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SEBF: A single-chain based extension model of blockchain for fintech 29

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SEBF: A single-chain based extension model of blockchain for...

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Ji, Yimu Gu, Weiheng Chen, Fei Xiao, Xiaoying Sun, Jing Liu, Shangdong He, Jing Li, Yunyao Zhang, Kaixiang Mei, Fen Wu, Fei School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China Institue of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China Nanjing Center of HPC China Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China Institute of Information Technology Nanjing University of Finance and Economics Nanjing China School of Software and Electrical Engineering Swinburne University of Technology Hawthorn Australia WeBank Co. Ltd. Shenzhen China College of Automation Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9780999241165

The traditional blockchain has the shortcoming that a single-chain can only deal with one or a few specific data types. The research question of how to make blockchain be able to deal with various data types has not been well studied. In this paper, we propose a single-chain based extension model of blockchain for fintech (SEBF). In the financial environment, we design a four-layer architecture for this model. By employing the external trusted oracle group and a financial regulator agency, a variety types of data can be effectively stored in the blockchain, such that the data type extension based on a single-chain is realized. The experimental results indicate that the proposed model can improve the efficiency of simplified payment verification. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Blockchain

Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound

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arXiv 2025年

作者： Huang, Yuhao Chang, Ao Dou, Haoran Tao, Xing Zhou, Xinrui Cao, Yan Huang, Ruobing Frangi, Alejandro F. Bao, Lingyun Yang, Xin Ni, Dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China School of Computing University of Leeds Leeds United Kingdom Shenzhen RayShape Medical Technology Co. Ltd Shenzhen China Division of Informatics Imaging and Data Science School of Health Sciences University of Manchester Manchester United Kingdom Department of Computer Science School of Engineering University of Manchester Manchester United Kingdom Department of Electrical Engineering Department of Cardiovascular Sciences KU Leuven Belgium Alan Turing Institute London United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom Department of Ultrasound Affiliated Hangzhou First People’s Hospital School of Medicine Westlake University China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: 1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. 2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. 3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

SQUID pattern disruption in transition metal dichalcogenide Josephson junctions due to nonparabolic dispersion of the edge states

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Physical Review B 2020年第16期102卷 165407-165407页

作者： D. Sticlet P. Wójcik M. P. Nowak National Institute for Research and Development of Isotopic and Molecular Technologies 67-103 Donat 400293 Cluj-Napoca Romania AGH University of Science and Technology Faculty of Physics and Applied Computer Science al. A. Mickiewicza 30 30-059 Krakow Poland AGH University of Science and Technology Academic Centre for Materials and Nanotechnology al. A. Mickiewicza 30 30-059 Krakow Poland

We theoretically study Josephson junctions with a transition metal dichalcogenide zigzag ribbon as a weak link. We demonstrate that the spatial profile of the supercurrent carried by the edge modes determines the critical current dependence on the perpendicular magnetic field. We explore this finding and analyze the impact of Zeeman interaction and the orbital effects of the magnetic field on the Andreev bound states energies. We show that the unequal Fermi velocities of the spin-opposite edge modes lead to an anomalous shift of the Andreev bound states in the presence of the magnetic field. This is manifested in a pronounced modification of the SQUID critical current oscillations when two opposite edges of the ribbon are conducting and can be exploited in order to reveal the anomalous phase shift of the Andreev bound states in a single Josephson junction device.

关键词： Josephson junctions Nanoribbon SQUID Transition metal dichalcogenides

Multi-thread concurrent compression algorithm for genomic big data 20

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Multi-thread concurrent compression algorithm for genomic bi...

20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019

作者： Ji, Yimu Chen, Shuai Yao, Haichang Fang, Houzhi Li, Kui Liu, Shangdong Xie, Zhengyuan Wang, Kairui School of Computer Science Nanjing University of Posts and Telecommunications Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing Center of HPC China Jiangsu HPC Intelligent Processing Engineer Research Center Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecom. School of Computer and Software Nanjing Institute of Industry Technology Nanjing China University of Toronto Toronto Canada BSc Mathematics with Finance University of Liverpool Liverpool United Kingdom

ISBN: (纸本)9781728126166

At present, there are many excellent genome compression algorithms with high genome compression ratio. However, there is a lack of highly efficient compression algorithms for simultaneous compression of a large number of genomes. This manuscript presents an algorithm, which is called FastLNGC, for simultaneous compression of a large amount of genome data based on multi-thread concurrency. This algorithm is based on the LNGC (Large Number of Genomes Compressor) algorithm, and adopts multi-thread technology to achieve concurrent processing of genome data compression. A large number of experiments show that FastLNGC has better performance on compression of a large number of genes. The source code of FastLNGC is available at https://***/APandaThief/FastLNGC. © 2019 IEEE.

关键词： Big data

Reverse-engineering bar charts using neural networks

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arXiv 2020年

作者： Zhou, Fangfang Zhao, Yong Chen, Wenjiang Tan, Yijing Xu, Yaqi Chen, Yi Liu, Chao Zhao, Ying School of Computer Science and Engineer Central South University Changsha China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China Institute of Systems Engineering Academy of Military Sciences Beijing China Rail Data Research and Application Key Laboratory of Hunan Province Changsha China

Reverse-engineering bar charts extracts textual and numeric information from the visual representations of bar charts to support application scenarios that require the underlying information. In this paper, we propose a neural network-based method for reverse-engineering bar charts. We adopt a neural network-based object detection model to simultaneously localize and classify textual information. This approach improves the efficiency of textual information extraction. We design an encoder–decoder framework that integrates convolutional and recurrent neural networks to extract numeric information. We further introduce an attention mechanism into the framework to achieve high accuracy and robustness. Synthetic and real-world datasets are used to evaluate the effectiveness of the method. To the best of our knowledge, this work takes the lead in constructing a complete neural network-based method of reverse-engineering bar charts. Copyright © 2020, The Authors. All rights reserved.

关键词： Reverse engineering

Global, regional, and national age-sex-specific burden of diarrhoeal diseases, their risk factors, and aetiologies, 1990-2021, for 204 countries and territories: a systematic analysis for the Global Burden of Disease Study 2021

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LANCET INFECTIOUS DISEASES 2025年第5期25卷 519-536页

作者： Kyu, Hmwe Hmwe Vongpradith, Avina Dominguez, Regina-Mae Villanueva Ma, Jianing Albertson, Samuel B. Novotney, Amanda Khalil, Ibrahim A. Troeger, Christopher E. Doxey, Matthew C. Ledesma, Jorge R. Sirota, Sarah Brooke Bender, Rose Grace Swetschinski, Lucien R. Cunningham, Matthew Spearman, Sandra Abate, Yohannes Habtegiorgis Abd Al Magied, Abdallah H. A. Abd ElHafeez, Samar Abdoun, Meriem Abera, Bayeh Abidi, Hassan Aboagye, Richard Gyan Abtew, Yonas Derso Abualruz, Hasan Abu-Gharbieh, Eman Abukhadijah, Hana J. Aburuz, Salahdein Addo, Isaac Yeboah Adekanmbi, Victor Adetunji, Charles Oluwaseun Oluwaseun Adeyeoluwa, Temitayo Esther Adhikary, Ripon Kumar Adnani, Qorinah Estiningtyas Sakilah Adra, Saryia Adzigbli, Leticia Akua Afolabi, Aanuoluwapo Adeyimika Afzal, Muhammad Sohail Afzal, Saira Agampodi, Suneth Buddhika Agide, Feleke Doyore Ahinkorah, Bright Opoku Ahmad, Aqeel Ahmad, Sajjad Ahmed, Ali Ahmed, Ayman Ahmed, Haroon Ahmed, Saeed Akinosoglou, Karolina Akter, Ema Al Awaidy, Salah Alajlani, Muaaz M. Alam, Khurshid Albakri, Almaza Albashtawy, Mohammed Aldhaleei, Wafa A. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Syed Shujait Ali Ali, Waad Alif, Sheikh Mohammad Aljunid, Syed Mohamed Al-Marwani, Sabah Almazan, Joseph Uy Al-Mekhlafi, Hesham M. Almustanyir, Sami Alqahatni, Saleh A. Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Altaf, Awais Altirkawi, Khalid A. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Alyahya, Mohammad Sharif Ibrahim Al-Zyoud, Walid A. Amugsi, Dickson A. Andrei, Catalina Liliana Antoni, Sebastien Anuoluwa, Boluwatife Stephen Anuoluwa, Iyadunni Adesola Anwari, Saleha Anwar Palwasha Apostol, Geminn Louis Carace Arabloo, Jalal Arafat, Mosab Aravkin, Aleksandr Y. Areda, Demelash Aregawi, Brhane Berhe Aremu, Abdulfatai Arndt, Michael Benjamin Asgedom, Akeza Awealom Ashraf, Tahira Athari, Seyyed Shamsadin Atreya, Alok Ayele, Firayad Azadi, Davood Azhar, Gulrez Shah Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Taghanaki, Pegah Bahrami Bahramian, Saeed Balakrishna Department of Health Metrics Sciences School of Medicine Institute for Health Metrics and Evaluation University of Washington Seattle 98195 WA United States Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Center for Biostatistics Ohio State University Columbus OH United States Department of Global Health University of Washington Seattle WA United States Urban Indian Health Institute Seattle Indian Health Board Seattle WA United States School of Medicine Yale University New Haven CT United States Department of Clinical Governance and Quality Improvement Aleta Wondo General Hospital Aleta Wondo Ethiopia College of Pharmacy Ajman University Ajman United Arab Emirates Department of Epidemiology Alexandria University Alexandria Egypt Department of Medicine University of Sétif Algeria Sétif Algeria Department of Health Sétif Algeria Department of Medical Microbiology Bahir Dar University Bahir Dar Ethiopia Department of Biology Ghent University Ghent Belgium Laboratory Technology Sciences Department Yasuj University of Medical Sciences Yasuj Iran Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Department of Biomedical Science Arba Minch University Arba Minch Ethiopia Department of Nursing Al Zaytoonah University of Jordan Amman Jordan Amman Jordan Department of Clinical Sciences Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center-Academic Health System Hamad Medical Corporation Doha Qatar College of Pharmacy University of Jordan Amman Jordan Department of Therapeutics United Arab Emirates University Al Ain United Arab Emirates School of Medicine University of Sydney Sydney NSW Australia Centre for Social Research in Health University of New South Wales Syd

Background Diarrhoeal diseases claim more than 1 million lives annually and are a leading cause of death in children younger than 5 years. Comprehensive global estimates of the diarrhoeal disease burden for specific age groups of children younger than 5 years are scarce, and the burden in children older than 5 years and in adults is also understudied. We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to assess the burden of, and trends in, diarrhoeal diseases overall and attributable to 13 pathogens, as well as the contributions of associated risk factors, in children and adults in 204 countries and territories from 1990 to 2021. Methods We used the Cause of Death Ensemble modelling strategy to analyse vital registration data, verbal autopsy data, mortality surveillance data, and minimally invasive tissue sampling data. We used DisMod-MR (version 2.1), a Bayesian meta-regression tool, to analyse incidence and prevalence data identified via systematic reviews, population-based surveys, and claims and inpatient data. We calculated diarrhoeal disability-adjusted life-years (DALYs) as the sum of years of life lost (YLLs) and years lived with disability (YLDs) for each location, year, and age-sex group. For aetiology estimation, we used a counterfactual approach to quantify population-attributable fractions (PAFs). Additionally, we estimated the diarrhoeal disease burden attributable to the independent effects of risk factors using the comparative risk assessment framework. Findings In 2021, diarrhoeal diseases caused an estimated 1.17 million (95% uncertainty interval 0.793-1.62) deaths globally, representing a 60.3% (50.6-69.0) decrease since 1990 (2.93 million [2.31-3.73] deaths). The most pronounced decline was in children younger than 5 years, with a 79.2% (72.4-84.6) decrease in diarrhoeal deaths. Global YLLs also decreased substantially, from 186 million (147-221) in 1990 to 51.4 million (39.9-65.9) in 2021. In 2021, an esti

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Deep learning of cell classification using microscope images of intracellular microtubule networks

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arXiv 2020年

作者： Shpilman, Aleksei Boikiy, Dmitry Polyakova, Marina Kudenko, Daniel Burakov, Anton Nadezhdina, Elena Saint Petersburg National Research Academic University of the Russian Academy of Sciences St Petersburg Russia Moscow Institute of Physics and Technology Dolgoprudny Russia Department of Computer Science University of York United Kingdom A.N. Belozersky Intstitute of Physico-Chemical Biology Lomonosov Moscow State University Moscow Russia Institute of Protein Research of the Russian Academy of Sciences Puschino Russia

Microtubule networks (MTs) are a component of a cell that may indicate the presence of various chemical compounds and can be used to recognize properties such as treatment resistance. Therefore, the classification of MT images is of great relevance for cell diagnostics. Human experts find it particularly difficult to recognize the levels of chemical compound exposure of a cell. Improving the accuracy with automated techniques would have a significant impact on cell therapy. In this paper we present the application of Deep Learning to MT image classification and evaluate it on a large MT image dataset of animal cells with three degrees of exposure to a chemical agent. The results demonstrate that the learned deep network performs on par or better at the corresponding cell classification task than human experts. Specifically, we show that the task of recognizing different levels of chemical agent exposure can be handled significantly better by the neural network than by human experts. © 2020, CC BY.

关键词： Cells

Risk-based decision making: estimands for sequential prediction under interventions

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arXiv 2023年

作者： Luijken, Kim Morzywolek, Pawel van Amsterdam, Wouter Cinà, Giovanni Hoogland, Jeroen Keogh, Ruth Krijthe, Jesse Magliacane, Sara van Ommen, Thijs Peek, Niels Putter, Hein van Smeden, Maarten Sperrin, Matthew Wang, Junfeng Weir, Daniala Didelez, Vanessa van Geloven, Nan Department of Epidemiology Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Statistics University of Washington Seattle United States Department of Medical Informatics Amsterdam University Medical Centers Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands Department of Epidemiology and Data Science Amsterdam University Medical Centers Amsterdam Netherlands Department of Medical Statistics London School of Hygiene & Tropical Medicine Keppel Street London United Kingdom Pattern Recognition and Bio-Informatics Group EEMCS Delft University of Technology Delft Netherlands Amsterdam Machine Learning Lab University of Amsterdam Amsterdam Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology Department of Pharmaceutical Sciences Utrecht University Utrecht Netherlands Department of Biometry and Data Management Leibniz Institute for Prevention Research Epidemiology - BIPS Bremen Germany Faculty of Mathematics/Computer Science University of Bremen Bremen Germany

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions. © 2023, CC BY.

关键词： Forecasting

Striped magnetization plateau and chirality-reversible anomalous Hall effect in a magnetic kagome metal

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arXiv 2024年

作者： Cheng, Erjian Mao, Ning Yang, Xiaotian Song, Boqing Lou, Rui Ying, Tianping Nie, Simin Fedorov, Alexander Bertran, François Ding, Pengfei Suvorov, Oleksandr Zhang, Shu Changdar, Susmita Schnelle, Walter Koban, Ralf Yi, Changjiang Burkhardt, Ulrich Büchner, Bernd Wang, Shancai Zhang, Yang Wang, Wenbo Felser, Claudia Max Planck Institute for Chemical Physics of Solids Dresden01187 Germany School of Physical Science and Technology ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai201210 China Institute of Physics University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100190 China Leibniz Institute for Solid State and Materials Research IFW Dresden Dresden01069 Germany Helmholtz-Zentrum Berlin für Materialien und Energie Albert-Einstein-Straße 15 Berlin12489 Germany Joint Laboratory "Functional Quantum Materials" BESSY II Berlin12489 Germany Department of Mechanical Engineering Stanford University StanfordCA94305 United States Synchrotron SOLEIL L'Orme des Merisiers Départementale 128 Saint-Aubin91190 France Department of Physics Key Laboratory of Quantum State Construction and Manipulation Ministry of Education Beijing Key Laboratory of Opto-electronic Functional Materials & Micronano Devices Renmin University of China Beijing100872 China Kyiv Academic University Kyiv03142 Ukraine Max Planck Institute for the Physics of Complex Systems Dresden01187 Germany Institute for Solid State and Materials Physics TU Dresden Dresden01062 Germany Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States

Kagome materials with magnetic frustration in two-dimensional networks are known for their exotic properties, such as the anomalous Hall effect (AHE) with non-collinear spin textures. However, the effects of one-dimensional (1D) spin chains within these networks are less understood. Here, we report a distinctive AHE in the bilayer-distorted kagome material GdTi3Bi4, featuring 1D Gd zigzag spin chains, a one-third magnetization plateau, and two successive metamagnetic transitions. At these metamagnetic transitions, Hall resistivity shows abrupt jumps linked to the formation of stripe domain walls, while within the plateau, the absence of detectable domain walls suggests possible presence of skyrmion phase. Reducing the sample size to a few microns reveals additional Hall resistivity spikes, indicating domain wall skew scattering contributions. Magnetic atomistic spin dynamics simulations reveal that the magnetic textures at these transitions have reverse chirality, explaining the evolution of AHE and domain walls with fields. These results underscore the potential of magnetic and crystal symmetry interplay, and magnetic field-engineered spin chirality, for controlling domain walls and tuning transverse properties, advancing spintronic applications. © 2024, CC BY.

关键词： Chirality