Fons J Verbeek, Tanja Schultz, Hui Liu
As an essential task in data mining, outlier detection identifies abnormal patterns in numerous applications, among which clustering-based outlier detection is one of the most popular methods for its effectiveness in detecting cluster-related outliers, especially in medical applications. This article presents an advanced method to extract cluster-based outliers by employing a scaled minimum spanning
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ATSFCNN: a novel attention-based triple-stream fused CNN model for hyperspectral image classification Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-12 Jizhen Cai, Clotilde Boust, Alamin Mansouri
Recently, the convolutional neural network (CNN) has gained increasing importance in hyperspectral image (HSI) classification thanks to its superior performance. However, most of the previous research has mainly focused on 2D-CNN, and the limited applications of 3D-CNN have been attributed to its complexity, despite its potential to enhance information extraction between adjacent channels of the image
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Circumventing data imbalance in magnetic ground state data for magnetic moment predictions Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-08 Rohan Yuri Sanspeur, John R Kitchin
Magnetic materials play a crucial role in the transition to more sustainable forms of energy and electric vehicles. There is an anticipated shortage in magnetic materials in the future, and as a result there is an urgent need to discover and design new magnetic materials. Computational magnetic material design using density functional theory is daunting because of the challenge in identifying magnetic
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Sparse optical flow outliers elimination method based on Borda stochastic neighborhood graph Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-05 Yifan Wang, Yang Li, Jiaqi Wang, Haofeng Lv, Jinshi Guo
During the tracking of moving targets in dynamic scenes, efficiently handling outliers in the optical flow and maintaining robustness across various motion amplitudes represents a critical challenge. So far, studies have used thresholding and local consistency based approaches to deal with optical outliers. However, there is subjectivity through expert-defined thresholds or delineated regions, and
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Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-02 Enes Ozelbas, Emine Elif Tülay, Serhat Ozekes
Motor imagery brain-computer interfaces (MI-BCIs) have gained a lot of attention in recent years thanks to their potential to enhance rehabilitation and control of prosthetic devices for individuals with motor disabilities. However, accurate classification of motor imagery signals remains a challenging task due to the high inter-subject variability and non-stationarity in the electroencephalogram (EEG)
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Amortized simulation-based frequentist inference for tractable and intractable likelihoods Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-02 Ali Al Kadhim, Harrison B Prosper, Olivia F Prosper
High-fidelity simulators that connect theoretical models with observations are indispensable tools in many sciences. If the likelihood is known, inference can proceed using standard techniques. However, when the likelihood is intractable or unknown, a simulator makes it possible to infer the parameters of a theoretical model directly from real and simulated observations when coupled with machine learning
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ML-based regionalization of climate variables to forecast seasonal precipitation for water resources management Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-31 Taesam Lee, Chang-Hee Won, Vijay P Singh
Numerous dams and reservoirs have been constructed in South Korea, considering the distribution of seasonal precipitation which highly deviates from the actual one with high precipitation amount in summer and very low amount in other seasons. These water-related structures should be properly managed in order to meet seasonal demands of water resources wherein the forecasting of seasonal precipitation
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ELUQuant: event-level uncertainty quantification in deep inelastic scattering Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-30 C Fanelli, J Giroux
We introduce a physics-informed Bayesian neural network with flow-approximated posteriors using multiplicative normalizing flows for detailed uncertainty quantification (UQ) at the physics event-level. Our method is capable of identifying both heteroskedastic aleatoric and epistemic uncertainties, providing granular physical insights. Applied to deep inelastic scattering (DIS) events, our model effectively
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CoRe optimizer: an all-in-one solution for machine learning Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-30 Marco Eckhoff, Markus Reiher
The optimization algorithm and its hyperparameters can significantly affect the training speed and resulting model accuracy in machine learning (ML) applications. The wish list for an ideal optimizer includes fast and smooth convergence to low error, low computational demand, and general applicability. Our recently introduced continual resilient (CoRe) optimizer has shown superior performance compared
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Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-29 Tanujit Chakraborty, Ujjwal Reddy K S, Shraddha M Naik, Madhurima Panja, Bayapureddy Manvitha
Generative adversarial networks (GANs) have rapidly emerged as powerful tools for generating realistic and diverse data across various domains, including computer vision and other applied areas, since their inception in 2014. Consisting of a discriminative network and a generative network engaged in a minimax game, GANs have revolutionized the field of generative modeling. In February 2018, GAN secured
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Variance extrapolation method for neural-network variational Monte Carlo Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-25 Weizhong Fu, Weiluo Ren, Ji Chen
Constructing more expressive ansatz has been a primary focus for quantum Monte Carlo, aimed at more accurate ab initio calculations. However, with more powerful ansatz, e.g. various recent developed models based on neural-network architectures, the training becomes more difficult and expensive, which may have a counterproductive effect on the accuracy of calculation. In this work, we propose to make
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Phase transitions in the mini-batch size for sparse and dense two-layer neural networks Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-23 Raffaele Marino, Federico Ricci-Tersenghi
The use of mini-batches of data in training artificial neural networks is nowadays very common. Despite its broad usage, theories explaining quantitatively how large or small the optimal mini-batch size should be are missing. This work presents a systematic attempt at understanding the role of the mini-batch size in training two-layer neural networks. Working in the teacher-student scenario, with a
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Deep kernel methods learn better: from cards to process optimization Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-19 Mani Valleti, Rama K Vasudevan, Maxim A Ziatdinov, Sergei V Kalinin
The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL)
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δARD loss for low-contrast medical image segmentation Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-19 Yu Zhao, Xiaoyan Shen, Jiadong Chen, Wei Qian, He Ma, Liang Sang
Medical image segmentation is essential to image-based disease analysis and has proven to be significantly helpful for doctors to make decisions. Due to the low-contrast of some medical images, the accurate segmentation of medical images has always been a challenging problem. The experiment found that UNet with current loss functions cannot capture subtle information in target contours or regions in
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Optimizing collective behavior of communicating active particles with machine learning Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-19 Jens Grauer, Fabian Jan Schwarzendahl, Hartmut Löwen, Benno Liebchen
Bacteria and other self-propelling microorganisms produce and respond to signaling molecules to communicate with each other (quorum sensing) and to direct their collective behavior. Here, we explore agents (active particles) which communicate with each other to coordinate their collective dynamics for maximizing nutrient consumption. Using reinforcement learning and neural networks, we identify three
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A multi-stage machine learning algorithm for estimating personal dose equivalent using thermoluminescent dosimeter Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-18 Munir S Pathan, S M Pradhan, T Palani Selvam, B K Sapra
In the present age, marked by data-driven advancements in various fields, the importance of machine learning (ML) holds a prominent position. The ability of ML algorithms to resolve complex patterns and extract insights from large datasets has solidified its transformative potential in various scientific domains. This paper introduces an innovative application of ML techniques in the domain of radiation
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High-resolution imaging in acoustic microscopy using deep learning Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-18 Pragyan Banerjee, Shivam Milind Akarte, Prakhar Kumar, Muhammad Shamsuzzaman, Ankit Butola, Krishna Agarwal, Dilip K Prasad, Frank Melandsø, Anowarul Habib
Acoustic microscopy is a cutting-edge label-free imaging technology that allows us to see the surface and interior structure of industrial and biological materials. The acoustic image is created by focusing high-frequency acoustic waves on the object and then detecting reflected signals. On the other hand, the quality of the acoustic image’s resolution is influenced by the signal-to-noise ratio, the
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Application of autoencoders artificial neural network and principal component analysis for pattern extraction and spatial regionalization of global temperature data Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-17 Chibuike Chiedozie Ibebuchi, Omon A Obarein, Itohan-Osa Abu
Spatial regionalization is instrumental in simplifying the spatial complexity of the climate system. To identify regions of significant climate variability, pattern extraction is often required prior to spatial regionalization with a clustering algorithm. In this study, the autoencoder (AE) artificial neural network was applied to extract the inherent patterns of global temperature data (from 1901
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Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-17 Niall Jeffrey, Benjamin D Wandelt
Evidence Networks can enable Bayesian model comparison when state-of-the-art methods (e.g. nested sampling) fail and even when likelihoods or priors are intractable or unknown. Bayesian model comparison, i.e. the computation of Bayes factors or evidence ratios, can be cast as an optimization problem. Though the Bayesian interpretation of optimal classification is well-known, here we change perspective
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Mud-Net: multi-domain deep unrolling network for simultaneous sparse-view and metal artifact reduction in computed tomography Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-17 Baoshun Shi, Ke Jiang, Shaolei Zhang, Qiusheng Lian, Yanwei Qin, Yunsong Zhao
Sparse-view computed tomography (SVCT) is regarded as a promising technique to accelerate data acquisition and reduce radiation dose. However, in the presence of metallic implants, SVCT inevitably makes the reconstructed CT images suffer from severe metal artifacts and streaking artifacts due to the lack of sufficient projection data. Previous stand-alone SVCT and metal artifact reduction (MAR) methods
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Redefining the North Atlantic Oscillation index generation using autoencoder neural network Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-16 Chibuike Chiedozie Ibebuchi
Understanding the spatial patterns of the North Atlantic Oscillation (NAO) is vital for climate science. For this reason, empirical orthogonal function (EOF) analysis is commonly applied to sea-level pressure (SLP) anomaly data in the North Atlantic region. This study evaluated the traditional EOF-based definition of the NAO index against the autoencoder (AE) neural network-based definition, using
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Deep learning of crystalline defects from TEM images: a solution for the problem of ‘never enough training data’ Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-16 Kishan Govind, Daniela Oliveros, Antonin Dlouhy, Marc Legros, Stefan Sandfeld
Crystalline defects, such as line-like dislocations, play an important role for the performance and reliability of many metallic devices. Their interaction and evolution still poses a multitude of open questions to materials science and materials physics. In-situ transmission electron microscopy (TEM) experiments can provide important insights into how dislocations behave and move. The analysis of
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Discovering interpretable physical models using symbolic regression and discrete exterior calculus Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-16 Simone Manti, Alessandro Lucantonio
Computational modeling is a key resource to gather insight into physical systems in modern scientific research and engineering. While access to large amount of data has fueled the use of machine learning to recover physical models from experiments and increase the accuracy of physical simulations, purely data-driven models have limited generalization and interpretability. To overcome these limitations
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Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-10 Sara Ayoub Miskovich, Willie Neiswanger, William Colocho, Claudio Emma, Jacqueline Garrahan, Timothy Maxwell, Christopher Mayes, Stefano Ermon, Auralee Edelen, Daniel Ratner
Although beam emittance is critical for the performance of high-brightness accelerators, optimization is often time limited as emittance calculations, commonly done via quadrupole scans, are typically slow. Such calculations are a type of multipoint query, i.e. each query requires multiple secondary measurements. Traditional black-box optimizers such as Bayesian optimization are slow and inefficient
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Synthetic pre-training for neural-network interatomic potentials Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-10 John L A Gardner, Kathryn T Baker, Volker L Deringer
Machine learning (ML) based interatomic potentials have transformed the field of atomistic materials modelling. However, ML potentials depend critically on the quality and quantity of quantum-mechanical reference data with which they are trained, and therefore developing datasets and training pipelines is becoming an increasingly central challenge. Leveraging the idea of ‘synthetic’ (artificial) data
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Neural network field theories: non-Gaussianity, actions, and locality Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-09 Mehmet Demirtas, James Halverson, Anindita Maiti, Matthew D Schwartz, Keegan Stoner
Both the path integral measure in field theory (FT) and ensembles of neural networks (NN) describe distributions over functions. When the central limit theorem can be applied in the infinite-width (infinite-N) limit, the ensemble of networks corresponds to a free FT. Although an expansion in 1/N corresponds to interactions in the FT, others, such as in a small breaking of the statistical independence
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Explainable representation learning of small quantum states Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-05 Felix Frohnert, Evert van Nieuwenburg
Unsupervised machine learning models build an internal representation of their training data without the need for explicit human guidance or feature engineering. This learned representation provides insights into which features of the data are relevant for the task at hand. In the context of quantum physics, training models to describe quantum states without human intervention offers a promising approach
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Generating artificial displacement data of cracked specimen using physics-guided adversarial networks Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-01-04 David Melching, Erik Schultheis, Eric Breitbarth
Digital image correlation (DIC) has become a valuable tool to monitor and evaluate mechanical experiments of cracked specimen, but the automatic detection of cracks is often difficult due to inherent noise and artefacts. Machine learning models have been extremely successful in detecting crack paths and crack tips using DIC-measured, interpolated full-field displacements as input to a convolution-based
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FAIR AI models in high energy physics Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-29 Javier Duarte, Haoyang Li, Avik Roy, Ruike Zhu, E A Huerta, Daniel Diaz, Philip Harris, Raghav Kansal, Daniel S Katz, Ishaan H Kavoori, Volodymyr V Kindratenko, Farouk Mokhtar, Mark S Neubauer, Sang Eon Park, Melissa Quinnan, Roger Rusack, Zhizhen Zhao
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning models—algorithms that have been trained on data without being explicitly programmed—and
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Exploring explainable AI: category theory insights into machine learning algorithms Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-28 Ares Fabregat-Hernández, Javier Palanca, Vicent Botti
Explainable artificial intelligence (XAI) is a growing field that aims to increase the transparency and interpretability of machine learning (ML) models. The aim of this work is to use the categorical properties of learning algorithms in conjunction with the categorical perspective of the information in the datasets to give a framework for explainability. In order to achieve this, we are going to define
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Role of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-27 Ryan Jacobs, Philip E Goins, Dane Morgan
Materials discovery and design typically proceeds through iterative evaluation (both experimental and computational) to obtain data, generally targeting improvement of one or more properties under one or more constraints (e.g. time or budget). However, there can be great variation in the quality and cost of different data, and when they are mixed together in what we here call multifidelity data, the
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MLGN: multi-scale local-global feature learning network for long-term series forecasting Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-22 Maowei Jiang, Kai Wang, Yue Sun, Wenbo Chen, Bingjie Xia, Ruiqi Li
Although Transformer-based methods have achieved remarkable performance in the field of long-term series forecasting, they can be computationally expensive and lack the ability to specifically model local features as CNNs. CNN-based methods, such as temporal convolutional network (TCN), utilize convolutional filters to capture local temporal features. However, the intermediate layers of TCN suffer
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Differentiable Earth mover’s distance for data compression at the high-luminosity LHC Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-22 Rohan Shenoy, Javier Duarte, Christian Herwig, James Hirschauer, Daniel Noonan, Maurizio Pierini, Nhan Tran, Cristina Mantilla Suarez
The Earth mover’s distance (EMD) is a useful metric for image recognition and classification, but its usual implementations are not differentiable or too slow to be used as a loss function for training other algorithms via gradient descent. In this paper, we train a convolutional neural network (CNN) to learn a differentiable, fast approximation of the EMD and demonstrate that it can be used as a substitute
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High-dimensional reinforcement learning for optimization and control of ultracold quantum gases Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-21 N Milson, A Tashchilina, T Ooi, A Czarnecka, Z F Ahmad, L J LeBlanc
Machine-learning (ML) techniques are emerging as a valuable tool in experimental physics, and among them, reinforcement learning (RL) offers the potential to control high-dimensional, multistage processes in the presence of fluctuating environments. In this experimental work, we apply RL to the preparation of an ultracold quantum gas to realize a consistent and large number of atoms at microkelvin
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Bayesian experimental design and parameter estimation for ultrafast spin dynamics Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-18 Zhantao Chen, Cheng Peng, Alexander N Petsch, Sathya R Chitturi, Alana Okullo, Sugata Chowdhury, Chun Hong Yoon, Joshua J Turner
Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and materials physics, which often suffer from the scarcity of large-scale facility resources, such as x-ray or neutron scattering centers. To address these limitations, we introduce a methodology that leverages the Bayesian optimal experimental design paradigm to efficiently
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Physics-enhanced neural networks for equation-of-state calculations Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-18 Timothy J Callow, Jan Nikl, Eli Kraisler, Attila Cangi
Rapid access to accurate equation-of-state (EOS) data is crucial in the warm-dense matter (WDM) regime, as it is employed in various applications, such as providing input for hydrodynamic codes to model inertial confinement fusion processes. In this study, we develop neural network models for predicting the EOS based on first-principles data. The first model utilises basic physical properties, while
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A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-15 Francesco Pio Barone, Daniele Dell’Aquila, Marco Russo
Advanced LIGO and Advanced Virgo ground-based interferometers are instruments capable to detect gravitational wave (GW) signals exploiting advanced laser interferometry techniques. The underlying data analysis task consists in identifying specific patterns in noisy timeseries, but it is made extremely complex by the incredibly small amplitude of the target signals. In this scenario, the development
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Using random forest for brain tissue identification by Raman spectroscopy Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-14 Weiyi Zhang, Chau Minh Giang, Qingan Cai, Behnam Badie, Jun Sheng, Chen Li
The traditional definitive diagnosis of brain tumors is performed by needle biopsy under the guidance of imaging-based exams. This paradigm is based on the experience of radiogolists, and accuracy could be affected by uncertainty in imaging interpretation and needle placement. Raman spectroscopy has the potential to improve needle biopsy by providing fingerprints of different materials and performing
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A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-14 Mohammad Rezasefat, James D Hogan
This study presents a data-driven finite element-machine learning surrogate model for predicting the end-to-end full-field stress distribution and stress concentration around an arbitrary-shaped inclusion. This is important because the model’s capacity to handle large datasets, consider variations in size and shape, and accurately replicate stress fields makes it a valuable tool for studying how inclusion
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Scalable quantum measurement error mitigation via conditional independence and transfer learning Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-13 Changwon Lee, Daniel K Park
Mitigating measurement errors in quantum systems without relying on quantum error correction is of critical importance for the practical development of quantum technology. Deep learning-based quantum measurement error mitigation (QMEM) has exhibited advantages over the linear inversion method due to its capability to correct non-linear noise. However, scalability remains a challenge for both methods
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Rescuing off-equilibrium simulation data through dynamic experimental data with dynAMMo Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-12 Christopher Kolloff, Simon Olsson
Long-timescale behavior of proteins is fundamental to many biological processes. Molecular dynamics (MD) simulations and biophysical experiments are often used to study protein dynamics. However, high computational demands of MD limit what timescales are feasible to study, often missing rare events, which are critical to explain experiments. On the other hand, experiments are limited by low resolution
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Application of kernel principal component analysis for optical vector atomic magnetometry Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-08 James A McKelvy, Irina Novikova, Eugeniy E Mikhailov, Mario A Maldonado, Isaac Fan, Yang Li, Ying-Ju Wang, John Kitching, Andrey B Matsko
Vector atomic magnetometers that incorporate electromagnetically induced transparency (EIT) allow for precision measurements of magnetic fields that are sensitive to the directionality of the observed field by virtue of fundamental physics. However, a practical methodology of accurately recovering the longitudinal angle of the local field through observations of EIT spectra has not been established
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Parameter-free basis allocation for efficient multiple metric learning Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-08 Dongyeon Kim, Yejin Kan, Seungmin Lee, Gangman Yi
Metric learning involves learning a metric function for distance measurement, which plays an important role in improving the performance of classification or similarity-based algorithms. Multiple metric learning is essential for efficiently reflecting the local properties between instances, as single metric learning has limitations in reflecting the nonlinear structure of complex datasets. Previous
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Breast cancer diagnosis through knowledge distillation of Swin transformer-based teacher–student models Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-07 Bhavannarayanna Kolla, Venugopal P
Breast cancer is a significant global health concern, emphasizing the crucial need for a timely and accurate diagnosis to enhance survival rates. Traditional diagnostic methods rely on pathologists analyzing whole-slide images (WSIs) to identify and diagnose malignancies. However, this task is complex, demanding specialized expertise and imposing a substantial workload on pathologists. Additionally
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Deep learning for enhanced free-space optical communications Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-07 M P Bart, N J Savino, P Regmi, L Cohen, H Safavi, H C Shaw, S Lohani, T A Searles, B T Kirby, H Lee, R T Glasser
Atmospheric effects, such as turbulence and background thermal noise, inhibit the propagation of light used in ON–OFF keying (OOK) free-space optical (FSO) communication. Here we present and experimentally validate a convolutional neural network (CNN) to reduce the bit error rate of FSO communication in post-processing that is significantly simpler and cheaper than existing solutions based on advanced
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GA-based weighted ensemble learning for multi-label aerial image classification using convolutional neural networks and vision transformers Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-07 Ming-Hseng Tseng
Multi-label classification (MLC) of aerial images is a crucial task in remote sensing image analysis. Traditional image classification methods have limitations in image feature extraction, leading to an increasing use of deep learning models, such as convolutional neural networks (CNN) and vision transformers (ViT). However, the standalone use of these models may have limitations when dealing with
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Machine learning of microscopic structure-dynamics relationships in complex molecular systems Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-06 Martina Crippa, Annalisa Cardellini, Matteo Cioni, Gábor Csányi, Giovanni M Pavan
In many complex molecular systems, the macroscopic ensemble’s properties are controlled by microscopic dynamic events (or fluctuations) that are often difficult to detect via pattern-recognition approaches. Discovering the relationships between local structural environments and the dynamical events originating from them would allow unveiling microscopic-level structure-dynamics relationships fundamental
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Improved decision making with similarity based machine learning: applications in chemistry Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-06 Dominik Lemm, Guido Falk von Rudorff, O Anatole von Lilienfeld
Despite the fundamental progress in autonomous molecular and materials discovery, data scarcity throughout chemical compound space still severely hampers the use of modern ready-made machine learning models as they rely heavily on the paradigm, ‘the bigger the data the better’. Presenting similarity based machine learning (SML), we show an approach to select data and train a model on-the-fly for specific
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Harnessing data augmentation to quantify uncertainty in the early estimation of single-photon source quality Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-12-04 David Jacob Kedziora, Anna Musiał, Wojciech Rudno-Rudziński, Bogdan Gabrys
Novel methods for rapidly estimating single-photon source (SPS) quality have been promoted in recent literature to address the expensive and time-consuming nature of experimental validation via intensity interferometry. However, the frequent lack of uncertainty discussions and reproducible details raises concerns about their reliability. This study investigates the use of data augmentation, a machine
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Intelligent processing of electromagnetic data using detrended and identification Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-11-30 Xian Zhang, Diquan Li, Bei Liu, Yanfang Hu, Yao Mo
The application of the electromagnetic method has accelerated due to the demand for the development of mineral resource, however the strong electromagnetic interference seriously lowers the data quality, resolution and detect effect. To suppress the electromagnetic interference, this paper proposes an intelligent processing method based on detrended and identification, and applies for wide field electromagnetic
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Fast neural network inference on FPGAs for triggering on long-lived particles at colliders Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2023-11-29 Andrea Coccaro, Francesco Armando Di Bello, Stefano Giagu, Lucrezia Rambelli, Nicola Stocchetti
Experimental particle physics demands a sophisticated trigger and acquisition system capable to efficiently retain the collisions of interest for further investigation. Heterogeneous computing with the employment of FPGA cards may emerge as a trending technology for the triggering strategy of the upcoming high-luminosity program of the Large Hadron Collider at CERN In this context, we present two machine-learning