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arXiv 2022年

作者： Ali, Sharib Ghatwary, Noha Jha, Debesh Isik-Polat, Ece Polat, Gorkem Yang, Chen Li, Wuyang Galdran, Adrian Ballester, Miguel-Ángel González Thambawita, Vajira Hicks, Steven Poudel, Sahadev Lee, Sang-Woong Jin, Ziyi Gan, Tianyuan Yu, ChengHui Yan, JiangPeng Yeo, Doyeob Lee, Hyunseok Tomar, Nikhil Kumar Haithmi, Mahmood Ahmed, Amr Riegler, Michael A. Daul, Christian Halvorsen, Pål Rittscher, Jens Salem, Osama E. Lamarque, Dominique Cannizzaro, Renato Realdon, Stefano de Lange, Thomas East, James E. Institute of Biomedical Engineering Department of Engineering Science University of Oxford OxfordOX3 7DQ United Kingdom Oxford National Institute for Health Research Biomedical Research Centre OxfordOX4 2PG United Kingdom Big Data Institute University of Oxford Li Ka Shing Centre for Health Information and Discovery OxfordOX3 7LF United Kingdom Translational Gastroenterology Unit Nuffield Department of Medicine Experimental Medicine Div. John Radcliffe Hospital University of Oxford OxfordOX3 9DU United Kingdom Computer Engineering Department Arab Academy for Science and Technology Alexandria1029 Egypt SimulaMet Oslo0167 Norway Department of Computer Science UiT The Arctic University of Norway Hansine Hansens veg 18 Tromsø9019 Norway Medical Department Sahlgrenska University Hospital-Mölndal Blå stråket 5 Göteborg413 45 Sweden Veneto Institute of Oncology IOV-IRCCS Via Gattamelata 64 Padua35128 Italy CRO Centro Riferimento Oncologico IRCCS Aviano Italy Via Franco Gallini 2 PN Aviano33081 Italy Université de Versailles St-Quentin en Yvelines Hôpital Ambroise Paré 9 Av. Charles de Gaulle Boulogne-Billancourt92100 France Faculty of Medicine University of Alexandria Alexandria21131 Egypt Graduate School of Informatics Middle East Technical University Ankara06800 Turkey City University of Hong Kong Kowloon Hong Kong BCN MedTech Dept. of Information and Communication Technologies Universitat Pompeu Fabra Barcelona08018 Spain Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Automation Tsinghua University Beijing100084 China Smart Sensing & Diagnosis Research Division Korea Atomic Energy Research Institute 34057 Korea Republic of Daegu-Gyeongbuk Medical Innovation Foundation Medical Device Deve

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realising automated methods for both detection and segmentation of these polyps using machine learning. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets that come from different centres, modalities and acquisition systems. To test this hypothesis rigorously we curated a multi-centre and multi-population dataset acquired from multiple colonoscopy systems and challenged teams comprising machine learning experts to develop robust automated detection and segmentation methods as part of our crowd-sourcing Endoscopic computer vision challenge (EndoCV) 2021. In this paper, we analyse the detection results of the four top (among seven) teams and the segmentation results of the five top teams (among 16). Our analyses demonstrate that the top-ranking teams concentrated on accuracy (i.e., accuracy > 80% on overall Dice score on different validation sets) over real-time performance required for clinical applicability. We further dissect the methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets. © 2022, CC BY-NC-SA.

关键词： Endoscopy

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Learning From Experience: An Automatic pH Neutralization System Using Hybrid Fuzzy System and Neural Network

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Procedia Computer science 2018年 140卷 206-215页

作者： Ethar H.K. Alkamil Seaar Al-Dabooni Ahmed K. Abbas Ralph Flori Donald C. Wunsch Dept. of Geosciences and Geological and Petroleum Engineering Missouri University of Science and Technology Rolla Missouri 65401 USA Dept. of Petroleum Engineering University of Basrah 61004 Iraq Basrah Oil Company Basrah 61004 Iraq Applied Computational Intelligence Laboratory Rolla Missouri 65401 USA Iraqi Drilling Company Basrah 61004 Iraq Dept. of Electrical and Computer Engineering Missouri University of Science and Technology Rolla Missouri 65401 USA

In oil and gas industry, the pH level is one of the most important indicators of mud contamination while drilling. Although the process has simple components, the pH neutralization process is complicated in the mud circulation system. This difficulty is due to the high nonlinearity of the process. In this paper, the fuzzy neural network (FNN) is integrated to the fuzzy logic controller (FLC) to create a system which identifies pH fluctuation into a drilling mud system, assesses and signals its severity. And then, it automatically actuates a chemical treating fluids valve (CTFV), such as Caustic Soda (CS), for neutralizing the drilling fluid pH level while circulating. In this work, an integrated mud circulation lab-scaled system is used to mimic the pH neutralization (e.g., because of the H2S intrusion) while drilling the hydrogen sulfide bearing zones. The system contains several significant nonlinearities such as sudden pH reduction appearances that stretch the ability of simple controllers in achieving satisfactory performance. The results show, the FLC can be used successfully to stabilize any chosen operating point of the system and to deal with nonlinearities (uncertainties of a model). All derived results are validated by computer simulation of a nonlinear mathematical model of the lab-scaled mud neutralization system. The presented FNN in this work demonstrates a reliable response to neutralize the circulated drilling fluid pH, through a quick, safe, and automatic initiation of appropriate treatment.

关键词： Automatic pH Neutralization System Fuzzy Neural Network

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LinearPartition: Linear-Time Approximation of RNA Folding Partition Function and Base Pairing Probabilities

arXiv

引用

arXiv 2019年

作者： Zhang, He Zhang, Liang Mathews, David H. Huang, Liang Baidu Research USA SunnyvaleCA94089 United States School of Electrical Engineering & Computer Science Oregon State University CorvallisOR97330 United States Dept. of Biochemistry & Biophysics University of Rochester Medical Center RochesterNY14642 United States Center for RNA Biology University of Rochester Medical Center RochesterNY14642 United States Dept. of Biostatistics & Computational Biology University of Rochester Medical Center RochesterNY14642 United States

RNA secondary structure prediction is widely used to understand RNA function. Recently, there has been a shift away from the classical minimum free energy (MFE) methods to partition function-based methods that account for folding ensembles and can therefore estimate structure and base pair probabilities. However, the classical partition function algorithm scales cubically with sequence length, and is therefore a slow calculation for long sequences. This slowness is even more severe than cubic-time MFE-based methods due to a larger constant factor in runtime. Inspired by the success of our recently proposed LinearFold algorithm that predicts the approximate MFE structure in linear time, we design a similar linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base pairing probabilities, which is shown to be orders of magnitude faster than Vienna RNAfold and CONTRAfold (e.g., 2.5 days vs. 1.3 minutes on a sequence with length 32,753 nt). More interestingly, the resulting base pairing probabilities are even better correlated with the ground truth structures. LinearPartition also leads to a small accuracy improvement when used for downstream structure prediction on families with the longest length sequences (16S and 23S rRNA), as well as a substantial improvement on long-distance base pairs (500+ nt apart). Copyright © 2019, The Authors. All rights reserved.

关键词： Heuristic algorithms

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Chatter classification in turning using machine learning and topological data analysis

arXiv

引用

arXiv 2018年

作者： Khasawneh, Firas A. Munch, Elizabeth Perea, Jose A. Dept. of Mechanical Engineering Michigan State University East LansingMI48824 United States Dept. of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Dept. of Mathematics Michigan State University East LansingMI48824 United States Dept. of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Dept. of Mathematics Michigan State University East LansingMI48824 United States

Chatter identification and detection in machining processes has been an active area of research in the past two decades. Part of the challenge in studying chatter is that machining equations that describe its occurrence are often nonlinear delay differential equations. The majority of the available tools for chatter identification rely on defining a metric that captures the characteristics of chatter, and a threshold that signals its occurrence. The difficulty in choosing these parameters can be somewhat alleviated by utilizing machine learning techniques. However, even with a successful classification algorithm, the transferability of typical machine learning methods from one data set to another remains very limited. In this paper we combine supervised machine learning with Topological data Analysis (TDA) to obtain a descriptor of the process which can detect chatter. The features we use are derived from the persistence diagram of an attractor reconstructed from the time series via Takens embedding. We test the approach using deterministic and stochastic turning models, where the stochasticity is introduced via the cutting coefficient term. Our results show a 97% successful classification rate on the deterministic model labeled by the stability diagram obtained using the spectral element method. The features gleaned from the deterministic model are then utilized for characterization of chatter in a stochastic turning model where there are very limited analysis methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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A convex program for bilinear inversion of sparse vectors 18

A convex program for bilinear inversion of sparse vectors

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Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Alireza Aghasi Ali Ahmed Paul Hand Babhru Joshi Georgia State Business School GSU GA Dept. of Electrical Engineering ITU Lahore Dept. of Mathematics and College of Computer and Information Science Northeastern University MA Dept. of Computational and Applied Mathematics Rice University TX

We consider the bilinear inverse problem of recovering two vectors, x ∈ ℝL and w ∈ ℝL, from their entrywise product. We consider the case where x and w have known signs and are sparse with respect to known dictionaries of size K and N, respectively. Here, K and N may be larger than, smaller than, or equal to L. We introduce ℓ1-BranchHull, which is a convex program posed in the natural parameter space and does not require an approximate solution or initialization in order to be stated or solved. We study the case where x and w are S1- and S2-sparse with respect to a random dictionary, with the sparse vectors satisfying an effective sparsity condition, and present a recovery guarantee that depends on the number of measurements as L ≥ Ω(S1 + S2) log2(K + N). Numerical experiments verify that the scaling constant in the theorem is not too large. One application of this problem is the sweep distortion removal task in dielectric imaging, where one of the signals is a nonnegative reflectivity, and the other signal lives in a known subspace, for example that given by dominant wavelet coefficients. We also introduce a variants of ℓ1-BranchHull for the purposes of tolerating noise and outliers, and for the purpose of recovering piecewise constant signals. We provide an ADMM implementation of these variants and show they can extract piecewise constant behavior from real images.

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P10.25.A ATR INHIBITION IN EXPERIMENTAL GLIOMA: DISCOVERY AND VALIDATION OF MOLECULAR TARGETS FOR FUNCTIONALLY-INSTRUCTED COMBINATION THERAPIES

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Neuro-Oncology 2023年第SUPPLEMENT_2期25卷 ii68–ii68页

作者： Walter, B Hirsch, S Kuhlburger, L Stahl, A Schnabel, L Haeusser, L A Tsiami, F Ploeger, S Aghaallaei, N Dick, A M Skokowa, J Schmees, C Templin, M Schenke-Layland, K Tatagiba, M Nahnsen, S Merk, D Tabatabai, G Hertie Institute for Clinical Brain Research Tübingen Germany Quantitative Biology Center Eberhard Karls University Tübingen Tübingen Germany Biomedical Data Science Department of Computer Science Eberhard Karls University Tübingen Tübingen Germany NMI Natural and Medical Sciences Institute at the University of Tübingen Reutlingen Germany German Consortium for Translational Cancer Research (DKTK) Partner site Tübingen Tübingen Germany Division of Translational Oncology Department of Internal Medicine II University Hospital Tübingen Tübingen Germany Institute of Biomedical Engineering Dept. for Medical Technologies and regenerative Medicine Eberhard Karls University Tübingen Tübingen Germany Center for Neuro-Oncology Comprehensive Cancer Center Tübingen-Stuttgart Eberhard Karls University Tübingen Tübingen Germany

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A Model for Recommending Actions during the Collaborative Design of Visual Analytics Flows

A Model for Recommending Actions during the Collaborative De...

引用

International Conference on Information Systems and Computer science (INCISCOS)

作者： Armando Arce-Orozco Antonio González-Torres Erick Mata-Montero Ayse Bener Hans Jara-Watson School of Computing Costa Rica Institute of Technology Cartago Costa Rica School of Computing National University Heredia Costa Rica Costa Rica Institute of Technology Cartago Costa Rica Data Science Lab Ryerson University Toronto Canada Dept. of Computer Engineering Costa Rica Institute of Technology Cartago Costa Rica

ISBN: (数字)9781728155814

ISBN: (纸本)9781728155821

Visual Analytics (VA) is a process to facilitate the uncovering of knowledge from large and complex datasets to enable users to make informed decisions. However, the design of VA tools is frequently performed by multidisciplinary teams that can be physically in the same place or distributed across different locations. Therefore, there is an intrinsic collaboration need between team members and also of proposing methods to support these teams to work collaboratively during the design and implementation of VA systems. Although some proposals have made on the use of visual data flows and recommender systems, its combination with a Multi-Device Environment can benefit the discussion and exchange of knowledge during design and programming. Therefore, this article presents the design of an architecture that combines a Multi-Device Environment (MDE) with visual data flows and recommender systems for the design of VA tools in a collaborative way. The intention is to support designers and programmers during the design of visual data flows through the sharing and analysis of ideas in a collaborative environment supported by a recommender system that aids decision making during the design of flows.

关键词： data visualization Visualization Collaboration Tools Recommender systems Proposals Task analysis

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A blockchain-based self-tallying voting scheme in decentralized IoT

arXiv

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arXiv 2019年

作者： Li, Yannan Susilo, Willy Yang, Guomin Yu, Yong Liu, Dongxi Guizani, Mohsen Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia School of Computer Science Shaanxi Normal University Xi’an710062 China Data61 CSIRO MarsfieldNSW2122 Australia Dept. of Electrical and Computer Engineering University of Idaho MoscowID United States

The Internet of Things (IoT) is experiencing explosive growth and has gained extensive attention from academia and industry in recent years. Most of the existing IoT infrastructures are centralized, in which the presence of a cloud server is mandatory. However, centralized frameworks suffer from the issues of unscalability and single-point-of-failure. Consequently, decentralized IoT has been proposed by taking advantage of the emerging technology of Blockchain. Voting systems are widely adopted in IoT, such as a leader election in wireless sensor networks. Self-tallying voting systems are alternatives to traditional centralized voting systems in decentralized IoT since the traditional ones are not suitable for such scenarios. Unfortunately, self-tallying voting systems inherently suffer from fairness issues, such as adaptive and abortive issues caused by malicious voters. In this paper, we introduce a framework of self-tallying systems in decentralized IoT based on Blockchain. We propose a concrete construction and prove the proposed system satisfies all the security requirements including fairness, dispute-freeness and maximal ballot secrecy. The implementations on mobile phones demonstrate the practicability of our system. Copyright © 2019, The Authors. All rights reserved.

关键词： Internet of things

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Survey of Middlewares for Internet of Things

Survey of Middlewares for Internet of Things

引用

International Conference on Recent Trends in Advance Computing (ICRTAC)

作者： Dilip Rathod Girish Chowdhary Dept. of Computer Science and Engineering Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded Maharashtra India Department of Computational Science Swami Ramanand Teerth Marathwada University Nanded Maharashtra India

ISBN: (纸本)9781538658543;9781538658536

With advancements in communications and sensor technologies Internet of Things (IoT) is witnessing rapid growth in this decade. IoT is a complex system consisting of variety of sensing devices, network protocols, connecting networks, and architectures etc. Middleware is well known approach to cope up with the complexities inherent in building IoT system. Middleware is software component which helps in developing scalable IoT system, integrating data from variety of IoT devices, offering security, discovering service, providing sensor data analytics, context awareness etc. In this paper, we present the necessity of middleware along with requirement it should addresses for IoT. Followed by survey of middlewares which are relevant to IoT using identified requirements. We also discuss challenges and possible directions for future research.

关键词： Middleware Internet of Things Wireless sensor networks Security Radiofrequency identification Computer architecture

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Toward finding latent cities with non-negative matrix factorization

arXiv

引用

arXiv 2018年

作者： Graells-Garrido, Eduardo Caro, Diego Parra, Denis Data Science Institute Universidad Del Desarrollo Telefonica RandD Santiago Chile Dept. of Computer Science Faculty of Engineering Pontificia Universidad Catolica Santiago Chile

In the last decade, digital footprints have been used to cluster population activity into functional areas of cities. However, a key aspect has been overlooked: we experience our cities not only by performing activities at specific destinations, but also by moving from one place to another. In this paper, we propose to analyze and cluster the city based on how people move through it. Particularly, we introduce Mobilicities, automatically generated travel patterns inferred from mobile phone network data using NMF, a matrix factorization model. We evaluate our method in a large city and we find that mobilicities reveal latent but at the same time interpretable mobility structures of the city. Our results provide evidence on how clustering and visualization of aggregated phone logs could be used in planning systems to interactively analyze city structure and population activity. Copyright © 2018, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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