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Beyond Bounds on Light Scattering with Complex Frequency Excitations

Physical Review Letters 2022年第20期129卷 203601-203601页

作者： Seunghwi Kim Sergey Lepeshov Alex Krasnok Andrea Alù Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA DTU Electro Department of Electrical and Photonics Engineering Technical University of Denmark Ørsteds Plads 343 Kgs. Lyngby DK-2800 Denmark Department of Electrical and Computer Engineering Florida International University Miami Florida 33174 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Light scattering is one of the most established wave phenomena in optics, lying at the heart of light-matter interactions and of crucial importance for nanophotonic applications. Passivity, causality, and energy conservation imply strict bounds on the degree of control over scattering from small particles, with implications on the performance of many optical devices. Here, we demonstrate that these bounds can be surpassed by considering excitations at complex frequencies, yielding extreme scattering responses as tailored nanoparticles reach a quasi-steady-state regime. These mechanisms can be used to engineer light scattering of nanostructures beyond conventional limits for noninvasive sensing, imaging, and nanoscale light manipulation.

关键词： Metamaterials Mie scattering

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Model-informed deep-learning photoacoustic reconstruction for low-element linear array

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Photoacoustics 2025年 44卷 100732页

作者： Paul, Souradip Lee, S. Alex Zhao, Shensheng Chen, Yun-Sheng Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign UrbanaIL United States Nick Holonyak Micro and Nanotechnology Laboratory University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL United States Department of Bioengineering University of Illinois Urbana-Champaign UrbanaIL United States Department of Biomedical and Translational Sciences Carle Illinois College of Medicine University of Illinois Urbana-Champaign UrbanaIL United States Cancer Center at Illinois University of Illinois Urbana-Champaign UrbanaIL United States

Photoacoustic tomography (PAT), widely applied using linear array ultrasound transducers for clinical and preclinical imaging, faces significant challenges due to sparse sensor arrangements and limited sensor pitch. These factors often compromise image quality, particularly in devices designed to have fewer sensors to reduce complexity and power consumption, such as wearable systems. Conventional reconstruction methods, including delay-and-sum and iterative model-based techniques, either lack accuracy or are computationally intensive. Recent advancements in deep learning offer promising improvements. In particular, model-based deep learning combines physics-informed priors with neural networks to enhance reconstruction quality and reduce computational demands. However, model matrix inversion during adjoint transformations presents computational challenges in model-based deep learning. To address the challenges, we introduce a simplified, efficient GE-CNN framework specifically tailored for linear array transducers. Our lightweight GE-CNN architecture significantly reduces computational demand, achieving a 4-fold reduction in model matrix size (2.09 GB for 32 elements vs. 8.38 GB for 128 elements) and accelerating processing by approximately 46.3 %, reducing the processing time from 7.88 seconds to 4.23 seconds. We rigorously evaluated this approach using synthetic models, experimental phantoms, and in-vivo rat liver imaging, highlighting the improved reconstruction performance with minimal hardware. © 2025 The Authors

关键词： Ultrasonic imaging

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Subdiffusive wave transport and weak localization transition in three-dimensional stealthy hyperuniform disordered systems

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Physical Review B 2022年第6期105卷 064204-064204页

作者： F. Sgrignuoli S. Torquato L. Dal Negro Department of Electrical and Computer Engineering & Photonics Center Boston University 8 Saint Mary's Street Boston Massachusetts 02215 USA Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08540 USA Division of Material Science and Engineering Boston University 15 Saint Mary's Street Brookline Massachusetts 02446 USA Department of Physics Boston University 590 Commonwealth Avenue Boston Massachusetts 02215 USA

The purpose of this work is to understand the fundamental connection between structural correlations and light localization in three-dimensional (3D) open scattering systems of finite size. We numerically investigate the transport of vector electromagnetic waves scattered by resonant electric dipoles spatially arranged in 3D space by stealthy hyperuniform disordered point patterns. Three-dimensional stealthy hyperuniform disordered systems are engineered with different structural correlation properties determined by their degree of stealthiness χ. Such fine control of exotic states of amorphous matter enables the systematic design of optical media that interpolate in a tunable fashion between uncorrelated random structures and crystalline materials. By solving the electromagnetic multiple scattering problem using Green's matrix spectral method, we establish a transport phase diagram that demonstrates a distinctive transition from a diffusive to a weak localization regime beyond a critical scattering density that depends on χ. The transition is characterized by studying the Thouless number and the spectral statistics of the scattering resonances. In particular, by tuning the χ parameter, we demonstrate large spectral gaps and suppressed subradiant proximity resonances, facilitating light localization. Moreover, consistently with previous studies, our results show a region of the transport phase diagram where the investigated scattering systems become transparent. Our work provides a systematic description of the transport and weak localization properties of light in stealthy hyperuniform structures and motivates the engineering of photonic systems with enhanced light-matter interactions for applications to both classical and quantum devices.

关键词： Anderson localization Light-matter interaction Mesoscopics Photonics Scattering theory Weak localization Metamaterials

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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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Spatially-varying microscope calibration from unstructured sparse inputs

Spatially-varying microscope calibration from unstructured s...

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Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020

作者： Kuo, Grace Monakhova, Kristina Yanny, Kyrollos Ng, Ren Waller, Laura Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley BerkeleyCA94720 United States

We propose a method based on blind deconvolution to calibrate the spatially-varying point spread functions of a coded-aperture microscope system. From easy-to-acquire measurements of unstructured fluorescent beads, we... 详细信息

ISBN: (纸本)9781557528209

关键词： Optical transfer function

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The 2020 power blackout in the brazilian state of amapá: Lessons learned and opportunities

The 2020 power blackout in the brazilian state of amapá: Le...

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2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2021

作者： Do Prado, Josue Campos Santos, Marcos Vinicius Ramos, Lucas Feksa Canha, Luciane Neves Konzen, Gabriel School of Engineering Computer Science Washington State University VancouverWA98686 United States School of Engineering Newcastle University Newcastle Upon TyneNE1 7RU United Kingdom Federal University of Rondônia Department of Electrical Engineering Porto Velho Brazil Graduate Program in Electrical Engineering Federal University of Santa Maria Santa Maria Brazil Division of Energy Economics and Environmental Studies Rio de Janeiro Brazil

ISBN: (纸本)9781665444217

The Brazilian state of Amapa=-notable for having 70% of its area consisting of unexplored Amazonian forests-experienced a massive power blackout in November 2020. An internal short-circuit in a transformer led to an explosion followed by a fire at the Macapa substation. This event left about 90% of the Amapa's population without power for several days in the middle of the COVID-19 pandemic, disrupting the lives of hundreds of thousands of people and raising concerns from several electricity sector stakeholders. This paper discusses select lessons learned from the power blackout and presents future opportunities to improve power system reliability and resilience for the grids in Amapá, and other similar Brazilian states, with a special focus on the increasingly important role of of distributed energy resources and smart grid technologies. © 2021 IEEE.

关键词： Outages

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Estimating Causal Effects using a Multi-task Deep Ensemble

arXiv

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arXiv 2023年

作者： Jiang, Ziyang Hou, Zhuoran Liu, Yiling Ren, Yiman Li, Keyu Carlson, David Department of Civil and Environmental Engineering Duke University DurhamNC United States Department of Biostatistics and Bioinformatics Duke University School of Medicine DurhamNC United States Program in Computational Biology and Bioinformatics Duke University School of Medicine DurhamNC United States Department of Economics University of Michigan Ross School of Business Ann ArborMI United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Computer Science Duke University DurhamNC United States

A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap, we propose Causal Multi-task Deep Ensemble (CMDE), a novel framework that learns both shared and group-specific information from the study population. We provide proofs demonstrating equivalency of CDME to a multi-task Gaussian process (GP) with a coregionalization kernel a priori. Compared to multi-task GP, CMDE efficiently handles high-dimensional and multi-modal covariates and provides pointwise uncertainty estimates of causal effects. We evaluate our method across various types of datasets and tasks and find that CMDE outperforms state-of-the-art methods on a majority of these tasks. © 2023, CC BY.

关键词： Data handling

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Plug-and-play stability for intracortical brain-computer interfaces: a one-year demonstration of seamless brain-to-text communication 23

Plug-and-play stability for intracortical brain-computer int...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Chaofei Fan Nick Hahn Foram Kamdar Donald Avansino Guy H. Wilson Leigh Hochberg Krishna V. Shenoy Jaimie M. Henderson Francis R. Willett Department of Computer Science Stanford University Department of Neurosurgery Stanford University Howard Hughes Medical Institute at Stanford University Department of Neuroscience Stanford University School of Engineering and Carney Institute for Brain Science Brown University and VA RR & D Center for Neurorestoration and Neurotechnology VA Providence Healthcare System and Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Harvard Medical School Bio-X Program Stanford University and Department of Neurobiology Stanford University and Department of Bioengineering Stanford University and Wu Tsai Neurosciences Institute Stanford University and Howard Hughes Medical Institute at Stanford University and Department of Electrical Engineering Stanford University Department of Neurosurgery Stanford University and Wu Tsai Neurosciences Institute Stanford University

Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.

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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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Heterogeneous Oblique Double Random Forest

arXiv

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arXiv 2023年

作者： Ganaie, M.A. Tanveer, M. Beheshti, I. Ahmad, N. Suganthan, P.N. Department of Robotics University of Michigan Ann ArborMI United States Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Department of International Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

The decision tree ensembles use a single data feature at each node for splitting the data. However, splitting in this manner may fail to capture the geometric properties of the data. Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node. Oblique decision trees capture the geometric properties of the data and hence, show better generalization. The performance of the oblique decision trees depends on the way oblique hyperplanes are generate and the data used for the generation of those hyperplanes. Recently, multiple classifiers have been used in a heterogeneous random forest (RaF) classifier, however, it fails to generate the trees of proper depth. Moreover, double RaF studies highlighted that larger trees can be generated via bootstrapping the data at each non-leaf node and splitting the original data instead of the bootstrapped data recently. The study of heterogeneous RaF lacks the generation of larger trees while as the double RaF based model fails to take over the geometric characteristics of the data. To address these shortcomings, we propose heterogeneous oblique double RaF. The proposed model employs several linear classifiers at each non-leaf node on the bootstrapped data and splits the original data based on the optimal linear classifier. The optimal hyperplane corresponds to the models based on the optimized impurity criterion. The experimental analysis indicates that the performance of the introduced heterogeneous double random forest is comparatively better than the baseline models. To demonstrate the effectiveness of the proposed heterogeneous double random forest, we used it for the diagnosis of Schizophrenia disease. The proposed model predicted the disease more accurately compared to the baseline models. © 2023, CC BY-NC-ND.

关键词： Decision trees

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