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arXiv 2021年

作者： Wang, Dong-Sheng Wang, Yun-Jiang Cao, Ningping Zeng, Bei Laflamme, Raymond Institute of Theoretical Physics Chinese Academy of Sciences Beijing China Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Xian China Department of Mathematics and Statistics University of Guelph GuelphONN1G 2W1 Canada Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada

In this work, we develop the theory of quasi-exact fault-tolerant quantum (QEQ) computation, which uses qubits encoded into quasi-exact quantum error-correction codes ("quasi codes"). By definition, a quasi code is a parametric approximate code that can become exact by tuning its parameters. The model of QEQ computation lies in between the two well-known ones: the usual noisy quantum computation without error correction and the usual fault-tolerant quantum computation, but closer to the later. Many notions of exact quantum codes need to be adjusted for the quasi setting. Here we develop quasi error-correction theory using quantum instrument, the notions of quasi universality, quasi code distances, and quasi thresholds, etc. We find a wide class of quasi codes which are called valence-bond-solid codes, and we use them as concrete examples to demonstrate QEQ computation. © 2021, CC BY-NC-ND.

关键词： Quantum computers

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EEG Based Depression Recognition by Combining Functional Brain Network and Traditional Biomarkers

EEG Based Depression Recognition by Combining Functional Bra...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuting Sun Huayu Chen Xuexiao Shao Liangliang Liu Xiaowei Li Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in BrainScience and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Beijing China Ministry of Education Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University) Lanzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Resting-state EEG data were collected from 24 major depressive patients (MDD) and 29 normal controls using 128-electrode geodesic sensor net. To better identify depression, we extracted multi-type of EEG features including linear features (L), nonlinear features (NL), functional connectivity features phase lagging index (PLI) and network measures (NM) to comprehensively characterize the EEG signals in patients with MDD. And machine learning algorithms and statistical analysis were used to evaluate the EEG features. Combined multi-types features (All: L+ NL + PLI + NM) outperformed single-type features for classifying depression. Analyzing the optimal features set we found that compared to other type features, PLI occupied the largest proportion of which functional connections in intra-hemisphere were much more than that of in inter-hemisphere. In addition, when using PLI features and All features, high frequency bands (alpha, beta) could achieve obviously higher classification accuracy than low frequency bands (delta, theta). Parietal-occipital lobe in the high frequency bands had great effect in depression identification. In conclusion, combined multi-types EEG features along with a robust classifier can better distinguish depressive patients from normal controls. And intra-hemispheric functional connections might be an effective biomarker to detect depression. Hence, this paper may provide objective and potential electrophysiological characteristics in depression recognition.

关键词： Electroencephalography Depression Feature extraction Electrodes Biomarkers Support vector machines Entropy

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Multi-criteria collaborative filtering based on matrix filling 4

Multi-criteria collaborative filtering based on matrix filli...

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4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Ye, Li Wu, Chunming Qiang, Baohua Xie, Wu College of Computer Information Science Southwest University Chongqing China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781538683392

In multi-criteria collaborative filtering, the data sparsity is a critical factor affecting the effectiveness of the algorithm. Existing solutions generally remove low-efficiency data through dimensionality reduction, decomposition, etc. While at lower data dimension, they also lose original data information, which impacts the accuracy of predicted ratings. This paper adopts a new processing strategy and proposes to use the matrix filling method to handle the missing data. Firstly, an improved similarity algorithm combined with the Jaccard Similarity Coefficient is used to calculate the predicted rating. Secondly, the Reliable Factor is introduced to prevent the error of filling data. Finally, multiple linear regression is employed for aggregating the ratings of the criteria to eventually yield preferred predicted ratings. A series of experiments show that the proposed method can precisely reflect the user's similarity and achieve better results in predicted ratings, in contrast to traditional methods. © 2018 IEEE.

关键词： Collaborative filtering

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MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis

arXiv

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arXiv 2020年

作者： Cai, Hanshu Gao, Yiwen Sun, Shuting Li, Na Tian, Fuze Xiao, Han Li, Jianxiu Yang, Zhengwu Li, Xiaowei Zhao, Qinglin Liu, Zhenyu Yao, Zhijun Yang, Minqiang Peng, Hong Zhu, Jing Zhang, Xiaowei Gao, Guoping Zheng, Fang Li, Rui Guo, Zhihua Ma, Rong Yang, Jing Zhang, Lan Hu, Xiping Li, Yumin Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Open Source Software and Real-Time System Lanzhou University Ministry of Education China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Lanzhou University Second Hospital China

According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation;the 3-electrode EEG signals of 55 subjects were recorded in resting state;the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Semantic image segmentation with fused CNN features

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Optoelectronics Letters 2017年第5期13卷 381-385页

作者：耿慧强张桦薛彦兵周冕徐光平高赞 Key Laboratory of Computer Vision and System of Ministry of Education Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology

Semantic image segmentation is a task to predict a category label for every image pixel. The key challenge of it is to design a strong feature representation. In this paper, we fuse the hierarchical convolutional neural network(CNN) features and the region-based features as the feature representation. The hierarchical features contain more global information, while the region-based features contain more local information. The combination of these two kinds of features significantly enhances the feature representation. Then the fused features are used to train a softmax classifier to produce per-pixel label assignment probability. And a fully connected conditional random field(CRF) is used as a post-processing method to improve the labeling consistency. We conduct experiments on SIFT flow dataset. The pixel accuracy and class accuracy are 84.4% and 34.86%, respectively.

关键词： Neural networks Pixels Random processes Semantics

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Enhancing countries' fitness with recommender systems on the international trade network

arXiv

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arXiv 2019年

作者： Liao, Hao Wu, Xing-Tong Vidmer, Alexandre Huang, Xiao-Min Liu, Ming-Kai Zhou, Mingyang Zhang, Yi-Cheng National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Physics University of Fribourg Fribourg1700 Switzerland

Prediction is one of the major challenges in complex systems. The prediction methods have shown to be effective predictors of the evolution of networks. These methods can help policy makers to solve practical problems successfully and make better strategy for the future. In this work, we focus on exporting countries' data of the international trading network. A recommendation system is then used to identify the products corresponding to the production capacity of each individual country, but are somehow overlook by the country. Then, we simulate the evolution of the country's fitness if it would have followed the recommendations. The result of this work is the combination combine these two methods to provide insights to countries on how to enhance the diversification of their exported products in a scientific way and improve national competitiveness significantly, especially for developing countries. Copyright © 2019, The Authors. All rights reserved.

关键词： Recommender systems

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A Dynamic Road Incident Information Delivery Strategy to Reduce Urban Traffic Congestion

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IEEE/CAA Journal of Automatica Sinica 2018年第5期5卷 934-945页

作者： Liang Qi Mengchu Zhou Wenjing Luan IEEE Department of Computer Scicnce and Technology Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Tongji UniversityShanghai 201804China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102USA Key Laboratory of Embedded System and Service Computing Ministry of EducationShanghai Electronic Transactions and Information Service Collaborative Innovation CenterDepartment of Com-puter ScienceTongji UniversityShanghai 201804China

Advanced information and communication technolo-gies can be used to facilitate traffic incident *** an incident is detected and blocks a road link,in order to reduce the incident-induced traffic congestion,a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this ***-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information.A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well *** results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles,operation time interval of the proposed strategy,traffic density in the traffic network,and the scope of the area where traffic incident information is *** results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

关键词： Cell transmission model(CTM) intelligent transportation systems(ITS) traffic incident management(TIM) urban traffic congestion

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Designing and Application of Wearable Fatigue Detection system Based on Multimodal Physiological Signals

Designing and Application of Wearable Fatigue Detection Syst...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoxuan Qiu Fuze Tian Qiuxia Shi Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Lanzhou China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University) Ministry of Education Lanzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Judging mental fatigue can be guided by acquiring and analyzing various physiological data. However, the existing equipment focuses on single physiological signals, such as electroencephalogram (EEG) and electrocardiogram (ECG), which ignores the preponderance of multimodal physiological signals in fatigue states. Alternatively, some equipment is difficult to operate and thus unsuitable for portable applications. To this end, this paper details the design of a miniaturized multi-physiological signal acquisition system. The system not only acquires EEG and ECG based on combining wavelet transform with Kalman filter, but also uses precise temperature sensors to synchronously acquire proximal skin temperature signals which are not easily interfered with by environmental noise or other physiological signals of human body. Through a fatigue assessment experiment on ten healthy subjects, it was verified that our equipment reliably detects mental fatigue states by monitoring and analyzing EEG, ECG, and proximal skin temperature data. In actual applications, this system appears to the traits of easy operation and good stability. It provides better hardware support for the pervasive applications and concrete implementation of mental health in multi-scene, and can popularize use.

关键词： Fatigue Electrocardiography Electroencephalography Skin Temperature measurement Temperature distribution Temperature sensors

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Publication recommendations of manuscripts using improved C4.5 decision tree algorithm 2nd

Publication recommendations of manuscripts using improved C4...

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2nd International Conference on Intelligent and Interactive systems and Applications, IISA 2017

作者： Jia, Didi Xie, Wu Chen, Zhiyong Qiang, Baohua School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex System Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin541004 China

ISBN: (纸本)9783319690957

In view of the problem that the result accuracy of the research publication recommendations of manuscripts is often low. It is very difficult for many contributors to find the suitable journals from many recommendation results. To solve this problem, a novel method of publication recommendations for authors’ manuscripts is proposed using our improved C4.5 decision tree algorithm. For the shortcomings of too many values of data samples during dealing with the traditional C4.5 methods, the five-value logic ideas are adopted to improve this C4.5 algorithm to apply in the research fields of publication recommendations. The experimental results show that the related publications are easier to obtain for authors via the decision trees with the improved C4.5 method than before, and this method has higher accuracy than ever. It is of great significance to help the researchers, especially for those who have no enough experience to choose the most suitable journals from a lot of publications. © 2018, Springer International Publishing AG.

关键词： Decision trees

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