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GWAK: gravitational-wave anomalous knowledge with recurrent autoencoders Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-24 Ryan Raikman, Eric A Moreno, Ekaterina Govorkova, Ethan J Marx, Alec Gunny, William Benoit, Deep Chatterjee, Rafia Omer, Muhammed Saleem, Dylan S Rankin, Michael W Coughlin, Philip C Harris and Erik Katsavounidis
Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such techniques have been traditionally used in searches for compact binary coalescences (CBCs), and have been employed in all known GW detections so far. However, interesting science cases aside from compact mergers do not yet have accurate
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Multimodal protein representation learning and target-aware variational auto-encoders for protein-binding ligand generation Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-24 Nhat Khang Ngo and Truong Son Hy
Without knowledge of specific pockets, generating ligands based on the global structure of a protein target plays a crucial role in drug discovery as it helps reduce the search space for potential drug-like candidates in the pipeline. However, contemporary methods require optimizing tailored networks for each protein, which is arduous and costly. To address this issue, we introduce TargetVAE, a target-aware
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Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-22 Marco Knipfer, Stefan Meier, Tobias Volk, Jonas Heimerl, Peter Hommelhoff and Sergei Gleyzer
Accurate observation of two or more particles within a very narrow time window has always been a challenge in modern physics. It creates the possibility of correlation experiments, such as the ground-breaking Hanbury Brown–Twiss experiment, leading to new physical insights. For low-energy electrons, one possibility is to use a Microchannel plate with subsequent delay lines for the readout of the incident
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Generalization of graph-based active learning relaxation strategies across materials Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-21 Xiaoxiao Wang, Joseph Musielewicz, Richard Tran, Sudheesh Kumar Ethirajan, Xiaoyan Fu, Hilda Mera, John R Kitchin, Rachel C Kurchin and Zachary W Ulissi
Although density functional theory (DFT) has aided in accelerating the discovery of new materials, such calculations are computationally expensive, especially for high-throughput efforts. This has prompted an explosion in exploration of machine learning (ML) assisted techniques to improve the computational efficiency of DFT. In this study, we present a comprehensive investigation of the broader application
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Neural networks for separation of cosmic gamma rays and hadronic cosmic rays in air shower observation with a large area surface detector array Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-17 Sousuke Okukawa, Kazuyuki Hara, Kinya Hibino, Yusaku Katayose, Kazumasa Kawata, Munehiro Ohnishi, Takashi Sako, Takashi K Sako, Makio Shibata, Atsushi Shiomi and Masato Takita
The Tibet ASγ experiment has been observing cosmic gamma rays and cosmic rays in the energy range from teraelectron volts to several tens of petaelectron volts with a surface detector array since 1990. The derivation of cosmic gamma-ray flux is made by finding the excess distribution of the arrival direction of air showers above background cosmic rays. In 2014, the underground water Cherenkov muon
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GRINN: a physics-informed neural network for solving hydrodynamic systems in the presence of self-gravity Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-17 Sayantan Auddy, Ramit Dey, Neal J Turner and Shantanu Basu
Modeling self-gravitating gas flows is essential to answering many fundamental questions in astrophysics. This spans many topics including planet-forming disks, star-forming clouds, galaxy formation, and the development of large-scale structures in the Universe. However, the nonlinear interaction between gravity and fluid dynamics offers a formidable challenge to solving the resulting time-dependent
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Training quantum Boltzmann machines with the β-variational quantum eigensolver Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-17 Onno Huijgen, Luuk Coopmans, Peyman Najafi, Marcello Benedetti and Hilbert J Kappen
The quantum Boltzmann machine (QBM) is a generative machine learning model for both classical data and quantum states. Training the QBM consists of minimizing the relative entropy from the model to the target state. This requires QBM expectation values which are computationally intractable for large models in general. It is therefore important to develop heuristic training methods that work well in
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Transferring predictions of formation energy across lattices of increasing size* Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-17 Massimiliano Lupo Pasini, Mariia Karabin and Markus Eisenbach
In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of the nickel-platinum solid solution alloy across atomic structures of increasing sizes. The original dataset was generated with the large-scale atomic/molecular massively parallel simulator using the second nearest-neighbor modified embedded-atom method empirical interatomic
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Classifying rock types by geostatistics and random forests in tandem Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-16 Parag Jyoti Dutta and Xavier Emery
Rock type classification is crucial for evaluating mineral resources in ore deposits and for rock mechanics. Mineral deposits are formed in a variety of rock bodies and rock types. However, the rock type identification in drill core samples is often complicated by overprinting and weathering processes. An approach to classifying rock types from drill core data relies on whole-rock geochemical assays
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A method for quantifying the generalization capabilities of generative models for solving Ising models Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-15 Qunlong Ma, Zhi Ma, Ming Gao
For Ising models with complex energy landscapes, whether the ground state can be found by neural networks depends heavily on the Hamming distance between the training datasets and the ground state. Despite the fact that various recently proposed generative models have shown good performance in solving Ising models, there is no adequate discussion on how to quantify their generalization capabilities
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Generation of conformational ensembles of small molecules via surrogate model-assisted molecular dynamics Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-15 Juan Viguera Diez, Sara Romeo Atance, Ola Engkvist, Simon Olsson
The accurate prediction of thermodynamic properties is crucial in various fields such as drug discovery and materials design. This task relies on sampling from the underlying Boltzmann distribution, which is challenging using conventional approaches such as simulations. In this work, we introduce surrogate model-assisted molecular dynamics (SMA-MD), a new procedure to sample the equilibrium ensemble
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Differentiable simulation of a liquid argon time projection chamber Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-14 Sean Gasiorowski, Yifan Chen, Youssef Nashed, Pierre Granger, Camelia Mironov, Ka Vang Tsang, Daniel Ratner and Kazuhiro Terao
Liquid argon time projection chambers (LArTPCs) are widely used in particle detection for their tracking and calorimetric capabilities. The particle physics community actively builds and improves high-quality simulators for such detectors in order to develop physics analyses in a realistic setting. The ability of these simulators to mimic real, measured data is limited by the modeling of the physical
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Neural networks as effective surrogate models of radio-frequency quadrupole particle accelerator simulations Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-12 Joshua Villarreal, Daniel Winklehner, Daniel Koser, Janet M Conrad
Radio-frequency quadrupoles (RFQs) are multi-purpose linear particle accelerators that simultaneously bunch and accelerate charged particle beams. They are ubiquitous in accelerator physics, especially as injectors to higher-energy machines, owing to their impressive efficiency. The design and optimization of these devices can be lengthy due to the need to repeatedly perform high-fidelity simulations
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Analysis and Benchmarking of feature reduction for classification under computational constraints Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-11 Omer Subasi, Sayan Ghosh, Joseph Manzano, Bruce Palmer, Andrés Marquez
Machine learning is most often expensive in terms of computational and memory costs due to training with large volumes of data. Current computational limitations of many computing systems motivate us to investigate practical approaches, such as feature selection and reduction, to reduce the time and memory costs while not sacrificing the accuracy of classification algorithms. In this work, we carefully
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A machine learning constitutive model for plasticity and strain hardening of polycrystalline metals based on data from micromechanical simulations Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-11 Ronak Shoghi, Alexander Hartmaier
Machine learning (ML) methods have emerged as promising tools for generating constitutive models directly from mechanical data. Constitutive models are fundamental in describing and predicting the mechanical behavior of materials under arbitrary loading conditions. In recent approaches, the yield function, central to constitutive models, has been formulated in a data-oriented manner using ML. Many
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Using convolutional neural networks for stereological characterization of 3D hetero-aggregates based on synthetic STEM data Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-08 Lukas Fuchs, Tom Kirstein, Christoph Mahr, Orkun Furat, Valentin Baric, Andreas Rosenauer, Lutz Mädler, Volker Schmidt
The 3D nano/microstructure of materials can significantly influence their macroscopic properties. In order to enable a better understanding of such structure-property relationships, 3D microscopy techniques can be deployed, which are however often expensive in both time and costs. Often 2D imaging techniques are more accessible, yet they have the disadvantage that the 3D nano/microstructure of materials
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Graph convolutional multi-mesh autoencoder for steady transonic aircraft aerodynamics Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-04 David Massegur, Andrea Da Ronch
Calculating aerodynamic loads around an aircraft using computational fluid dynamics is a user’s and computer-intensive task. An attractive alternative is to leverage neural networks (NNs) bypassing the need of solving the governing fluid equations at all flight conditions of interest. NNs have the ability to infer highly nonlinear predictions if a reference dataset is available. This work presents
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Generation model meets swin transformer for unsupervised low-dose CT reconstruction Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-04 Yu Li, Xueqin Sun, Sukai Wang, Yingwei Qin, Jinxiao Pan, Ping Chen
Computed tomography (CT) has evolved into an indispensable tool for clinical diagnosis. Reducing radiation dose crucially minimizes adverse effects but may introduce noise and artifacts in reconstructed images, affecting diagnostic processes for physicians. Scholars have tackled deep learning training instability by exploring diffusion models. Given the scarcity of clinical data, we propose the unsupervised
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Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-03 Yun-Wen Mao, Roman V Krems
We demonstrate accurate data-starved models of molecular properties for interpolation in chemical compound spaces with low-dimensional descriptors. Our starting point is based on three-dimensional, universal, physical descriptors derived from the properties of the distributions of the eigenvalues of Coulomb matrices. To account for the shape and composition of molecules, we combine these descriptors
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Active robotic search for victims using ensemble deep learning techniques Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-03 Jorge F García-Samartín, Christyan Cruz Ulloa, Jaime del Cerro, Antonio Barrientos
In recent years, legged quadruped robots have proved to be a valuable support to humans in dealing with search and rescue operations. These robots can move with great ability in complex terrains, unstructured environments or regions with many obstacles. This work employs the quadruped robot A1 Rescue Tasks UPM Robot (ARTU-R) by Unitree, equipped with an RGB-D camera and a lidar, to perform victim searches
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Generative adversarial networks for data-scarce radiative heat transfer applications Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-03 J J García-Esteban, J C Cuevas, J Bravo-Abad
Generative adversarial networks (GANs) are one of the most robust and versatile techniques in the field of generative artificial intelligence. In this work, we report on an application of GANs in the domain of synthetic spectral data generation for data-scarce radiative heat transfer applications, an area where their use has not been previously reported. We demonstrate the proposed approach by applying
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Using positional tracking to improve abdominal ultrasound machine learning classification Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-02 Alistair Lawley, Rory Hampson, Kevin Worrall, Gordon Dobie
Diagnostic abdominal ultrasound screening and monitoring protocols are based around gathering a set of standard cross sectional images that ensure the coverage of relevant anatomical structures during the collection procedure. This allows clinicians to make diagnostic decisions with the best picture available from that modality. Currently, there is very little assistance provided to sonographers to
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On the expressivity of embedding quantum kernels Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-02 Elies Gil-Fuster, Jens Eisert, Vedran Dunjko
One of the most natural connections between quantum and classical machine learning has been established in the context of kernel methods. Kernel methods rely on kernels, which are inner products of feature vectors living in large feature spaces. Quantum kernels are typically evaluated by explicitly constructing quantum feature states and then taking their inner product, here called embedding quantum
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Laziness, barren plateau, and noises in machine learning Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-02 Junyu Liu, Zexi Lin, Liang Jiang
We define laziness to describe a large suppression of variational parameter updates for neural networks, classical or quantum. In the quantum case, the suppression is exponential in the number of qubits for randomized variational quantum circuits. We discuss the difference between laziness and barren plateau in quantum machine learning created by quantum physicists in McClean et al (2018 Nat. Commun
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Ensemble classifiers fed by functional connectivity during cognitive processing differentiate Parkinson’s disease even being under medication Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-04-02 Emine Elif Tülay
Brain–computer interface technologies, as a type of human-computer interaction, provide a control ability on machines and intelligent systems via human brain functions without needing physical contact. Moreover, it has a considerable contribution to the detection of cognitive state changes, which gives a clue for neurodegenerative diseases, including Parkinson’s disease (PD), in recent years. Although
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Qudit machine learning Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-28 Sebastián Roca-Jerat, Juan Román-Roche, David Zueco
We present a comprehensive investigation into the learning capabilities of a simple d-level system (qudit). Our study is specialized for classification tasks using real-world databases, specifically the Iris, breast cancer, and MNIST datasets. We explore various learning models in the metric learning framework, along with different encoding strategies. In particular, we employ data re-uploading techniques
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Leveraging trust for joint multi-objective and multi-fidelity optimization Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-27 Faran Irshad, Stefan Karsch, Andreas Döpp
In the pursuit of efficient optimization of expensive-to-evaluate systems, this paper investigates a novel approach to Bayesian multi-objective and multi-fidelity (MOMF) optimization. Traditional optimization methods, while effective, often encounter prohibitively high costs in multi-dimensional optimizations of one or more objectives. Multi-fidelity approaches offer potential remedies by utilizing
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Actively learning costly reward functions for reinforcement learning Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-26 André Eberhard, Houssam Metni, Georg Fahland, Alexander Stroh, Pascal Friederich
Transfer of recent advances in deep reinforcement learning to real-world applications is hindered by high data demands and thus low efficiency and scalability. Through independent improvements of components such as replay buffers or more stable learning algorithms, and through massively distributed systems, training time could be reduced from several days to several hours for standard benchmark tasks
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Optimized multifidelity machine learning for quantum chemistry Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-25 Vivin Vinod, Ulrich Kleinekathöfer, Peter Zaspel
Machine learning (ML) provides access to fast and accurate quantum chemistry (QC) calculations for various properties of interest such as excitation energies. It is often the case that high accuracy in prediction using a ML model, demands a large and costly training set. Various solutions and procedures have been presented to reduce this cost. These include methods such as Δ-ML, hierarchical-ML, and
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Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-21 Siwoo Lee, Stefan Heinen, Danish Khan, O Anatole von Lilienfeld
We present an automated data-collection pipeline involving a convolutional neural network and a large language model to extract user-specified tabular data from peer-reviewed literature. The pipeline is applied to 74 reports published between 1957 and 2014 with experimentally-measured oxidation potentials for 592 organic molecules (−0.75 to 3.58 V). After data curation (solvents, reference electrodes
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The impact of memory on learning sequence-to-sequence tasks Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-21 Alireza Seif, Sarah A M Loos, Gennaro Tucci, Édgar Roldán, Sebastian Goldt
The recent success of neural networks in natural language processing has drawn renewed attention to learning sequence-to-sequence (seq2seq) tasks. While there exists a rich literature that studies classification and regression tasks using solvable models of neural networks, seq2seq tasks have not yet been studied from this perspective. Here, we propose a simple model for a seq2seq task that has the
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Observing Schrödinger’s cat with artificial intelligence: emergent classicality from information bottleneck Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-21 Zhelun Zhang, Yi-Zhuang You
We train a generative language model on the randomized local measurement data collected from Schrödinger’s cat quantum state. We demonstrate that the classical reality emerges in the language model due to the information bottleneck: although our training data contains the full quantum information about Schrödinger’s cat, a weak language model can only learn to capture the classical reality of the cat
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Inverting the Kohn–Sham equations with physics-informed machine learning Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-19 Vincent Martinetto, Karan Shah, Attila Cangi, Aurora Pribram-Jones
Electronic structure theory calculations offer an understanding of matter at the quantum level, complementing experimental studies in materials science and chemistry. One of the most widely used methods, density functional theory, maps a set of real interacting electrons to a set of fictitious non-interacting electrons that share the same probability density. Ensuring that the density remains the same
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Determination of droplet size from wide-angle light scattering image data using convolutional neural networks Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-15 Tom Kirstein, Simon Aßmann, Orkun Furat, Stefan Will, Volker Schmidt
Wide-angle light scattering (WALS) offers the possibility of a highly temporally and spatially resolved measurement of droplets in spray-based methods for nanoparticle synthesis. The size of these droplets is a critical variable affecting the final properties of synthesized materials such as hetero-aggregates. However, conventional methods for determining droplet sizes from WALS image data are labor-intensive
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Transforming two-dimensional tensor networks into quantum circuits for supervised learning Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-14 Zhihui Song, Jinchen Xu, Xin Zhou, Xiaodong Ding, Zheng Shan
There have been numerous quantum neural networks reported, but they struggle to match traditional neural networks in accuracy. Given the huge improvement of the neural network models’ accuracy by two-dimensional tensor network (TN) states in classical tensor network machine learning (TNML), it is promising to explore whether its application in quantum machine learning can extend the performance boundary
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An iterative deep learning procedure for determining electron scattering cross-sections from transport coefficients Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-14 Dale L Muccignat, Gregory G Boyle, Nathan A Garland, Peter W Stokes, Ronald D White
We propose improvements to the artificial neural network (ANN) method of determining electron scattering cross-sections from swarm data proposed by coauthors. A limitation inherent to this problem, known as the inverse swarm problem, is the non-unique nature of its solutions, particularly when there exists multiple cross-sections that each describe similar scattering processes. Considering this, prior
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WaveFormer: transformer-based denoising method for gravitational-wave data Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-13 He Wang, Yue Zhou, Zhoujian Cao, Zongkuan Guo, Zhixiang Ren
With the advent of gravitational-wave astronomy and the discovery of more compact binary coalescences, data quality improvement techniques are desired to handle the complex and overwhelming noise in gravitational wave (GW) observational data. Though recent machine learning-based studies have shown promising results for data denoising, they are unable to precisely recover both the GW signal amplitude
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Gaussian-process-regression-based method for the localization of exceptional points in complex resonance spectra Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-13 Patrick Egenlauf, Patric Rommel, Jörg Main
Resonances in open quantum systems depending on at least two controllable parameters can show the phenomenon of exceptional points (EPs), where not only the eigenvalues but also the eigenvectors of two or more resonances coalesce. Their exact localization in the parameter space is challenging, in particular in systems, where the computation of the quantum spectra and resonances is numerically very
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Robust errant beam prognostics with conditional modeling for particle accelerators Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-08 Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta
Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output. To avoid these faults, we apply anomaly detection techniques to predict unusual behavior and perform preemptive actions to improve the total availability. Supervised
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Spectral density classification for environment spectroscopy Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-08 J Barr, G Zicari, A Ferraro, M Paternostro
Spectral densities encode the relevant information characterizing the system–environment interaction in an open-quantum system problem. Such information is key to determining the system’s dynamics. In this work, we leverage the potential of machine learning techniques to reconstruct the features of the environment. Specifically, we show that the time evolution of a system observable can be used by
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Online meta-learned gradient norms for active learning in science and technology Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-08 Haiqi Dong, Amanda S Barnard, Amanda J Parker
Acquisition of scientific data can be expensive and time-consuming. Active learning is a solution to reduce costs and time by guiding the selection of scientific experiments. Autonomous and automatic identification of the most essential samples to annotate by active learning can also help to mitigate human bias. Previous research has demonstrated that unlabelled samples causing the largest gradient
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Quantum machine learning for image classification Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-08 Arsenii Senokosov, Alexandr Sedykh, Asel Sagingalieva, Basil Kyriacou, Alexey Melnikov
Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution
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WATUNet: a deep neural network for segmentation of volumetric sweep imaging ultrasound Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-08 Donya Khaledyan, Thomas J Marini, Avice O’Connell, Steven Meng, Jonah Kan, Galen Brennan, Yu Zhao, Timothy M Baran, Kevin J Parker
Limited access to breast cancer diagnosis globally leads to delayed treatment. Ultrasound, an effective yet underutilized method, requires specialized training for sonographers, which hinders its widespread use. Volume sweep imaging (VSI) is an innovative approach that enables untrained operators to capture high-quality ultrasound images. Combined with deep learning, like convolutional neural networks
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Stochastic gradient descent with random label noises: doubly stochastic models and inference stabilizer Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-04 Haoyi Xiong, Xuhong Li, Boyang Yu, Dongrui Wu, Zhanxing Zhu, Dejing Dou
Random label noise (or observational noise) widely exists in practical machine learning settings. While previous studies primarily focused on the effects of label noise to the performance of learning, our work intends to investigate the implicit regularization effects of label noise, under mini-batch sampling settings of stochastic gradient descent (SGD), with the assumption that label noise is unbiased
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Data-driven Lie point symmetry detection for continuous dynamical systems Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-01 Alex Gabel, Rick Quax, Efstratios Gavves
Symmetry detection, the task of discovering the underlying symmetries of a given dataset, has been gaining popularity in the machine learning community, particularly in science and engineering applications. Most previous works focus on detecting ‘canonical’ symmetries such as translation, scaling, and rotation, and cast the task as a modeling problem involving complex inductive biases and architecture
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Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-01 Mohammad Rezasefat, James D Hogan
This study investigates the application of machine learning models to predict time-evolving stress fields in complex three-dimensional structures trained with full-scale finite element simulation data. Two novel architectures, the multi-decoder CNN (MUDE-CNN) and the multiple encoder–decoder model with transfer learning (MTED-TL), were introduced to address the challenge of predicting the progressive
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Comparison of neural network architectures for feature extraction from binary black hole merger waveforms Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-03-01 Osvaldo Gramaxo Freitas, Juan Calderón Bustillo, José A Font, Solange Nunes, Antonio Onofre, Alejandro Torres-Forné
We evaluate several neural-network architectures, both convolutional and recurrent, for gravitational-wave time-series feature extraction by performing point parameter estimation on noisy waveforms from binary-black-hole mergers. We build datasets of 100 000 elements for each of four different waveform models (or approximants) in order to test how approximant choice affects feature extraction. Our
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Bridging the gap between high-level quantum chemical methods and deep learning models Mach. Learn. Sci. Technol. (IF 6.013) Pub Date : 2024-02-29 Viki Kumar Prasad, Alberto Otero-de-la-Roza, Gino A DiLabio
Supervised deep learning (DL) models are becoming ubiquitous in computational chemistry because they can efficiently learn complex input-output relationships and predict chemical properties at a cost significantly lower than methods based on quantum mechanics. The central challenge in many DL applications is the need to invest considerable computational resources in generating large ( N>1×105 ) training
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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