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11th International Conference on Bio-inspired computing – Theories and Applications, BIC-TA 2016

作者： Yahya, Rafaa I. Shamsuddin, Siti Mariyam Yahya, Salah I. Hasan, Shafatnnur Al-Salibi, Bisan Al-Khafaji, Ghada Faculty of Computing Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Computer College of Science University of Al-Mustansiriyah Baghdad Iraq UTM Big Data Center Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Software Engineering Koya University Iraq School of Computer Sciences Universiti Sains Malaysia USM GelugorPenang11800 Malaysia Department of Computer Science College of Science Baghdad University Baghdad Iraq

ISBN: (纸本)9789811036101

Membrane computing, a recent branch of natural computing, has been gaining momentum attention in the last few decades due to its massive parallelism and efficient computation. Many researchers in the field of membrane computing have proposed sophisticated techniques inspired by cell biology for computer science applications, especially when they considered cell organization in tissues, organs, and most recently, from the organization of neurons. The interdisciplinary applications of membrane computing include, but not limited to computer science, biology, biomedicine, bioinformatics and several other fields such as mathematics, artificial intelligence, automation, economics, to name but a few. Their applications are pertaining to computer graphics, approximate optimization, cryptography, parallel computing and image processing. Hence, in this paper we present an up to date comprehensive literature review pertaining to the application of membrane computing in the area of digital image analysis, especially image segmentation, comprehensively and systematically. We thoroughly investigate the recent advancement in the field of image segmentation using membrane system. Furthermore, we highlight the merits and demerits of various software tools and methods. Finally, we suggest some intuitive future directions in light of the current limitations. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Image segmentation

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Bag of compact HKS-based feature descriptors 8

Bag of compact HKS-based feature descriptors

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8th Eurographics Workshop on 3D Object Retrieval, 3DOR 2015

作者： ElNaghy, H. Hamad, S. Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9783905674781

3D object retrieval has become an integral part in many today's applications attracting extensive research efforts. This paper introduces an enhanced 3D object retrieval technique using a compact and highly discriminative feature point descriptor. The key idea is based on integrating Bag of features (BoF) paradigm with Heat Kernel Signature (HKS) for feature description and detection. Initially, HKS computation phase defines HKS point signatures for each 3D model. Then, an innovative feature point detection algorithm provides a succinct set of feature points to be associated with a compact HKS-based descriptor vectors computed at local time scales. Finally, we take advantage of the BoF paradigm to encode a given 3D model with an informative feature frequency vector. The proposed approach has been evaluated on SHREC 2015 dataset of non-rigid models. The experimental results demonstrate the effective retrieval performance, invariance to different kinds of deformation and possible noise. © The Eurographics Association 2015.

关键词： Feature extraction

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Correction to: Multivariate statistical and trend analyses of surface water quality in the central Indian River Lagoon area, Florida

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Environmental Earth sciences 2018年第5期77卷 1-1页

作者： Sayem Zaman Ming Ye Fan Zhang Mingming Zhu Department of Scientific Computing Florida State University Tallahassee USA Draper Aden Associates Richmond USA School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin China Department of Earth Ocean and Atmospheric Science Florida State University Tallahassee USA Key Laboratory of Tibetan Environmental Changes and Land Surface Processes Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing China Center of Hydraulic Engineering Regulation and Management Jinzhou Waterwork Office Jinzhou China

Unfortunately, the online version of the original article has an error in authors’ names.

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Recent developments in the PySCF program package

arXiv

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arXiv 2020年

作者： Sun, Qiming Zhang, Xing Banerjee, Samragni Bao, Peng Barbry, Marc Blunt, Nick S. Bogdanov, Nikolay A. Booth, George H. Chen, Jia Cui, Zhi-Hao Eriksen, Janus Juul Gao, Yang Guo, Sheng Hermann, Jan Hermes, Matthew R. Koh, Kevin Koval, Peter Lehtola, Susi Li, Zhendong Liu, Junzi Mardirossian, Narbe McClain, James D. Motta, Mario Mussard, Bastien Pham, Hung Q. Pulkin, Artem Purwanto, Wirawan Robinson, Paul J. Ronca, Enrico Sayfutyarova, Elvira Scheurer, Maximilian Schurkus, Henry F. Smith, James E.T. Sun, Chong Sun, Shi-Ning Upadhyay, Shiv Wagner, Lucas K. Wang, Xiao White, Alec Whitfield, James Daniel Williamson, Mark J. Wouters, Sebastian Yang, Jun Yu, Jason M. Zhu, Tianyu Berkelbach, Timothy C. Sharma, Sandeep Sokolov, Alexander Yu. Chan, Garnet Kin-Lic AxiomQuant Investment Management LLC Shanghai200120 China Division of Chemistry and Chemical Engineering California Institute of Technology PasadenaCA91125 United States Department of Chemistry and Biochemistry The Ohio State University ColumbusOH43210 United States Beijing National Laboratory for Molecular Sciences State Key Laboratory for Structural Chemistry of Unstable and Stable Species Institute of Chemistry Chinese Academy of Sciences Beijing100190 China Simbeyond B.V. P.O. Box 513 EindhovenNL-5600 MB Netherlands Department of Chemistry Lensfield Road CambridgeCB2 1EW United Kingdom Max Planck Institute for Solid State Research Heisenbergstraße 1 Stuttgart70569 Germany Department of Physics King's College London Strand LondonWC2R 2LS United Kingdom Department of Physics University of Florida GainesvilleFL32611 United States Quantum Theory Project University of Florida GainesvilleFL32611 United States School of Chemistry University of Bristol Cantock's Close BristolBS8 1TS United Kingdom Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States Google Inc. Mountain ViewCA94043 United States FU Berlin Department of Mathematics and Computer Science Arnimallee 6 Berlin14195 Germany TU Berlin Machine Learning Group Marchstr. 23 Berlin10587 Germany Department of Chemistry Chemical Theory Center Supercomputing Institute University of Minnesota 207 Pleasant Street SE MinneapolisMN55455 United States Department of Chemistry and Biochemistry The University of Notre Dame du Lac 251 Nieuwland Science Hall Notre DameIN46556 United States Simune Atomistics S.L. Avenida Tolosa 76 Donostia-San Sebastian Spain HelsinkiFI-00014 Finland Key Laboratory of Theoretical and Computational Photochemistry Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Chemistry The Johns Hopkins University BaltimoreMD21218 United States AMGEN

PYSCF is a Python-based general-purpose electronic structure platform that both supports first-principles simulations of molecules and solids, as well as accelerates the development of new methodology and complex computational workflows. The present paper explains the design and philosophy behind PYSCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PYSCF as a development environment. We then summarize the capabilities of PYSCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PYSCF across the domains of quantum chemistry, materials science, machine learning and quantum information science. Copyright © 2020, The Authors. All rights reserved.

关键词： Python

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A real time dynamic model for optimizing hydrocarbons' production based on deep recurrent network

A real time dynamic model for optimizing hydrocarbons' produ...

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International Conference on computer Engineering and Systems

作者： A. S. Elons M. Y. Elgendy Dalia Ahmed Magdy Scientific Computing Department Ain-Shams University Cairo Egypt Research and Development Departmen Apex Company Cairo Egypt Computer and Information System Department Sadat Academy for Management Sciences Cairo Egypt

ISBN: (纸本)9781509032686

Contrary to common sense that oil and gas products do exist on a constant vertical depth in a specific region, they exist on varying depths in the same region. This fact forces the drilling directions to change deviations to keep track in the production zone “Pay-Zone”. Drilling a new borehole can be directed by the knowledge of the surrounding already drilled boreholes. Geo-steering process is a real-time determining for drilling directions based on drilling sensors readings and surrounding boreholes data to maximize oil and gas production inside a new borehole. A previous work was done by us that proposes an initial pre-drilling plan based on sensors measurements of the drilled surrounding boreholes. This work extends it by adding a dynamic adaptation feature for this plan based on the current borehole sensors measurements. This adaptation considers the current borehole previous measurements to re-direct the drilling path to keep track the Pay-Zone. This paper presents Long Short-Term Memory (LSTM) Recurrent Neural Network for modeling the previous sensors measurements sequence to adapt the drilling directions. The dataset consists of 3 groups of boreholes in 3 different Areas, each group consists of 5 boreholes, 4 of them considered as surrounding boreholes for the 5 th . Two groups is exploited as training set while the 3 rd is used for testing issue. The results shows that LSTM Recurrent network outperforms both the conventional Recurrent Network and Deep Forward Network. The prediction results reached 98% accuracy for the testing data.

关键词： Decision support systems Recurrent neural networks Azimuth Oils Production

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Towards a novel infrastructure for conducting high productive cloud-based scientific analytics

Towards a novel infrastructure for conducting high productiv...

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Proceedings of the International Convention MIPRO

作者： Peter Brezany Thomas Ludescher Thomas Feilhauer SIX Research Centre Brno University of Technology Brno Czech Republic Research Group Scientific Computing University of Vienna Vienna Austria Department of Computer Science University of Applied Sciences Dornbirn Austria

The life-science and health care research environments offer an abundance of new opportunities for improvement of their efficiency and productivity using big data in collaborative research processes. A key component of this development is e-Science analytics, which is typically supported by Cloud computing nowadays. However, the state-of-the-art Cloud technology does not provide an appropriate support for high-productivity e-Science analytics. In this paper, we show how productivity of Cloud-based analytics systems can be increased by (a) supporting researchers with integrating multiple problem solving environments into the life cycle of data analysis, (b) parallel code execution on top of multiple cores or computing machines, (c) enabling safe inclusion of sensitive datasets into analytical processes through improved security mechanisms, (d) introducing scientific dataspace-a novel data management abstraction, and (e) automatic analysis services enabling a faster discovery of scientific insights and providing hints to detect potential new topics of interests. Moreover, an appropriate formal productivity model for evaluating infrastructure design decisions was developed. The result of the realization of this vision, a key contribution of this effort, is called the High-Productivity Framework that was tested and evaluated using real life-science application domain addressing breath gas analysis applied e.g. in the cancer treatment.

关键词： Productivity Cloud computing Security Mathematical model computers computer science Computational modeling

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A proposed model for predicting the drilling path based on hybrid Pso-Bp neural network

A proposed model for predicting the drilling path based on h...

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Science and Information Conference (SAI)

作者： A. S. Elons Dalia Ahmed Magdi M. Y. Elgendy Scientific Computing Department Ain-Shams University Cairo Egypt Computer and Information System Department Sadat Academy for Management Sciences Cairo Egypt Research and Development Department Apex Company Cairo Egypt

ISBN: (纸本)9781467384612

In the early days Hydrocarbon wells were normally drilled vertically which rarely requires making decisions. As the technology progressed, the capability to do some deviation of the bit was used resulting in deviated or directional wells to follow the Hydrocarbon formation. This type of drilling requires real-time drilling directions decisions. In this paper, the aim is to develop a real-time intelligent system that can model and predict the optimal drilling path for a new well before actual drilling is done based on the geological layers of the pre-drilled surrounding wells. In the presented work, a hybrid method of stochastic population-based search algorithm (Particle Swarm Optimization) and a gradient based algorithm (Back Propagation) is used to train a Multiplicative Neural Network. The proposed network topology and training algorithms have shown superiority on the traditional Neural Network for determining the drilling optimal drilling path. The proposed system generated proposed drilling plans that achieved more than 88% drilling decision accuracy which is measured according to the actual drilling path.

关键词： Reservoirs Rocks Multi-layer neural network Neurons Predictive models

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Integrable discretization of soliton equations via bilinear method and Backlund transformation

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Science China Mathematics 2015年第2期58卷 279-296页

作者： ZHANG Ying Nan CHANG Xiang Ke HU Juan HU Xing Biao TAM Hon-Wah Institute of Computational Mathematics and Scientific Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Department of Applied Mathematics Zhejiang University of Technology Department of Computer Science Hong Kong Baptist University

We present a systematic procedure to derive discrete analogues of integrable PDEs via Hirota’s bilinear *** approach is mainly based on the compatibility between an integrable system and its B¨acklund *** apply this procedure to several equations,including the extended Korteweg-deVries(Kd V)equation,the extended Kadomtsev-Petviashvili(KP)equation,the extended Boussinesq equation,the extended Sawada-Kotera(SK)equation and the extended Ito equation,and obtain their associated semidiscrete *** the continuum limit,these differential-difference systems converge to their corresponding smooth *** these new integrable systems,their B¨acklund transformations and Lax pairs are derived.

关键词： integrable discretization bilinear method Backlund transformation

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Advances in Nature and Biologically Inspired computing: Proceedings of the 7th World Congress on Nature and Biologically Inspired computing (NaBIC2015) in Pietermaritzburg, South Africa, held December 01-03, 2015 7th

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7th World Congress on Nature and Biologically Inspired computing, NaBIC 2015

作者： Pillay, Nelishia Engelbrecht, Andries P. Abraham, Ajith Du Plessis, Mathys C. Snášel, Václav Muda, Azah Kamilah School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Pietermaritzburg South Africa Department of Computer Science School of Information Technology University of Pretoria Pretoria South Africa Machine Intelligence Research Labs Scientific Network for Innovation and Research Excellence AuburnWA United States Department of Computing Sciences Nelson Mandela Metropolitan University Port Elizabeth South Africa Technical University of Ostrava Ostrava-Poruba Czech Republic Computational Intelligences and Technologies Lab Universiti Teknikal Malaysia Melaka Durian Tunggal Malaysia

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Tensor fem for spectral fractional diffusion

arXiv

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arXiv 2017年

作者： Banjai, Lehel Melenk, Jens M. Nochetto, Ricardo H. Otárola, Enrique Salgado, Abner J. Schwab, Christoph Maxwell Institute for Mathematical Sciences School of Mathematical & Computer Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom Institut für Analysis und Scientific Computing Technische Universität Wien ViennaA-1040 Austria Department of Mathematics Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States Departamento de Matemática Universidad Técnica Federico Santa María Valparaíso Chile Department of Mathematics University of Tennessee KnoxvilleTN37996 United States Seminar for Applied Mathematics ETH Zürich ETH Zentrum HG G57.1 ZürichCH8092 Switzerland

We design and analyze several Finite Element Methods (FEMs) applied to the Caffarelli-Silvestre extension that localizes the fractional powers of symmetric, coercive, linear elliptic operators in bounded domains with Dirichlet boundary conditions. We consider open, bounded, polytopal but not necessarily convex domains Ω ⊂ Rd with d = 1, 2. For the solution to the extension problem, we establish analytic regularity with respect to the extended variable y ∈ (0, ∞). We prove that the solution belongs to countably normed, power–exponentially weighted Bochner spaces of analytic functions with respect to y, taking values in corner-weighted Kondat’ev type Sobolev spaces in Ω. In Ω ⊂ R2, we discretize with continuous, piecewise linear, Lagrangian FEM (P1-FEM) with mesh refinement near corners, and prove that first order convergence rate is attained for compatible data f ∈ H1−s(Ω). We also prove that tensorization of a P1-FEM in Ω with a suitable hp-FEM in the extended variable achieves log-linear complexity with respect to NΩ, the number of degrees of freedom in the domain Ω. In addition, we propose a novel, sparse tensor product FEM based on a multilevel P1FEM in Ω and on a P1-FEM on radical–geometric meshes in the extended variable. We prove that this approach also achieves log-linear complexity with respect to NΩ. Finally, under the stronger assumption that the data is analytic in Ω, and without compatibility at ∂Ω, we establish exponential rates of convergence of hp-FEM for spectral, fractional diffusion operators in energy norm. This is achieved by a combined tensor product hp-FEM for the Caffarelli-Silvestre extension in the truncated cylinder Ω × (0, Y) with anisotropic geometric meshes that are refined towards ∂Ω. We also report numerical experiments for model problems which confirm the theoretical results. We indicate several extensions and generalizations of the proposed methods to other problem classes and to other boundary conditions on ∂Ω. Copyright © 2017, The

关键词： Anisotropy

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