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arXiv 2024年

作者： Zhang, Xiran Abdulah, Sameh Cao, Jian Ltaief, Hatem Sun, Ying Genton, Marc G. Keyes, David E. Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Statistics Program King Abdullah University of Science and Technology Saudi Arabia Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Mathematics The University of Houston HoustonTX United States

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of- Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n3) and space complexity of O(n2), mainly due to a Cholesky factorization operation for an n × n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on largescale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificing the

关键词： Statistics

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Landau Polaritons in a Full-Dielectric Three-Dimensional Photonic-Crystal Cavity

Landau Polaritons in a Full-Dielectric Three-Dimensional Pho...

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CLEO: Science and Innovations, S and I 2022

作者： Tay, Fuyang Mojibpour, Ali Liang, Shuang Baydin, Andrey Ahmadivand, Arash Peraca, Nicolas Marquez Xu, Hongjing Gardner, Geoff C. Manfra, Michael J. Hagenmüller, David Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Purdue University West LafayetteIN47907 United States Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Microsoft Quantum Purdue Purdue University West LafayetteIN47907 United States School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States School of Materials Engineering Purdue University West LafayetteIN47907 United States Université de Strasbourg CNRS Strasbourg67000 France Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

ISBN: (纸本)9781557528209

We fabricated a full-dielectric three-dimensional photonic-crystal cavity containing an ultrahigh-mobility two-dimensional electron gas. By applying a strong perpendicular magnetic field, we created Landau polaritons originating from the ultrastrong coupling of electrons with cavity modes. © Optica Publishing Group 2022, © 2022 The Author(s)

关键词： Polariton

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Opto-thermoelectric pulling of light-absorbing particles

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Light(Science & Applications) 2020年第1期9卷 1686-1696页

作者： Linhan Lin Pavana Siddhartha Kollipara Abhay Kotnala Taizhi Jiang Yaoran Liu Xiaolei Peng Brian A.Korgel Yuebing Zheng Walker Department of Mechanical Engineering The University of Texas at AustinAustinTX 78712USA Materials Science&Engineering Program and Texas Materials Institute The University of Texas at AustinAustinTX 78712USA State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentTsinghua UniversityBeijing 100084People’s Republic of China McKetta Department of Chemical Engineering The University of Texas at AustinAustinTX 78712USA Department of Electrical and Computer Engineering The University of Texas at AustinAustinTX 78712USA

Optomechanics arises from the photon momentum and its exchange with low-dimensional *** is well known that optical radiation exerts pressure on objects,pushing them along the light ***,optical pulling of an object against the light path is still a counter-intuitive ***,we present a general concept of optical pulling-opto-thermoelectric pulling(OTEP)—where the optical heating of a light-absorbing particle using a simple plane wave can pull the particle itself against the light *** irradiation orientation-directed pulling force imparts self-restoring behaviour to the particles,and three-dimensional(3D)trapping of single particles is achieved at an extremely low optical intensity of 10^(−2)mWμm^(−2).Moreover,the OTEP force can overcome the short trapping range of conventional optical tweezers and optically drive the particle flow up to a macroscopic *** concept of selfinduced opto-thermomechanical coupling is paving the way towards freeform optofluidic technology and lab-on-achip devices.

关键词： absorbing light trapping

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A Light Weight Multi-Head SSD Model for ADAS Applications 1

A Light Weight Multi-Head SSD Model for ADAS Applications

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1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020

作者： Lai, Chun-Yu Wu, Bo-Xun Lee, Tsung-Han Shivanna, Vinay Malligere Guo, Jiun-In National Chiao Tung University Department of Electronics Engineering and Institute of Electronics Hsinchu Taiwan National Chiao Tung University College Doctoral Program of Electrical and Computer Engineering Hsinchu Taiwan

ISBN: (纸本)9781665404839

Moving objects detection is considered as one of the prime safety indicators in the Advanced Driver Assistance System (ADAS). For implementing on resource-limited embedded platforms and still yield sufficient frame rate and quality, the paper proposes a lightweight multi-head single shot detector (SSD) model that strengthens the moving object detection significantly. The paper also introduces focal loss method to deal with imbalance problem of detecting pedestrians and bikes in training datasets (vehicles, bikes, and pedestrians). Lastly, the proposed lightweight network can be deployed on low-power embedded devices to achieve real-time processing performance (512x256) yielding 30fps. © 2020 IEEE.

关键词： Object detection

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All-antiferromagnetic electrically controlled memory on silicon featuring large tunneling magnetoresistance

arXiv

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arXiv 2023年

作者： Shi, Jiacheng Lopez-Dominguez, Victor Arpaci, Sevdenur Sangwan, Vinod K. Mahfouzi, Farzad Kim, Jinwoong Athas, Jordan G. Hamdi, Mohammad Aygen, Can Phatak, Charudatta Carpentieri, Mario Jiang, Jidong S. Grayson, Matthew A. Kioussis, Nicholas Finocchio, Giovanni Hersam, Mark C. Amiri, Pedram Khalili Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Universitat Jaume I Castellón12006 Spain Applied Physics Program Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Physics and Astronomy California State University Northridge NorthridgeCA91330 United States Materials Science Division Argonne National Laboratory LemontIL60439 United States Department of Electrical and Information Engineering Politecnico di Bari Bari Italy Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina Messina98166 Italy Department of Chemistry Northwestern University EvanstonIL60208 United States

Antiferromagnetic (AFM) materials are a pathway to spintronic memory and computing devices with unprecedented speed, energy efficiency, and bit density. Realizing this potential requires AFM devices with simultaneous electrical writing and reading of information, which are also compatible with established silicon-based manufacturing. Recent experiments have shown tunneling magnetoresistance (TMR) readout in epitaxial AFM tunnel junctions. However, these TMR structures were not grown using a silicon-compatible deposition process, and controlling their AFM order required external magnetic fields. Here we show three-terminal AFM tunnel junctions based on the noncollinear antiferromagnet PtMn3, sputter-deposited on silicon. The devices simultaneously exhibit electrical switching using electric currents, and electrical readout by a large room-temperature TMR effect. First-principles calculations explain the TMR in terms of the momentum-resolved spin-dependent tunneling conduction in tunnel junctions with noncollinear AFM electrodes. Copyright © 2023, The Authors. All rights reserved.

关键词： Silicon

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Guiding Brain-to-Vocalization Decoder Design Using Structured Generalization Error

Guiding Brain-to-Vocalization Decoder Design Using Structure...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jingya Huang Pablo Tostado-Marcos Sowmya Manojna Narasimha Aashish N. Patel Ezequiel. M. Arneodo Timothy Q. Gentner Gal Mishne Vikash Gilja Department of Electrical and Computer Engineering University of California San Diego CA Department of Bioengineering University of California San Diego CA Institute for Neural Computation University of California San Diego CA Neuroscience Graduate Program University of California San Diego CA Department of Physics University of California San Diego CA Department of Psychology University of California San Diego CA Halıcıoğlu Data Science Institute University of California San Diego CA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

State-of-the-art intracortical neuroprostheses currently enable communication at 60+ words per minute for anarthric individuals by training on over 10K sentences to account for phoneme variability in different word contexts. There is limited understanding about whether this performance can be maintained in decoding naturalistic speech with 40K+ word vocabularies across elicited, spontaneous, and conversational speech contexts. We introduce a vocal-unit-level generalization test to explicitly evaluate neural decoder performance with an expanded and more diverse behavioral repertoire. Tested on neural decoders modeling zebra finch vocalization, an analog to human vocal production, we compare three decoders with different input types: spike trains, neural factors, and firing rates. The factors and rates are latent neural features inferred using trained Latent Factor Analysis via Dynamical Systems (LFADS) models that capture the population neural dynamics during vocal production. While the conventional random holdout generalization error measure is similar for all three decoders, factor- and rate-based decoders outperform spike-based decoders when testing vocal-unit-holdout generalization error. These results suggest the later models better adapt to flexible vocalization inference when trained with partial observation of data variation, motivating further exploration of decoders incorporating latent neural and vocalization dynamics.

关键词： Training Adaptation models Vocabulary Firing Measurement uncertainty Neuroprostheses Production Decoding engineering in medicine and biology Testing

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Terahertz spin dynamics in rare-earth orthoferrites

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Photonics Insights 2022年第2期1卷 1-56页

作者： Xinwei Li Dasom Kim Yincheng Liu Junichiro Kono Department of Physics California Institute of TechnologyPasadenaUSA Department of Electrical and Computer Engineering Rice UniversityHoustonUSA Applied Physics Graduate Program Smalley-Curl InstituteRice UniversityHoustonUSA Department of Physics and Astronomy Rice UniversityHoustonUSA Department of Materials Science and NanoEngineering Rice UniversityHoustonUSA

Recent interest in developing fast spintronic devices and laser-controllable magnetic solids has sparked tremendous experimental and theoretical efforts to understand and manipulate ultrafast dynamics in *** of spin dynamics in the terahertz(THz)frequency range are particularly important for elucidating microscopic pathways toward novel device ***,we review THz phenomena related to spin dynamics in rare-earth orthoferrites,a class of materials promising for antiferromagnetic *** expand this topic into a description of four key elements.(1)We start by describing THz spectroscopy of spin excitations for probing magnetic phase transitions in thermal *** acoustic magnons are useful indicators of spin reorientation transitions,electromagnons that arise from dynamic magnetoelectric couplings serve as a signature of inversion-symmetry-breaking phases at low temperatures.(2)We then review the strong laser driving scenario,where the system is excited far from equilibrium and thereby subject to modifications to the free-energy *** pathways for ultrafast laser manipulation of magnetic order are discussed.(3)Furthermore,we review a variety of protocols to manipulate coherent THz magnons in time and space,which are useful capabilities for antiferromagnetic spintronic applications.(4)Finally,new insights into the connection between dynamic magnetic coupling in condensed matter and the Dicke superradiant phase transition in quantum optics are *** presenting a review on an array of THz spin phenomena occurring in a single class of materials,we hope to trigger interdisciplinary efforts that actively seek connections between subfields of spintronics,which will facilitate the invention of new protocols of active spin control and quantum phase engineering.

关键词： terahertz spin dynamics magnetism orthoferrite ultrafast spectroscopy magneto-optics

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Increasing the mobility and power-electronics figure of merit of AlGaN with atomically thin AlN/GaN digital-alloy superlattices

arXiv

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arXiv 2022年

作者： Pant, Nick Lee, Woncheol Sanders, Nocona Kioupakis, Emmanouil Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

Alloy scattering in random AlGaN alloys drastically reduces the electron mobility and therefore the power-electronics figure of merit. As a result, Al compositions greater than 75% are required to obtain even a two-fold increase of the Baliga figure of merit compared to GaN. However, beyond approximately 80% Al composition, donors in AlGaN undergo the DX transition which makes impurity doping increasingly more difficult. Moreover, the contact resistance increases exponentially with increasing Al content, and integration with dielectrics becomes difficult due to the upward shift of the conduction band. Atomically thin superlattices of AlN and GaN, also known as digital alloys, are known to grow experimentally under appropriate growth conditions. These chemically ordered nanostructures could offer significantly enhanced figure of merit compared to their random-alloy counterparts due to the absence of alloy scattering, as well as better integration with contact metals and dielectrics. In this work, we investigate the electronic structure and phonon-limited electron mobility of atomically thin AlN/GaN digital-alloy superlattices using first-principles calculations based on density-functional and many-body perturbation theory. The band gap of the atomically thin superlattices reaches 4.8 eV, and the in-plane (out-of-plane) mobility is 369 (452) cm2 V-1 s-1. Using the modified Baliga figure of merit that accounts for the dopant ionization energy, we demonstrate that atomically thin AlN/GaN superlattices with a monolayer sublattice periodicity have the highest modified Baliga figure of merit among several technologically relevant ultra-wide band-gap materials, including random AlGaN, β-Ga2O3, cBN, and diamond. Copyright © 2022, The Authors. All rights reserved.

关键词： Aluminum gallium nitride

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Optimizing System-on-Chip Performance Using AI and SDN: Approaches and Challenges

Optimizing System-on-Chip Performance Using AI and SDN: Appr...

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International Conference on Software Defined Systems (SDS)

作者： Mohammad Alja'afreh Muath Obaidat Ali Karime Sahel Alouneh Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE Computer Science- City University of New York USA Department of Electrical and Computer Engineering The Royal Military College of Canada Canada Cybersecurity Program Al Ain University UAE and German Jordanian University Jordan

The advancement of modern multimedia and data-intensive classes of applications demands the development of hardware that delivers better performance. Due to the evolution of 5G, Edge-Computing, the Internet of Things, Software-Defined networks, etc., the data produced by the devices such as sensors are increasing. A software-Defined network is a powerful paradigm that is capable of automating networking and cloud computing. Software-Defined Network has controllers, devices, and applications which produce a huge amount of data. The processing of data inside the device as well as between the devices needs a better hardware architecture with more cores to ensure speedy performance. The System-on-Chip approach alone will not be capable to handle this dense core comprised of hardware. We have to blend Network-on-Chip along with System-on-Chip to increase the potential to include more cores capable to handle more threads. Artificial Intelligence, a key enabler in next-generation devices is capable of producing a better architecture design with optimized performance. In this paper, we are discussing and endeavouring how System-on-Chip, Network-on-Chip, Software-Defined Networks, and Artificial Intelligence can be physically, logically, and contextually incorporated to deliver improved computation and networking outcomes.

关键词： Performance evaluation computer architecture Network-on-chip Solids Hardware Software Artificial intelligence

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Multiscale Numerical Modeling for Near-Field Microwave Impedance Microscopy

Multiscale Numerical Modeling for Near-Field Microwave Imped...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Diego Tami Douglas A. A. Ohlberg Jhonattan C. Ramirez Cássio Gonçalves Do Rego Gilberto Medeiros-Ribeiro Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Brazil Microscopy Center Universidade Federal de Minas Gerais Brazil Department of Electronic Engineering Universidade Federal de Minas Gerais Brazil Computer Science Department Universidade Federal de Minas Gerais Brazil

In this work, we analyzed numerically a multiscale nanosystem based on sMIM on TBG. Spontaneous formation of a water-meniscus by the approximation between the tip-sample concentrates the microwave fields, reaching res... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Microscopy Masers Lasers and electrooptics Laser modes Numerical models Impedance Electrooptical waveguides

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