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Retraction Note: Advanced artificial intelligence in heart rate and blood pressure monitoring for stress management

Journal of Ambient Intelligence and Humanized computing 2024年第1期15卷 255-255页

作者： Lin, Qiang Li, Tongtong Shakeel, P. Mohamed Samuel, R. Dinesh Jackson School of Mathematics and Computer Science Northwest Minzu University LanzhouGansu China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University LanzhouGansu China Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University LanzhouGansu China Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka (UTeM) Durian Tunggal Malaysia Faculty of Technology Design and Environment Visual Artificial Intelligence Lab Oxford Brookes University Oxford UK

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Association of structural MRI with Braak staging using neural network-based risk scoring

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Alzheimer's & Dementia 2023年第S3期19卷

作者： Matthew I. Miller Shangran Qiu Prajakta S. Joshi Joyce Lee Rhoda Au Vijaya B. Kolachalama Boston University School of Medicine Boston MA USA College of Arts & Sciences Boston University Boston MA USA Framingham Heart Study Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University Alzheimer’s Disease Center Boston University Boston MA USA Boston University School of Public Health Boston MA USA Department of Anatomy and Neurobiology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA Boston University Alzheimer’s Disease Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Whitaker Cardiovascular Institute Boston University Chobanian & Avedisian School of Medicine Boston MA USA

Background The gold standard of Alzheimer’s disease (AD) diagnosis remains histopathologic examination of neuropathologic changes. However, the dependence on post-mortem microscopic analysis limits the role of pathology in clinical care. Magnetic resonance imaging (MRI) is routinely used to diagnose suspected dementia (DE) due to AD, but correlating neuroimaging to underlying pathologic changes remains challenging. Herein, we report the development of two risk scores derived from convolutional neural network (CNN) processing of MRI scans, which we then validate against Braak staging of neurofibrillary tangles from autopsy reports. Method Using T1-weighted MRIs from the National Alzheimer’s Coordinating Center (NACC), we trained two CNNs (Figure 1) to predict continuous scores for overall cognitive status (“COG” score) and AD probability (“ALZ” score). The COG score, ranging from 0-2, graded a subject’s cognitive status on a spectrum from normal cognition (NC, 0), to mild cognitive impairment (MCI, 1), and DE due to any cause (2). The ALZ score then graded each DE individual’s probability of AD from 0 to 1. These CNNs were then applied to a subset of participants with autopsy reports available within 6 months of MRI. CNN performance was evaluated by area under receiver operating characteristic curve (AUC). Differences in risk scores were assessed by Kruskal-Wallis H testing with post hoc Dunn testing. Result The CNN models for COG and ALZ score estimation were trained on 4,822 NACC participants. Thresholding of COG scores distinguished individuals with DE from those with NC and MCI (AUC = 0.87). ALZ scores distinguished individuals with AD from those with non-AD causes of DE (AUC = 0.77). Among 74 persons with available neuropathology data, COG scores differed significantly among Braak staging groups (p<0.001), increasing from a mean of 0.99±0.36 in Stage 1 to 1.47±0.41 in Stage 6 (p = 0.010). Significant differences in ALZ scores were also observed (p = 0.039) betwe

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Multi-graph Convolution Network with Jump Connection for Event Detection

Multi-graph Convolution Network with Jump Connection for Eve...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xiangbin Meng Pengfei Wang Haoran Yan Liutong Xu Jiafeng Guo Yixing Fan School of Computer Science Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Event detection is an important information extraction task in nature language processing. Recently, the method based on syntactic information and graph convolution network has been wildly used in event detection task and achieved good performance. For event detection, graph convolution network (GCN) based on dependency arcs can capture the sentence syntactic representations and the syntactic information, which is from candidate triggers to arguments. However, existing methods based on GCN with dependency arcs suffer from imbalance and redundant information in graph. To capture important and refined information in graph, we propose Multi-graph Convolution Network with Jump Connection (MGJ-ED). The multi-graph convolution network module adds a core subgraph splitted from dependency graph which selects important one-hop neighbors' syntactic information in breadth via GCN. Also the jump connection architecture aggregate GCN layers' representation with different attention score, which learns the importance of neighbors' syntactic information of different hops away in depth. The experimental results on the widely used ACE 2005 dataset shows the superiority of the other state-of-the-art methods.

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Hybrid loss guided densely connected convolutional neural network for Ischemic Stroke Lesion segmentation

Hybrid loss guided densely connected convolutional neural ne...

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International Conference for Convergence of Technology (I2CT)

作者： Saqib Qamar Hai Jin Ran Zheng Mohd Faizan Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electronics Aligarh Muslim University India

ISBN: (数字)9781538680759

ISBN: (纸本)9781538680766

The exact position of dead tissue in ischemic stroke lesions plays an important role to cure the life-threatening condition. However, this issue stays difficult caused by variation of ischemic strokes such as shape and location. Fully convolutional neural networks (FCN) have great potential for semantic image segmentation. Recently, UNet based CNN architectures have secured outstanding performance in the application of medical imaging. The primary problem is data imbalanced in the application of such networks which are very common in medical data application such as lesion applications where non-lesion voxels are higher than lesion voxels. data imbalance generates low recall and high precision on the prediction of trained networks. Biases towards the particular class are not suitable for many medical works where false positives are less than false negatives. A lot of approaches are developed to tackle this issue including balanced sampling, sample re-weighting, and recently focal loss and similarity loss functions. In this paper, we proposed hyper densely connected network along with hybrid loss functions for ischemic stroke lesions segmentation. The densely connected network exploits the contextual information of multi-modalities, and hybrid loss function uses to overcome the data imbalance. We evaluate our performance on ISLES dataset. Our approach performs well as compared to other existing methods.

关键词： Lesions Image segmentation Magnetic resonance imaging Biomedical imaging Convolutional neural networks Training Computer architecture

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A Review of Breast Cancer Histopathological Image Classification

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Journal of Physics: Conference Series 2021年第1期2129卷

作者： Lei Huang Azlan Mohd Zain Kai-Qing Zhou Chang-Feng Chen School of Computing Faculty of Engineering Universiti Teknologi Malaysia UTM Skudai Johor Bahru Johor Malaysia. UTM Big Data Centre School of Computing Universiti Teknologi Malaysia UTM Skudai Johor Bahru Johor Malaysia. College of Information Science and Engineering Jishou University Jishou Hunan China.

Breast Cancer (BC) is the most common malignant tumor for women in the world. Histopathological examination serves as basis for breast cancer diagnosis. Due to the low accuracy of histopathological images through manual judgment, the classification of histopathological images of breast cancer has become a research hotspot in the field of medical image processing. Accurate classification of images can help doctors to properly diagnoses and improve the survival rate of patients. This paper reviews the existing works on histopathological image classification of breast cancer and analysis the advantages and disadvantages of related algorithms. Findings of the histopathological image classification of the Breast Cancer study are drawn, and the possible future directions are also discussed.

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Outlier detection over distributed trajectory streams

Outlier detection over distributed trajectory streams

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2018 SIAM International Conference on data Mining, SDM 2018

作者： Mao, Jiali Sun, Pengda Jin, Cheqing Zhou, Aoying School of Data Science and Engineering East China Normal University School of Computing China West Normal University China School of Computer Science and Software Engineering East China Normal University China

The wide deployments of GPS-embedded devices have produced multiple rapid voluminous trajectory streams, which needs to be analyzed to extract abnormal behaviors of moving objects in real-time. To date, outlier detection over distributed trajectory streams has not received enough focuses due to the constraint factors like skewness distribution and evolving nature of trajectory data, and on-the-y execution requirement with minimal communication cost. In this paper, we present the first scalable decentralized outlier detection framework over distributed trajectory streams, called ODDTS. It consists of remote site processing and co-ordinator processing, with the aim of continuously provid-ing feature-grouping based outliers detection over distribut-ed trajectory streams. Extensive experiments over real data demonstrate high detecting validity, less communication cost and linear scalability of ODDTS method for online identify-ing outliers upon distributed trajectory streams. © 2018 by SIAM.

关键词： Scalability

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How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities

arXiv

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arXiv 2024年

作者： Bunne, Charlotte Roohani, Yusuf Rosen, Yanay Gupta, Ankit Zhang, Xikun Roed, Marcel Alexandrov, Theo AlQuraishi, Mohammed Brennan, Patricia Burkhardt, Daniel B. Califano, Andrea Cool, Jonah Dernburg, Abby F. Ewing, Kirsty Fox, Emily B. Haury, Matthias Herr, Amy E. Horvitz, Eric Hsu, Patrick D. Jain, Viren Johnson, Gregory R. Kalil, Thomas Kelley, David R. Kelley, Shana O. Kreshuk, Anna Mitchison, Tim Otte, Stephani Shendure, Jay Sofroniew, Nicholas J. Theis, Fabian Theodoris, Christina V. Upadhyayula, Srigokul Valer, Marc Wang, Bo Xing, Eric Yeung-Levy, Serena Zitnik, Marinka Karaletsos, Theofanis Regev, Aviv Lundberg, Emma Leskovec, Jure Quake, Stephen R. Department of Computer Science Stanford University StanfordCA United States Genentech South San FranciscoCA United States Chan Zuckerberg Initiative Redwood CityCA United States School of Computer and Communication Sciences School of Life Sciences EPFL Lausanne Switzerland Arc Institute Palo AltoCA United States KTH Royal Institute of Technology Science for Life Laboratory Department of Protein Science Stockholm Sweden Department of Bioengineering Stanford University StanfordCA United States Department of Pharmacology University of California San DiegoCA United States Department of Bioengineering University of California San DiegoCA United States Department of Systems Biology Columbia University New YorkNY United States Cellarity SomervilleMA United States Vagelos College of Physicians and Surgeons Columbia University Irving Medical Center New YorkNY United States Chan Zuckerberg Biohub New YorkNY United States Department of Molecular and Cell Biology University of California Berkeley BerkeleyCA United States Department of Statistics Stanford University StanfordCA United States Chan Zuckerberg Biohub San Francisco CA United States Chan Zuckerberg Institute for Advanced Biological Imaging Redwood CityCA United States Department of Bioengineering University of California BerkeleyCA United States Microsoft Research RedmondWA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Google Research Mountain ViewCA United States NewLimit San FranciscoCA United States Schmidt city United States Calico Life Sciences LLC San FranciscoCA United States Chan Zuckerberg Biohub Chicago IL United States Northwestern University EvanstonIL United States Cell Biology and Biophysics Unit European Molecular Biology Laboratory Heidelberg Germany Department of Systems Biology Harvard Medical School BostonMA United States Department of Genome Sciences University of Washington SeattleWA Unit

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using Virtual Instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach. © 2024, CC BY.

关键词： Virtual environments

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Rethinking performance estimation in neural architecture search

arXiv

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arXiv 2020年

作者： Zheng, Xiawu Ji, Rongrong Wang, Qiang Ye, Qixiang Li, Zhenguo Tian, Yonghong Tian, Qi Media Analytics and Computing Lab Department of Artificial Intelligence School of Informatics Xiamen University 361005 China National Institute for Data Science in Health and Medicine Xiamen University Peng Cheng Laboratory Shenzhen China University of Chinese Academy of Sciences China Noah's Ark Lab Huawei Technologies Peking University

Neural architecture search (NAS) remains a challenging problem, which is attributed to the indispensable and time-consuming component of performance estimation (PE). In this paper, we provide a novel yet systematic rethinking of PE in a resource constrained regime, termed budgeted PE (BPE), which precisely and effectively estimates the performance of an architecture sampled from an architecture space. Since searching an optimal BPE is extremely time-consuming as it requires to train a large number of networks for evaluation, we propose a Minimum Importance Pruning (MIP) approach. Given a dataset and a BPE search space, MIP estimates the importance of hyper-parameters using random forest and subsequently prunes the minimum one from the next iteration. In this way, MIP effectively prunes less important hyper-parameters to allocate more computational resource on more important ones, thus achieving an effective exploration. By combining BPE with various search algorithms including reinforcement learning, evolution algorithm, random search, and differentiable architecture search, we achieve 1, 000× of NAS speed up with a negligible performance drop comparing to the SOTA.1. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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Transparent concurrency control: Decoupling concurrency control from DBMS

arXiv

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arXiv 2019年

作者： Zhou, Ningnan Zhou, Xuan Tan, Kian-Lee Wang, Shan DEKE Lab Renmin University of China Beijing China School of Data Science & Engineering East China Normal University Shanghai China School of Computing National University of Singapore Singapore

For performance reasons, conventional DBMSes adopt monolithic architectures. A monolithic design cripples the adaptability of a DBMS, making it difficult to customize, to meet particular requirements of different applications. In this paper, we propose to completely separate the code of concurrency control (CC) from a monolithic DBMS. This allows us to add / remove functionalities or data structures to / from a DBMS easily, without concerning the issues of data consistency. As the separation deprives the concurrency controller of the knowledge about data organization and processing, it may incur severe performance issues. To minimize the performance loss, we devised a two-level CC mechanism. At the operational level, we propose a robust scheduler that guarantees to complete any data operation at a manageable cost. At the transactional level, the scheduler can utilize data semantics to achieve enhanced performance. Extensive experiments were conducted to demonstrate the feasibility and effectiveness of our approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Concurrency control

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$\mathsf{NCF}$NCF: A Neural Context Fusion Approach to Raw Mobility Annotation

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IEEE Transactions on Mobile computing 2020年第1期21卷 226-238页

作者： Renjun Hu Jingbo Zhou Xinjiang Lu Hengshu Zhu Shuai Ma Hui Xiong SKLSDE Lab Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application Beijing China Talent Intelligence Center Baidu Inc. Beijing China Management Science and Information Systems Department Rutgers Business School Rutgers University Newark NJ USA

Understanding human mobility patterns at the point-of-interest (POI) scale plays an important role in enhancing business intelligence in mobile environments. While large efforts have been made in this direction, most studies simply utilize POI check-ins to mine the concerned mobility patterns, the effectiveness of which is usually hindered due to data sparsity. To obtain better POI-based human mobility for mining, in this paper, we strive to directly annotate the POIs associated with raw user-generated mobility records. We propose a neural context fusion approach which integrates various context factors in people's POI-visiting behaviors. Our approach evaluates the preference and transition factors via representation learning. Notably, we incorporate an attention mechanism to deal with the randomized transitions in raw mobility. The domain knowledge factors, i.e., distance, time and popularity, remain effective and our approach further includes them from a data-driven perspective. Factors are automatically fused with a feed-forward neural network. Furthermore, we exploit a multi-head architecture to enhance the model expressiveness. Using two real-life data sets, we conduct our experimental study and find that our approach consistently outperforms the state-of-the-art baselines by at least 32 percent in accuracy. Besides, we demonstrate the utility of the obtained POI-based human mobility with a POI recommendation example.

关键词： Annotations Trajectory Mobile computing Computer architecture Business intelligence Neural networks Semantics

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