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Counting subwords and regular languages

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arXiv 2018年

作者： Colbourn, Charles J. Dougherty, Ryan E. Lidbetter, Thomas Finn Shallit, Jeffrey Computing Informatics and Decision Systems Engineering Arizona State University P.O. Box 878809 TempeAZ85287-8809 United States School of Computer Science University of Waterloo WaterlooONN2L3G1 Canada

Let x and y be words. We consider the languages whose words z are those for which the numbers of occurrences of x and y, as subwords of z, are the same (resp., the number of x's is less than the number of y's, resp., is less than or equal). We give a necessary and sufficient condition on x and y for these languages to be regular, and we show how to check this condition efficiently. Copyright © 2018, The Authors. All rights reserved.

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Sequential vessel speed optimization under dynamic weather conditions 5

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Sequential vessel speed optimization under dynamic weather c...

5th International Maritime-Port Technology and Development Conference, MTEC 2017

作者： Basu, Debabrota Pedrielli, Giulia Chen, Weidong Hui Ng, Szu Hay Lee, Loo Bressan, Stéphane School of Computing National University of Singapore Singapore Singapore School of Computing Informatics and Design Systems Engineering Arizona State University United States Department of Industrial and Systems Engineering National University of Singapore Singapore Singapore

ISBN: (纸本)9811127220

The International Maritime Organization (IMO) identifies speed optimization as a key operational measure for achieving energy efficiency through reduced emissions. Ocean Liner services have fixed port rotations and schedules. While the speed can be optimized for emissions, the service level in terms of schedules arrival and departure need to be carefully considered not to loose market share. This already challenging problem is further complicated when dynamic weather conditions along the service route are considered. In fact, few contributions can be found that address this issue. We study the operational problem of dynamically determining a vessel's speed, departure time and arrival time at each port of call under dynamic weather conditions. We model the minimization of cost, namely bunkering costs and early and delayed departure and arrival penalties, using calculus of variations. The proposed algorithm leverages upon a discretization technique based on the Weierstrass-Erdmann condition. The numerical tests show the efficiency and effectiveness of this algorithm over standard techniques like IVP. Copyright © 2017 by MTEC 2017 Organizers.

关键词： Energy efficiency

Spatial proteomics in three-dimensional intact specimens

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四川生理科学杂志 2022年第12期44卷 2202-2202页

作者： Harsharan Singh Bhatia Insititute for Tissue Engineering and Regenerative Medicine (iTERM) Helmholtz Zentrum München 85764 Neuherberg Germany Institute for Stroke and Dementia Research Klinikum der Universität München Ludwig-Maximilians University Munich 81377 Munich Germany Department for Proteomics and Signal Transduction Max Planck Institute of Biochemistry 82152 Martinsried Germany Boehringer Ingelheim Pharma GmbH & Co. KG Drug Discovery Sciences Birkendorfer Str. 65 D-88400 Biberach Riss Germany Graduate School of Neuroscience (GSN) 82152 Munich Germany Munich Cluster for Systems Neurology (SyNergy) 81377 Munich Germany Center for Translational Cancer Research (TranslaTUM) of the TUM 81675 Munich Germany Image-Based Biomedical Modeling Department of Informatics Technical University of Munich 85748 Garching Germany Helmholtz AI Helmholtz Zentrum München 85764 Neuherberg Germany Biomedical Image Analysis Group Department of Computing Imperial College London London SW7 2AZ UK Department for Quantitative Biomedicine University of Zurich 8006 Zurich Switzerland Department of Neuroradiology Klinikum rechts der Isar 81675 Munich Germany Munich Medical Research School (MMRS) 80336 Munich Germany Institute for Diabetes and Cancer Helmholz Zentrum München 85764 Neuherberg Germany Institute for Diabetes and Obesity Helmholz Zentrum München 85764 Neuherberg Germany German Center for Diabetes Research Helmholz Zentrum München 85764 Neuherberg Germany German Center for Neurodegenerative Diseases (DZNE) 81377 Munich Germany Neuroproteomics School of Medicine Klinikum Rechts der Isar Technical University of Munich 81675 Munich Germany Institute of Anatomy University of Leipzig 04109 Leipzig Germany Institute of Computational Biology Helmholz Zentrum München 85764 Neuherberg Germany TUM School of Life Sciences Weihenstephan Technical University of Munich 85354 Freising Germany Department of Mathematics Technical University of Munich 85748 Garching Germany NNF Center for Protein Res

Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological ***,methods for molecular analysis of large biological specimens imaged in 3D are ***,we present DISCO-MS,a technology that combines whole-organ/whole-organism clearing and imaging,deep-learning-based image analysis,robotic tissue extraction,and ultra-high-sensitivity mass spectrometry.

关键词： Spatial combine analysis

MTDeep: Boosting the security of deep neural nets against adversarial attacks with moving target defense

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arXiv 2017年

作者： Sengupta, Sailik Chakraborti, Tathagata Kambhampati, Subbarao School of Computing Informatics Decision Systems Engineering Arizona State University United States

Recent works on gradient based attacks and universal perturbations can adversarially modify images to bring down the accuracy of state-of-the-art classification techniques based on deep neural networks to as low as 10% on popular datasets like MNIST and ImageNet. The design of general defense strategies against a wide range of such attacks remains a challenging problem. In this paper, we derive inspiration from recent advances in the fields of cybersecurity and multi-agent systems and propose to use the concept of Moving Target Defense (MTD) for increasing the robustness of a set of deep networks against such adversarial attacks. To this end, we formalize and exploit the notion of differential immunity of an ensemble of networks to specific attacks. To classify an input image, a trained network is picked from this set of networks by formulating the interaction between a Defender (who hosts the classification networks) and their (Legitimate and Malicious) Users as a repeated Bayesian Stackelberg Game (BSG). We empirically show that our approach, MTDeep reduces misclassification on perturbed images for MNIST and ImageNet datasets while maintaining high classification accuracy on legitimate test images. Lastly, we demonstrate that our framework can be used in conjunction with any existing defense mechanism to provide more resilience to adversarial attacks than those defense mechanisms by themselves. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

A structured approach to predicting image enhancement parameters

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arXiv 2017年

作者： Chandakkar, Parag Shridhar Li, Baoxin School of Computing Informatics and Decision Systems Engineering Arizona State University United States

Social networking on mobile devices has become a commonplace of everyday life. In addition, photo capturing process has become trivial due to the advances in mobile imaging. Hence people capture a lot of photos everyday and they want them to be visually-attractive. This has given rise to automated, one-touch enhancement tools. However, the inability of those tools to provide personalized and content-adaptive enhancement has paved way for machine-learned methods to do the same. The existing typical machine-learned methods heuristically (e.g. kNN-search) predict the enhancement parameters for a new image by relating the image to a set of similar training images. These heuristic methods need constant interaction with the training images which makes the parameter prediction sub-optimal and computationally expensive at test time which is undesired. This paper presents a novel approach to predicting the enhancement parameters given a new image using only its features, without using any training images. We propose to model the interaction between the image features and its corresponding enhancement parameters using the matrix factorization (MF) principles. We also propose a way to integrate the image features in the MF formulation. We show that our approach outperforms heuristic approaches as well as recent approaches in MF and structured prediction on synthetic as well as real-world data of image enhancement. Copyright © 2017, The Authors. All rights reserved.

关键词： Factorization

A computational approach to relative aesthetics

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arXiv 2017年

作者： Gattupalli, Vijetha Chandakkar, Parag S. Li, Baoxin School of Computing Informatics and Decision Systems Engineering Arizona State University United States

Computational visual aesthetics has recently become an active research area. Existing state-of-art methods formulate this as a binary classification task where a given image is predicted to be beautiful or not. In many applications such as image retrieval and enhancement, it is more important to rank images based on their aesthetic quality instead of binary-categorizing them. Furthermore, in such applications, it may be possible that all images belong to the same category. Hence determining the aesthetic ranking of the images is more appropriate. To this end, we formulate a novel problem of ranking images with respect to their aesthetic quality. We construct a new dataset of image pairs with relative labels by carefully selecting images from the popular AVA dataset. Unlike in aesthetics classification, there is no single threshold which would determine the ranking order of the images across our entire dataset. We propose a deep neural network based approach that is trained on image pairs by incorporating principles from relative learning. Results show that such relative training procedure allows our network to rank the images with a higher accuracy than a state-of-art network trained on the same set of images using binary labels. Copyright © 2017, The Authors. All rights reserved.

关键词： Image enhancement

Uncertainty Reduction for Stochastic Processes on Complex Networks

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Physical Review Letters 2018年第19期120卷 198301-198301页

作者： Filippo Radicchi Claudio Castellano Center for Complex Networks and Systems Research School of Informatics Computing and Engineering Indiana University Bloomington Indiana 47408 USA Istituto dei Sistemi Complessi (ISC-CNR) Via dei Taurini 19 00185 Roma Italy

Many real-world systems are characterized by stochastic dynamical rules where a complex network of interactions among individual elements probabilistically determines their state. Even with full knowledge of the network structure and of the stochastic rules, the ability to predict system configurations is generally characterized by a large uncertainty. Selecting a fraction of the nodes and observing their state may help to reduce the uncertainty about the unobserved nodes. However, choosing these points of observation in an optimal way is a highly nontrivial task, depending on the nature of the stochastic process and on the structure of the underlying interaction pattern. In this paper, we introduce a computationally efficient algorithm to determine quasioptimal solutions to the problem. The method leverages network sparsity to reduce computational complexity from exponential to almost quadratic, thus allowing the straightforward application of the method to mid-to-large-size systems. Although the method is exact only for equilibrium stochastic processes defined on trees, it turns out to be effective also for out-of-equilibrium processes on sparse loopy networks.

关键词： Guided network search Network resilience Spreading

A novel visible-infrared image fusion framework for smart city

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International Journal of Simulation and Process Modelling 2018年第2期13卷 144-155页

作者： Zhu, Zhiqin Qi, Guanqiu Chai, Yi Yin, Hongpeng Sun, Jian School of Automation Chongqing University Chongqing400044 China State Key Laboratory of Power Transmission Equipment and System Security and New Technology College of Automation Chongqing University Chongqing400044 China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85287 United States Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400030 China

Image fusion technology is widely used in different areas and can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. Image fusion technology as an efficient way to integrate information from multiple images plays a more and more important role in smart city. The quality of fused image affects the accuracy, efficiency, and robustness of the related applications. Existing sparse representation-based image fusion methods consist of overly complete and redundant dictionaries learning and sparse coding. However, overly complete and redundant dictionary does not consider the discriminative ability of dictionaries that may seriously affect the image fusion. A good dictionary is the key to a successful image fusion technique. To construct a discriminative dictionary, a novel framework that integrates an image-patches clustering and online dictionary learning methods is proposed for visible-infrared image fusion. The comparison experiments with existing solutions are used to validate and demonstrate the effectiveness of the proposed solution for image fusion. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Image fusion

The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image computing and Computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging