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arXiv 2025年

作者： Rico, Albert Physics department Universitat Autònoma de Barcelona Bellaterra BarcelonaES-08193 Spain Faculty of Physics Astronomy and Applied Computer Science Institute of Theoretical Physics Jagiellonian University Kraków30-348 Poland

Recently, a toolkit of highly symmetric techniques employing matrix inequalities has been developed to detect entanglement in various ways. Here we concisely review these methods providing a unified framework, and expand them to a new family of positive maps with further detection capabilities. In the simplest case, we generalize the reduction map to detect more generic states using both multiple copies and local filters. Through the Choi-Jamiolkowski isomorphism, this family of maps leads to a construction of multipartite entanglement witnesses. Discussions and examples are provided regarding the detection of states with local positive partial transposition and the use of multiple copies. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum entanglement

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State-witness contraction

arXiv

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arXiv 2025年

作者： Rico, Albert Physics Department Universitat Autònoma de Barcelona Bellaterra BarcelonaES-08193 Spain Faculty of Physics Astronomy and Applied Computer Science Institute of Theoretical Physics Jagiellonian University Kraków30-348 Poland

We present a method to construct entanglement witnesses for arbitrarily large multipartite systems, by tensoring and partial tracing existing states and witnesses. As a proof of principle we show that, using little shared quantum resources, the method allows to reuse witnesses unable to detect states with local positive partial transpositions into new ones able to do so. Moreover, we show that this technique allows to tailor both linear and nonlinear witnesses to specific states using semidefinite programming with comparatively low-dimensional variables. As an example, existing trace polynomial witnesses are significantly improved while preserving their symmetries and implementability with randomized measurements. Besides detecting entanglement, the method is shown to detect k-copy distillability. A recipe for the single-copy case is shown to be effective for generic and Werner states. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum entanglement

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Effect of transverse momentum conservation and flow on symmetric cumulants sc2,3 {4} and sc2,3,4 {6}

arXiv

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arXiv 2025年

作者： Pei, Jia-Lin Ma, Guo-Liang Bzdak, Adam Institute of Modern Physics Fudan University Shanghai200433 China Shanghai Research Center for Theoretical Nuclear Physics NSFC and Fudan University Shanghai200438 China AGH University of Science and Technology Faculty of Physics and Applied Computer Science Kraków30-059 Poland

Symmetric cumulants can improve our understanding of the joint probability distribution function P (vm, vn, vk, . . ., Ψm, Ψn, Ψk, . . .), potentially offering new insights into the nature of the fluctuations of the quark-gluon plasma produced in relativistic heavy-ion collisions. In this work, the four-particle symmetric cumulants sc2,3 {4}, six-particle symmetric cumulants sc2,3,4 {6}, and the normalized cumulants nsc2,3 {4} and nsc2,3,4 {6} originating from transverse momentum conservation, collective flow, and the interplay between the two effects are calculated. Our results are consistent with the ATLAS data using the subevent cumulant method and the simulations using the realistic Monte Carlo models of iEBE-VISHNU and HIJING, facilitating a more profound understanding of the origins of these symmetric cumulants in small systems. © 2025, CC BY.

关键词： Distribution functions

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Scalar-assisted magnetogenesis during the radiation-dominated epoch

arXiv

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arXiv 2025年

作者： Ganz, Alexander Lin, Chunshan Zhu, Mian College of Physics Sichuan University Chengdu610065 China Institute for Theoretical Physics Leibniz University Hannover Appelstraße 2 Hannover30167 Germany Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow30-348 Poland

We propose a novel mechanism to generate primordial magnetic fields (PMFs) strong enough to explain the observed cosmic magnetic fields. We employ a scalar field charged under U(1) gauge symmetry with a non-trivial VEV to provide an effective mass term to the EM field and thus break its conformal invariance. The primordial magnetogenesis takes place in the radiation dominated (RD) epoch, after the electroweak symmetry breaking (EWSB) phase. As a result, our mechanism is naturally free from the overproduction of electric fields due to high conductivity in the RD epoch, and the baryon isocurvature problem which takes place only if magneto-genesis happens before the ESWB phase. In addition, we find that a significant amount of PMFs can be generated when the scalar field experiences a tachyonic phase. In this case, the scalar field is light and weakly coupled and has negligible energy density compared to the cold dark matter, hence the strong coupling problem and the back-reaction problem are also absent. Therefore, our model is free from the above-mentioned problems that frequently appear in other primordial magneto-genesis scenarios. © 2025, CC BY.

关键词： Magnetic fields

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The Effect of Reduced Co Content in CrMnFeCoNi Alloys: A First Principles Study

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Metals 2025年第4期15卷 362-362页

作者： Johansson, Gustav Kuorak, Johan-Michael Gorbatov, Oleg Etz, Corina Applied Physics Division of Material Science Department of Engineering Sciences and Mathematics Luleå University of Technology Luleå97187 Sweden Faculty of Industrial Engineering Mechanical Engineering and Computer Science University of Iceland Sæmundargötu 2 Reykjavík102 Iceland

This study investigates high-entropy CrMnFeCoNi alloys with reduced Co content using density functional theory. The muffin-tin orbital method and coherent potential approximation successfully predict experimental values for volume, magnetic moment, and elastic constants. Thermodynamic properties, analyzed using the Debye–Gruneisen model, emphasize the need to consider both electronic and magnetic contributions to the free energy. The alloys exhibit anti-Invar behavior, with a significant increase in the linear thermal expansion coefficient with increased temperature. This effect is slightly more pronounced for reduced Co content, leading to a larger lattice parameter and a decrease in elastic constants. However, the changes are small, suggesting that similar mechanical properties can be achieved with lower Co content. © 2025 by the authors.

关键词： Thermal expansion

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DFDFTr + RAC-DAC: Dense Fusion with Deep Fusion Transformer and Realistic Actor-Critic combined with Decoupled Actor-Critic for end-to-end continuous multi-task robotic arm control

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Expert Systems with Applications 2025年 276卷

作者： Dang, Thi Phuc Nguyen, Truong Chinh Dau, Sy Hieu Department of Computer Science Faculty of Information Technology Industrial University of Ho Chi Minh City 12 Nguyen Van Bao Ho Chi Minh7000 Viet Nam Department of Applied Physics Faculty of Applied Science University of Technology - Viet Nam National University HCMC Ho Chi Minh City To Hien Thanh Ho Chi Minh7000 Viet Nam

In this paper, we propose novel methods to address the challenges of intelligent robotic arm operations in complex environments. Our approach includes a deep learning method for 6D object pose estimation and a deep reinforcement learning method for controlling complex tasks. For 6D object pose estimation, we introduce the Dense Fusion combined with Deep Fusion Transformer model (DFDFTr). This method integrates Dense Fusion and Deep Fusion Transformer, applies re-parameterization techniques such as Reparameterized Convolution and Batch Normalization Fusion, and incorporates specialized neural networks for each stage of the model. Experimental results on the LineMOD and Occlusion LineMOD datasets demonstrate that our method outperforms one-stage approaches like DenseFusion and MPF6D in accuracy and achieves results comparable to advanced two-stage methods such as PVN3D and DTr. Leveraging re-parameterization, our method maintains competitive inference speed with significantly fewer parameters than PVN3D and MPF6D, PoseCNN+ICP without sacrificing accuracy. Additionally, it efficiently processes multiple objects, handling 8 objects in 0.1 s compared to 0.07 s for a single object. For continuous task control, we propose a deep reinforcement learning approach using Realistic Actor-Critic combined with Decoupled Actor-Critic (RAC-DAC), integrated with techniques such as Relay Hindsight Experience Replay (RHER), Multi-step Hindsight Experience Replay (MHER), Curiosity-Driven Prioritization (CDP), and Projected Conflicting Gradients (PCGrad). Experimental results show that RAC-DAC-RHER achieves significantly higher success rates than DDPG-RHER and TD3-RHER, particularly in challenging tasks such as object grasping. It also ensures efficient learning and high accuracy. Furthermore, by refining hyperparameters and integrating MHER, CDP, PCGrad, MLP, and the Clipping Mechanism, we enhanced learning performance, mitigated instabilities, and enabled higher learning rates in spars

关键词： Robotic arms

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Enhancement of the topological regime in elongated Josephson junctions

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Physical Review B 2025年第8期111卷 085416-085416页

作者： D. Kuiri P. Wójcik M. P. Nowak AGH University of Krakow Academic Centre for Materials and Nanotechnology al. A. Mickiewicza 30 30-059 Krakow Poland AGH University of Krakow Faculty of Physics and Applied Computer Science al. A. Mickiewicza 30 30-059 Krakow Poland

We theoretically study topological superconductivity in elongated planar Josephson junctions. In the presence of spin-orbit coupling and an in-plane magnetic field, the Josephson junction can enter the topological phase and host zero-energy Majorana bound states over a range of the superconducting phase difference centered around π, with the span of this range determined by the strength of the magnetic field. We demonstrate that the topological superconducting phase range can be greatly increased by elongation of the junction, which causes an amplification of the Zeeman-induced phase shift of Andreev bound states. We show that the appearance of trivial in-gap states that occurs in elongated junctions can prohibit the creation of Majorana modes, but it can be mitigated by further proximitization of the junction with additional superconducting contacts. The topological transition in this system can be probed by measurements of the critical current and we show that the elongation of the junction leads to a linear decrease of the transition critical magnetic field beneficial for experimental studies.

关键词： Magnetic fields

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Unique Properties of the Electronic Structure of Alluaudite Sodium Iron Sulfate Cathode Material and the Impact on its Electrochemical Performance

SSRN

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SSRN 2025年

作者： Molenda, Janina Tobola, Janusz Milewska, Anna Budziak, Andrzej Zając, Wojciech Wolczko, Marta Dziedzic-Kocurek, Katarzyna Nowak, Mikolaj Kalahurska, Katarzyna Imam, Neama Henkel, Karsten Flege, Jan Ingo Zschech, Ehrenfried Dresden University of Technology Germany AGH University of Krakow Faculty of Energy and Fuels Al. Mickiewicza 30 Krakow30-059 Poland AGH University of Krakow Faculty of Physics and Applied Computer Science Al. Mickiewicza 30 Krakow30-059 Poland Jagiellonian University Faculty of Physics Astronomy and Applied Computer Science ul. S. Lojasiewicza 11 Krakow30-348 Poland Brandenburg University of Technology Cottbus-Senftenberg Institute of Physics Konrad-Zuse-Str. 1 Cottbus03046 Germany

This work explores the unique properties of the electronic structure of alluaudite-type Na2.5Fe1.75(SO4)3, which impacts its electrochemical properties as a cathode for sodium-ion batteries (SIBs). Density functional theory calculations using the implemented KKR-CPA package (Korringa-Kohn-Rostoker method combined with the coherent potential approximation) showed the diverse electrochemical activity of sodium at different sites within the alluaudite structure. This revealed unprecedented contributions of the Na atoms to the overall electronic density of states near the Fermi energy (dominated by d-Fe and p-O states), undetected before in the group of layered transition oxide cathode materials. A high-purity Na2.5Fe1.75(SO4)3 cathode material was synthesized by optimizing the solid-state synthesis route. Based on a multi-technique approach including: X-ray diffraction, scanning electron microscopy, Mössbauer, X-ray photoelectron, Fourier Transform Infrared, and Raman spectroscopy studies, the electronic structure and electrochemical properties of the cathode material and its evolution during the sodium deintercalation/intercalation processes have been characterized, providing a comprehensive understanding of the sodium storage mechanism. Operando and in-situ X-ray diffraction studies monitored changes in the crystal structure of the cathode material during charge/discharge cycles showing that the system exhibits a reversible tendency to amorphize at deep sodium deintercalation. The electrochemical analysis showed reversible cycling with minimal capacity loss (only 2.5% after 300 cycles at discharge at C/5 current loading), demonstrating high structural stability of this optimized cathode material. © 2025, The Authors. All rights reserved.

关键词： Sodium-ion batteries

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Explainable Machine Learning for Radio Environment Mapping: An Intelligent System for Electric Field Strength Monitoring.

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IEEE Access 2025年 13卷 75104-75122页

作者： Kiouvrekis, Yiannis Panagiotakopoulos, Theodor Nousi, Efthymia Filippopoulos, Ioannis Ploussi, Agapi Spyratou, Ellas Efstathopoulos, Efstathios P. University of Thessaly Mathematics Computer Science and Artificial Intelligence Lab Faculty of Public and One Health Karditsa43100 Greece University of Limassol Department of Information Technologies Limassol Cyprus University of Nicosia Business School Cyprus University of Patras Department of Management Science and Technology Patras26334 Greece Infralabs Ltd Agiou Pavlou 61 Nicosia Cyprus University of Limassol Limassol Cyprus National and Kapodistrian University of Athens Medical School Department of Applied Medical Physics Athens11527 Greece

The accurate characterization of signal propagation is critical for optimizing wireless network performance and supporting applications such as electromagnetic field (EMF) exposure assessment and the development of Radio Environmental Maps (REMs). This study proposes a novel, explainable machine learning system to predict electric field strength across diverse urban, semi-urban, and rural environments in Cyprus. The system is trained on a rich dataset comprising 6,543 EMF measurements collected in 2023 at mobile phone and digital TV stations, following CEPT/ECC/REC/(02)04 recommendations. The dataset includes geospatial and environmental features such as antenna distance, population density, urbanization level, and detailed built environment characteristics (e.g., volume, surface, and height). We evaluate multiple machine learning models—kNN, neural networks, decision trees, random forests, XGBoost, and LightGBM—using a two-semester split for training and assessment. Best performance was achieved with the Random Forest model, which yielded the lowest RMSE among all models. Gradient boosting models (XGBoost and LightGBM) also performed well, with RMSE values slightly higher than RF while offering flexible and scalable configurations. In contrast, k-NN and neural networks showed higher RMSE values, indicating they were less effective for this specific task. Across all models, confidence intervals were narrow, demonstrating stable and reliable predictions. Explainable AI techniques revealed that antenna distance, building volume, and population density are the most influential predictors of EMF intensity. Our approach outperforms traditional signal models by incorporating urban morphology and demographic context. As part of this system, we also create a Geographic Information System (GIS) that displays electromagnetic field strength maps derived from our explainable machine learning models. This contributes a scalable, interpretable framework for EMF exposure mapping t

关键词： Machine learning Predictive models Radio frequency Accuracy Electric fields Random forests Population density Explainable AI Estimation Buildings

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DFE: Deep Flow Embedding for Robust Network Traffic Classification

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IEEE Transactions on Network science and Engineering 2025年第3期12卷 1597-1612页

作者： Wang, Zhijiong Zhang, Anguo Li, Hung Chun Yin, Yadong Chen, Wei Lam, Chan Tong Mak, Peng Un Vai, Mang I. Gao, Yueming Pun, Sio Hang Zhuhai UM Science and Technology Research Institute Lingyange Semiconductor Incorporated Joint Laboratory Zhuhai519031 China Lingyange Semiconductor Inc. Zhuhai519030 China University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI Institute of Microelectronics 999078 China Fuzhou University School of Physics and Information Engineering Fuzhou350108 China Fudan University Center for Intelligent Medical Electronics School of Information Science and Technology Human Phenome Institute Shanghai200437 China Fudan University Human Phenome Institute Shanghai200437 China Macau Polytechnic University Faculty of Applied Sciences 999078 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology 999078 China Fuzhou University Key Lab of Medical Instrumentation and Pharmaceutical Technology of Fujian Province Fuzhou350108 China

People's increasing demand for high-quality network services has prompted the continuous attention and development of network traffic classification (NTC). In recent years, deep flow inspection (DFI) is considered to be the most effective and promising method to solve the NTC. However, DFI still cannot effectively address the problem of changes in flow characteristics of complex packet flows and the discovery of new traffic categories. In this paper, we propose a metric learning based deep learning solution with feature compressor, named deep flow embedding (DFE). The feature compressor is used to compress the feature information transmitted layer by layer in DL backbone while maintaining the computational accuracy, so that the backbone can remove as much noise, redundancy, and other irrelevant information from the input data as possible, and achieve more robust feature extraction of network traffic flow. The deep learning (DL) backbone generates an embedding vector for each network packet flow. Then the embedding vector is compared with the vector template preset for each traffic type in the template library to determine the category of the packet flow. Experimental results verify that our method is more effective than the traditional DFI methods in overcoming the problems of flow characteristics variation and new category discovery. © 2013 IEEE.

关键词： Network embeddings

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