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arXiv 2022年

作者： Alser, Mohammed Waymost, Sharon Ayyala, Ram Lawlor, Brendan Abdill, Richard J. Rajkumar, Neha LaPierre, Nathan Brito, Jaqueline Ribeiro-Dos-Santos, André M. Firtina, Can Almadhoun, Nour Sarwal, Varuni Eskin, Eleazar Hu, Qiyang Strong, Derek Kim, Byoung-Do Abedalthagafi, Malak S. Mutlu, Onur Mangul, Serghei Department of Information Technology and Electrical Engineering ETH Zürich Zürich8092 Switzerland Department of Computer Science UCLA Los AngelesCA90095 United States Department of Translational Biomedical Informatics University of Southern California Los AngelesCA90089 United States Department of Clinical Pharmacy University of Southern California Los AngelesCA90089 United States Department of Computer Science Munster Technological University CorkT12 P928 Ireland Department of Genetics Cell Biology and Development University of Minnesota MinneapolisMN55417 United States Department of Bioengineering UCLA Los AngelesCA90095 United States Institute for Systems Genetics NYU Grossman School of Medicine New YorkNY10016 United States Department of Computational Medicine University of California Los AngelesCA90095 United States Department of Human Genetics University of California Los AngelesCA90095 United States Office of Advanced Research Computing UCLA Los AngelesCA90095 United States Center for Advanced Research Computing University of Southern California Los AngelesCA90089 United States Genomics Research Department Saudi Human Genome Project King Fahad Medical City King Abdulaziz City for Science and Technology Riyadh Saudi Arabia King Salman Center for Disability Research Riyadh12512 Saudi Arabia Riyadh Saudi Arabia

Omics software tools have reshaped the landscape of modern biology and become an essential component of biomedical research. The increasing dependence of biomedical scientists on these powerful tools creates a need for easier installation and greater usability. Packaging, virtualization, and containerization are different approaches to satisfy this need by wrapping omics tools in additional software that makes the omics tools easier to install and use. Here, we systematically review practices across prominent packaging, virtualization, and containerization platforms. We outline the challenges, advantages, and limitations of each approach and some of the most widely used platforms from the perspectives of users, software developers, and system administrators. We also propose principles to make packaging, virtualization, and containerization of omics software more sustainable and robust to increase the reproducibility of biomedical and life science research. © 2022, CC BY.

关键词： Containers

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A Hybrid Scheme Using PCA and ICA Based Statistical Feature for Epileptic Seizure Recognition from EEG Signal

A Hybrid Scheme Using PCA and ICA Based Statistical Feature ...

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International Conference on informatics, Electronics & Vision (ICIEV)

作者： Abdul Matin Rasel Ahmed Bhuiyan Shafiur Raihan Shafi Amit Kumar Kundu Muhammad Usama Islam Department of Computer Science and Engineering Uttara University Bangladesh Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh Department of Electrical and Electronic Engineering Uttara University Bangladesh School of Computing and Informatics University of Louisiana Lafayette USA

Epilepsy is commonly regarded as a neurological disorder which can be characterized by repetitive unprovoked seizures. Electroencephalogram (EEG) is the neuro-physiological measurement of the brain's electrical activity recorded by electrodes placed in the cerebral cortex. The EEG signals play an essential role in the diagnosis of epilepsy. This paper proposes an approach for classifying the epileptic seizure patterns that carry significant indications regarding chronic neurological disorders. In this regard, a dimensionality reduction scheme, hybrid in nature, utilizing Independent and Principal Component Analysis (ICA and PCA) is developed followed by the extraction of statistical features for epileptic seizure identification. At first, a particular number of sub-frames is extracted from the given EEG signal. The extracted sub-frames are considered as the input of PCA and ICA. After that, the significant components of ICA and PCA are utilized for statistical feature extraction. Lastly, the supervised support vector machine (SVM) classifier is employed for the seizure classification purpose. To evaluate the raised method, the publicly available EPILEPTIC dataset is used. According to the experiments and result analysis, the proposed scheme achieves convincing performance in terms of accuracy when the first components of ICA and PCA algorithms are used for feature extraction.

关键词： Feature extraction Principal component analysis Electroencephalography Support vector machines Brain modeling Time series analysis Dimensionality reduction

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False-Locality Attack Detection Using CNN in Named Data Networking

False-Locality Attack Detection Using CNN in Named Data Netw...

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IEEE Global Communications Conference

作者： Yujie Zeng Guowei Wu Rui Wang Mohammad S. Obaidat Kuei-Fang Hsiao School of Software Dalian University of Technology Dalian China College of Computing and Informatics University fo Sharjah UAE Nazarbayev University Astana Kazakhstan KASIT University of Jordan Amman Jordan University of Science and Technology Beijing China Department of Information Management Ming Chuan University Taiwan

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Named data networking (NDN) is a very promising architecture for future network, which can improve the network performance due to its in-network caching feature. However, the pervasive caching is vulnerable against False-Locality Attack (FLA), one kind of cache pollution attack, where attackers repeatedly request a specific set of non-popular contents to replace popular contents. Therefore, the cache hit of legal requests is reduced and the response delay is increased. To mitigate this attack and improve the network performance, we propose a detection scheme based on Convolutional Neural Network (CNN) by fully exploiting the regularity of past requests. The input data of CNN are related to the inherent characteristics of the cached contents including the request ratio, the standard deviation of repeated Interests, the variance of request interval and the change of cache hit ratio. The output of CNN indicates whether FLA has been launched. Simulations through multi-topologies are conducted to validate the performance of our scheme. Compared with other state-of-the-art schemes, it is more effective in detecting FLA with higher detecting ratio, higher cache hit and lower hop count.

关键词： Convolution Feature extraction Pollution Standards Blacklisting Kernel Law

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A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System

arXiv

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arXiv 2019年

作者： Sirlanci, Melike Levine, Matthew E. Wang, Cecilia Low Albers, David J. Stuart, Andrew M. Department of Biomedical Informatics School of Medicine University of Colorado Anschutz Medical Campus AuroraCO United States Department of Bioengineering College of Engineering Design and Computing AuroraCO United States Department of Biostatistics & Informatics Colorado School of Public Health AuroraCO United States Department of Biomedical Informatics Columbia University New YorkNY United States Department of Computing and Mathematical Sciences California Institute of Technology PasadenaCA United States Division of Endocrinology Metabolism and Diabetes Department of Medicine School of Medicine University of Colorado Anschutz Medical Campus AuroraCO United States

In this paper, we build a new, simple, and interpretable mathematical model to estimate and forecast physiology related to the human glucose-insulin system, constrained by available data. By constructing a simple yet flexible model class with interpretable parameters, this general model can be specialized to work in different settings, such as type 2 diabetes mellitus (T2DM) and intensive care unit (ICU);different choices of appropriate model functions describing uptake of nutrition and removal of glucose differentiate between the models. In addition to data-driven decision-making, the model has the potential also to be useful for the basic quantification of endocrine physiology. In both cases, the available data is sparse and collected in clinical settings, major factors that have constrained our model choice to the simple form adopted. The model has the form of a linear stochastic differential equation (SDE) to describe the evolution of the BG level. The model includes a term quantifying glucose removal from the bloodstream through the regulation system of the human body and two other terms representing the effect of nutrition and externally delivered insulin. The stochastic fluctuations encapsulate model error necessitated by the simple model form and enable flexible incorporation of data. The parameters entering the equation must be learned in a patient-specific fashion, leading to personalized models. We present experimental results on patient-specific parameter estimation and future BG level forecasting in T2DM and ICU settings. The resulting model leads to the prediction of the BG level as an expected value accompanied by a band around this value which accounts for uncertainties in the prediction. Such predictions, then, have the potential for use as part of control systems that are robust to model imperfections and noisy data. Finally, the model's predictive capability is compared with two different models built explicitly for T2DM and ICU contexts. Our simp

关键词： Insulin

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Unsupervised embedding of trajectories captures the latent structure of scientific migration

arXiv

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arXiv 2020年

作者： Murray, Dakota Yoon, Jisung Kojaku, Sadamori Costas, Rodrigo Jung, Woo-Sung Milojević, Staša Ahn, Yong-Yeol Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Network Science Institute Northeastern University BostonMA02115 United States Kellogg School of Management Northwestern University EvanstonIL60208 United States Northwestern Institute on Complex Systems EvanstonIL60208 United States Centre for Science and Technology Studies Leiden University AX Leiden2300 Netherlands DST-NRF Centre of Excellence in Scientometrics and Science Technology and Innovation Policy Stellenbosch University Stellenbosch South Africa Department of Physics Pohang University of Science and Technology Pohang37673 Korea Republic of Department of Industrial and Management Engineering Pohang University of Science and Technology Pohang37673 Korea Republic of

Human migration and mobility drives major societal phenomena including epidemics, economies, innovation, and the diffusion of ideas. Although human mobility and migration have been heavily constrained by geographic distance throughout the history, advances and globalization are making other factors such as language and culture increasingly more important. Advances in neural embedding models, originally designed for natural language, provide an opportunity to tame this complexity and open new avenues for the study of migration. Here, we demonstrate the ability of the model word2vec to encode nuanced relationships between discrete locations from migration trajectories, producing an accurate, dense, continuous, and meaningful vector-space representation. The resulting representation provides a functional distance between locations, as well as a "digital double" that can be distributed, re-used, and itself interrogated to understand the many dimensions of migration. We show that the unique power of word2vec to encode migration patterns stems from its mathematical equivalence with the gravity model of mobility. Focusing on the case of scientific migration, we apply word2vec to a database of three million migration trajectories of scientists derived from the affiliations listed on their publication records. Using techniques that leverage its semantic structure, we demonstrate that embeddings can learn the rich structure that underpins scientific migration, such as cultural, linguistic, and prestige relationships at multiple levels of granularity. Our results provide a theoretical foundation and methodological framework for using neural embeddings to represent and understand migration both within and beyond science. © 2020, CC BY.

关键词： Embeddings

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Partition-aware adaptive switching neural networks for post-processing in HEVC

arXiv

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arXiv 2019年

作者： Lin, Weiyao He, Xiaoyi Han, Xintong Liu, Dong See, John Zou, Junni Xiong, Hongkai Wu, Feng Department of Electronic Engineering Shanghai Jiao Tong University China Huya Inc. China Faculty of Computing and Informatics Multimedia University Malaysia Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Information Science and Technology University of Science and Technology of China

This paper addresses neural network based post-processing for the state-of-the-art video coding standard, High Efficiency Video Coding (HEVC). We first propose a partition-aware Convolution Neural Network (CNN) that utilizes the partition information produced by the encoder to assist in the post-processing. In contrast to existing CNN-based approaches, which only take the decoded frame as input, the proposed approach considers the coding unit (CU) size information and combines it with the distorted decoded frame such that the artifacts introduced by HEVC are efficiently reduced. We further introduce an adaptive-switching neural network (ASN) that consists of multiple independent CNNs to adaptively handle the variations in content and distortion within compressed-video frames, providing further reduction in visual artifacts. Additionally, an iterative training procedure is proposed to train these independent CNNs attentively on different local patch-wise classes. Experiments on benchmark sequences demonstrate the effectiveness of our partition-aware and adaptive-switching neural networks. The project page can be found in http://***/lwydemo/***. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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Approximate amplitude encoding in shallow parameterized quantum circuits and its application to financial market indicators

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Physical Review Research 2022年第2期4卷 023136-023136页

作者： Kouhei Nakaji Shumpei Uno Yohichi Suzuki Rudy Raymond Tamiya Onodera Tomoki Tanaka Hiroyuki Tezuka Naoki Mitsuda Naoki Yamamoto Graduate School of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Kanagawa 223- 8522 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Kanagawa 223-8522 Japan Mizuho Research & Technologies Ltd. 2-3 Kanda-Nishikicho Chiyoda-ku Tokyo 101-8443 Japan IBM Quantum IBM Research-Tokyo 19-21 Nihonbashi Hakozaki-cho Chuo-ku Tokyo 103-8510 Japan Mitsubishi UFJ Financial Group Inc. and MUFG Bank Ltd. 2-7-1 Marunouchi Chiyoda-ku Tokyo 100-8388 Japan Sony Group Corporation 1-7-1 Konan Minato-ku Tokyo 108-0075 Japan Sumitomo Mitsui Trust Bank Ltd. 1-4-1 Marunouchi Chiyoda-ku Tokyo 100-8233 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama 223-8522 Japan

Efficient methods for loading given classical data into quantum circuits are essential for various quantum algorithms. In this paper, we propose an algorithm called Approximate Amplitude Encoding that can effectively load all the components of a given real-valued data vector into the amplitude of quantum state, while the previous proposal can load only the absolute values of those components. The key of our algorithm is to variationally train a shallow parameterized quantum circuit, using the results of two types of measurement: the standard computational-basis measurement plus the measurement in the Hadamard-transformed basis, introduced in order to handle the sign of the data components. The variational algorithm changes the circuit parameters so as to minimize the sum of two costs corresponding to those two measurement basis, both of which are given by the efficiently computable maximum mean discrepancy. We also consider the problem of constructing the singular value decomposition entropy via the stock market data set to give a financial market indicator; a quantum algorithm (the variational singular value decomposition algorithm) is known to produce a solution faster than classical, which yet requires the sign-dependent amplitude encoding. We demonstrate, with an in-depth numerical analysis, that our algorithm realizes loading of time series of real stock prices on quantum state with small approximation error, and thereby it enables constructing an indicator of the financial market based on the stock prices.

关键词： Machine learning Quantum algorithms Quantum gates

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Approximate complex amplitude encoding algorithm and its application to data classification problems

arXiv

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arXiv 2022年

作者： Mitsuda, Naoki Ichimura, Tatsuhiro Nakaji, Kouhei Suzuki, Yohichi Tanaka, Tomoki Raymond, Rudy Tezuka, Hiroyuki Onodera, Tamiya Yamamoto, Naoki Sumitomo Mitsui Trust Bank Ltd. 1-4-1 Marunouchi Chiyoda-ku Tokyo100-8233 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan Department of Chemistry University of Toronto TorontoONM5G 1Z8 Canada 1-1-1 Umezono Ibaraki Tsukuba305-8568 Japan Mitsubishi UFJ Financial Group Inc. MUFG Bank Ltd. 2-7-1 Marunouchi Chiyoda-ku Tokyo100-8388 Japan IBM Quantum IBM Japan 19-21 Nihonbashi Hakozaki-cho Chuo-ku Tokyo103-8510 Japan Department of Computer Science University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Advanced Research Laboratory Technology Infrastructure Center Sony Group Corporation Technology Platform 1-7-1 Konan Minato-ku Tokyo108-0075 Japan Graduate School of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223- 8522 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Kanagawa Yokohama223-8522 Japan

Quantum computing has a potential to accelerate data processing efficiency, especially in machine learning, by exploiting special features such as the quantum interference. The major challenge in this application is that, in general, the task of loading a classical data vector into a quantum state requires an exponential number of quantum gates. The approximate amplitude encoding (AAE) method, which uses a variational means to approximately load a given real-valued data vector into the amplitude of a quantum state, was recently proposed as a general approach to this problem mainly for near-term devices. However, AAE cannot load a complex-valued data vector, which narrows its application range. In this work, we extend AAE so that it can handle a complex-valued data vector. The key idea is to employ the fidelity distance as a cost function for optimizing a parametrized quantum circuit, where the classical shadow technique is used to efficiently estimate the fidelity and its gradient. We apply this algorithm to realize the complex-valued-kernel binary classifier called the compact Hadamard classifier, and then we present a numerical experiment showing that it enables classification of the Iris dataset and credit card fraud detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Cost functions

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Developing Univariate Neurodegeneration Biomarkers with low-rank and sparse subspace decomposition

arXiv

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arXiv 2020年

作者： Wang, Gang Dong, Qunxi Wu, Jianfeng Su, Yi Chen, Kewei Su, Qingtang Zhang, Xiaofeng Hao, Jinguang Yao, Tao Liu, Li Zhang, Caiming Caselli, Richard J. Reiman, Eric M. Wang, Yalin Ulsan Ship and Ocean College Ludong University Yantai China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Banner Alzheimer’s Institute Banner Good Samaritan Pet Center PhoenixAZ United States School of Information and Electrical Engineering Ludong University Yantai China Shandong Province Key Lab of Digital Media Technology Shandong University of Finance and Economics Jinan China Department of Neurology Mayo Clinic Arizona ScottsdaleAZ United States

Cognitive decline due to Alzheimer’s disease (AD) is closely associated with brain structure alterations captured by structural magnetic resonance imaging (sMRI). It supports the validity to develop sMRI-based univariate neurodegeneration biomarkers (UNB). However, existing UNB work either fails to model large group variances or does not capture AD dementia (ADD) induced changes. We propose a novel low-rank and sparse subspace decomposition method capable of stably quantifying the morphological changes induced by ADD. Specifically, we propose a numerically efficient rank minimization mechanism to extract group common structure and impose regularization constraints to encode the original 3D morphometry connectivity. Further, we generate regions-of-interest (ROI) with group difference study between common subspaces of Aβ+ AD and Aβ− cognitively unimpaired (CU) groups. A univariate morphometry index (UMI) is constructed from these ROIs by summarizing individual morphological characteristics weighted by normalized difference between Aβ+ AD and Aβ− CU groups. We use hippocampal surface radial distance feature to compute the UMIs and validate our work in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25% reduction in the mean annual change with 80% power and two-tailed P = 0:05 are 116, 279 and 387 for the longitudinal Aβ+ AD, Aβ+ mild cognitive impairment (MCI) and Aβ+ CU groups, respectively. Additionally, for MCI patients, UMIs well correlate with hazard ratio of conversion to AD (4:3, 95% CI=2:3 – 8:2) within 18 months. Our experimental results outperform traditional hippocampal volume measures and suggest the application of UMI as a potential UNB. Copyright © 2020, The Authors. All rights reserved.

关键词： Biomarkers

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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

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arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

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