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arXiv 2017年

作者： Javarone, Marco Alberto Marinazzo, Daniele School of Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom Chain Ltd LondonW1W 8AP United Kingdom Dept. of Mathematics and Computer Science University of Cagliari Cagliari09123 Italy Department of Data Analysis Faculty of Psychology and Educational Sciences University of Ghent Ghent Belgium

The dynamics and behavior of ferromagnets have a great relevance even beyond the domain of statistical physics. In this work, we propose a Monte Carlo method, based on random graphs, for modeling their dilution. In particular, we focus on ferromagnets with dimension D ≥ 4, which can be approximated by the Curie-Weiss model. Since the latter has as graphic counterpart a complete graph, a dilution can be in this case viewed as a pruning process. Hence, in order to exploit this mapping, the proposed strategy uses a modified version of the Erdos-Renyi graph model. In doing so, we are able both to simulate a continuous dilution, and to realize diluted ferromagnets in one step. The proposed strategy is studied by means of numerical simulations, aimed to analyze main properties and equilibria of the resulting diluted ferromagnets. To conclude, we also provide a brief description of further applications of our strategy in the field of complex networks. Copyright © 2017, The Authors. All rights reserved.

关键词： Ferromagnetism

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Tackling the ordinal and imbalance nature of a melanoma image classification problem

Tackling the ordinal and imbalance nature of a melanoma imag...

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International Joint Conference on Neural Networks

作者： M. Perez-Ortiz A. Saez J. Sanchez-Monedero P. A. Gutierrez C. Hervas-Martinez Dept. of Quantitative Methods Universidad Loyola Andalucía 14004 Córdoba Spain Dept. of Signal Theory and Communications University of Seville 41092 Spain Dept. of Computer Science and Numerical Analysis University of Córdoba 14071 Spain

ISBN: (纸本)9781509006212

Melanoma is a type of cancer that usually occurs on the skin. Early detection is crucial for ensuring five-year survival (which varies between 15% and 99% depending on the melanoma stage). Melanoma severity is typically diagnosed by invasive methods (e.g. a biopsy). In this paper, we propose an alternative system combining image analysis and machine learning for detecting melanoma presence and severity. The 86 features selected consider the shape, colour, pigment network and texture of the melanoma. As opposed to previous studies that have focused on distinguishing melanoma and non-melanoma images, our work considers a finer-grain classification problem using five categories: benign lesions and 4 different stages of melanoma. The dataset presents two main characteristics that are approached by specific machine learning methods: 1) the classes representing melanoma severity follow a natural order, and 2) the dataset is imbalanced, where benign lesions clearly outnumber melanoma ones. Different nominal and ordinal classifiers are considered, one of them being based on an ordinal cascade decomposition method. The cascade method is shown to obtain good performance for all classes, while respecting and exploiting the order information. Moreover, we explore the alternative of applying a class balancing technique, presenting good synergy with the ordinal and nominal methods.

关键词： Melanoma computer vision Machine learning Ordinal classification Imbalanced classification

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Machine Learning paradigms for Weed Mapping via Unmanned Aerial Vehicles

Machine Learning paradigms for Weed Mapping via Unmanned Aer...

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IEEE Symposium Series on Computational Intelligence

作者： M. Pérez-Ortiz P.A. Gutiérrez J.M. Pena J. Torres-Sanchez F. López-Granados C. Hervás-Martínez Dept. of Quantitative Methods Universidad Loyola Andalucía 14004 Córdoba Spain Dept. of Computer Science and Numerical Analysis University of Córdoba 14071 Spain Institute for Sustainable Agriculture CSIC 14080 Córdoba Spain

ISBN: (纸本)9781509042418

This paper presents a novel strategy for weed monitoring, using images taken with unmanned aerial vehicles (UAVs) and concepts of image analysis and machine learning. Weed control in precision agriculture designs site-specific treatments based on the coverage of weeds, where the key is to provide precise weed maps timely. Most traditional remote platforms, e.g. piloted planes or satellites, are, however, not suitable for early weed monitoring, given their low temporal and spatial resolutions, as opposed to he ultra-high spatial resolution of UAVs. The system here proposed makes use of UAV-imagery and is based on: 1) Divide the image, 2) compute and binarise the vegetation indexes, 3) detect crop rows, 4) optimise the parameters and 4) learn a classification model. Since crops are usually organised in rows, the use of a crop row detection algorithm helps to separate properly weed and crop pixels, which is a common handicap given the spectral similitude of both. Several artificial intelligence paradigms are compared in this paper to identify the most suitable strategy for this topic (i.e. unsupervised, supervised and semi-supervised approaches). Our experiments also study the effect of different parameteres: the flight altitude, the sensor and the use of previously trained models at a different height. Our results show that 1) very promising performance can be obtained, even when using very few labelled data and 2) the classification model can be learnt in a subplot of the experimental field at low altitude and then applied to the whole field at a higher height, which simplifies the whole process. These results motivate the use of this strategy to design weed monitoring strategies for early post-emergence weed control.

关键词： paradigms Weed Mapping

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Exclusion Method for Finding Nash Equilibrium in Multi-Player Games 16

Exclusion Method for Finding Nash Equilibrium in Multi-Playe...

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International Conference on Autonomous Agents and Multiagent Systems

作者： Kimmo Berg Tuomas Sandholm Aalto University School of Science Dept. of Mathematics and Systems Analysis Carnegie Mellon University Computer Science Department

ISBN: (纸本)9781510855083

We present a complete algorithm for finding an ε-Nash equilibrium, for arbitrarily small ε, in games with more than two players. The best prior complete algorithm has significantly worse complexity and has, to our knowledge, never been implemented. The main components of our tree-search-based method are a node-selection strategy, an exclusion oracle, and a subdivision scheme. The node-selection strategy determines the next region to be explored - based on the region's size and an estimate of whether the region contains an equilibrium. The exclusion oracle provides a provably correct sufficient condition for there not to exist an equilibrium in the region. The subdivision scheme determines how the region is split if it cannot be excluded. Unlike well-known incomplete methods, our method does not need to proceed locally, which avoids it getting stuck in a local minimum that may be far from any actual equilibrium. The run time grows rapidly with the game size, and this suggests a hybrid scheme where one of the relatively fast prior incomplete algorithms is run, and if it fails to find an equilibrium, then our method is used.

关键词： Nash equilibrium Multi-player games Noncooperative games Nash equilibrium Game theory exclusion Information retrieval systems-Teletext and videotex Local Minimum Runtime

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100 Million dimensions large-scale global optimization using distributed GPU computing

100 Million dimensions large-scale global optimization using...

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Congress on Evolutionary Computation

作者： Alberto Cano Carlos García-Martínez Computer Science Dept. Virginia Commonwealth University Richmond Virginia USA Computing and Num. Analysis Dept. University of Córdoba Córdoba Spain

ISBN: (纸本)9781509006243

At this time, many industrial and science problems deal with a large number of decision variables. Classic metaheuristics have shown excellent search abilities on bounded problems, but they often lose their efficacy when applied to large ones. This is known as the curse of dimensionality. To this issue, we have to add the simple fact that the solution evaluation becomes excessively demanding in time. To push the research state forward on this type of problems, the IEEE Congress on Evolutionary Computation regularly organises a competition on large-scale global optimization since 2008. On the other hand, general purpose computing with graphics processing units has become very attractive in the last years, because they may attain very high speed-up ratios on problems with high data parallelism levels. In this work, we study the benefits of exploiting a scalable and distributed computational architecture with multiple GPUs for large scale function optimisation. The study is carried out in terms of 1) evaluation speed-up, 2) quality of the results, and 3) extremely large scale optimisation with real-parameter functions with up to 10 8 variables.

关键词： Graphics processing units Optimization Instruction sets Sociology Statistics Parallel processing Benchmark testing

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An extensible JCLEC-based solution for the implementation of multi-objective evolutionary algorithms 15

An extensible JCLEC-based solution for the implementation of...

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17th Genetic and Evolutionary Computation Conference, GECCO 2015

作者： Ramírez, Aurora Romero, José Raúl Ventura, Sebastián Dept. of Computer Sciences and Numerical Analysis University of Córdoba Campus of Rabanales Córdoba14071 Spain

ISBN: (纸本)9781450334884

The ongoing advances in multi-objective optimisation (MOO) are improving the way that complex real-world optimisation problems, mostly characterised by the definition of many conflicting objectives, are currently addressed. To put it into practice, developers require flexible implementations of these algorithms so that they can be adapted to the problemspecific needs. Here, metaheuristic optimisation frameworks (MOFs) are essential tools to provide end-user oriented development solutions. Even though consolidated MOFs are continuously evolving, they seem to have paid little attention to the new trends in MOO. Recently, new frameworks have emerged with the aim of providing support to these approaches, but they often offer less variety of basic functionalities like diversity of encodings and operators than other general-purpose solutions. In this paper we identify a number of relevant features serving to satisfy the requirements demanded by MOO nowadays, and propose a solution, called JCLEC-MOEA, on the basis of the JCLEC framework. As a key contribution, its architecture has been designed with a twofold purpose: reusing all the features already given by a mature framework like JCLEC, and extending it to enable new developments more flexibly than current alternatives.

关键词： Multiobjective optimization

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Comparative analysis of drug supply and inventory management methods literature review 4th

Comparative analysis of drug supply and inventory management...

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Colloquium in Information science and Technology (CIST)

作者： Sara Jebbor Abdellatif El Afia Raddouane Chiheb Fatima Ouzayd Dept. Logistical and Operational Research National Superior School of Computer Science and Systems Analysis-Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9781509007523

Nowadays, scientific researches attribute a crucial importance to hospital sector. The improvement of this sector is considered by the literature as strongly depending on well mastered hospital supply chain, more exactly drug supply chain. Hence, several research studies have worked on the management of drug supply chain by offering diverse approaches and management models. In this paper, we focus on stochastic drug supply chain and our goal is to present a literature review about the drug supply chain and drug supply and inventory management methods, in order to establish a comparative analysis of the methods and approaches recently proposed by various research projects to identify the most appropriate one.

关键词： Drugs Supply chains Inventory management Hospitals Stochastic processes Uncertainty

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From-below approximations in Boolean matrix factorization: Geometry and new algorithm

From-below approximations in Boolean matrix factorization: G...

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作者： Belohlavek, Radim Trnecka, Martin Data Analysis and Modeling Lab Dept. Computer Science Palacký University Czech Republic

We present new results on Boolean matrix factorization and a new algorithm based on these results. The results emphasize the significance of factorizations that provide from-below approximations of the input matrix. While the previously proposed algorithms do not consider the possibly different significance of different matrix entries, our results help measure such significance and suggest where to focus when computing factors. An experimental evaluation of the new algorithm on both synthetic and real data demonstrates its good performance in terms of good coverage by the first few factors as well as a small number of factors needed for an almost exact decomposition and indicates that the algorithm outperforms the available ones in these terms. We also propose future research topics. © 2015 Elsevier Inc.

关键词： Approximation algorithms

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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A multi-start simheuristic for the stochastic two-dimensional vehicle routing problem 16

A multi-start simheuristic for the stochastic two-dimensiona...

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Simulation Winter Conference

作者： Daniel Guimarans Oscar Dominguez Angel A. Juan Enoc Martinez Aviation Academy Amsterdam University of Applied Sciences Amsterdam NETHERLANDS Institute of Intelligent Systems and Numerical Applications in Engineering University of Las Palmas de Gran Canaria Las Palmas de Gran Canaria SPAIN Computer Science Dept. - IN3 Open University of Catalonia SPAIN

ISBN: (纸本)9781509044849

The two-dimensional vehicle routing problem (2L-VRP) is a realistic extension of the classical vehicle routing problem where customers' demands are composed by sets of non-stackable items. Examples of such problems can be found in many real-life applications, e.g. furniture or industrial machinery transportation. Often, these real-life instances have to deal with uncertainty in many aspects of the problem, such as variable traveling times due to traffic conditions or customers availability. We present a hybrid simheuristic algorithm that combines biased-randomized routing and packing heuristics within a multi-start framework. Monte Carlo simulation is used to deal with uncertainty at different stages of the search process. With the goal of minimizing total expected cost, we use this methodology to solve a set of stochastic instances of the 2L-VRP with unrestricted oriented loading. Our results show that accounting for systems variability during the algorithm search yields more robust solutions with lower expected costs.

关键词： Routing Vehicles Loading Vehicle routing Computational modeling Heuristic algorithms Cache memory

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