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Magnetic Feshbach resonances in ultracold atom-molecule collisions

Physical Review A 2024年第2期110卷 L021301-L021301页

作者： Masato Morita Maciej B. Kosicki Piotr S. Żuchowski Paul Brumer Timur V. Tscherbul Chemical Physics Theory Group Department of Chemistry and Center for Quantum Information and Quantum Control Faculty of Physics Institute of Physics Faculty of Physics Astronomy and Informatics Department of Physics

We report numerically exact quantum scattering calculations on magnetic Feshbach resonances in ultracold, strongly anisotropic atom-molecule [Rb(S2) + SrF(2Σ+)] collisions based on state-of-the-art ab initio potential energy surfaces. We find broad resonances mediated by the intermolecular spin-exchange interaction, as well as narrow resonances due to the intramolecular spin-rotation interaction, which are unique to atom-molecule collisions. Remarkably, the density of resonances in atom-molecule collisions is not much higher than that in atomic collisions despite the presence of a dense manifold of molecular rotational states, which can be rationalized by analyzing the adiabatic states of the collision complex.

关键词： Cold and ultracold molecules Scattering theory Ultracold collisions

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Deep learning with photonic neural cellular automata

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Light(Science & Applications) 2024年第12期13卷 3030-3040页

作者： Gordon H.Y.Li Christian R.Leefmans James Williams Robert M.Gray Midya Parto Alireza Marandi Department of Applied Physics California Institute of TechnologyPasadenaCAUSA Department of Electrical Engineering California Institute of TechnologyPasadenaCAUSA Physics and Informatics Laboratories NTT Research Inc.SunnyvaleCAUSA

Rapid advancements in deep learning over the past decade have fueled an insatiable demand for efficient and scalable *** offers a promising solution by leveraging the unique properties of ***,conventional neural network architectures,which typically require dense programmable connections,pose several practical challenges for photonic *** overcome these limitations,we propose and experimentally demonstrate Photonic Neural Cellular Automata(PNCA)for photonic deep learning with sparse *** harnesses the speed and interconnectivity of photonics,as well as the self-organizing nature of cellular automata through local interactions to achieve robust,reliable,and efficient *** utilize linear light interference and parametric nonlinear optics for all-optical computations in a time-multiplexed photonic network to experimentally perform self-organized image *** demonstrate binary(two-class)classification of images using as few as 3 programmable photonic parameters,achieving high experimental accuracy with the ability to also recognize out-ofdistribution *** proposed PNCA approach can be adapted to a wide range of existing photonic hardware and provides a compelling alternative to conventional photonic neural networks by maximizing the advantages of lightbased computing whilst mitigating their practical *** results showcase the potential of PNCA in advancing photonic deep learning and highlights a path for next-generation photonic computers.

关键词： automata connectivity utilize

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Evaluate Recent Numerical Methods for Long-Term Simulation to Study the Effect of Different Shapes of Thermal Bridges in Walls

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Defect and Diffusion Forum 2024年 436卷 77-90页

作者： Omle, Issa Kovács, Endre Department of Fluid and Heat Engineering Faculty of Mechanical Engineering and Informatics University of Miskolc Miskolc3515 Hungary Institute of Physics and Electrical Engineering Faculty of Mechanical Engineering and Informatics University of Miskolc Miskolc3515 Hungary

According to previous studies, the most effective, stable, and explicit numerical methods to deal with problems of heat transfer in building walls are the two recently published approaches, which are the modified Dufort-Frankel and leapfrog-hopscotch techniques, which are used in this study to make transient and long-term simulations (three months of the winter season) of 2-D space systems that enable us to execute these simulations with relatively short computational times to evaluate the two most effective versions of these methods. Our solution to a real-world engineering challenge involves investigating thermal bridges of different forms inside multilayer walls of buildings exposed to environmental factors specific to Hungary's climate, such as the outside temperature and sun radiation, to improve energy efficiency. The distributions of temperatures and the total heat loss (across the walls) are calculated for all cases (three layers without a thermal bridge three layers with thermal bridges in straight, bent, and L-shaped shapes). © 2024 Trans Tech Publications Ltd, All Rights Reserved.

关键词： Walls (structural partitions)

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Classification of Tree Species by Federated Learning 21

Classification of Tree Species by Federated Learning

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21st IEEE International Symposium on Intelligent Systems and informatics, SISY 2023

作者： Stano, Michal Dlugolinsky, Stefan Hluchy, Ladislav Kvassay, Marcel Krammer, Peter Tran, Viet Institute of Informatics Slovak Academy of Sciences Comenius University Faculty of Mathematics Physics and Informatics Bratislava Slovakia Institute of Informatics Slovak Academy of Sciences Bratislava Slovakia

ISBN: (纸本)9798350343366

This article focuses on applying federated learning methods to the use case of tree species classification on the basis of hyperspectral imaging data. A pixel-based approach is applied, using multiple inputs representing various spectral frequency bands. A deep neural network with a relatively simple structure was used for the classification of 14 different types of trees. The article includes an investigation of how the number of federated learning sites (worker nodes) and the communication frequency with the central aggregation server affect the overall accuracy of the classification model and the convergence speed. The presented work also introduces and explains the most commonly used Federated Learning techniques. © 2023 IEEE.

关键词： Classification (of information)

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Non-Markovian quantum dynamics from symmetric measurements

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Physical Review A 2024年第1期110卷 012440-012440页

作者： Katarzyna Siudzińska Institute of Physics Faculty of Physics Astronomy and Informatics

We use symmetric measurement operators to construct quantum channels that provide a further generalization of generalized Pauli channels. The resulting maps are bistochastic but, in general, no longer mixed unitary. We analyze their important properties, such as complete positivity and the ability to break quantum entanglement. In the main part, we consider the corresponding open quantum systems dynamics with time-local generators. From the divisibility properties of dynamical maps, we derive sufficient Markovianity and non-Markovianity conditions. As instructive examples, we present the generators of P-divisible generalized Pauli dynamical maps that allow for more negativity in the decoherence rates.

关键词： Open quantum systems & decoherence Quantum channels Quantum communication Quantum correlations in quantum information Quantum entanglement Quantum information theory Quantum measurements

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Reliability Assessment of Arm Cortex-M Processors under Heavy Ions and Emulated Fault Injection 25

Reliability Assessment of Arm Cortex-M Processors under Heav...

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25th IEEE Latin American Test Symposium, LATS 2024

作者： Gobatto, Leonardo R. Benevenuti, Fabio Added, Nemitala Alberton, Saulo G. MacChione, Eduardo L. A. Aguiar, Vitor A. P. Medina, Nilberto H. Kastensmidt, Fernanda L. Azambuja, Jose Rodrigo Institute Of Informatics - Ppgc - PGMicro Porto Alegre Brazil Institute Of Physics São Paulo Brazil

ISBN: (纸本)9798350365559

This paper evaluates radiation-induced software failure detection in embedded processors from the Cortex-M family. Our proposed methodology performs heavy ion irradiation experiments and establishes a connection with emulated fault injection for a benchmark of applications. We experimented on a set of case-studies in bare-metal and with the FreeRTOS operating system with different configurations, tailoring our approach for deployment in small satellite missions. Additionally, we tested a fault tolerance technique for fault detection. Results show coherence between irradiation and fault injection and can help designers build better fault tolerance mechanisms. © 2024 IEEE.

关键词： Heavy ions

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EEG Biomarkers Based on Microstates and RQA 17th

EEG Biomarkers Based on Microstates and RQA

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17th International Conference on Brain informatics, BI 2024

作者： Duch, Wlodzislaw Tolpa, Krzysztof Furman, Lukasz Ratajczak, Ewa Department of Informatics Institute of Engineering and Technology Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Toruń Poland Institute of Psychology Faculty of Philosophy and Social Sciences Nicolaus Copernicus University Toruń Poland

ISBN: (纸本)9789819632930

Although many sophisticated methods for analyzing electroencephalography (EEG) recordings have been developed, they are rarely used in clinical practice. To create robust EEG biomarkers that provide insight into the character of brain processes and distinguish mental disorders, we analyze neurodynamics using Recurrence Quantification Analysis (RQA) and simplify complex, non-stationary spatiotemporal oscillatory patterns using microstates. Transition patterns between microstates reflect brain dynamics. Average transition probability matrices between microstates may be used as reference prototypes for the classification of mental disorders. RQA enhances the feature space that microstate analysis provides, allowing for better interpretation of the results. We have tested this approach on adolescent schizophrenia data, comparing results based on microstate transitions with results based on features derived from RQA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electrophysiology

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Catalytic Advantage in Otto-like Two-Stroke Quantum Engines

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Physical Review Letters 2024年第26期132卷 260403-260403页

作者： Marcin Łobejko Tanmoy Biswas Paweł Mazurek Michał Horodecki Institute of Theoretical Physics and Astrophysics Faculty of Mathematics Physics and Informatics International Centre for Theory of Quantum Technologies Theoretical Division (T4) Institute of Informatics Faculty of Mathematics Physics and Informatics

We demonstrate how to incorporate a catalyst to enhance the performance of a heat engine. Specifically, we analyze efficiency in one of the simplest engine models, which operates in only two strokes and comprises of a pair of two-level systems, potentially assisted by a d-dimensional catalyst. When no catalysis is present, the efficiency of the machine is given by the Otto efficiency. Introducing the catalyst allows for constructing a protocol which overcomes this bound, while new efficiency can be expressed in a simple form as a generalization of Otto’s formula: 1−(1/d)(ωc/ωh). The catalyst also provides a bigger operational range of parameters in which the machine works as an engine. Although an increase in engine efficiency is mostly accompanied by a decrease in work production (approaching zero as the system approaches Carnot efficiency), it can lead to a more favorable trade-off between work and efficiency. The provided example introduces new possibilities for enhancing performance of thermal machines through finite-dimensional ancillary systems.

关键词： Quantum correlations in quantum information Quantum information processing Quantum information theory Quantum thermodynamics Quantum heat engines & refrigerators

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A State-of-the-Art Survey on Local Training Methods in Federated Learning 23

A State-of-the-Art Survey on Local Training Methods in Feder...

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23rd IEEE International Symposium on Computational Intelligence and informatics, CINTI 2023

作者： Stano, Michal Hluchy, Ladislav Institute of Informatics Slovak Academy of Sciences Bratislava Slovakia Physics and Informatics Comenius University Faculty of Mathematics Bratislava Slovakia

ISBN: (纸本)9798350342949

Federated learning is a machine learning paradigm that enables the training of machine learning models on decentralised data while significantly reducing the amount of communication required. This paper comprehensively overviews the five generations of local training methods in federated learning. We discuss the key contributions of each generation and the challenges that remain to be addressed. The first generation of local training methods proposed in the early 2010s was based on heuristic approaches. These methods had no theoretical guarantees, but they were often effective in practice. The second generation of local training methods, which was proposed in the late 2010s, was based on the assumption that the data on the different clients was homogeneous. This assumption allowed for the development of more theoretical guarantees for local training methods. The third generation of local training methods, which was proposed in the early 2020s, relaxed the assumption of homogeneous data and allowed for heterogeneous data. This made local training methods more applicable to real-world scenarios. The fourth generation of local training methods, which was proposed in the mid-2020s, further improved the convergence rate of local training methods. This enabled training machine learning models on federated learning with less communication and less computation. The fifth generation of local training methods, which is still under development, is exploring the use of advanced techniques such as acceleration and quantisation to improve the efficiency of local training methods further. We believe that the five generations of local training methods have made significant progress in the field of federated learning. We believe that the future of federated learning is bright, and we are excited to see what the next generation of local training methods will bring. Research on federated learning with adaptive communication has the potential to improve the efficiency and effectiveness

关键词： Efficiency

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Federated Learning Methods for Analytics of Big and Sensitive Distributed Data and Survey 17

Federated Learning Methods for Analytics of Big and Sensitiv...

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17th IEEE International Symposium on Applied Computational Intelligence and informatics, SACI 2023

作者： Stano, Michal Hluchy, Ladislav Bobak, Martin Krammer, Peter Tran, Viet Institute of Informatics Slovak Academy of Sciences Bratislava Slovakia Comenius University Faculty of Mathematics Physics and Informatics Bratislava Slovakia

ISBN: (纸本)9798350321104

This article focuses on analytics of big distributed sensitive data on a federated learning base. The main current focus is on the most common use technology platforms: TensorFlow Federated, PySyft, Flower and IBM Federated Learning of the point of view edge computing usability. Training PyTorch models with differential privacy (DP) is more scalable than existing state-of-the-art methods. Differential privacy is a mathematically rigorous framework for quantifying the anonymisation of sensitive data. It's often used in analytics, with growing interest in the machine learning (ML) community. Training distributed data at the edge is interesting for privacy sensitivity and the transfer of huge data. Sensitivity and huge data is the main challenge in federated learning. Federated learning is a solution for protecting huge device data through model updates. © 2023 IEEE.

关键词： Sensitive data

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