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Large Spin-Orbit-Torque Efficiency and Room-Temperature Magnetization Switching in SrIrO3/Co-Fe-B Heterostructures

Physical Review applied 2023年第2期19卷 024076-024076页

作者： Peng Li Sanyum Channa Xiang Li Laith Alahmed Chunli Tang Di Yi Lauren Riddiford Jacob Wisser Purnima P. Balakrishnan Xin Yu Zheng Di Lu Arturas Vailionis Shan X. Wang Yuri Suzuki Geballe Laboratory for Advanced Materials Stanford University Stanford California 94305 USA Department of Physics Stanford University Stanford California 94305 USA Department of Electrical Engineering Stanford University Stanford California 94305 USA Department of Electrical and Computer Engineering Auburn University Auburn AL 36849 USA Department of Applied Physics Stanford University Stanford California 94305 USA School of Microelectronics University of Science and Technology of China Hefei 230052 China Stanford Nano Shared Facilities Stanford University Stanford California 94305 USA Department of Physics Kaunas University of Technology Kaunas Lithuania

Spin-orbit-torque (SOT) switching is a potentially energy-efficient mechanism by which electrical current can control magnetization. We demonstrate room-temperature SOT switching in nanometer-thick CoFeB/SrIrO3 bilayers despite the amorphous nature of CoFeB and the interface. This behavior is attributed to the large spin Hall angle in SrIrO3 as well as high spin transmission at the interface that contribute to the SOT efficiency ξSOT. Our bilayers exhibit large ξSOT of up to 1.4 and effective spin Hall conductivity of 0.9×105 (ℏ/2e)Ω−1m−1. In our bilayers, we observe unidirectional magnetoresistance and current-induced magnetization switching with a low critical current density of 1.38×1010A/m2 at room temperature. Our results are promising for heterostructures combining technologically relevant CoFeB with high spin-orbit-coupling oxides.

关键词： Magnetization switching Magnetoresistance Spin current Spin-orbit torque Devices for digital logic, storage & processing Ferromagnets Magnetic multilayers Transition metal oxides

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Entanglement purification on quantum networks

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Physical Review Research 2023年第3期5卷 033171-033171页

作者： Michelle Victora Spyros Tserkis Stefan Krastanov Alexander Sanchez de la Cerda Steven Willis Prineha Narang Aliro Technologies Inc. Boston Massachusetts 02135 USA John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA College of Letters and Science University of California Los Angeles (UCLA) California 90095 USA

We present an approach to entanglement purification on complex quantum network architectures, that is, how a quantum network built from repeaters with limited processing fidelity can purify and distribute entanglement between users. In particular, we explore how noisy gates, depolarizing channels, and finite memory storage time influence the performance of entanglement purification in a quantum network. Finally, we apply the purification techniques we developed in the context of entanglement routing for different quantum network topologies in an effort to inform future design choices for quantum network configurations.

关键词： Quantum channels Quantum communication Quantum information architectures & platforms Quantum networks Quantum repeaters

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Frozen Food Supply Chain Monitoring with IoT

Frozen Food Supply Chain Monitoring with IoT

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Mohd Sallehin Mohd Kassim Muhammad Haziq bin Rozman Faridah Yahya Advanced Telecommunication Technology Cluster International Industrial and Institutional Partnership Department Computer Engineering Technology Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia Bachelor of Information Technology (Hons) Internet of Thing Computer Engineering Technology Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia Applied Statistics and Data Science Clusters (ASAD) Computer Engineering Technology Section Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

This project aimed to implement a prototype capable of real-time monitoring and control of various parameters in information management setting with Internet of Things (IoT). Research methodology involved designing and implementing the system prototype to resemble a supply container or transportation truck with installed sensors controlled by a microcontroller. Main components used temperature sensors (AHT20), RFID reader for access control, GPS module for location tracking, and a web/mobile dashboard for remote monitoring. Additionally, a robust database system was developed to store sensor readings and transaction history, ensuring data integrity and facilitating seamless communication between components for real-time data transmission. Key findings indicate that the system effectively monitors temperature, manages access control through RFID authentication, tracks real-time location via GPS, and provides a user-friendly dashboard interface for data visualization.

关键词： Temperature sensors Access control Temperature measurement Supply chain management Supply chains Prototypes Data visualization Real-time systems Temperature control Sensors

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Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel

arXiv

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arXiv 2022年

作者： Jiang, Ziyang Zheng, Tongshu Liu, Yiling Carlson, David Department of Civil and Environmental Engineering Duke University United States Division of Natural and Applied Science Duke Kunshan University China Program in Computational Biology and Bioinformatics Duke University School of Medicine United States Department of Civil and Environmental Engineering Department of Biostatistics and Bioinformatics Department of Computer Science Duke University United States

It is challenging to guide neural network (NN) learning with prior knowledge. In contrast, many known properties, such as spatial smoothness or seasonality, are straightforward to model by choosing an appropriate kernel in a Gaussian process (GP). Many deep learning applications could be enhanced by modeling such known properties. For example, convolutional neural networks (CNNs) are frequently used in remote sensing, which is subject to strong seasonal effects. We propose to blend the strengths of NNs and the clear modeling capabilities of GPs by using a composite kernel that combines a kernel implicitly defined by a neural network with a second kernel function chosen to model known properties (e.g., seasonality). We implement this idea by combining a deep network and an efficient mapping function based on either Nyström approximation or random Fourier features, which we call Implicit Composite Kernel (ICK). We then adopt a sample-then-optimize approach to approximate the full GP posterior distribution. We demonstrate that ICK has superior performance and flexibility on both synthetic and real-world datasets including a remote sensing dataset. The ICK framework can be used to include prior information into neural networks in many applications. © 2022, CC BY.

关键词： Remote sensing

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Semantic Data Management in Data Lakes

arXiv

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arXiv 2023年

作者： Hoseini, Sayed Theissen-Lipp, Johannes Quix, Christoph Department of Electrical Engineering and Computer Science Hochschule Niederrhein University of Applied Sciences Reinarzstrasse 49 NRW Krefeld47805 Germany Databases and Information Systems RWTH Aachen University Ahornstraße 55 Aachen NRW Aachen52074 Germany Department of Data Science and Artificial Intelligence Fraunhofer Institute for Applied Information Technology FIT Schloss Birlinghoven NRW St. Augustin53757 Germany

In recent years, data lakes emerged as a way to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. Some approaches propose the linkage of metadata to knowledge graphs based on the Linked Data principles to provide more meaning and semantics to the data in the lake. Such a semantic layer may be utilized not only for data management but also to tackle the problem of data integration from heterogeneous sources, in order to make data access more expressive and interoperable. In this survey, we review recent approaches with a specific focus on the application within data lake systems and scalability to Big Data. We classify the approaches into (i) basic semantic data management, (ii) semantic modeling approaches for enriching metadata in data lakes, and (iii) methods for ontology-based data access. In each category, we cover the main techniques and their background, and compare latest research. Finally, we point out challenges for future work in this research area, which needs a closer integration of Big Data and Semantic Web technologies. © 2023, CC BY.

关键词： Big data

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Navigating the Artificial Intelligence Revolution in NeuroOncology: A Multidisciplinary Viewpoint

SSRN

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SSRN 2024年

作者： Saxena, Sanjay Panda, Soumyaranjan Tiwari, Ekta Singh, Rajesh Fouda, Mostafa M. Kalra, Mannudeep K. Kotecha, Ketan Saba, Luca Suri, Jasjit S. Department of Computer Science & Engineering International Institute of Information Technology Odisha Bhubaneswar India Department of Computer Science & Information Technology Mahatma Gandhi Central University Bihar India Department of Computer & Engineering Visvesvaraya National Institute of Technology Maharashtra Nagpur India Department of Research and Innovation Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Department of Electrical and Computer Engineering Idaho State University PocatelloID83209 United States Department of Radiology Massachusetts General Hospital BostonMA United States Symbiosis centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune India Cagliari Italy Department of ECE Idaho State University PocatelloID83209 United States Department of CE Graphics Era Deemed to be University Dehradun248002 India University Center for Research & Development Chandigarh University Mohali India Pune India Stroke Diagnostic and Monitoring Division AtheroPoint RosevilleCA95661 United States Harvard Medical School United States University Hospital of Cagliari Italy

This comprehensive review presents the role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in neuro-oncology, focussing their groundbreaking impact on the field. It gives a historical overview of AI's evolution in area of neuro-oncology, representing its progress from basic data management to high-level predictive analytics that now significantly makes personalized treatment ***, This study examines various ML based methods that emphasize brain tumor classification, genetic profiling, and individualized care plans proposals, which basically reflects the shift towards precision medicine. This paper also highlights, developments in DL particularly in the development of neuro-imaging analysis, which has significantly guided surgical planning and radiation therapy for improved patient outcomes. Through various case studies, the paper presents recent breakthroughs where AI interventions have noticeably enhanced the treatment of neuro-oncological conditions. Moreover, It also addresses the ethical considerations encompassing the use of automated decision-making tools, emphasizing the need for ethical governance in AI deployment. By integrating multidisciplinary perspectives, the study underscores the collaborative effort between clinicians, data scientists, and ethicists, which is very crucial for the ethical and effective implementation of AI in current clinical practice. The main aim of discussion is to equip neurooncologists/sergeons and doctors with understandings into sustainable AI adoption which ensures that AI's path in neuro-oncology aligns with both clinical needs for personalized medicine and ethical *** paper concludes by outlining considered methods and frameworks for the future integration of AI in neuro-oncology, predicting trends, and preparing to meet the challenges ahead. © 2024, The Authors. All rights reserved.

关键词： Personalized medicine

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Unsupervised Single Valued Neutrosophic Sets Approach for Cloud Clustering

Unsupervised Single Valued Neutrosophic Sets Approach for Cl...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Byeon, Haewon Kumar, Pramod Khan, Ihtiram Raza Alghayadh, Faisal Yousef Rusho, Maher Ali Soni, Mukesh Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of Ganga Institute of Technology and Management Jhajjar - Bahadurgarh Rd Bhadana Haryana Kablana124104 India Computer Science Department Jamia Hamdard Delhi India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Lockheed Martin Engineering Management University of Colorado Boulder BoulderCO United States Dr. D.Y. Patil Vidyapeeth Pune Dr. D.Y. Patil School of Science & Technology Tathawade Pune India

This work presents an innovative approach to integrating restricted rationality and strategic manipulation into multi-attribute decision-making in product style design, resulting in more precise and objective outcomes. The suggested approach employs cloud model clustering and unsupervised Single-Valued Neutrosophic Sets (SVNS). SVNS is derived by constructing pairwise comparison ratio square matrices between baseline attributes and prospective alternatives, following the scientific clustering of decision experts. SSVNC denotes the degrees of real, false, and questionable membership. The relative weights of the standard attributes are established by analysing the evaluation outcomes of different solutions for each common trait. Cloud model clustering is employed to exclude conflicting and unreasonable experts, hence attaining precise weights for design judgements. This model incorporates multi-granularity linguistic factors. The integration of the significance of generic attributes and human assessment yields a composite score for each prospective response. The feasibility and efficacy of the proposed strategy are demonstrated through a case study in automotive design aesthetics. The results emphasise the method's capability to safeguard against nefarious strategic manipulation and effectively address complex and confusing multi-attribute decision-making challenges in vehicle stylistic design. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Integrated circuit layout

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Layer-dependent optically induced spin polarization in InSe

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Physical Review B 2023年第11期107卷 115304-115304页

作者： Jovan Nelson Teodor K. Stanev Dmitry Lebedev Trevor LaMountain J. Tyler Gish Hongfei Zeng Hyeondeok Shin Olle Heinonen Kenji Watanabe Takashi Taniguchi Mark C. Hersam Nathaniel P. Stern Applied Physics Program Northwestern University Evanston Illinois 60208 USA Department of Physics and Astronomy Northwestern University Evanston Illinois 60208 USA Department of Material Science and Engineering Northwestern University Evanston Illinois 60208 USA Computational Science Division Argonne National Laboratory Lemont Illinois 60439 USA Material Science Division Argonne National Laboratory Lemont Illinois 60439 USA Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan Department of Chemistry Northwestern University Evanston Illinois 60208 USA Department of Electrical Engineering and Computer Science Northwestern University Evanston Illinois 60208 USA

Optical control of spin in semiconductors has been pioneered using nanostructures of III-V and II-VI semiconductors, but the emergence of two-dimensional van der Waals materials offers an alternative low-dimensional platform for spintronic phenomena. Indium selenide (InSe), a group-III monochalcogenide van der Waals material, has shown promise for optoelectronics due to its high electron mobility, tunable direct band gap, and quantum transport. In addition to these confirmed properties, there are predictions of spin-dependent optical selection rules, suggesting the potential for all-optical excitation and control of spin in a two-dimensional layered material. Despite these predictions, layer-dependent optical spin phenomena in InSe have yet to be explored. Here, we present measurements of layer-dependent optical spin dynamics in few-layer and bulk InSe. Polarized photoluminescence reveals layer-dependent optical orientation of spin, thereby demonstrating the optical selection rules in few-layer InSe. Spin dynamics are also studied in many-layer InSe using time-resolved Kerr rotation spectroscopy. By applying out-of-plane and in-plane static magnetic fields for polarized emission measurements and Kerr measurements, respectively, the g factor for InSe was extracted. Further investigations are done by calculating precession values using a k·p model, which is supported by ab initio density functional theory. Comparison of predicted precession rates with experimental measurements highlights the importance of excitonic effects in InSe for understanding spin dynamics. Optical orientation of spin is an important prerequisite for optospintronic phenomena and devices, and these first demonstrations of layer-dependent optical excitation of spins in InSe lay the foundation for combining layer-dependent spin properties with advantageous electronic properties found in this material.

关键词： Magneto-optics Spin dynamics Chalcogenides Layered semiconductors Van der Waals systems Density functional theory Kerr effect Photoluminescence k dot p method

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Out-of-band authenticated group key exchange: From strong authentication to immediate key delivery 1

Out-of-band authenticated group key exchange: From strong au...

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1st Conference on Information-Theoretic Cryptography, ITC 2020

作者： Naor, Moni Rotem, Lior Segev, Gil Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot76100 Israel School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem91904 Israel

ISBN: (纸本)9783959771511

Given the inherent ad-hoc nature of popular communication platforms, out-of-band authenticated key-exchange protocols are becoming widely deployed: Key exchange protocols that enable users to detect man-in-the-middle attacks by manually authenticating one short value. In this work we put forward the notion of immediate key delivery for such protocols, requiring that even if some users participate in the protocol but do not complete it (e.g., due to losing data connectivity or to other common synchronicity issues), then the remaining users should still agree on a shared secret. A property of a similar flavor was introduced by Alwen, Coretti and Dodis (EUROCRYPT19) asking for immediate decryption of messages in user-to-user messaging while assuming that a shared secret has already been established – but the underlying issue is crucial already during the initial key exchange and goes far beyond the context of messaging. Equipped with our immediate key delivery property, we formalize strong notions of security for out-of-band authenticated group key exchange, and demonstrate that the existing protocols either do not satisfy our notions of security or are impractical (these include, in particular, the protocols deployed by Telegram, Signal and WhatsApp). Then, based on the existence of any passively-secure key-exchange protocol (e.g., the Diffie-Hellman protocol), we construct an out-of-band authenticated group key-exchange protocol satisfying our notions of security. Our protocol is inspired by techniques that have been developed in the context of fair string sampling in order to minimize the effect of adversarial aborts, and offers the optimal tradeoff between the length of its out-of-band value and its security. © Moni Naor, Lior Rotem, and Gil Segev;licensed under Creative Commons License CC-BY

关键词： Authentication

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Strong convergence of an fractional exponential integrator scheme for the finite element discretization of time-fractional SPDE driven by standard and fractional Brownian motions

arXiv

引用

arXiv 2022年

作者： Noupelah, Aurelien Junior Tambue, Antoine Woukeng, Jean Louis Department of Mathematics and Computer Sciences University of Dschang P.O. BOX 67 Dschang Cameroon Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Inndalsveien 28 Bergen5063 Norway

The aim of this work is to provide the first strong convergence result of numerical approximation of a general time-fractional second order stochastic partial differential equation involving a Caputo derivative in time of order α ϵ (1/2;1) and driven simultaneously by a multiplicative standard Brownian motion and additive fBm with Hurst parameter H ϵ (1.2, 1), more realistic to model the random effects on transport of particles in medium with thermal memory. We prove the existence and uniqueness results and perform the spatial discretization using the finite element and the temporal discretization using a fractional exponential integrator scheme. We provide the temporal and spatial convergence proofs for our fully discrete scheme and the result shows that the convergence orders depend on the regularity of the initial data, the power of the fractional derivative, and the Hurst parameter H. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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