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Electrically tunable planar liquid-crystal singlets for simultaneous spectrometry and imaging

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Light(science & Applications) 2024年第11期13卷 2589-2598页

作者： Zhou Zhou Yiheng Zhang Yingxin Xie Tian Huang Zile Li Peng Chen Yan-qing Lu Shaohua Yu Shuang Zhang Guoxing Zheng Electronic Information School and School of MicroelectronicsWuhan UniversityWuhan 430072China NUS Graduate School National University of SingaporeSingapore 119077Singapore Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and Manipulationand College of Engineering and Applied SciencesNanjing UniversityNanjing 210093China Peng Cheng Laboratory Shenzhen 518055China New Cornerstone Science Laboratory Department of PhysicsUniversity of Hong KongHong KongChina Department of Electrical and Electronic Engineering University of Hong KongHong KongChina Wuhan Institute of Quantum Technology Wuhan 430206China

Conventional hyperspectral cameras cascade lenses and spectrometers to acquire the spectral datacube,which forms the fundamental framework for hyperspectral ***,this cascading framework involves tradeoffs among spectral and imaging performances when the system is driven toward ***,we propose a spectral singlet lens that unifies optical imaging and computational spectrometry functions,enabling the creation of minimalist,miniaturized and high-performance hyperspectral *** a paradigm,we capitalize on planar liquid crystal optics to implement the proposed framework,with each liquid-crystal unit cell acting as both phase modulator and electrically tunable spectral *** with various targets show that the resulting millimeter-scale hyperspectral camera exhibits both high spectral fidelity(>95%)and high spatial resolutions(~1.7 times the diffraction limit).The proposed“two-in-one”framework can resolve the conflicts between spectral and imaging resolutions,which paves a practical pathway for advancing hyperspectral imaging systems toward miniaturization and portable applications.

关键词： crystal planar singlet

CONSUMER TRANSACTIONS SIMULATION THROUGH GENERATIVE ADVERSARIAL NETWORKS UNDER STOCKS CONSTRAINTS IN LARGE-SCALE RETAIL

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arXiv 2024年

作者： Tkachuk, Sergiy Lukasik, Szymon Wróblewska, Anna Systems Research Institute Polish Academy of Sciences ul. Newelska 6 Warsaw01-447 Poland Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland Faculty of Physics and Applied Computer Science AGH University of Science and Technology al. Mickiewicza 30 Kraków30-059 Poland

In the rapidly evolving domain of large-scale retail data systems, envisioning and simulating future consumer transactions has become a crucial area of interest. It offers significant potential to fortify demand forecasting and fine-tune inventory management. This paper presents an innovative application of Generative Adversarial Networks (GANs) to generate synthetic retail transaction data, specifically focusing on a novel system architecture that combines consumer behavior modeling with stock-keeping unit (SKU) availability constraints to address real-world assortment optimization challenges. We diverge from conventional methodologies by integrating SKU data into our GAN architecture and using more sophisticated embedding methods (e.g., hyper-graphs). This design choice enables our system to generate not only simulated consumer purchase behaviors but also reflects the dynamic interplay between consumer behavior and SKU availability – an aspect often overlooked, among others, because of data scarcity in legacy retail simulation models. Our GAN model generates transactions under stock constraints, pioneering a resourceful experimental system with practical implications for real-world retail operation and strategy. Preliminary results demonstrate enhanced realism in simulated transactions measured by comparing generated items with real ones using methods employed earlier in related studies. This underscores the potential for more accurate predictive modeling. © 2024, CC BY-NC-ND.

关键词： Generative adversarial networks

Symbrain: A large-scale dataset of MRI images for neonatal brain symmetry analysis

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arXiv 2024年

作者： Gucciardi, Arnaud El Ghazouali, Safouane Venturini, Francesca Groznik, Vida Michelucci, Umberto University of Ljubljana Faculty of Computer and Information Science Ljubljana Slovenia TOELT llc Machine Learning Research and Development LAB Dübendorf Switzerland Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Winterthur Switzerland Faculty of Mathematics Natural Sciences and Information Technologies University of Primorska Koper Slovenia

This paper presents an annotated dataset of brain MRI images designed to advance the field of brain symmetry study. Magnetic resonance imaging (MRI) has gained interest in analyzing brain symmetry in neonatal infants, and challenges remain due to the vast size differences between fetal and adult brains. Classification methods for brain structural MRI use scales and visual cues to assess hemisphere symmetry, which can help diagnose neonatal patients by comparing hemispheres and anatomical regions of interest in the brain. Using the Developing Human Connectome Project dataset, this work presents a dataset comprising cerebral images extracted as slices across selected portions of interest for clinical evaluation . All the extracted images are annotated with the brain’s midline. All the extracted images are annotated with the brain’s midline. From the assumption that a decrease in symmetry is directly related to possible clinical pathologies, the dataset can contribute to a more precise diagnosis because it can be used to train deep learning model application in neonatal cerebral MRI anomaly detection from postnatal infant scans thanks to computer vision. Such models learn to identify and classify anomalies by identifying potential asymmetrical patterns in medical MRI images. Furthermore, this dataset can contribute to the research and development of methods using the relative symmetry of the two brain hemispheres for crucial diagnosis and treatment planning. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Application of HPC and big data in post-pandemic times

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Earthquake Research Advances 2021年第3期1卷 21-28页

作者： Henry M.Tufo David A.Yuen Gabriele Morra Matthew G.Knepley Bei Zhang Shi Chen Dept.of Computer Science University of ColoradoBoulderUSA Dept.of Applied Physics and Applied Mathematics Columbia UniversityUSA College of Information Science and Engineering and College of Marine Geosciences Ocean University of ChinaQingdaoChina Dept.of Physics and Earth Sciences University of LouisianaLafayetteLouisianaUSA Dept.of Computer Science and Engineering State University of New YorkBuffaloUSA Institute of Geophysics China Earthquake AdministrationBeijingChina Beijing Baijiatuan Earth Science National Observation and Research StationBeijingChina

We lay out the ramifications of the 2020 pandemic for all people in geosciences,especially the young,and argue for significant changes on training and career *** focus primarily on its devastating impact in USA and compare with that in other countries especially *** review the potential effect for the next four years or so on the aspirations of an academic career versus more realistic career *** urge people in mid-career about the need to reassess previous *** stress the need for students or researchers to acquire technical skills in high-performance computing(HPC),data analytics,artificial intelligence,and/or visualization along with a broad set of technical skills in applied computer science and *** give advice about hot prospects in several areas that have great potential for advancement in the coming decade,such as visualization,deep learning,quantum computing and information,and cloud computing,all of which lie within the aegis of *** forecast is that the pandemic will significantly reshape the job landscape and career paths for both young and established researchers and we discuss bluntly the dire situation facing junior people in geosciences in the aftermath of the pandemic around the world until 2024.

关键词： Geophysics High-performance computing Higher education Post-pandemic era

Backtesting Framework for Concentrated Liquidity Market Makers on Uniswap V3 Decentralized Exchange

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arXiv 2024年

作者： Urusov, Andrey Berezovskiy, Rostislav Yanovich, Yury Vega Institute Foundation Moscow Russia Faculty of Applied Mathematics Physics and Information Technologies Chuvash State University Cheboksary Russia International Laboratory of Stochastic Analysis and its Applications HSE University Moscow Russia Skolkovo Institute of Science and Technology Moscow Russia Faculty of Computer Science HSE University Moscow Russia

Decentralized finance (DeFi) has revolutionized the financial landscape, with protocols like Uniswap offering innovative automated market-making mechanisms. This article explores the development of a backtesting framework specifically tailored for concentrated liquidity market makers (CLMM). The focus is on leveraging the liquidity distribution approximated using a parametric model, to estimate the rewards within liquidity pools. The article details the design, implementation, and insights derived from this novel approach to backtesting within the context of Uniswap V3. The developed backtester was successfully utilized to assess reward levels across several pools using historical data from 2023 (pools Uniswap v3 for pairs of altcoins, stablecoins and USDC/ETH with different fee levels). Moreover, the error in modeling the level of rewards for the period under review for each pool was less than 1%. This demonstrated the effectiveness of the backtester in quantifying liquidity pool rewards and its potential in estimating LP’s revenues as part of the pool rewards, as focus of our next research. The backtester serves as a tool to simulate trading strategies and liquidity provision scenarios, providing a quantitative assessment of potential returns for liquidity providers (LP). By incorporating statistical tools to mirror CLMM pool liquidity dynamics, this framework can be further leveraged for strategy enhancement and risk evaluation for LPs operating within decentralized exchanges. © 2024, CC BY.

关键词： Decentralized finance

Classifying extended, localized and critical states in quasiperiodic lattices via unsupervised learning

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arXiv 2024年

作者： Zheng, Bohan Zhu, Siyu Zhou, Xingping Liu, Tong School of Computer Science and Technology School of Software Nanjing University of Posts and Telecommunications Nanjing210023 China Institute of Quantum Information and Technology Nanjing University of Posts and Telecommunications Nanjing210003 China Department of Applied Physics School of Science Nanjing University of Posts and Telecommunications Nanjing210003 China

Classification of quantum phases is one of the most important areas of research in condensed matter physics. In this work, we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised learning. Firstly, we choose two advanced unsupervised learning algorithms, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS), to explore the distinct phases of Aubry-André-Harper model and quasiperiodic p-wave model. The unsupervised learning results match well with traditional numerical diagonalization. Finally, we compare the similarity of different algorithms and find that the highest similarity between the results of unsupervised learning algorithms and those of traditional algorithms has exceeded 98%. Our work sheds light on applications of unsupervised learning for phase classification. © 2024, CC BY.

关键词： Contrastive Learning

Efficiently Verifiable Quantum Advantage on Near-Term Analog Quantum Simulators

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PRX Quantum 2025年第1期6卷 010341-010341页

作者： Zhenning Liu Dhruv Devulapalli Dominik Hangleiter Yi-Kai Liu Alicia J. Kollár Alexey V. Gorshkov Andrew M. Childs Joint Center for Quantum Information and Computer Science NIST/University of Maryland College Park Maryland 20742 USA Joint Quantum Institute NIST/University of Maryland College Park Maryland 20742 USA Department of Computer Science University of Maryland College Park Maryland 20742 USA Simons Institute for the Theory of Computing University of California at Berkeley California 94720 USA Applied and Computational Mathematics Division National Institute of Standards and Technology (NIST) Gaithersburg Maryland 20899 USA Department of Physics University of Maryland College Park Maryland 20742 USA Institute for Advanced Computer Studies University of Maryland College Park Maryland 20742 USA

Existing schemes for demonstrating quantum computational advantage are subject to various practical restrictions, including the hardness of verification and challenges in experimental implementation. Meanwhile, analog quantum simulators have been realized in many experiments to study novel physics. In this work, we propose a quantum advantage protocol based on single-step Feynman-Kitaev verification of an analog quantum simulation, in which the verifier need only run an O(λ2)-time classical computation, and the prover need only prepare O(1) samples of a history state and perform O(λ2) single-qubit measurements, for a security parameter λ. We also propose a near-term feasible strategy for honest provers and discuss potential experimental realizations.

关键词： Quantum computers

Observation of the symmetry-protected signature of 3-body interactions

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arXiv 2024年

作者： Zhukas, Liudmila A. Wang, Qingfeng Katz, Or Monroe, Christopher Marvian, Iman Duke Quantum Center Departments of Electrical and Computer Engineering and Physics Duke University DurhamNC27708 United States Chemical Physics Program Institute for Physical Science and Technology University of Maryland College ParkMD United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States

Identifying and characterizing multi-body interactions in quantum processes remains a significant challenge. This is partly because 2-body interactions can produce an arbitrary time evolution, a fundamental fact often called the universality of 2-local gates in the context of quantum computing. However, when an unknown Hamiltonian respects a U(1) symmetry such as charge or particle number conservation, N-body interactions exhibit a distinct symmetry-protected signature known as the N-body phase, which fewer-body interactions cannot mimic. We develop and demonstrate an efficient technique for the detection of 3-body interactions despite the presence of unknown 2-body interactions. This technique, which takes advantage of GHZ states for phase estimation, requires probing the unitary evolution and measuring its determinant in a small subspace that scales linearly with the system size, making it an efficient approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

Frequency Aware and Graph Fusion Network for Polyp Segmentation

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Frequency Aware and Graph Fusion Network for Polyp Segmentat...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yan Li Zhuoran Zheng Wenqi Ren Yunfeng Nie Jingang Zhang Xiuyi Jia School of Future Technology University of Chinese Academy of Sciences Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Engineering Nanjing University of Science and Technology Department of Applied Physics and Photonics Vrije Universiteit Brussel and Flanders Make

Polyp segmentation plays a crucial role in the prevention of colon cancer. However, the diverse shapes of polyps and their similarity to normal areas in terms of color and texture make polyp segmentation a challenging task. Currently, most polyp segmentation methods solely focus on spatial domain features, ignoring the valuable features in the frequency domain. Consequently, many polyp segmentation algorithms struggle with the camouflage of polyps. To tackle this issue, we propose the Frequency Aware and Graph Fusion Network (FAGF-Net). Specifically, it begins with a Frequency-based Global Extraction Module (FGEM), which provides an initial estimation of the polyp regions to guide subsequent modules. Next, we design a Frequency-based Feature Attention Module (FFAM) that leverages amplitude and phase information to amplify appearance differences and enhance semantic representations. Moreover, we present a Graph-based Fusion Module (GFM), which infers the geometric characteristic of polyps through aggregating and interacting with enhanced features. Extensive experiments show that our method outperforms state-of-the-art methods with better quantitative and qualitative evaluations.

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