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Roadmap of spin-orbit torques

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arXiv 2021年

作者： Shao, Qiming Li, Peng Liu, Luqiao Yang, Hyunsoo Fukami, Shunsuke Razavi, Armin Wu, Hao Wang, Kang Freimuth, Frank Mokrousov, Yuriy Stiles, Mark D. Emori, Satoru Hoffmann, Axel Åkerman, Johan Roy, Kaushik Wang, Jian-Ping Yang, See-Hun Garello, Kevin Zhang, Wei Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Electrical and Computer Engineering Auburn University Electrical Engineering and Computer Science Massachusetts Institute of Technology Department of Electrical and Computer Engineering National University of Singapore Research Institute of Electrical Communication Tohoku University Japan Department of Electrical and Computer Engineering University of California Los Angeles United States Forschungszentrum Juelich GmbH University of Mainz Alternative Computing Group National Institute of Standards and Technology Department of Physics Virginia Tech Department of Materials Science and Engineering University of Illinois Urbana-Champaign Physics Department University of Gotherburg Department of Electrical and Computer Engineering Purdue University Electrical and Computer Engineering Department University of Minnesota IBM Research - Almaden United States IMEC Leuven Belgium CEA-Spintec Grenoble France Physics Department Oakland University

Spin-orbit torque (SOT) is an emerging technology that enables the efficient manipulation of spintronic devices. The initial processes of interest in SOTs involved electric fields, spin-orbit coupling, conduction electron spins and magnetization. More recently interest has grown to include a variety of other processes that include phonons, magnons, or heat. Over the past decade, many materials have been explored to achieve a larger SOT efficiency. Recently, holistic design to maximize the performance of SOT devices has extended material research from a nonmagnetic layer to a magnetic layer. The rapid development of SOT has spurred a variety of SOT-based applications. In this Roadmap paper, we first review the theories of SOTs by introducing the various mechanisms thought to generate or control SOTs, such as the spin Hall effect, the Rashba-Edelstein effect, the orbital Hall effect, thermal gradients, magnons, and strain effects. Then, we discuss the materials that enable these effects, including metals, metallic alloys, topological insulators, two-dimensional materials, and complex oxides. We also discuss the important roles in SOT devices of different types of magnetic layers, such as magnetic insulators, antiferromagnets, and ferrimagnets. Afterward, we discuss device applications utilizing SOTs. We discuss and compare three-terminal and two-terminal SOT-magnetoresistive random-access memories (MRAMs);we mention various schemes to eliminate the need for an external field. We provide technological application considerations for SOT-MRAM and give perspectives on SOT-based neuromorphic devices and circuits. In addition to SOT-MRAM, we present SOT-based spintronic terahertz generators, nano-oscillators, and domain wall and skyrmion racetrack memories. This paper aims to achieve a comprehensive review of SOT theory, materials, and applications, guiding future SOT development in both the academic and industrial sectors. © 2021, CC BY.

关键词： Spin Hall effect

Gmr Biosensing with Magnetic Nanowires as Labels for the Detection of Osteosarcoma Cells

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SSRN

SSRN 2022年

作者： Su, Diqing Um, Joseph Moreno, Julian Nemati, Zohreh Srinivasan, Karthik Chen, Junyang Kouhpanji, M. Reza Zamani Shore, Daniel Wu, Kai Kosel, Jürgen Modiano, Jaime F. Franklin, Rhonda Wang, Jian-Ping Stadler, Bethanie Department of Chemical Engineering and Materials Science University of Minnesota MinneapolisMN55455 United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Electrical Engineering Department King Abdullah University of Science and Technology Thuwal 23955 Saudi Arabia Animal Cancer Care and Research Program University of Minnesota St. PaulMN55108 United States Department of Veterinary Clinical Sciences College of Veterinary Medicine University of Minnesota St. PaulMN55108 United States Masonic Cancer Center University of Minnesota MinneapolisMN55455 United States Center for Immunology University of Minnesota MinneapolisMN55455 United States Stem Cell Institute University of Minnesota MinneapolisMN55455 United States Institute for Engineering in Medicine University of Minnesota MinneapolisMN55455 United States

Magnetic nanowires (MNWs) were explored as potential magnetic tags for cell detection with giant magnetoresistance (GMR) biosensors based on a handheld system. Due to size, shape anisotropy and higher moment materials, the signal detected from a single MNW was 2500 times larger than that from a single magnetic iron oxide nanobead, which is important for ultra-low concentration cell detection. A model was used to determine how the MNW orientation with respect to the GMR sensor impacts detection performance, and the results aligned well with the experimental results. As a proof of concept OSCA-8 cells tagged with Ni MNWs were also detected using the same handheld system. The limit of detection (LOD) in aqueous solution appeared to be 133 cells, and single-cell detection can be realized if the cell is in direct contact with the sensor surface. Since MNWs are already employed in magnetic separation of cells, directly using MNWs as tags in cell detection eliminates the need of additional functionalization with other labels. This largely simplifies the detection process and reduces the risk of contamination during sample preparation. © 2022, The Authors. All rights reserved.

关键词： Biosensors

The Genomic Computing Revolution: Defining the Next Decades of Accelerating Genomics

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The Genomic Computing Revolution: Defining the Next Decades ...

IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Harisankar Sadasivan Artur Klauser Juergen Hench Yatish Turakhia Gagandeep Singh Alberto Zeni Sarah Beecroft Satish Narayanasamy Jeff Nivala Bob Robey Onur Mutlu Kristof Denolf Sriranjani Sitaraman AI Group Advanced Micro Devices Inc. Bellevue WA USA Seattle WA USA Institute for Pathology and Medical Genetics University Hospital Basel Basel Switzerland Electrical and Computer Engineering University of California San Diego USA Research and Development Advanced Micro Devices Inc. Zug Switzerland AI Group- Silicon Advanced Micro Devices Inc. Dublin Ireland Pawsey Supercomputing Research Centre Perth WA Australia Computer Science and Engineering University of Michigan Ann Arbor MI USA Computer Science and Engineering University of Washington Seattle WA USA HPC Solutions DCGPU Advanced Micro Devices Inc. Los Alamos NM USA Information Technology and Electrical Engineering ETH Zürich Zürich Switzerland Research and Development Advanced Micro Devices Inc. Longmont CO USA HPC Solutions DCGPU Advanced Micro Devices Inc. Austin TX USA

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

The rapid growth of genomic data has positioned genomics to become one of the world's most storage-intensive and computationally demanding fields. This review paper provides a comprehensive analysis of emerging trends in software and hardware for genomics, addressing a critical gap in the current literature. We categorize and examine key algorithms in genomic sequencing workflows, including sequence data indexing, retrieval and pile-up, dynamic programming, and machine learning. We analyze how computational and memory requirements scale with input sizes for each category, offering insights into potential bottlenecks and optimization opportunities. Our review synthesizes recent advancements in hardware-software co-design, compression schemes, and acceleration techniques for genomic data processing. We propose graphics processing units (GPUs) as a promising first step for deploying genomics workflows due to their high throughput, memory bandwidth, and programmability. By providing a holistic perspective on the computational challenges in genomics, this paper attempts to set a promising direction for future researchers to work collectively to optimize all the components of genomic data processing workflows. Our work aims to foster more targeted and effective advancements in the field, potentially leading to significant improvements in the efficiency and scalability of genomic analyses. We also discuss performance portability as a second step to meet the diverse requirements in point-of-care workflows.

关键词： Sequential analysis Reviews Software algorithms Genomics Graphics processing units Hardware Software Dynamic programming Bioinformatics Optimization

Large Language Models for Disease Diagnosis: A Scoping Review

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arXiv 2024年

作者： Zhou, Shuang Xu, Zidu Zhang, Mian Xu, Chunpu Guo, Yawen Zhan, Zaifu Ding, Sirui Wang, Jiashuo Xu, Kaishuai Fang, Yi Xia, Liqiao Yeung, Jeremy Zha, Daochen Lin, Mingquan Zhang, Rui Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States School of Nursing Columbia University New YorkNY United States Erik Jonsson School of Engineering and Computer Science University of Texas at Dallas RichardsonTX United States Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Informatics University of California Irvine IrvineCA United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN United States Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States Shanghai China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong Department of Computer Science Rice University HoustonTX United States

Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the growing attention in this field, many critical research questions remain under-explored. For instance, what diseases and LLM techniques have been investigated for diagnostic tasks? How can suitable LLM techniques and evaluation methods be selected for clinical decision-making? To answer these questions, we performed a comprehensive analysis of LLM-based methods for disease diagnosis. This scoping review examined the types of diseases, associated organ systems, relevant clinical data, LLM techniques, and evaluation methods reported in existing studies. Furthermore, we offered guidelines for data preprocessing and the selection of appropriate LLM techniques and evaluation strategies for diagnostic tasks. We also assessed the limitations of current research and delineated the challenges and future directions in this research field. In summary, our review outlined a blueprint for LLM-based disease diagnosis, helping to streamline and guide future research endeavors. Copyright © 2024, The Authors. All rights reserved.

关键词： Diseases

Probabilistic Hardware Trojan Attacks on Multiple Layers of Reconfigurable Network Infrastructure

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Journal of Hardware and Systems Security 2020年第4期4卷 343-360页

作者： Mukherjee, Rijoy Govindan, Vidya Koteshwara, Sandhya Das, Amitabh Parhi, Keshab K. Chakraborty, Rajat Subhra Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Electrical and Computer Engineering University of Minnesota Minneapolis USA

Over the past decades, there has been an exponential growth in the number of connected devices, often without well-thought out security mechanisms in place for the relevant network standards and protocols. As a result, security loopholes have been discovered and widely exploited for these vulnerable connected devices, often with devastating consequences. As a countermeasure to these attacks, subsequently some of these original network standards have been enhanced with addition of security features, e.g., the original insecure Ethernet protocol (IEEE 802.3) was supplemented by the IEEE 802.1AE Media Access Control Security (MACSec) standard. In this paper, we present a network packet redirection attack on reconfigurable network devices, specifically a MACSec-enabled NetFPGA-SUME based Ethernet switch, as well as on a NetFPGA-SUME based IPv4 router, by means of Hardware Trojan (HT) insertion. Our HT design is probabilistic in its functionality, with multi-level trigger mechanism. In the MAC layer attack, an activated HT redirects a frame to an incorrect port leading to possible eavesdropping by a malicious attacker as well as denial-of-service, while in the network layer attack, upon activation it forwards all IP packets through a sub-optimal router port causing a denial-of-service attack on the receiver. The proposed HT evades most state-of-the-art HT detection schemes, while having very low resource footprint. We present the complete architecture, detailed description of the mode of operation, and implementation of the HT, with promising experimental results.

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Spatio-temporal dynamics of malaria in Rwanda between 2012 and 2022: a demography-specific analysis

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Infectious Diseases of Poverty 2024年第5期13卷 100-100页

作者： Felix K.Rubuga Paula Moraga Ayman Ahmed Emmanuel Siddig Eric Remera Giovenale Moirano Guéladio Cissé Jürg Utzinger Swiss Tropical and Public Health Institute Allschwil Switzerland University of Basel Basel Switzerland College of Medicine and Health Sciences University of Rwanda Kigali Rwanda Center for Impact Innovation and Capacity building for Health Information systems and Nutrition (CIIC-HIN) Kigali Rwanda Institute of Endemic Diseases University of Khartoum Khartoum Sudan Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Medical Microbiology and Infectious Diseases Erasmus Medical Center University Medical Center Rotterdam Rotterdam The Netherlands Faculty of Medical Laboratory Sciences University of Khartoum Khartoum Sudan Rwanda Biomedical Center Kigali Rwanda Department of Medical Science University of Turin Torino Italy Barcelona Supercomputing Center Barcelona Spain

Background Despite global efforts to reduce and eventually interrupt malaria transmission,the disease remains a pressing public health problem,especially in sub-Saharan *** study presents a detailed spatio-temporal analysis of malaria transmission in Rwanda from 2012 to *** main objective was to gain insights into the evolving patterns of malaria and to inform and tailor effective public health *** The study used yearly aggregated data of malaria cases from the Rwanda health management information *** employed a multifaceted analytical approach,including descriptive statistics and spatio-temporal analysis across three demographic groups:children under the age of 5 years,and males and females above 5 *** spatially explicit models and spatio scan statistics were utilised to examine geographic and temporal patterns of relative risks and to identify clusters of malaria *** We observed a significant increase in malaria cases from 2014 to 2018,peaking in 2016 for males and females aged above 5 years with counts of 98,645 and 116,627,respectively and in 2018 for under 5-year-old children with 84,440 cases with notable geographic *** like Kamonyi(Southern Province),Ngoma,Kayonza and Bugesera(Eastern Province)exhibited high burdens,possibly influenced by factors such as climate,vector control practices,and cross-border *** spatially explicit modeling revealed elevated relative risks in numerous districts,underscoring the heterogeneity of malaria transmission in these districts,and thus contributing to an overall rising trend in malaria cases until 2018,followed by a subsequent *** findings emphasize that the heterogeneity of malaria transmission is potentially driven by ecologic,socioeconomic,and behavioural *** The study underscores the complexity of malaria transmission in Rwanda and calls for climate adaptive,gender-,age-and district-specific strategies in

关键词： Malaria transmission Epidemiology Public health Rwanda Spatio-temporal analysis Rwanda

Reflections from the Workshop on AI-Assisted Decision Making for Conservation

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arXiv 2023年

作者： Xu, Lily Rolf, Esther Beery, Sara Bennett, Joseph R. Berger-Wolf, Tanya Birch, Tanya Bondi-Kelly, Elizabeth Brashares, Justin Chapman, Melissa Corso, Anthony Davies, Andrew Garg, Nikhil Gaylard, Angela Heilmayr, Robert Kerner, Hannah Klemmer, Konstantin Kumar, Vipin MacKey, Lester Monteleoni, Claire Moorcroft, Paul Palmer, Jonathan Perrault, Andrew Thau, David Tambe, Milind School of Engineering and Applied Sciences Harvard University United States Harvard University United States United States Department of Computer Science University of Colorado Boulder United States Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Department of Biology Carleton University Canada Department of Computer Science and Engineering The Ohio State University United States Department of Electrical and Computer Engineering The Ohio State University United States Department of Evolution Ecology and Organismal Biology The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States Google United States Electrical Engineering and Computer Science University of Michigan United States Department of Environmental Science Policy and Management University of California Berkeley United States Department of Aeronautics and Astronautics Stanford University United States Department of Organismic and Evolutionary Biology Harvard University United States Operations Research and Information Engineering Cornell Tech United States African Parks South Africa Bren School of Environmental Science and Management University of California Santa Barbara United States School of Computing and Augmented Intelligence Arizona State University United States Microsoft Research United States Department of Computer Science and Engineering University of Minnesota United States Inria Paris France United States Wwf Global Science United States

In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022. We identify key open research questions in resource allocation, planning, and interventions for biodiversity conservation, highlighting conservation challenges that not only require AI solutions, but also require novel methodological advances. In addition to providing a summary of the workshop talks and discussions, we hope this document serves as a call-to-action to orient the expansion of algorithmic decision-making approaches to prioritize real-world conservation challenges, through collaborative efforts of ecologists, conservation decision-makers, and AI researchers. Copyright © 2023, The Authors. All rights reserved.

关键词： Conservation

Giant magnetoresistance, Fermi surface topology, Shoenberg effect and vanishing quantum oscillations in type-II Dirac semimetal candidates MoSi2 and WSi2

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arXiv 2022年

作者： Pavlosiuk, Orest Swatek, Przemyslaw Wojciech Wang, Jian-Ping Wiśniewski, Piotr Kaczorowski, Dariusz Institute of Low Temperature and Structure Research Polish Academy of Sciences Okólna 2 Wroclaw50-422 Poland Electrical and Computer Engineering Department University of Minnesota MinneapolisMN55455 United States Institute of Molecular Physics Polish Academy of Sciences Smoluchowskiego 17 Poznan60-179 Poland

We performed comprehensive theoretical and experimental studies of the electronic structure and the Fermi surface topology of two novel quantum materials, MoSi2 and WSi2. The theoretical predictions of the electronic structure in the vicinity of the Fermi level was verified experimentally by thorough analysis of the observed quantum oscillations in both electrical resistivity and magnetostriction. We established that the Fermi surface sheets in MoSi2 and WSi2 consist of 3D dumbbell-shaped hole-like pockets and rosette-shaped electron-like pockets, with nearly equal volumes. Based on this finding, both materials were characterized as almost perfectly compensated semimetals. In conjunction, the magnetoresistance attains giant values of 104 and 105 % for WSi2 and MoSi2, respectively. In turn, the anisotropic magnetoresistance achieves −95 and −98 % at T = 2 K and in B = 14 T for WSi2 and MoSi2, respectively. Furthermore, for both compounds we observed the Shoenberg effect in their Shubnikov-de Haas oscillations that persisted at as high temperature as T = 25 K in MoSi2 and T = 12 K in WSi2. In addition, we found for MoSi2 a rarely observed spin-zero phenomenon. Remarkably, the electronic structure calculations revealed type-II Dirac cones located near 480 meV and 710 meV above the Fermi level in MoSi2 and WSi2, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Electronic structure

Broadband focusing of acoustic plasmons in graphene with an applied current

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Physical Review B 2021年第16期104卷 L161409-L161409页

作者： Michael Sammon Dionisios Margetis E. J. Mele Tony Low Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota 55455 USA Institute for Physical Science and Technology and Department of Mathematics and Center for Scientific Computation and Mathematical Modeling University of Maryland College Park Maryland 20742 USA Dept. of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA

Nonreciprocal plasmons in current-driven, isotropic, and homogenous graphene with proximal metallic gates are theoretically explored. Nearby metallic gates screen the Coulomb interactions, leading to linearly dispersive acoustic plasmons residing close to its particle-hole continuum counterpart. We show that the applied bias leads to spectral broadband focused plasmons whose resonance linewidth is dependent on the angular direction relative to the current flow due to Landau damping. We predict that forward focused nonreciprocal plasmons are possible with accessible experimental parameters and setup.

关键词： Plasmons Surface plasmon polariton Graphene