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International IEEE Conference on Intelligent Transportation Systems

作者： Shuai Di Honggang Zhang Xue Mei Danil Prokhorov Haibin Ling School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Toyota Research Institute North America Ann Arbor USA Center for Data Analytics and Biomedical Informatics Department of Computer and Information Sciences Temple University Philadelphia USA

ISBN: (纸本)9781467365970

Parsing road scene images taken from vehicle mounted cameras provides important information for high level tasks in automated on-road vehicles. In this paper we adopt the nonparametric framework for this problem and present a simple yet effective strategy to integrate spatial prior into the framework. Unlike natural scene images, road scene images in our problem typically have very stable scene layout, which motivates us to explore such layout for improving scene labeling. In particular, the spatial distribution of each semantic category is obtained from a set of previously observed data. Then, such distributions, in the form of histograms, are integrated into the nonparametric labeling framework to guide scene parsing. Compared with previous approaches, our solution is very efficient in both computation and memory usage, since there is no complicated semantic training involved. For evaluation, we collected three video datasets on three different trips and ran the proposed algorithm on all of them, both within each trip or cross trip. The experimental results show advantages of our algorithm.

关键词： high level tasks nonparametric framework histograms histograms Parsing Road layouts rise level vehicle Frameworks

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Tensor Based Tumor Tissue Type Differentiation Using Magnetic Resonance Spectroscopic Imaging

Tensor Based Tumor Tissue Type Differentiation Using Magneti...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： H. N. Bharath D. M. Sima N. Sauwen U. Himmelreich L. De Lathauwer S. Van Huffel Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Biomedical MRI Unit/Molecular Small Animal Imaging Center Department of Imaging and Pathology KU Leuven Leuven Belgium

ISBN: (纸本)9781424492695

Magnetic resonance spectroscopic imaging (MRSI) has the potential to characterise different tissue types in brain tumors. Blind source separation techniques are used to extract the specific tissue profiles and their corresponding distribution from the MRSI data. A 3-dimensional MRSI tensor is constructed from in vivo 2D-MRSI data of individual tumor patients. Non-negative canonical polyadic decomposition (NCPD) with common factor in mode-1 and mode-2 and l1 regularization on mode-3 is applied on the MRSI tensor to differentiate various tissue types. Initial in vivo study shows that NCPD has better performance in identifying tumor and necrotic tissue type in high grade glioma patients compared to previous matrix-based decompositions, such as non-negative matrix factorization and hierarchical non-negative matrix factorization.

关键词： MRSI NCPD 2D-MRSI

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EasyESA: A low-effort infrastructure for explicit semantic analysis

EasyESA: A low-effort infrastructure for explicit semantic a...

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ISWC 2014 Posters and Demonstrations Track, ISWC-P and D 2014, 13th International Semantic Web Conference, ISWC 2014

作者： Carvalho, Danilo Çalli, Çaatay Freitas, André Curry, Edward Insight Centre for Data Analytics National University of Ireland Galway Ireland Brazil Department of Computer Engineering METU Ankara Turkey

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A mixed integer programming model for multi-period dyeing and batching problem in a yarn plant 44

A mixed integer programming model for multi-period dyeing an...

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Joint International Symposium on "The Social Impacts of Developments in Information, Manufacturing and Service Systems" 44th International Conference on Computers and Industrial engineering, CIE 2014 and 9th International Symposium on Intelligent Manufacturing and Service Systems, IMSS 2014

作者： Ozturk, C. Ornek, A. Insight Centre for Data Analytics University College Cork Cork Ireland Department of Industrial Engineering Izmir University of Economics Turkey

In a so-called multi-period yarn dyeing and batching problem, we try to determine the optimal batching of customer orders to be dyed in dye machines in each shift to minimize total lateness and earliness costs. In addition to weight, production quantity and volume capacity of the machines, there is a set of technical dyeing interaction constraints such as flotte, colour types, colour percentages and chemical recipe of customer orders when yarns are immersed in a large vat of coloured water known as the dye-liquor that includes dyestuffs, plus a range of chemicals to assist the dyeing process in the same shift. Furthermore, because of multi-period multi-shift nature of the problem, there is a setup carryover restriction which enforces that from shift to shift the colours must be processed in the increasing degree of darkness, i.e., in technical terms, the colour percentage of the batch increases. To the best of our knowledge, there is no study in the literature to solve this combinatorial optimization problem. Hence, in this paper, we first develop a novel mixed integer programming (MIP) formulation and then, we present a case study in a worldwide known yarn manufacturing company.

关键词： Yarn

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Dependability Issues of Android Games: A First Look via Software Analysis

Dependability Issues of Android Games: A First Look via Soft...

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2015 IEEE Symposium on Service-Oriented System engineering

作者： Jiaojiao Fu Yangfan Zhang Yu Kang School of Computer Science Fudan University Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications The Chinese University of Hong Kong Fudan University Shanghai Shanghai CN Dept. of Computer Science & Engineering The Chinese University of Hong Kong

Smart phones have surged into popularity in recent years, which has dramatically changed the way people live, work, and have fun. Smart phone games are an important type of Smart phone applications, which attract many software developers. However, they still have not caught much research attention in the software dependability community. In this paper, we study the characteristics of over 2000 Android games with software analysis techniques, with a focus on their dependability issues. Our study suggests that a new security paradigm is of great importance to Smart phone games to prevent potential privilege abuse. Moreover, a new set of testing and debugging approaches should be specifically tailored for Smart phone games, since games are becoming more complicated. Our study also reveals that most games are not specifically optimized according to the user pattern of Smart phones. We expect these open problems can bring more attentions to the software dependability community.

关键词： Games Internet Androids Humanoid robots Libraries Java Software

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Activity recognition using a few label samples

Activity recognition using a few label samples

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18th Pacific-Asia Conference on Advances in Knowledge Discovery and data Mining, PAKDD 2014

作者： Davoudi, Heidar Li, Xiao-Li Nhut, Nguyen Minh Krishnaswamy, Shonali Priyadarsini School of Computer Engineering Nanyang Technological University Singapore Singapore Data Analytics Department Institute for Infocomm Research A Star Singapore Singapore

ISBN: (纸本)9783319066073

Sensor-based human activity recognition aims to automatically identify human activities from a series of sensor observations, which is a crucial task for supporting wide range applications. Typically, given sufficient training examples for all activities (or activity classes), supervised learning techniques have been applied to build a classification model using sufficient training samples for differentiating various activities. However, it is often impractical to manually label large amounts of training data for each individual activities. As such, semi-supervised learning techniques sound promising alternatives as they have been designed to utilize a small training set L, enhanced by a large unlabeled set U. However, we observe that directly applying semi-supervised learning techniques may not produce accurate classification. In this paper, we have designed a novel dynamic temporal extension technique to extend L into a bigger training set, and then build a final semi-supervised learning model for more accurate classification. Extensive experiments demonstrate that our proposed technique outperforms existing 7 state-of-the-art supervised learning and semi-supervised learning techniques. © 2014 Springer International Publishing.

关键词： Supervised learning

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Jackknifed variance estimators for simulation output analysis 15

Jackknifed variance estimators for simulation output analysi...

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Simulation Winter Conference

作者： Kemal Dinçer Dingeç Christos Alexopoulos David Goldsman James R. Wilson Wenchi Chiu Tûba Aktaran-Kalaycı H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta GA USA Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University Raleigh NC USA Fenxi LLC Belmont CA USA AT&T Advanced Analytics Big Data Atlanta GA USA

ISBN: (纸本)9781467397414

We develop new point estimators for the variance parameter of a steady-state simulation process. The estimators are based on jackknifed versions of nonoverlapping batch means, overlapping batch means, and standardized time series variance estimators. The new estimators have reduced bias-and can be manipulated to reduce their variance and mean-squared error-compared with their predecessors, facts which we demonstrate analytically and empirically.

关键词： Time series analysis Steady-state Analytical models Mathematical model Random variables

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PennyLane: Automatic differentiation of hybrid quantum-classical computations

arXiv

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arXiv 2018年

作者： Bergholm, Ville Izaac, Josh Schuld, Maria Gogolin, Christian Ahmed, Shahnawaz Ajith, Vishnu Alam, M. Sohaib Alonso-Linaje, Guillermo AkashNarayanan, B. Asadi, Ali Arrazola, Juan Miguel Azad, Utkarsh Banning, Sam Blank, Carsten Bromley, Thomas R. Cordier, Benjamin A. Ceroni, Jack Delgado, Alain Matteo, Olivia Di Dusko, Amintor Garg, Tanya Guala, Diego Hayes, Anthony Hill, Ryan Ijaz, Aroosa Isacsson, Theodor Ittah, David Jahangiri, Soran Jain, Prateek Jiang, Edward Khandelwal, Ankit Kottmann, Korbinian Lang, Robert A. Lee, Christina Loke, Thomas Lowe, Angus McKiernan, Keri Meyer, Johannes Jakob Montañez-Barrera, J.A. Moyard, Romain Niu, Zeyue O’Riordan, Lee James Oud, Steven Panigrahi, Ashish Park, Chae-Yeun Polatajko, Daniel Quesada, Nicolás Roberts, Chase Sá, Nahum Schoch, Isidor Shi, Borun Shu, Shuli Sim, Sukin Singh, Arshpreet Strandberg, Ingrid Soni, Jay Száva, Antal Thabet, Slimane Vargas-Hernández, Rodrigo A. Vincent, Trevor Vitucci, Nicola Weber, Maurice Wierichs, David Wiersema, Roeland Willmann, Moritz Wong, Vincent Zhang, Shaoming Killoran, Nathan Xanadu 777 Bay Street Toronto Canada Wallenberg Centre for Quantum Technology Department of Microtechnology and Nanoscience Chalmers University of Technology Gothenburg412 96 Sweden Indian Institute of Information Technology Kottayam India NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States data cybernetics Martin-Kolmsperger-Str 26 Landsberg86899 Germany Department of Medical Informatics and Clinical Epidemiology Oregon Health and Science University PortlandOR97202 United States Dept. of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Department of Physics Indian Institute of Technology Roorkee Uttarakhand Roorkee India qBraid 5235 South Harper Court ChicagoIL60615 United States Factal Analytics Level 2 Chimes Building Plot 61 Sector - 44 Haryana Gurgaon122003 India Centre for High Energy Physics Indian Institute of Science Karnataka Bengaluru560012 India ICFO - Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Av. Carl Friedrich Gauss 3 Castelldefels Barcelona08860 Spain Chemical Physics Theory Group Department of Chemistry University of Toronto Canada DUG Technology 76 Kings Park Rd West PerthWA6005 Australia Rigetti Computing 2919 Seventh Street BerkeleyCA94710 United States Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute for Advanced Simulation Jülich Supercomputing Centre Forschungszentrum Jülich Jülich52425 Germany University of Amsterdam Netherlands School of Physical Sciences National Institute of Science Education and Research HBNI Odisha Jatni752 050 India Cervest United Kingdom Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 Rio de Janeiro22290-180 Brazil Zurich8092 Switzerland Neo4j UK Ltd United Kingdom Zapata Computing Inc. United States ITC Infotech Bangalore India Department of Microtechnology and Nanoscienc

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane’s core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Comparison of speech envelope extraction methods for EEG-based auditory attention detection in a cocktail party scenario

Comparison of speech envelope extraction methods for EEG-bas...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Wouter Biesmans Jonas Vanthornhout Jan Wouters Marc Moonen Tom Francart Alexander Bertrand KU Leuven Dept. Electrical Engineering (ESAT) Stadius Center for Dynamical Systems Signal Processing and Data Analytics. Kasteelpark Arenberg 10 B-3001 Leuven Belgium KU Leuven Dept. of Neurosciences ExpORL. Herestraat 49 bus 721 B-3000 Leuven Belgium iMinds Medical IT

ISBN: (纸本)9781424492695

Recent research has shown that it is possible to detect which of two simultaneous speakers a person is attending to, using brain recordings and the temporal envelope of the separate speech signals. However, a wide range of possible methods for extracting this speech envelope exists. This paper assesses the effect of different envelope extraction methods with varying degrees of auditory modelling on the performance of auditory attention detection (AAD), and more specifically on the detection accuracy. It is found that sub-band envelope extraction with proper power-law compression yields best performance, and that the use of several more detailed auditory models does not yield a further improvement in performance.

关键词： AAD Comparison EEG-based envelopes Envelope Speech Cocktails

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Using simulation to examine appointment overbooking schemes for a medical imaging center 15

Using simulation to examine appointment overbooking schemes ...

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Simulation Winter Conference

作者： Yan Chen Yong-Hong Kuo Hari Balasubramanian Chaobai Wen Department of Decision Sciences Macau University of Science and Technology MACAU Stanley Ho Big Data Decision Analytics Research Centre The Chinese University of Hong Kong HONG KONG Department of Mechanical and Industrial Engineering University of Massachusetts Amherst MA USA Medical Imageology Center Macau University of Science and Technology Hospital MACAU

ISBN: (纸本)9781467397414

In this paper, we present an appointment scheduling problem faced by a medical imaging center in a major hospital in Macau. We developed an empirically calibrated simulation model to represent the appointment and medical diagnosis procedure as a multi-server queuing network with multiple patient classes. Four appointment overbooking schemes are proposed to compensate for patient no-shows and unpunctuality. The focus of this study is to integrate overbooking schemes with existing appointment rules to improve the operational efficiency of the center. Simulation results show that our proposed overbooking schemes significantly enhance the performance of the center. Compared with the current practice, the best performing overbooking scheme reduces the overtime by 58.32% and the idle time by 23.65%, increases the number of patients served by 15.9%, while still ensuring that patient waiting times remain acceptable.

关键词： data models Standards

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