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Strain-induced lateral heterostructures: Hole localization and the emergence of flat bands in rippled MoS2 monolayers

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Physical Review Materials 2025年第2期9卷 L021002-L021002页

作者： Meshal Alawein Joel W. Ager Ali Javey D. C. Chrzan Electrical Engineering and Computer Science University of California Berkeley California 94720 USA Materials Sciences Division Lawrence Berkeley National Laboratories Berkeley California 94720 USA Materials Science and Engineering University of California Berkeley California 94720 USA

The formation of 2D lateral heterostructures in rippled MoS2 and similar transition metal dichalcogenides (TMDs) is studied using density functional theory. Compression of rippled TMDs beyond a threshold compression leads to the formation of a flat valence band associated with strongly localized holes. The implications for exciton manipulation and the emergence of one-dimensional heavy fermion behavior are discussed.

关键词： Layered semiconductors

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Clustered Reinforcement Learning

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Frontiers of computer science 2025年第4期19卷 43-57页

作者： Xiao MA Shen-Yi ZHAO Zhao-Heng YIN Wu-Jun LI National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing UniversityNanjing 210023China Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720-1770USA

Exploration strategy design is a challenging problem in reinforcement learning(RL),especially when the environment contains a large state space or sparse *** exploration,the agent tries to discover unexplored(novel)areas or high reward(quality)*** existing methods perform exploration by only utilizing the novelty of *** novelty and quality in the neighboring area of the current state have not been well utilized to simultaneously guide the agent’s *** address this problem,this paper proposes a novel RL framework,called clustered reinforcement learning(CRL),for efficient exploration in *** adopts clustering to divide the collected states into several clusters,based on which a bonus reward reflecting both novelty and quality in the neighboring area(cluster)of the current state is given to the *** leverages these bonus rewards to guide the agent to perform efficient ***,CRL can be combined with existing exploration strategies to improve their performance,as the bonus rewards employed by these existing exploration strategies solely capture the novelty of *** on four continuous control tasks and six hard-exploration Atari-2600 games show that our method can outperform other state-of-the-art methods to achieve the best performance.

关键词： deep reinforcement learning exploration count-based method clustering K-means

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Linear Algorithmic Checksums for Deep-Neural-Network Error Detection: Fundamentals and Recent Advancements

IEEE Design and Test

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IEEE Design and Test 2025年第3期42卷 26-40页

作者： Ozen, Elbruz Ozerdem, Ozgur Orailoglu, Alex University of California at San Diego Department of Computer Science and Engineering La Jolla CA92093 United States

The widespread adoption of deep neural networks in safety-critical systems necessitates the examination of the safety issues raised by hardware errors. The consequent interest in fault tolerance methods that can deliver comprehensive coverage yet are sufficiently low-cost to meet the margin requirements of consumer deep learning applications can be satisfied through a rigorous exploration of the mathematical properties of the deep neural network computations. Concretely, this stringent set of requirements can be satisfied through a novel technique, Sanity-Check, that delivers error detection in fully-connected and convolutional layers through the use of linear algorithmic checksums. The interest in Sanity-Check was accentuated by its purely software-based implementation in a variety of off-the-shelf hardware platforms. Meanwhile, dedicated hardware extensions seamlessly integrate with modern deep learning accelerators and eliminate the performance overhead of the software-based implementation. The perfect critical error coverage of Sanity-Check offers a promising alternative for low-cost error detection in safety-critical deep neural network applications. The success of this approach in delivering frugal and highly comprehensive error detection has spurred a slew of subsequent research work, which we survey in this article. © 2013 IEEE.

关键词： Deep neural networks

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MF-CGAN: Multi-Feature Conditional GAN for Synthetic Data Generation in Internet of Medical Things

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IEEE Internet of Things Journal 2025年第10期12卷 13469-13476页

作者： Pandey, Chandrasen Tiwari, Vaibhav Francis, Sharmila A. J. Pal, Vipin Roy, Diptendu Sinha Manipal University Department of Computer Science and Engineering Jaipur India Bennett University School of Computer Science Engineering and Technology Greater Noida India King Khalid University Department of Computer Science and Engineering Rijal Alma Abha Campus Saudi Arabia Netaji Subhash University of Technology Department of Computer Science and Engineering New Delhi India Department of Computer Science and Engineering Meghalaya India

Synthetic data generation via Generative Artificial Intelligence (GenAI) is essential for enhancing cybersecurity and safeguarding privacy in the Internet of Medical Things (IoMT) and healthcare. We introduce Multi-Feature Conditional GAN (MF-CGAN), a Conditional Generative Adversarial Network with multi-feature integration, that generates realistic synthetic data using the WUSTL EHMS 2020 dataset of network traffic and health metrics. MF-CGAN’s architecture integrates conditional variables such as cyberattack types, traffic direction, and status flags, preserving their intricate interdependencies. The generator employs dense layers with batch normalization and Leaky ReLU activations to produce conditional synthetic data;the discriminator evaluates authenticity using the same conditional inputs. Experimental results show that MF-CGAN produces high-fidelity synthetic data, closely mirroring real data characteristics. In addition, we quantitatively demonstrate (MAE=0.012, RMSE=0.035, and classification accuracy up to 99%) that MF-CGAN-generated data effectively supports cybersecurity tasks, indicating its potential to significantly enhance data availability for IoMT. © 2014 IEEE.

关键词： Generative adversarial networks

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Optimizing medical image analysis through MViTX on multiple datasets with explainable AI

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Neural Computing and Applications 2025年第14期37卷 8479-8507页

作者： Sheakh, Md. Alif Tahosin, Mst. Sazia Alam, Mohammad Jahangir Begum, Mahbuba Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Mawlana Bhashani Science and Technology University Tangail Bangladesh

Cancer remains a leading cause of mortality worldwide, with early detection and accurate diagnosis critical to improving patient outcomes. While computer-aided diagnosis systems powered by deep learning have shown considerable promise, their widespread clinical adoption faces significant challenges in maintaining consistent performance across diverse imaging modalities and datasets. This research addresses the critical challenge of developing robust, generalizable deep learning models by proposing a comprehensive framework utilizing seven diverse medical imaging datasets encompassing fundus photography, histopathology, endoscopy, and MRI, covering diseases such as ocular toxoplasmosis, endometrial cancer, colorectal cancer, gastrointestinal disease, breast cancer, brain tumor, and tympanic membrane conditions. Our methodology combines customized data augmentation strategies (photometric, geometric, and elastic transformations) with an optimized vision transformer with external attention (MViTX) architecture. The MViTX model demonstrated exceptional performance with test accuracies ranging from 94.1 to 99.1% across all datasets, achieving superior metrics in accuracy, precision, recall, F1-score, and AUC compared to state-of-the-art CNNs. The model's effectiveness was further validated through ablation studies and explainable AI techniques, while its practical utility was demonstrated through deployment as a user-friendly web application. Our research establishes the effectiveness of combining tailored data augmentation with attention-based transformer architectures for medical image analysis, representing a significant step toward enhancing healthcare professionals' diagnostic capabilities and ultimately improving patient care outcomes. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Magnetic resonance imaging

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The Design and Realization of a Self-Hosted and Open-Source Agile Internal Development Platform

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IEEE Access 2025年 13卷 79516-79533页

作者： Ciancarini, Paolo Giancarlo, Raffaele Grimaudo, Gennaro Missiroli, Marcello Xia, Tian Cheng University of Bologna Department of Computer Science -Science and Engineering Bologna40126 Italy University of Palermo Department of Mathematics and Computer Science Palermo90123 Italy University of Palermo Department of Engineering Palermo90128 Italy University of Bologna Department of Computer Science and Engineering Bologna40126 Italy

Platform engineering (PE) is a growing area of Software engineering, with many facets, including the elusive concept of Internal Development Platforms (IDPs). They integrate various technologies and tools to support the realization of software projects, especially their managing and developing roles, fostering Agile collaboration and enhancing team productivity. In particular for the new trends of Agile remote working and for Agile applied to the Digital Transformation in the Public Sector. While proprietary IDPs abound, academic research that could naturally lead to open-source IDPs remain scarce. In particular for Scrum, a fundamental Agile methodology in the strategic area of Digital Transformation in the Public Sector, where IDP self-hosting is also appropriate. This paper addresses this gap by proposing the design and realization of the first, to the best of our knowledge, IDP supporting Scrum, being open-source and with self-hosting capabilities. The end-product of our research is the Compositional Agile System (CAS), an extendable microservice architecture-based IDP. Its design, as well as CAS, makes full use of hands-on previous successful experiences of IDP development by members of this team, specifically non-trivial tasks in Italian Public Administration and to foster remote working in university Software engineering classes, a practice started during the pandemic but nowa *** offer several architectural and example scenarios where CAS can be of use, concentrating on the Public Administration. Particularly relevant is the use of CAS in analyzing a pillar of Italian Digital Administration available to its citizens, i.e., AppIo. The software is publicly available. © 2013 IEEE.

关键词： Open source software

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Temperature-dependent microstructural evolution in a compositionally complex solid electrolyte:The role of a grain boundary transition

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Journal of Advanced Ceramics 2025年第3期14卷 163-176页

作者： Shu-Ting Ko Chaojie Du Huiming Guo Hasti Vahidi Jenna L.Wardini Tom Lee Yi Liu Jingjing Yang Francisco Guzman Timothy J.Rupert William J.Bowman Shen J.Dillon Xiaoqing Pan Jian Luo Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California San DiegoLa JollaCalifornia 92093USA Program in Materials Science and Engineering University of California San DiegoLa JollaCalifornia 92093USA Department of Materials Science and Engineering University of California IrvineIrvineCalifornia 92697USA Department of Physics and Astronomy University of California IrvineIrvineCalifornia 92697USA Irvine Materials Research Institute University of California IrvineIrvineCalifornia 92697USA

Compositionally complex solid electrolyte(Li_(0.375)Sr_(0.4375))(Ta_(0.375)Nb_(0.375)Zr_(0.125)Hf_(0.125))O_(3)(LSTNZH)samples are synthesized using different sintering temperatures,durations,and cooling conditions(furnace cooling(FC)*** quenching(AQ)).The temperature-dependent grain growth has been examined to investigate the microstructural evolution and the origin of exaggerated(abnormal)grain *** moderate temperatures,the grain growth of LSTNZH follows a cubic root growth model with an Arrhenius temperature *** increasing temperature,bimodal microstructures develop,and the Arrhenius temperature dependence breaks ***,increasing the temperature induces increased Nb segregation at general grain boundaries(GBs),in contrast to classical GB segregation models but suggesting premelting-like GB disordering,which can explain the observed abnormal grain growth(AGG).In addition,the large grains become faceted with increasing temperature,which occurs concurrently with the temperature-induced transitions in GB segregation and grain growth,thereby further supporting the occurrence of a GB phase-like(complexion)*** impacts on the densification,ionic conductivity,and hardness are also *** work provides a new insight into the fundamental understanding of the grain growth mechanisms of the emergent class of medium-and high-entropy compositionally complex ceramics(CCCs),which is essential for tailoring microstructures and material properties.

关键词： compositionally complex ceramics(CCCs) solid electrolytes perovskite grain growth grain boundary(GB) electron backscatter diffraction(EBSD)

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Drug–target interactions prediction based on similarity graph features extraction and deep learning

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Neural Computing and Applications 2025年第6期37卷 4303-4322页

作者： Torkey, Hanaa El-Behery, Heba Attia, Abdel-Fattah El-Fishawy, Nawal Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Computer Science and Engineering Faculty of Engineering Kafrelsheikh University Kafrelsheikh Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Identifying drug–target interactions (DTIs) is a critical step in both drug repositioning. The labor-intensive, time-consuming, and costly nature of classic DTI laboratory studies makes it imperative to create efficient computer algorithms to forecast possible DTIs. However, current computational approaches that predict potential drug–target interactions (DTIs) suffer from some limitations, like finding the best similarity measures or negative samples, and thus require substantial performance improvement. This study proposes an integrated approach based on feature representation and deep learning to predict DTIs. We extract the relevant features of drugs and proteins from heterogeneous networks using graph mining techniques. The proposed approach constructs a heterogeneous graph from the known drug–protein interactions, protein–protein, and drug–drug similarities. Then applying two feature extraction techniques to extract the features, then utilizing these features in training a deep learning model to predict the potential DTIs. Also, a novel algorithm is proposed to find the negative samples based on the drug and protein similarity matrices. Four Benchmark datasets are used to evaluate the proposed approach. Our approach achieves the highest AUC (area under the ROC curve) across all datasets (0.98) with around 2% increases over the existing methods. Experimental results demonstrate that our proposed approach outperforms the baseline methods in predicting DTI, and our negative sample-identifying algorithm could be established as a competitive solution. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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Safe Reinforcement Learning for Grid-forming Inverter Based Frequency Regulation with Stability Guarantee

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Journal of Modern Power Systems and Clean Energy 2025年第1期13卷 79-86页

作者： Hang Shuai Buxin She Jinning Wang Fangxing Li Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN37996USA

This study investigates a safe reinforcement learning algorithm for grid-forming(GFM)inverter based frequency *** guarantee the stability of the inverter-based resource(IBR)system under the learned control policy,a modelbased reinforcement learning(MBRL)algorithm is combined with Lyapunov approach,which determines the safe region of states and *** obtain near optimal control policy,the control performance is safely improved by approximate dynamic programming(ADP)using data sampled from the region of attraction(ROA).Moreover,to enhance the control robustness against parameter uncertainty in the inverter,a Gaussian process(GP)model is adopted by the proposed algorithm to effectively learn system dynamics from *** simulations validate the effectiveness of the proposed algorithm.

关键词： Inverter-based resource(IBR) virtual synchronous generator(VSG) safe reinforcement learning Lyapunov function frequency regulation grid-forming inverter

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Generalized Fractional Repetition Codes for Binary Coded Computations

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IEEE Transactions on Information Theory 2025年第3期71卷 2170-2194页

作者： Charalambides, Neophytos Mahdavifar, Hessam Hero, Alfred O. University of California at San Diego Department of Computer Science and Engineering La Jolla CA92093 United States Northeastern University Department of Electrical and Computer Engineering BostonMA02115 United States University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48104 United States

This paper addresses the gradient coding and coded matrix multiplication problems in distributed optimization and coded computing. We present a computationally efficient coding method which overcomes the drawbacks of the Fractional Repetition Coding gradient coding method proposed by Tandon et al., and can also be leveraged by coded computing networks whose servers are of heterogeneous nature. Specifically, we propose a construction for fractional repetition gradient coding;while ensuring that the generator matrix remains close to perfectly balanced for any set of coding parameters, as well as a low complexity decoding step. The proposed binary encoding avoids operations over the real and complex numbers which inherently introduce numerical and rounding errors, thereby enabling accurate distributed encodings of the partial gradients. We then make connections between gradient coding and coded matrix multiplication. Specifically, we show that any gradient coding scheme can be extended to coded matrix multiplication. Furthermore, we show how the proposed binary gradient coding scheme can be used to construct two different coded matrix multiplication schemes, each achieving different trade-offs. © 1963-2012 IEEE.

关键词： Encoding (symbols)

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