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Quantum Gravity in the Lab. I. Teleportation by Size and Traversable Wormholes

PRX Quantum 2023年第1期4卷 010320-010320页

作者： Adam R. Brown Hrant Gharibyan Stefan Leichenauer Henry W. Lin Sepehr Nezami Grant Salton Leonard Susskind Brian Swingle Michael Walter Google Mountain View California 94043 USA Department of Physics Stanford University Stanford California 94305 USA Institute for Quantum Information and Matter Caltech Pasadena California 91125 USA Physics Department Princeton University Princeton New Jersey 08540 USA Amazon Quantum Solutions Lab Seattle Washington 98170 USA Amazon Web Services (AWS) Center for Quantum Computing Pasadena California 91125 USA Condensed Matter Theory Center Joint Center for Quantum Information and Computer Science Maryland Center for Fundamental Physics and Department of Physics University of Maryland College Park Maryland 20742 USA Faculty of Computer Science Ruhr University Bochum D-44801 Bochum Germany Korteweg–de Vries Institute for Mathematics Institute for Theoretical Physics Institute for Logic Language and Computation & QuSoft University of Amsterdam 1090 GE Amsterdam Kingdom of the Netherlands

With the long-term goal of studying models of quantum gravity in the lab, we propose holographic teleportation protocols that can be readily executed in table-top experiments. These protocols exhibit similar behavior to that seen in the recent traversable-wormhole constructions of Gao et al. [J. High Energy Phys., 2017, 151 (2017)] and Maldacena et al. [Fortschr. Phys., 65, 1700034 (2017)]: information that is scrambled into one half of an entangled system will, following a weak coupling between the two halves, unscramble into the other half. We introduce the concept of teleportation by size to capture how the physics of operator-size growth naturally leads to information transmission. The transmission of a signal through a semiclassical holographic wormhole corresponds to a rather special property of the operator-size distribution that we call size winding. For more general systems (which may not have a clean emergent geometry), we argue that imperfect size winding is a generalization of the traversable-wormhole phenomenon. In addition, a form of signaling continues to function at high temperature and at large times for generic chaotic systems, even though it does not correspond to a signal going through a geometrical wormhole but, rather, to an interference effect involving macroscopically different emergent geometries. Finally, we outline implementations that are feasible with current technology in two experimental platforms: Rydberg-atom arrays and trapped ions.

关键词： Quantum algorithms Quantum entanglement Quantum gravity Quantum teleportation

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Consensus algorithms for biased labeling in crowdsourcing

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Information sciences 2017年 382-383卷 254-273页

作者： Jing Zhang Victor S. Sheng Qianmu Li Jian Wu Xindong Wu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 P. R. China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 P. R. China Department of Computer Science University of Central Arkansas Conway AR 72035 USA Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 P. R. China Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 P. R. China School of Computing and Informatics University of Louisiana at Lafayette LA 70504 USA

Although it has become an accepted lay view that when labeling objects through crowdsourcing systems, non-expert annotators often exhibit biases, this argument lacks sufficient evidential observation and systematic empirical study. This paper initially analyzes eight real-world datasets from different domains whose class labels were collected from crowdsourcing systems. Our analyses show that biased labeling is a systematic tendency for binary categorization; in other words, for a large number of annotators, their labeling qualities on the negative class (supposed to be the majority) are significantly greater than are those on the positive class (minority). Therefore, the paper empirically studies the performance of four existing EM-based consensus algorithms , DS, GLAD, RY, and ZenCrowd, on these datasets. Our investigation shows that all of these state-of-the-art algorithms ignore the potential bias characteristics of datasets and perform badly although they model the complexity of the systems. To address the issue of handling biased labeling, the paper further proposes a novel consensus algorithm, namely adaptive weighted majority voting (AWMV), based on the statistical difference between the labeling qualities of the two classes. AWMV utilizes the frequency of positive labels in the multiple noisy label set of each example to obtain a bias rate and then assigns weights derived from the bias rate to negative and positive labels. Comparison results among the five consensus algorithms (AWMV and the four existing) show that the proposed AWMV algorithm has the best overall performance. Finally, this paper notes some potential related topics for future study.

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Efficient visual recognition with deep neural networks: A survey on recent advances and new directions

arXiv

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arXiv 2021年

作者： Wu, Yang Wang, Dingheng Lu, Xiaotong Yang, Fan Li, Guoqi Dong, Weisheng Shi, Jianbo Institute for Research Initiatives Nara Institute of Science and Technology Takayama-cho Ikoma Nara630-0192 Japan School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi'An Jiaotong University Shaanxi Xi'an710049 China School of Artificial Intelligence Xidian University China Division of Information Science Nara Institute of Science and Technology Japan Department of Precision Instrumentation Center for Brain Inspired Computing Research Beijing Innovation Center for Future Chip Tsinghua University Beijing100084 China Department of Computer and Information Science University of Pennsylvania United States

visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence. It has great fundamental importance and strong industrial needs. Deep neural networks (DNNs) have largely boosted their performances on many concrete tasks, with the help of large amounts of training data and new powerful computation resources. Though recognition accuracy is usually the first concern for new progresses, efficiency is actually rather important and sometimes critical for both academic research and industrial applications. Moreover, insightful views on the opportunities and challenges of efficiency are also highly required for the entire community. While general surveys on the efficiency issue of DNNs have been done from various perspectives, as far as we are aware, scarcely any of them focused on visual recognition systematically, and thus it is unclear which progresses are applicable to it and what else should be concerned. In this paper, we present the review of the recent advances with our suggestions on the new possible directions towards improving the efficiency of DNNrelated visual recognition approaches. We investigate not only from the model but also the data point of view (which is not the case in existing surveys), and focus on three most studied data types (images, videos and points). This paper attempts to provide a systematic summary via a comprehensive survey which can serve as a valuable reference and inspire both researchers and practitioners who work on visual recognition problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Bob’s Project Guidelines: Writing a Dissertation for a BSc. in computer science

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Innovation in Teaching and Learning in Information and computer sciences 2011年第1期10卷

作者： Robert S Laramee Visual and Interactive Computing Group Computer Science Department Swansea University UK r.s.laramee@swansea.ac.u

These detailed guidelines attempt to explain the mechanics of writing a third year project dissertation in computer science. They serve as a useful starting point for students (and new advisers) in their third or fourth year of study in the computer science department of a UK-based university. Afterall, no one is born knowing how to write a dissertation. Yet, there are certain elements, a commonality, that can be found in virtually all good dissertations. We give our recommendations as to each section a good dissertation consists of as well as what each section contains. These guidelines are generic and can be customized to fit most projects. The guidelines are open and free to use at any university or institution without *** believe that the writing can start with the abstract, however, in practice writing usually starts with the background section. The abstract can be approximately 6–12 sentences. It is a difficult starting point, but it forces the author to write down a concise description of what it is they’re researching and what the benefits are. Chances are, if the author can’t write an abstract, then it is not clear in the author’s mind what the project is about. Of course the abstract will be refined and updated over the lifetime of the project. The abstract can concisely (1) identify the project topic, (2) identify the benefits and advantages that result (3) and if there is novelty, describe the novelty of the presented work. If there’s a description of the project in a previously existing project selection brochure then that can be used as a basis for an abstract. These guidelines are a starting point only and are not a substitute for an advisor.

关键词： project guidelines computer science

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Exploiting cone approximations in an augmented Lagrangian method for conic optimization

arXiv

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arXiv 2024年

作者： Fukuda, Mituhiro Gómez, Walter Haeser, Gabriel Mito, Leonardo M. Center for Mathematics Computing and Cognition Federal University of ABC Santo André São Paulo Brazil Department of Computer Science University of São Paulo São Paulo Brazil Department of Mathematical and Computing Science Tokyo Institute of Technology Meguro-ku Tokyo Japan Departamento de Ingeniería Matemática Universidad de la Frontera Temuco Chile Departamento de Matemática Aplicada Universidade de São Paulo São Paulo Brazil

We propose an algorithm for general nonlinear conic programming which does not require the knowledge of the full cone, but rather a simpler, more tractable, approximation of it. We prove that the algorithm satisfies a strong global convergence property in the sense that it generates a strong sequential optimality condition. In particular, a KKT point is necessarily found when a limit point satisfies Robinson’s condition. We conduct numerical experiments minimizing nonlinear functions subject to a copositive cone constraint. In order to do this, we consider a well known polyhedral approximation of this cone by means of refining the polyhedral constraints after each augmented Lagrangian iteration. We show that our strategy outperforms the standard approach of considering a close polyhedral approximation of the full copositive cone in every *** Codes 90C30, 49M37, 90C26 © 2024, CC BY.

关键词： Lagrange multipliers

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Fault-tolerant compiling of classically hard IQP circuits on hypercubes

arXiv

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arXiv 2024年

作者： Hangleiter, Dominik Kalinowski, Marcin Bluvstein, Dolev Cain, Madelyn Maskara, Nishad Gao, Xun Kubica, Aleksander Lukin, Mikhail D. Gullans, Michael J. Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Simons Insitute for the Theory of Computing University of California at Berkeley BerkeleyCA94720 United States Department of Physics Harvard University CambridgeMA02138 United States JILA Department of Physics University of Colorado BoulderCO80309 United States AWS Center for Quantum Computing PasadenaCA91125 United States California Institute of Technology PasadenaCA91125 United States

Realizing computationally complex quantum circuits in the presence of noise and imperfections is a challenging task. While fault-tolerant quantum computing provides a route to reducing noise, it requires a large overhead for generic algorithms. Here, we develop and analyze a hardware-efficient, fault-tolerant approach to realizing complex sampling circuits. We co-design the circuits with the appropriate quantum error correcting codes for efficient implementation in a reconfigurable neutral atom array architecture, constituting what we call a fault-tolerant compilation of the sampling algorithm. Specifically, we consider a family of [[2D, D, 2]] quantum error detecting codes whose transversal and permutation gate set can realize arbitrary degree-D instantaneous quantum polynomial (IQP) circuits. Using native operations of the code and the atom array hardware, we compile a fault-tolerant and fast-scrambling family of such IQP circuits in a hypercube geometry, realized recently in the experiments by Bluvstein et al. [Nature 626, 7997 (2024)]. We develop a theory of second-moment properties of degree-D IQP circuits for analyzing hardness and verification of random sampling by mapping to a statistical mechanics model. We provide strong evidence that sampling from these hypercube IQP circuits is classically hard to simulate even at relatively low depths. We analyze the linear cross-entropy benchmark (XEB) in comparison to the average fidelity and, depending on the local noise rate, find two different asymptotic regimes. To realize a fully scalable approach, we first show that Bell sampling from degree-4 IQP circuits is classically intractable and can be efficiently validated. We further devise new families of [[O(dD), D, d]] color codes of increasing distance d, permitting exponential error suppression for transversal IQP sampling. Our results highlight fault-tolerant compiling as a powerful tool in co-designing algorithms with specific error-correcting codes and realisti

关键词： Digital arithmetic

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Harnessing Smartphone Sensors for Enhanced Road Safety: A Comprehensive Dataset and Review

arXiv

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arXiv 2024年

作者： Khandakar, Amith Michelson, David G. Naznine, Mansura Salam, Abdus Nahiduzzaman, Md. Khan, Khaled M. Suganthan, Ponnuthurai Nagaratnam Ayari, Mohamed Arselene Menouar, Hamid Haider, Julfikar Department of Electrical Engineering Qatar University Doha2713 Qatar Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Department of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Department of Computer Science and Engineering Qatar University Doha2713 Qatar KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar Qatar University Doha Qatar Department of Engineering Manchester Metropolitan University Chester Street ManchesterM1 5GD United Kingdom

Severe collisions can result from aggressive driving and poor road conditions, emphasizing the need for effective monitoring to ensure safety. Smartphones, with their array of built-in sensors, offer a practical and affordable solution for road-sensing. However, the lack of reliable, standardized datasets has hindered progress in assessing road conditions and driving patterns. This study addresses this gap by introducing a comprehensive dataset derived from smartphone sensors, which surpasses existing datasets by incorporating a diverse range of sensors including accelerometer, gyroscope, magnetometer, GPS, gravity, orientation, and uncalibrated sensors. These sensors capture extensive parameters such as acceleration force, gravitation, rotation rate, magnetic field strength, and vehicle speed, providing a detailed understanding of road conditions and driving behaviors. The dataset is designed to enhance road safety, infrastructure maintenance, traffic management, and urban planning. By making this dataset available to the community, the study aims to foster collaboration, inspire further research, and facilitate the development of innovative solutions in intelligent transportation systems. © 2024, CC BY.

关键词： Urban planning

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Statistical descriptors-based automatic fingerprint identification: Machine learning approaches

arXiv

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arXiv 2019年

作者： Jan, Hamid Ali, Amjad Mahmood, Shahid Srivastava, Gautam Department of Electrical Engineering Sarhad University of Science & Information Technology Ring Road Peshawar25000 Kpk Pakistan Department of Mechanical Engineering Sarhad University of Science & Information Technology Ring Road Peshawar25000 Kpk Pakistan Department of Mathematics and Computer Science Brandon University 270 18th Street BrandonR7A 6A9 Canada Research Center for Interneural Computing China Medical University Taichung40402 Taiwan

Identification of a person from fingerprints of good quality has been used by commercial applications and law enforcement agencies for many years, however identification of a person from latent fingerprints is very difficult and challenging. A latent fingerprint is a fingerprint left on a surface by deposits of oils and/or perspiration from the finger. It is not usually visible to the naked eye but may be detected with special techniques such as dusting with fine powder and then lifting the pattern of powder with transparent tape. We have evaluated the quality of machine learning techniques that has been implemented in automatic fingerprint identification. In this paper, we use fingerprints of low quality from database DB1 of Fingerprint Verification Competition (FVC 2002) to conduct our experiments. Fingerprints are processed to find its core point using Poincare index and carry out enhancement using Diffusion coherence filter whose performance is known to be good in the high curvature regions of fingerprints. Grey-level Co-Occurrence Matrix (GLCM) based seven statistical descriptors with four different inter pixel distances are then extracted as features and put forward to train and test REPTree, RandomTree, J48, Decision Stump and Random Forest Machine Learning techniques for personal identification. Experiments are conducted on 80 instances and 28 attributes. Our experiments proved that Random Forests and J48 give good results for latent fingerprints as compared to other machine learning techniques and can help improve the identification accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images

arXiv

引用

arXiv 2024年

作者： Zhang, Xiaoge Wang, Tao Yan, Chao Najdawi, Fedaa Zhou, Kai Ma, Yuan Cheung, Yiu-Ming Malin, Bradley A. Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Kowloon Hong Kong Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Pathology Microbiology and Immunology Vanderbilt University Medical Center NashvilleTN United States Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong Department of Mechanical Engineering Research Institute for Intelligent Wearable Systems The Hong Kong Polytechnic University Kowloon Hong Kong Department of Computer Science Hong Kong Baptist University Kowloon Tong Kowloon Hong Kong Department of Computer Science Vanderbilt University NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States

Ensuring trustworthiness is fundamental to the development of artificial intelligence (AI) that is considered societally responsible, particularly in cancer diagnostics, where a misdiagnosis can have dire consequences. Current digital pathology AI models lack systematic solutions to address trustworthiness concerns arising from model limitations and data discrepancies between model deployment and development environments. To address this issue, we developed TRUECAM, a framework designed to ensure both data and model trustworthiness in non-small cell lung cancer subtyping with whole-slide images. TRUECAM integrates 1) a spectral-normalized neural Gaussian process for identifying out-of-scope inputs and 2) an ambiguity-guided elimination of tiles to filter out highly ambiguous regions, addressing data trustworthiness, as well as 3) conformal prediction to ensure controlled error rates. We systematically evaluated the framework across multiple large-scale cancer datasets, leveraging both task-specific and foundation models, illustrate that an AI model wrapped with TRUECAM significantly outperforms models that lack such guidance, in terms of classification accuracy, robustness, interpretability, and data efficiency, while also achieving improvements in fairness. These findings highlight TRUECAM as a versatile wrapper framework for digital pathology AI models with diverse architectural designs, promoting their responsible and effective applications in real-world settings. © 2024, CC BY.

关键词： Lung cancer

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