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Quantification of Si, Al, Ti and O Composition in Si/Al Oxide Based Synaptic Resistor Circuits

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 240-241页

作者： Xu, Mingjie Gao, Dawei Zheng, Jian-Guo Chen, Yong Irvine Materials Research Institute University of California Irvine CA United States Departments of Mechanical and Aerospace Engineering Materials Science and Engineering Electrical and Computer Engineering California NanoSystems Institute University of California Los Angeles CA United States

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Strong nonlinear optical processes with extraordinary polarization anisotropy in inversionsymmetry broken two-dimensional PdPSe

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Light(science & Applications) 2024年第8期13卷 1524-1533页

作者： Song Zhu Ruihuan Duan Xiaodong Xu Fangyuan Sun Wenduo Chen Fakun Wang Siyuan Li Ming Ye Xin Zhou Jinluo Cheng Yao Wu Houkun Liang Junichiro Kono Xingji Li Zheng Liu Qi Jie Wang School of Electrical and Electronic Engineering Nanyang Technological University639798 SingaporeSingapore School of Material Science and Engineering Nanyang Technological University639798 SingaporeSingapore CINTRA CNRS/NTU/THALES UMI 3288Research Techno PlazaNanyang Technological University637371 SingaporeSingapore School of Materials Science and Engineering Harbin Institute of Technology150001 HarbinChina Department of Chemistry National University of Singapore117543 SingaporeSingapore GPL Photonics Lab State Key Laboratory of Applied OpticsChangchun Institute of OpticsFine Mechanics and PhysicsChinese Academy of Sciences130033 ChangchunChina School of Electronics and Information Engineering Sichuan University610064 ChengduSichuanChina School of Physical and Mathematical Sciences Nanyang Technological University637371 SingaporeSingapore Departments of Electrical and Computer Engineering Physics and Astronomyand Materials Science and NanoEngineeringRice UniversityHoustonTXUSA

Nonlinear optical activities,especially second harmonic generation(SHG),are key phenomena in inversion-symmetrybroken two-dimensional(2D)transition metal dichalcogenides(TMDCs).On the other hand,anisotropic nonlinear optical processes are important for unique applications in nano-nonlinear photonic devices with polarization functions,having become one of focused research topics in the field of nonlinear ***,the strong nonlinearity and strong optical anisotropy do not exist simultaneously in common 2D ***,we demonstrate strong secondorder and third-order susceptibilities of 64 pm/V and 6.2×10^(-19)m^(2)/V^(2),respectively,in the even-layer PdPSe,which has not been discovered in other common TMDCs(e.g.,MoS2).Strikingly,it also simultaneously exhibited strong SHG anisotropy with an anisotropic ratio of~45,which is the largest reported among all 2D materials to date,to the best of our *** addition,the SHG anisotropy ratio can be harnessed from 0.12 to 45(375 times)by varying the excitation wavelength due to the dispersion ofχ^(2)*** an illustrative example,we further demonstrate polarized SHG imaging for potential applications in crystal orientation identification and polarization-dependent spatial *** findings in 2D PdPSe are promising for nonlinear nanophotonic and optoelectronic applications.

关键词： materials anisotropy nonlinear

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Metal carbonyl loaded nanoparticles for breast cancer biomarker detection 21

Metal carbonyl loaded nanoparticles for breast cancer biomar...

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Nanoscale Imaging, Sensing, and Actuation for Biomedical Applications XXI 2024

作者： Richard, Craig Hsieh, Pei-Hsuan Confer, Matthew Bhargava, Rohit Department of Bioengineering University of Illinois at Urbana-Champaign United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign United States Departments of Electrical and Computer Engineering Mechanical Science and Engineering Chemical and Biomolecular Engineering Chemistry United States Cancer Center at Illinois University of Illinois at UrbanaChampaign Urbana United States

ISBN: (纸本)9781510669758

The potential advantages of infrared spectroscopic imaging for examination of cells and tissue biopsies for cancer histopathology is high, however there are fundamental limitations of FTIR that must first be addressed. One limitation is that Infrared spectroscopy lacks the sensitivity to detect biomarkers of disease directly. With cancer being one of the leading causes of death and its increasing prevalence, it is important to develop tools that can rapidly screen and enable rapid and simultaneous fingerprinting of biologic content and metabolic state. To address these limitations, probed Infrared spectroscopy is being explored. We report the successful synthesis, characterization, and application of metal carbonyl loaded nanoparticle probes for the detection of breast cancer biomarkers. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Biomarkers

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Mitigating Scattering in a Quantum System Using Only an Integrating Sphere

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PRX Quantum 2024年第3期5卷 030351页

作者： Zhenfei Jiang Tian Li Matthew L. Boone Zhenhuan Yi Alexei V. Sokolov Girish S. Agarwal Marlan O. Scully Institute for Quantum Science and Engineering Department of Physics and Astronomy Department of Chemistry and Physics UTC Research Institute Department of Physics Department of Biological and Agricultural Engineering Department of Electrical and Computer Engineering

Strong quantum correlated sources are essential but delicate resources for quantum information science and engineering protocols. Decoherence and loss are the two main disruptive processes that lead to the loss of nonclassical behavior in quantum correlations. In quantum systems, scattering can contribute to both decoherence and loss. In this work, we present an experimental scheme capable of significantly mitigating the adverse impact of scattering in quantum systems. Our quantum system is composed of a two-mode squeezed light generated with the four-wave-mixing process in hot rubidium vapor and a scatterer is introduced to one of the two modes. An integrating sphere is then placed after the scatterer to recollect the scattered photons. We use mutual information between the two modes as the measure of quantum correlations and demonstrate a 47.5% mutual information recovery from scattering, despite an enormous photon loss of greater than 85%. Our scheme is the very first step toward recovering quantum correlations from disruptive random processes and thus has the potential to bridge the gap between proof-of-principle demonstrations and practical real-world implementations of quantum protocols.

关键词： Open quantum systems & decoherence Quantum correlations in quantum information Quantum correlations, foundations & formalism

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Optimizing Network Management and Virtualization Using Machine Learning Approach: Network Slice Prediction

Optimizing Network Management and Virtualization Using Machi...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinyemi, Lateef Adesola Oshinuga, Olamide Peter Oladejo, Sunday Oladayo Ekwe, Stephen Obono Sumbwanyanbe, Mbuyu Mnkandla, Ernest Shoewu, Oluwagbemiga Omotayo University of South Africa Departments of Computer Science and Elect. Engineering CSET Johannesburg South Africa Lagos State University Department of Electronic and Computer Engineering Lagos Nigeria Stellenbosch University School for Data Science and Computational Thinking Stellenbosch South Africa Cape Peninsula University of Technology Department of Elect. Electronic and Computer Engineering Cape Town South Africa University of South Africa Department of Electrical Engineering CSET Johannesburg South Africa University of South Africa Department for Computer Science CSET Johannesburg South Africa

ISBN: (纸本)9798350358155

This study demonstrates the utilization of Machine Learning (ML) for network slice prediction, enabling the optimization of resources for diverse network slices. Traditional methods for network slice prediction often lack efficiency and result in inaccuracies. By leveraging ML algorithms such as Naive Bayes and Random Forest, an intelligent framework that automates network slice prediction is developed. This framework enhances network virtualization and management, facilitating resource allocation. The ML algorithms take real-time network conditions and usages, such as packet delay and smart city, as input and output for selecting the most suitable network slice. Data analysis is further conducted to reveal the connections between the input parameters and how these parameters influence the selection of the accurate network slice. Network slicing plays a crucial role as it enables the customization of services and facilitates efficient scaling to meet the specific needs of different applications and industries. The accuracy scores of the employed ML algorithms were generally perfect, except for the KNN and SVM classifiers, which achieved an accuracy of 94.30% and 92.16%, respectively, for the prediction of network slices based on incoming network connections and usages. © 2024 IEEE.

关键词： Data Analysis Machine Learning Network Management Network Slicing Network Virtualization

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Towards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation

arXiv

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arXiv 2024年

作者： Chakraborty, Neeloy Fang, Yixiao Schreiber, Andre Ji, Tianchen Huang, Zhe Mihigo, Aganze Wall, Cassidy Almana, Abdulrahman Driggs-Campbell, Katherine Departments of Electrical and Computer Engineering Computer Science Industrial and Enterprise Systems Engineering The University of Illinois Urbana-Champaign United States

Teleoperation is an important technology to enable supervisors to control agricultural robots remotely. However, environmental factors in dense crop rows and limitations in network infrastructure hinder the reliability of data streamed to teleoperators. These issues result in delayed and variable frame rate video feeds that often deviate significantly from the robot’s actual viewpoint. We propose a modular learning-based vision pipeline to generate delay-compensated images in real-time for supervisors. Our extensive offline evaluations demonstrate that our method generates more accurate images compared to state-of-the-art approaches in our setting. Additionally, ours is one of the few works to evaluate a delay-compensation method in outdoor field environments with complex terrain on data from a real robot in real-time. Resulting videos and code are provided at https://***/***/comp-teleop. © 2024, CC BY.

关键词： Agricultural robots

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Boosting Monolayer Transition Metal Dichalcogenides Growth by Hydrogen-Free Ramping during Chemical Vapor Deposition

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Nano Letters 2024年第27期24卷 8277-8286页

作者： Liu, Hongwei Zhang, Tianyi Wu, Peng Lee, Hae Won Liu, Zhenjing Tang, Tsz Wing Tang, Shin-Yi Kang, Ting Park, Ji-Hoon Wang, Jun Zhang, Kenan Zheng, Xudong Peng, Yu-Ren Chueh, Yu-Lun Liu, Yuan Palacios, Tomás Kong, Jing Luo, Zhengtang Department of Chemical and Biological Engineering Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology William Mong Institute of Nano Science and Technology and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction The Hong Kong University of Science and Technology Kowloon999077 Hong Kong Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Materials Science and Engineering National Tsing-Hua University Hsinchu30013 Taiwan Department of Materials Science and Engineering Korea University Seoul02841 Korea Republic of Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education School of Physics and Electronics Hunan University Changsha410082 China

The controlled vapor-phase synthesis of two-dimensional (2D) transition metal dichalcogenides (TMDs) is essential for functional applications. While chemical vapor deposition (CVD) techniques have been successful for transition metal sulfides, extending these methods to selenides and tellurides often faces challenges due to uncertain roles of hydrogen (H2) in their synthesis. Using CVD growth of MoSe2 as an example, this study illustrates the role of a H2-free environment during temperature ramping in suppressing the reduction of MoO3, which promotes effective vaporization and selenization of the Mo precursor to form MoSe2 monolayers with excellent crystal quality. As-synthesized MoSe2 monolayer-based field-effect transistors show excellent carrier mobility of up to 20.9 cm2/(V·s) with an on-off ratio of 7 × 107. This approach can be extended to other TMDs, such as WSe2, MoTe2, and MoSe2/WSe2 in-plane heterostructures. Our work provides a rational and facile approach to reproducibly synthesize high-quality TMD monolayers, facilitating their translation from laboratory to manufacturing. © 2024 American Chemical Society.

关键词： Monolayers

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Efficient List-decoding of Polynomial Ideal Codes with Optimal List Size

arXiv

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arXiv 2024年

作者： Ron-Zewi, Noga Venkitesh, S. Wootters, Mary Department of Computer Science University of Haifa Departments of Computer Science Department of Electrical Engineering Stanford University United States

In a recent breakthrough [BGM23, GZ23, AGL23], it was shown that randomly punctured Reed-Solomon codes are list decodable with optimal list size with high probability, i.e., they attain the Singleton bound for list decoding [ST20, Rot22, GST22]. We extend this result to the family of polynomial ideal codes, a large class of error-correcting codes which includes several well-studied families of codes such as Reed-Solomon, folded Reed-Solomon, and multiplicity codes. More specifically, similarly to the Reed-Solomon setting, we show that randomly punctured polynomial ideal codes over an exponentially large alphabet exactly achieve the Singleton bound for list-decoding;while such codes over a polynomially large alphabet approximately achieve it. Combining our results with the efficient list-decoding algorithm for a large subclass of polynomial ideal codes of [BHKS21], implies as a corollary that a large subclass of polynomial ideal codes (over random evaluation points) is efficiently list decodable with optimal list size. To the best of our knowledge, this gives the first family of codes that can be efficiently list decoded with optimal list size (for all list sizes), as well as the first family of linear codes of rate R that can be efficiently list decoded up to a radius of 1 − R − ǫ with list size that is polynomial (and even linear) in 1/ǫ. Our result applies to natural families of codes with algebraic structure such as folded Reed-Solomon or multiplicity codes (over random evaluation points). Our proof follows the general framework of [BGM23, GZ23, AGL23], but several new ingredients are needed. The main two new ingredients are a polynomial-ideal GM-MDS theorem (extending the algebraic GM-MDS theorem of [YH19, Lov21]), as well as a duality theorem for polynomial ideal codes, both of which may be of independent interest. © 2024, CC BY.

关键词： Polynomials

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A Fast Algorithm for Adaptive Private Mean Estimation

arXiv

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arXiv 2023年

作者： Duchi, John Haque, Saminul Kuditipudi, Rohith Departments of Statistics Departments of Electrical Engineering Departments of Computer Science Stanford University United States

We design an (Ε, δ)-differentially private algorithm to estimate the mean of a d-variate distribution, with unknown covariance Σ, that is adaptive to Σ. To within polylogarithmic factors, the estimator achieves optimal rates of convergence with respect to the induced Mahalanobis norm k·kΣ, takes time Oe(nd2) to compute, has near linear sample complexity for sub-Gaussian distributions, allows Σ to be degenerate or low rank, and adaptively extends beyond sub-Gaussianity. Prior to this work, other methods required exponential computation time or the superlinear scaling n = Ω(d3/2) to achieve non-trivial error with respect to the norm k·kΣ © 2023, CC BY.

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Calibrated multiple-output quantile regression with representation learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 923-970页

作者： Shai Feldman Stephen Bates Yaniv Romano Department of Computer Science Technion — Israel Institute of Technology Technion City Haifa Israel Departments of Electrical Engineering and Computer Science and of Statistics University of California Berkeley Berkeley CA Departments of Electrical and Computer Engineering and of Computer Science Technion — Israel Institute of Technology Technion City Haifa Israel

We develop a method to generate predictive regions that cover a multivariate response variable with a user-specified probability. Our work is composed of two components. First, we use a deep generative model to learn a representation of the response that has a unimodal distribution. Existing multiple-output quantile regression approaches are effective in such cases, so we apply them on the learned representation, and then transform the solution to the original space of the response. This process results in a flexible and informative region that can have an arbitrary shape, a property that existing methods lack. Second, we propose an extension of conformal prediction to the multivariate response setting that modifies any method to return sets with a pre-specified coverage level. The desired coverage is theoretically guaranteed in the finite-sample case for any distribution. Experiments conducted on both real and synthetic data show that our method constructs regions that are significantly smaller compared to existing techniques.

关键词： conformal prediction uncertainty quantification quantile regression multiple regression variational auto-encoder

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