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A Quantum Secret Sharing Scheme Using Orbital Angular Momentum onto Multiple Spin States Based on Fibonacci Compression Encoding

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Communications in Theoretical Physics 2018年第10期70卷 384-390页

作者： Hong Lai Ming-Xing Luo Yong-Jian Xu Josef Pieprzyk Jun Zhang Lei Pan Mehmet A.Orgun School of Computer and Information Science and Centre for Research and Innovation in Software Engineering（RISE） Southwest UniversityChongqing 400715China Information Security and National Computing Grid Laboratory School of Information Science and TechnologySouthwest Jiaotong UniversityChengdu 610031China Data61 Commonwealth Scientific and Industrial Research OrganisationSydneyAustralia Institute of Computer Science Polish Academy of SciencesWarsaw01-248Poland School of Software and Electrical Engineering Swinburne University of TechnologyHawthornVIC3122Australia School of Information Technology Deakin UniversityGeelongVIC3220Australia Department of Computing Macquarie UniversitySydneyNSW 2109Australia Faculty of Information Technology Macao University of Science and TechnologyAvenida Wai LongTaipa999078Macao

Since the use of a quantum channel is very expensive for transmitting large messages, it is vital to develop an effective quantum compression encoding scheme that is easy to implement. Given that, with the single-photon spin-orbit entanglement, we propose a quantum secret sharing scheme using orbital angular momentum onto multiple spin states based on Fibonacci compression encoding. In our proposed scheme, we can represent the frequency of any secret message which is typically collection of bits encodings of text or integers as a bitstring using the base Fibonacci sequence, which is encoded multiple spin states for secret shares transmitted to participants. We demonstrate that Fibonacci compression encoding carries excellent properties that enable us to achieve more robust quantum secret sharing schemes with fewer number of photons.

关键词： spin-orbit entanglement multiple spin states Fibonacci compression encoding quantum secretsharing

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Closed Shop Scheduling Optimisation using Max-Plus Automata

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Journal of Physics: Conference Series 2019年第4期1341卷

作者： Dian Ayu Merdekawati Subiono Faculty of Mathematics Computing and Data Science Sepuluh Nopember Institute of Technology Surabaya Indonesia

An appropriate scheduling system is needed in manufacturing activities to minimize production time and costs. Flow shop is one type of production flow that widely used in manufacturing. Based on the demand criteria, it has been known as a scheduling problem called closed shop. This scheduling emphasizes the make-to-stock (MTS) which produces a stock. We take the closed shop scheduling case studies from an existing literature and optimize scheduling using max-plus automata optimum. We compare the result with regular scheduling and the previous result in the literature. We show that the result of max-plus automata optimum scheduling are optimum compared than the existing regular scheduling and the existing previous result of the literature.

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Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of covid-19 in ct images

arXiv

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arXiv 2020年

作者： He, Kelei Zhao, Wei Xie, Xingzhi Ji, Wen Liu, Mingxia Tang, Zhenyu Shi, Feng Gao, Yang Liu, Jun Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University China Department of Radiology Second Xiangya Hospital Central South University Changsha Hunan China Department of Radiology Quality Control Center Changsha Hunan China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Biomedical Research Imaging Center Department of Radiology University of North Carolina Chapel HillNC United States Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 China

Understanding chest CT imaging of the coronavirus disease 2019 (COVID-19) will help detect infections early and assess the disease progression. Especially, automated severity assessment of COVID-19 in CT images plays an essential role in identifying cases that are in great need of intensive clinical care. However, it is often challenging to accurately assess the severity of this disease in CT images, due to variable infection regions in the lungs, similar imaging biomarkers, and large inter-case variations. To this end, we propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification. Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice). A multi-task multi-instance deep network (called M2UNet) is then developed to assess the severity of COVID-19 patients and also segment the lung lobe simultaneously. Our M2UNet consists of a patch-level encoder, a segmentation subnetwork for lung lobe segmentation, and a classification subnetwork for severity assessment (with a unique hierarchical multi-instance learning strategy). Here, the context information provided by segmentation can be implicitly employed to improve the performance of severity assessment. Extensive experiments were performed on a real COVID-19 CT image dataset consisting of 666 chest CT images, with results suggesting the effectiveness of our proposed method compared to several state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Erratum: “Spin wave propagation in a ferrimagnetic thin film with perpendicular magnetic anisotropy” [Appl. Phys. Lett. 117, 232407 (2020)]

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Applied Physics Letters 2021年第2期118卷

作者： Lutong Sheng Yawen Liu Jilei Chen Hanchen Wang Jianyu Zhang Mingfeng Chen Ji Ma Chuanpu Liu Sa Tu Ce-Wen Nan Haiming Yu 1Fert Beijing Institute School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China 2Department of Physics and Astronomy University of California Riverside California 92521 USA 3State Key Lab of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing 100084 China

In the original article,1 the line spectrum in Fig. 2(b) is missing. The correct Fig. 2(b), given here, shows how we get the frequency difference from this spec

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Learning Orthodontic Cephalometry through Augmented Reality: A Conceptual Machine Learning Validation Approach 2

Learning Orthodontic Cephalometry through Augmented Reality:...

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2nd International Conference on Electrical Engineering and Informatics, ICELTICs 2018

作者： Lakshmana Rao, Gururajaprasad Kaggal Mokhtar, Norehan Iskandar, Yulita Hanum P. Channarayapatna Srinivasa, Arvind Craniofacial and Biomaterial Science Cluster Advanced Medical and Dental Institute Universiti Sains Malaysia Penang13200 Malaysia Graduate School of Busines Universiti Sains Malaysia Penang11800 Malaysia Cognitive Computing and Data Science Research Lab Global Technology Office Cognizant Technology Solutions India

ISBN: (纸本)9781538661406

The orthodontic cephalometry which forms a quintessential radiographic examination provides a valuable diagnostic resource for everyday orthodontic diagnosis and treatment planning. The method of analyzing the lateral cephalometric radiograph (LCR) has been found to lack effective methods of landmark identification and objective validation. This drawback, with the use of augmented reality (AR) for 3D visualization and machine learning for objective validation, can be effectively incorporated into the learning of cephalometrics. The two technologies together can produce a learning platform by reducing subjective errors and its implications on treatment planning in addition to effectively eliminating dissonance in learning. © 2018 IEEE.

关键词： Augmented reality

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Rapid simulation of glycoprotein structures by grafting and steric exclusion of glycan conformer libraries

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Cell 2024年第5期187卷 1296-1311.e26页

作者： Yu-Xi Tsai Ning-En Chang Klaus Reuter Hao-Ting Chang Tzu-Jing Yang Sören von Bülow Vidhi Sehrawat Noémie Zerrouki Matthieu Tuffery Michael Gecht Isabell Louise Grothaus Lucio Colombi Ciacchi Yong-Sheng Wang Min-Feng Hsu Kay-Hooi Khoo Gerhard Hummer Shang-Te Danny Hsu Cyril Hanus Mateusz Sikora Institute of Biological Chemistry Academia Sinica Taipei 11529 Taiwan Institute of Biochemical Sciences National Taiwan University Taipei 10617 Taiwan. Max Planck Computing and Data Facility 85748 Garching Germany. Department of Theoretical Biophysics Max Planck Institute for Biophysics 60438 Frankfurt Germany. Department of Theoretical Biophysics Max Planck Institute for Biophysics 60438 Frankfurt Germany Malopolska Centre of Biotechnology Jagiellonian University 31-007 Kraków Poland. Institute of Psychiatry and Neurosciences of Paris Inserm UMR1266 Université Paris-Cité 75014 Paris France. Hybrid Materials Interfaces Group Faculty of Production Engineering Bremen Center for Computational Materials Science and MAPEX Center for Materials and Processes University of Bremen 28359 Bremen Germany. Institute of Biological Chemistry Academia Sinica Taipei 11529 Taiwan. Institute of Biophysics Goethe University 60438 Frankfurt Germany. Institute of Biochemical Sciences National Taiwan University Taipei 10617 Taiwan International Institute for Sustainability with Knotted Chiral Meta Matter (WPI-SKCM(2)) Hiroshima University Hiroshima 739-8526 Japan. Electronic address: sthsu@gate.sinica.edu.tw. Institute of Psychiatry and Neurosciences of Paris Inserm UMR1266 Université Paris-Cité 75014 Paris France GHU Psychiatrie et Neurosciences de Paris 75014 Paris France. Electronic address: cyril.hanus@inserm.fr. Malopolska Centre of Biotechnology Jagiellonian University 31-007 Kraków Poland. Electronic address: mateusz.sikora@uj.edu.pl.

Most membrane proteins are modified by covalent addition of complex sugars through N- and O-glycosylation. Unlike proteins, glycans do not typically adopt specific secondary structures and remain very mobile, shielding potentially large fractions of protein surface. High glycan conformational freedom hinders complete structural elucidation of glycoproteins. Computer simulations may be used to model glycosylated proteins but require hundreds of thousands of computing hours on supercomputers, thus limiting routine use. Here, we describe GlycoSHIELD, a reductionist method that can be implemented on personal computers to graft realistic ensembles of glycan conformers onto static protein structures in minutes. Using molecular dynamics simulation, small-angle X-ray scattering, cryoelectron microscopy, and mass spectrometry, we show that this open-access toolkit provides enhanced models of glycoprotein structures. Focusing on N-cadherin, human coronavirus spike proteins, and gamma-aminobutyric acid receptors, we show that GlycoSHIELD can shed light on the impact of glycans on the conformation and activity of complex glycoproteins.

关键词： GABAA receptor N-cadherin SAXS coronavirus S protein cryo-EM glycans glycoproteins mass spectrometry molecular dynamics

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Detection of Pathological Condition of Heart Using Texture Complexity of the Heart Sound Signals in Kernel Space

Detection of Pathological Condition of Heart Using Texture C...

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International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)

作者： Ashok Mondal Ramaswamy Palaniappan National Institute of Technology Karnataka India Data Science Research Group School of Computing University of Kent UK

The heart sound signal is produced due to the mechanical activity of cardiovascular system and hence, it carries relevant information regarding the pathological and non-pathological condition of heart. Physicians listen to heart sounds using a stethoscope device and make interpretation regarding the diseases if any. Hence, its performance depends on the medical practitioners knowledge and experience. In this study, a very simple alternative approach of auscultation technique is introduced for early detection of cardiac abnormality based on a combined framework of kernel space decomposition and textural analysis. The experimental results are validated by physician's diagnosis and statistical analysis. The proposed diagnostic tool can be used in combination with audio clue of heart sound signal to train inexperienced physicians and as an assistive device for experienced doctors.

关键词： Heart Feature extraction Entropy Support vector machines Wavelet transforms Diseases

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A tree search algorithm for sequence labeling

arXiv

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arXiv 2018年

作者： Lao, Yadi Xu, Jun Lan, Yanyan Guo, Jiafeng Gao, Sheng Cheng, Xueqi Guo, Jun Beijing University of Posts and Telecommunications CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Science

In this paper we propose a novel reinforcement learning based model for sequence tagging, referred to as MM-Tag. Inspired by the success and methodology of the AlphaGo Zero, MM-Tag formalizes the problem of sequence tagging with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from le. to right, and each action corresponds to assign a tag to a word. Two long short-term memory networks (LSTM) are used to summarize the past tag assignments and words in the sentence. Based on the outputs of LSTMs, the policy for guiding the tag assignment and the value for predicting the whole tagging accuracy of thewhole sentence are produced.e policy and value are then strengthened with MCTS, which takes the produced raw policy and value as inputs, simulates and evaluates the possible tag assignments at the subsequent positions, and outputs a *** search policy for assigning tags. A reinforcement learning algorithm is proposed to train the model parameters. Our work is the first to apply the MCTS enhanced MDP model to the sequence tagging task. We show that MM-Tag can accurately predict the tags thanks to the exploratory decision making mechanism introduced by MCTS. Experimental results show based on a chunking benchmark showed that MM-Tag outperformed the state-of-the-art sequence tagging baselines including CRF and CRF with LSTM. Copyright © 2018, The Authors. All rights reserved.

关键词： Long short-term memory

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Complementary time-frequency domain networks for dynamic parallel MR image reconstruction

arXiv

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arXiv 2020年

作者： Qin, Chen Duan, Jinming Hammernik, Kerstin Schlemper, Jo Küstner, Thomas Botnar, René Prieto, Claudia Price, Anthony N. Hajnal, Joseph V. Rueckert, Daniel Institute for Digital Communications School of Engineering University of Edinburgh Edinburgh United Kingdom Department of Computing Imperial College London London United Kingdom School of Computer Science University of Birmingham Birmingham United Kingdom Institute for AI and Informatics Klinikum Rechts der Isar Technical University of Munich Munich Germany Hyperfine Research Inc. GuilfordCT United States School of Biomedical Engineering and Imaging Sciences King's College London St. Thomas' Hospital London United Kingdom Department of Diagnostic and Interventional Radiology Medical Image and Data Analysis University Hospital of Tuebingen Tuebingen Germany

Purpose To introduce a novel deep learning based approach for fast and high-quality dynamic multi-coil MR reconstruction by learning a complementary time-frequency domain network that exploits spatio-temporal correlations simultaneously from complementary domains. Theory and Methods Dynamic parallel MR image reconstruction is formulated as a multi-variable minimisation problem, where the data is regularised in combined temporal Fourier and spatial (x-f ) domain as well as in spatio-temporal image (x-t) domain. An iterative algorithm based on variable splitting technique is derived, which alternates among signal de-aliasing steps in x-f and x-t spaces, a closed-form point-wise data consistency step and a weighted coupling step. The iterative model is embedded into a deep recurrent neural network which learns to recover the image via exploiting spatio-temporal redundancies in complementary domains. Results Experiments were performed on two datasets of highly undersampled multi-coil short-axis cardiac cine MRI scans. Results demonstrate that our proposed method outperforms the current state-of-the-art approaches both quantitatively and qualitatively. The proposed model can also generalise well to data acquired from a different scanner and data with pathologies that were not seen in the training set. Conclusion The work shows the benefit of reconstructing dynamic parallel MRI in complementary time-frequency domains with deep neural networks. The method can effectively and robustly reconstruct high-quality images from highly undersampled dynamic multi-coil data (16× and 24× yielding 15s and 10s scan times respectively) with fast reconstruction speed (2.8s). This could potentially facilitate achieving fast single-breath-hold clinical 2D cardiac cine imaging. © 2020, CC BY.

关键词： Magnetic resonance imaging

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Word2Cluster: A New Multi-Label Text Clustering Algorithm with an Adaptive Clusters Number

Word2Cluster: A New Multi-Label Text Clustering Algorithm wi...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Kaili Mao Jianwei Niu Xuefeng Liu Shui Yu Longbo Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Hangzhou Innovation Research Institute Beihang University School of Computer Science and Cyber Engineering Guangzhou University Guangdong China China aerospace science and industry corporation China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Text clustering has been widely used in many Natural Language Processing (NLP) applications such as text summarization and news recommendation. However, most of the current algorithms need to predefine a clustering number, which is difficult to obtain. Moreover, the mutli-label clustering is useful in multiple clustering tasks in many applications, but related works are rarely available. Although several studies have attempted to solve above two problems, there is a need for methods that can solve the two issues simultaneously. Therefore, we propose a new text clustering algorithm called Word2Cluster. Word2Cluster can automatically generate an adaptive number of clusters and support multi-label clustering. To test the performance of Wrod2Cluster, we build a Chinese text dataset, Hotline, according to real world applications. To evaluate the clustering results better, we propose an improved evaluation method based on basic accuracy, precision and recall for multi-label text clustering. Experimental results on a Chinese text dataset (Hotline) and a public English text dataset (Reuters) demonstrate that our algorithm can achieve better F1-measure and runs faster than the state-of- the-art baselines.

关键词： Clustering algorithms Biological system modeling Merging Task analysis Computer science Technological innovation Natural language processing

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