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arXiv 2024年

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Faculty of Applied Science School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada The Chinese University of Hong Kong Shenzhen518172 China

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Towards Robust Bone Age Assessment: Rethinking Label Noise and Ambiguity 23rd

Towards Robust Bone Age Assessment: Rethinking Label Noise a...

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23rd International Conference on Medical Image computing and computer-Assisted Intervention, MICCAI 2020

作者： Gong, Ping Yin, Zihao Wang, Yizhou Yu, Yizhou Deepwise AI Lab Beijing China Department of Computer Science Peking University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China The University of Hong Kong Pokfulam Hong Kong

ISBN: (纸本)9783030597245

The effects of label noise and ambiguity are widespread, especially for subjective tasks such as bone age assessment (BAA). However, most existing BAA algorithms ignore these issues. We propose a robust framework for BAA supporting Tanner & Whitehouse 3 (TW3) method, which is clinically more objective and reproducible than Greulich & Pyle (GP) method, but has received less attention from the research community. Since the publicly available RSNA BAA dataset was annotated using GP method, we contribute additional TW3 annotations. We formulate TW3 BAA as an ordinal regression problem, and address both label noise and ambiguity with a two stage deep learning framework. The first stage focuses on correcting erroneous labels with ambiguity tolerated, while the latter stage introduces a module called Residual Context Graph (RCG) to conquer label ambiguity. Inspired by the way human experts handle ambiguity, we combine fine-grained local features with a graph based context. Experiments show the proposed framework outperforms previously reported TW3-based BAA systems by large margins. TW3 annotations of bone maturity levels for a portion of the RSNA BAA dataset will be made publicly available. © 2020, Springer Nature Switzerland AG.

关键词： Graphic methods

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Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices 23rd

Revisiting 3D Context Modeling with Supervised Pre-training ...

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23rd International Conference on Medical Image computing and computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Shu Xu, Jincheng Chen, Yu-Chun Ma, Jiechao Li, Zihao Wang, Yizhou Yu, Yizhou Department of Computer Science Peking University Beijing China Deepwise AI Lab Beijing China Advanced Institute of Information Technology Peking University Hangzhou China Center on Frontiers of Computing Studies Peking University Beijing China The University of Hong Kong Pokfulam Hong Kong

ISBN: (纸本)9783030597184

Universal lesion detection from computed tomography (CT) slices is important for comprehensive disease screening. Since each lesion can locate in multiple adjacent slices, 3D context modeling is of great significance for developing automated lesion detection algorithms. In this work, we propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) that leverages depthwise separable convolutional filters and a group transform module (GTM) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. To facilitate faster convergence, a novel 3D network pre-training method is derived using solely large-scale 2D object detection dataset in the natural image domain. We demonstrate that with the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset (3.48% absolute improvement in the sensitivity of FPs@0.5), significantly surpassing the baseline method by upto 6.06% (in MAP@0.5) which adopts 2D convolution for 3D context modeling. Moreover, the proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks. © 2020, Springer Nature Switzerland AG.

关键词： Object detection

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Priority based Adaptive Scheduling Algorithm for IoT Sensor Systems

Priority based Adaptive Scheduling Algorithm for IoT Sensor ...

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2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019

作者： Kavitha, K. Suseendran, G. Department of Computer Science VELS Institute of Science Technology and Advanced Studies Chellammal Women's College Chennai India Department of Information Technology School of Computing Sciences VELS Institute of Science Technology and Advanced Studies Chennai India

ISBN: (纸本)9781538680100

In IoT sensor systems, the existing scheduling approaches fail to satisfy the varying Quality-of-Service (QoS) requirements of heterogeneous applications and their demands. This paper proposes a priority based adaptive scheduling algorithm (PASA) for IoT sensor systems. Unlike the existing scheduling techniques, the proposed PASA considers the requirements of heterogeneous applications such as data rate, minimum delay, transmitting power, remaining energy, remaining buffer size of devices etc. The base station allocates collison-free time slots for each node based on their traffic priority. The duty-cycle (ST) of each node will be adaptively assigned based on the Priority of traffic, remaining buffer size of queue level (RBS), remaining energy (RE) and required transmitting power (TP). The proposed PASA is compared with the Energy Efficient Context Aware Traffic Scheduling (EE-CATS) algorithm. Simulation results have shown that PASA outperforms EE-CATS in terms of packet delivery ratio (PDR), average residual energy and throughput. © 2019 IEEE.

关键词： Scheduling algorithms

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Social Media Mining: A Genetic Based Multiobjective Clustering Approach to Topic Modelling

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第1期48卷

作者： Alfred, Rayner Jie, Loo Yew Obit, Joe Henry Lim, Yuto Haviluddin, Haviluddin Azman, Azreen Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Kota KinabaluSabah88400 Malaysia School of Information Science Security and Networks Area Japan Advanced Institute of Science and Technology Access 1-1 Asahidai NomiIshikawa923-1292 Japan Department of Informatics Universitas Mulawarman Samarinda Indonesia Department of Multimedia Faculty of Computer Science and Information Technology UPM Malaysia

Social media mining is the process of collecting large datasets from user-generated content and extracting and analyzing social media interactions to recognize meaningful patterns in individual and social behavior. Everyday, more contents related to social media are generated by social media users (e.g., Facebook, Twitter). As the components of big data continue to expand, the task of extracting useful information becomes critical. Topic extraction refers to the process of extracting main topics from the pool of news feed and a typical method to perform topic extraction is through clustering. Clustering defines or organizes a group of patterns or objects into clusters, allows high-dimensional data to be presented in an apprehensive fashion to humans. Although effective, the performance of the k-means clustering algorithm depends heavily on the initial centroids and the number of clusters, k. Recently, several effective supervised and unsupervised machine learning methods have been developed in the domain of topics extraction. However, less works have been conducted in applying multiobjective based algorithm for topic extraction. Most of these algorithms are not optimized, even if they are, they are only optimized by using a single objective method and may underperform when solving real-world problems which are typically multi-objectives in nature. This paper investigates the effects of using a multiobjective genetic algorithm (MOGA) based clustering technique to cluster texts for topic extraction which is designed based on the structure and purity of the clusters in order to determine the optimal initial centroids and the number of clusters, k. Then, the mapping percentages between the predefined and produced clusters are used to assess the performance of the proposed algorithm. The best mapping percentage of 62.7 obtained using the proposed algorithm when k = 15 is obtained to outperform the performance of the generic k-means. The top five most representative words

关键词： Genetic algorithms

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Approximate complex amplitude encoding algorithm and its application to data classification problems

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Physical Review A 2024年第5期109卷 052423-052423页

作者： Naoki Mitsuda Tatsuhiro Ichimura Kouhei Nakaji Yohichi Suzuki Tomoki Tanaka Rudy Raymond Hiroyuki Tezuka Tamiya Onodera Naoki Yamamoto Sumitomo Mitsui Trust Bank Ltd. 1-4-1 Marunouchi Chiyoda-ku Tokyo 100-8233 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Kanagawa 223-8522 Japan Department of Chemistry University of Toronto Toronto Ontario Canada M5G 1Z8 Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan Mitsubishi UFJ Financial Group Inc. and MUFG Bank Ltd. 2-7-1 Marunouchi Chiyoda-ku Tokyo 100-8388 Japan IBM Quantum IBM Japan 19-21 Nihonbashi Hakozaki-cho Chuo-ku Tokyo 103-8510 Japan Department of Computer Science University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Advanced Research Laboratory Technology Infrastructure Center Technology Platform Sony Group Corporation 1-7-1 Konan Minato-ku Tokyo 108-0075 Japan Graduate School of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Kanagawa 223-8522 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama 223-8522 Japan

Quantum computing has a potential to accelerate data processing efficiency, especially in machine learning, by exploiting special features such as the quantum interference. The major challenge in this application is that, in general, the task of loading a classical data vector into a quantum state requires an exponential number of quantum gates. The approximate amplitude encoding (AAE) method, which uses a variational means to approximately load a given real-valued data vector into the amplitude of a quantum state, was recently proposed as a general approach to this problem mainly for near-term devices. However, AAE cannot load a complex-valued data vector, which narrows its application range. In this work, we extend AAE so that it can handle a complex-valued data vector. The key idea is to employ the fidelity distance as a cost function for optimizing a parametrized quantum circuit, where the classical shadow technique is used to efficiently estimate the fidelity and its gradient. We apply this algorithm to realize the complex-valued-kernel binary classifier called the compact Hadamard classifier, and then we present a numerical experiment showing that it enables classification of the Iris dataset and credit card fraud detection.

关键词： Machine learning Quantum algorithms Quantum circuits Quantum information processing Quantum simulation

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Quantum computing for High-Energy Physics: State of the Art and Challenges

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PRX Quantum 2024年第3期5卷 037001-037001页

作者： Alberto Di Meglio Karl Jansen Ivano Tavernelli Constantia Alexandrou Srinivasan Arunachalam Christian W. Bauer Kerstin Borras Stefano Carrazza Arianna Crippa Vincent Croft Roland de Putter Andrea Delgado Vedran Dunjko Daniel J. Egger Elias Fernández-Combarro Elina Fuchs Lena Funcke Daniel González-Cuadra Michele Grossi Jad C. Halimeh Zoë Holmes Stefan Kühn Denis Lacroix Randy Lewis Donatella Lucchesi Miriam Lucio Martinez Federico Meloni Antonio Mezzacapo Simone Montangero Lento Nagano Vincent R. Pascuzzi Voica Radescu Enrique Rico Ortega Alessandro Roggero Julian Schuhmacher Joao Seixas Pietro Silvi Panagiotis Spentzouris Francesco Tacchino Kristan Temme Koji Terashi Jordi Tura Cenk Tüysüz Sofia Vallecorsa Uwe-Jens Wiese Shinjae Yoo Jinglei Zhang 1211 Geneva Switzerland CQTA Computation-based Science and Technology Research Center 8803 Rüschlikon Switzerland Department of Physics IBM Quantum Physics Division Deutsches Elektronen-Synchrotron (DESY) Notkestraße 85 22607 Hamburg Germany Templergraben 55 52062 Aachen Germany TIF Lab Dipartimento di Fisica Institut für Physik 〈aQa Physics Division Department of Computer Science Facultad de Ciencias Institute of Theoretical Physics 38116 Braunschweig Germany Transdisciplinary Research Area “Building Blocks of Matter and Fundamental Interactions” (TRA Matter) and Helmholtz Institute for Radiation and Nuclear Physics (HISKP) Institute for Theoretical Physics 6020 Innsbruck Austria Department of Physics and Arnold Sommerfeld Center for Theoretical Physics Munich Germany Institute of Physics CNRS/IN2P3 IJCLab Department of Physics and Astronomy Via Marzolo 8 35131 Padua Italy Science Park 105 1098 XG Amsterdam Netherlands Department of Gravitational Waves and Fundamental Physics International Center for Elementary Particle Physics Department of Physical Chemistry 20018 Donostia–San Sebastián Spain EHU Quantum Center University of the Basque Country UPV/EHU P.O. Box 644 48080 Bilbao Spain Basque Foundation for Science Via Sommarive 14 38123 Povo Trento Italy Department Física Center of Physics and Engineering of Advanced Materials (CeFEMA) Instituto Superior Técnico Lisbon Portugal Laboratory of Physics for Materials and Emergent Technologies (LaPMET) Lisbon Portugal Kirk and Pine Street Batavia Illinois 60510 USA Albert Einstein Center for Fundamental Physics Institute for Theoretical Physics 98 Rochester Sreet Upton New York 11973 USA Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada

Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage—namely, a significant (in some cases exponential) speedup of numerical simulations. The rapid development of hardware devices with various realizations of qubits enables the execution of small-scale but representative applications on quantum computers. In particular, the high-energy physics community plays a pivotal role in accessing the power of quantum computing, since the field is a driving source for challenging computational problems. This concerns, on the theoretical side, the exploration of models that are very hard or even impossible to address with classical techniques and, on the experimental side, the enormous data challenge of newly emerging experiments, such as the upgrade of the Large Hadron Collider. In this Roadmap paper, led by CERN, DESY, and IBM, we provide the status of high-energy physics quantum computations and give examples of theoretical and experimental target benchmark applications, which can be addressed in the near future. Having in mind hardware with about 100 qubits capable of executing several thousand two-qubit gates, where possible, we also provide resource estimates for the examples given using error-mitigated quantum computing. The ultimate declared goal of this task force is therefore to trigger further research in the high-energy physics community to develop interesting use cases for demonstrations on near-term quantum computers.

关键词： Quantum computation Quantum simulation

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Variational quantum algorithm for generalized eigenvalue problems and its application to the finite-element method

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Physical Review A 2023年第2期108卷 022429-022429页

作者： Yuki Sato Hiroshi C. Watanabe Rudy Raymond Ruho Kondo Kaito Wada Katsuhiro Endo Michihiko Sugawara Naoki Yamamoto Toyota Central R&D Labs. Inc. 41-1 Yokomichi Nagakute Aichi 480-1192 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Kanagawa 223-8522 Japan Department of Chemistry Graduate School of Science Kyushu University 744 Motooka Nishi-ku Fukuoka 819-0395 Japan IBM Quantum IBM Japan 19-21 Nihonbashi Hakozaki-cho Chuo-ku Tokyo 103-8510 Japan Department of Computer Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama 223-8522 Japan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan

Generalized eigenvalue problems (GEPs) play an important role in the variety of fields including engineering, machine learning, and quantum chemistry. Especially, many problems in these fields can be reduced to finding the minimum or maximum eigenvalue of GEPs. One of the key problems to handle GEPs is that the memory usage and computational complexity explode as the size of the system of interest grows. This paper aims at extending sequential quantum optimizers for GEPs. Sequential quantum optimizers are a family of algorithms that iteratively solve the analytical optimization of single-qubit gates in a coordinate descent manner. The contribution of this paper is as follows. First, we formulate the GEP as the minimization or maximization problem of the fractional form of the expectations of two Hermitians. We then show that the fractional objective function can be analytically minimized or maximized with respect to a single-qubit gate by solving a GEP of a 4×4 matrix. Second, we show that a system of linear equations characterized by a positive-definite Hermitian can be formulated as a GEP and thus be attacked using the proposed method. Finally, we demonstrate two applications to important engineering problems formulated with the finite-element method. Through the demonstration, we have the following bonus finding; a problem having a real-valued solution can be solved more effectively using quantum gates generating a complex-valued state vector, which demonstrates the effectiveness of the proposed method.

关键词： Quantum algorithms

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Preface

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CEUR Workshop Proceedings 2022年 3244卷

作者： Chaudhari, Narendra S. Ganesan, Geetha Satti, Srinivasa Rao Department of Computer Science and Engineering IIT Indore India Advanced Computing Research Society Tamilnadu Chennai India Norwegian Institute of Science and Technology Trondheim Norway

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Formation and Microwave Losses of Hydrides in Superconducting Niobium Thin Films Resulting from Fluoride Chemical Processing

arXiv

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arXiv 2024年

作者： Torres-Castanedo, Carlos G. Goronzy, Dominic P. Pham, Thang McFadden, Anthony Materise, Nicholas Das, Paul Masih Cheng, Matthew Lebedev, Dmitry Ribet, Stephanie M. Walker, Mitchell J. Garcia-Wetten, David A. Kopas, Cameron J. Marshall, Jayss Lachman, Ella Zhelev, Nikolay Sauls, James A. Mutus, Joshua Y. McRae, Corey Rae H. Dravid, Vinayak P. Bedzyk, Michael J. Hersam, Mark C. Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States National Institute of Standards and Technology BoulderCO80305 United States Department of Physics Colorado School of Mines GoldenCO80401 United States Rigetti Computing BerkeleyCA94710 United States Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University United EvanstonIL60208 United States Department of Physics University of Oregon EugeneOR97403 United States Hearne Institute of Theoretical Physics Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States Department of Physics University of Colorado BoulderCO80309 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States

Superconducting Nb thin films have recently attracted significant attention due to their utility for quantum information technologies. In the processing of Nb thin films, fluoride-based chemical etchants are commonly used to remove surface oxides that are known to affect superconducting quantum devices adversely. However, these same etchants can also introduce hydrogen to form Nb hydrides, potentially negatively impacting microwave loss performance. Here, we present comprehensive materials characterization of Nb hydrides formed in Nb thin films as a function of fluoride chemical treatments. In particular, secondary-ion mass spectrometry, X-ray scattering, and transmission electron microscopy reveal the spatial distribution and phase transformation of Nb hydrides. The rate of hydride formation is determined by the fluoride solution acidity and the etch rate of Nb2O5, which acts as a diffusion barrier for hydrogen into Nb. The resulting Nb hydrides are detrimental to Nb superconducting properties and lead to increased power-independent microwave loss in coplanar waveguide resonators. However, Nb hydrides do not correlate with two-level system loss or device aging mechanisms. Overall, this work provides insight into the formation of Nb hydrides and their role in microwave loss, thus guiding ongoing efforts to maximize coherence time in superconducting quantum devices. © 2024, CC BY-NC-ND.

关键词： Thin films

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