title: Exploiting shared Chinese characters in Chinese word segmentation optimization for Chinese-Japanese Machine Translation

abstract: Unknown words and word segmentation granularity are two main problems in Chinese word segmentation for ChineseJapanese Machine Translation (MT). In this paper, we propose an approach of exploiting common Chinese characters shared between Chinese and Japanese in Chinese word segmentation optimization for MT aiming to solve these problems. We augment the system dictionary of a Chinese segmenter by extracting Chinese lexicons from a parallel training corpus. In addition, we adjust the granularity of the training data for the Chinese segmenter to that of Japanese. Experimental results of Chinese-Japanese MT on a phrase-based SMT system show that our approach improves MT performance significantly.


title: Substrate Integrated Metamaterial-Based Leaky-Wave Antenna With Improved Boresight Radiation Bandwidth

abstract: A substrate integrated metamaterial-based leaky-wave antenna is proposed to improve its boresight radiation bandwidth. The proposed leaky-wave antenna based on a composite right/left-handed substrate integrated waveguide consists of two leaky-wave radiator elements which are with different unit cells. The dual-element antenna prototype features boresight gain of 12.0 dBi with variation of 1.0 dB over the frequency range of 8.775-9.15 GHz or 4.2%. In addition, the antenna is able to offer a beam scanning from to with frequency from 8.25 GHz to 13.0 GHz.


title: Comparing 2D vector field visualization methods: a user study

abstract: We present results from a user study that compared six visualization methods for two-dimensional vector data. Users performed three simple but representative tasks using visualizations from each method: 1) locating all critical points in an image, 2) identifying critical point types, and 3) advecting a particle. Visualization methods included two that used different spatial distributions of short arrow icons, two that used different distributions of integral curves, one that used wedges located to suggest flow lines, and line-integral convolution (LIC). Results show different strengths and weaknesses for each method. We found that users performed these tasks better with methods that: 1) showed the sign of vectors within the vector field, 2) visually represented integral curves, and 3) visually represented the locations of critical points. Expert user performance was not statistically different from nonexpert user performance. We used several methods to analyze the data including omnibus analysis of variance, pairwise t-tests, and graphical analysis using inferential confidence intervals. We concluded that using the inferential confidence intervals for displaying the overall pattern of results for each task measure and for performing subsequent pairwise comparisons of the condition means was the best method for analyzing the data in this study. These results provide quantitative support for some of the anecdotal evidence concerning visualization methods. The tasks and testing framework also provide a basis for comparing other visualization methods, for creating more effective methods and for defining additional tasks to further understand the tradeoffs among the methods. In the future, we also envision extending this work to more ambitious comparisons, such as evaluating two-dimensional vectors on two-dimensional surfaces embedded in three-dimensional space and defining analogous tasks for three-dimensional visualization methods.


title: Smoke detection using spatial and temporal analyses

abstract: Video-based fire detection is currently a fairly common application with the growth in the number of installed surveillance video systems. Moreover, the related pro- cessing units are becoming more powerful. Smoke is an early sign of most fires; therefore, selecting an appropriate smoke-detection method is essential. However, detecting smoke without creating a false alarm remains a challenging problem for open or large spaces with the disturbances of common moving objects, such as pedestrians and vehicles. This study proposes a novel video-based smoke-detection method that can be incorporated into a surveillance system to provide early alerts. In this study, the process of extracting smoke features from candidate regions was accomplished by analyzing the spatial and tempo- ral characteristics of video sequences for three important features: edge blurring, grad- ual energy changes, and gradual chromatic configuration changes. The proposed spatial- temporal analysis technique improves the feature extraction of gradual energy changes. In order to make the video smoke-detection results more reliable, these three features were combined using a support vector machine (SVM) technique and a temporal-based alarm decision unit (ADU) was also introduced. The effectiveness of the proposed algorithm was evaluated on a PC with an Intel R Core TM 2 Duo CPU (2.2 GHz) and 2 GB RAM. The average processing time was 32.27 ms per frame; i.e., the proposed algorithm can process 30.98 frames per second. Experimental results showed that the proposed system can detect smoke effectively with a low false-alarm rate and a short reaction time in many real-world scenarios. Keywords: Alarm decision unit (ADU), Support vector machine (SVM), Surveillance system, Video smoke detection (VSD), Wavelet transform


title: Recurrent Transformer Networks for Semantic Correspondence

abstract: We present recurrent transformer networks (RTNs) for obtaining dense correspondences between semantically similar images. Our networks accomplish this through an iterative process of estimating spatial transformations between the input images and using these transformations to generate aligned convolutional activations. By directly estimating the transformations between an image pair, rather than employing spatial transformer networks to independently normalize each individual image, we show that greater accuracy can be achieved. This process is conducted in a recursive manner to refine both the transformation estimates and the feature representations. In addition, a technique is presented for weakly-supervised training of RTNs that is based on a proposed classification loss. With RTNs, state-of-the-art performance is attained on several benchmarks for semantic correspondence.


title: Mindsets That Promote Resilience: When Students Believe That Personal Characteristics Can Be Developed

abstract: Because challenges are ubiquitous, resilience is essential for success in school and in life. In this article we review research demonstrating the impact of students’ mindsets on their resilience in the face of academic and social challenges. We show that students who believe (or are taught) that intellectual abilities are qualities that can be developed (as opposed to qualities that are fixed) tend to show higher achievement across challenging school transitions and greater course completion rates in challenging math courses. New research also shows that believing (or being taught) that social attributes can be developed can lower adolescents’ aggression and stress in response to peer victimization or exclusion, and result in enhanced school performance. We conclude by discussing why psychological interventions that change students’ mindsets are effective and what educators can do to foster these mindsets and create resilience in educational settings. When students struggle with their schoolwork, what determines whether they give up or embrace the obstacle and work to overcome it? And when students feel excluded or victimized by peers, what determines whether they seek revenge through aggression or seek more productive solutions? Resilience—or whether students respond positively to challenges—is crucial for success in school and in life. Yet what causes it? And what can be done to increase it? In this article we demonstrate the impact of students’ mindsets—or implicit theories about the malleability of human characteristics—on their academic and social resilience (Dweck, 2006; Dweck, Chiu, & Hong, 1995). We show how mindsets can contribute to two of the most important issues currently facing educators: (a) academic underachievement and (b) the impact of peer exclusion and victimization. Each of these problems is of great concern, yet each has been frustratingly difficult to address. For example, many of the large-scale interventions evaluated by the Institute of


title: Test method for empirically determining inertial properties of manual wheelchairs

abstract: The iMachine is a spring-loaded turntable used to measure inertial properties of irregularly shaped rigid bodies, specifically manual wheelchairs. We used a Newton-Euler approach to calculate wheelchair mass and center of mass (CM) location from static force measurements using load cells. We determined the moment of inertia about the vertical axis from the natural frequency of the system in simple harmonic motion. The device was calibrated to eliminate the effects of platform components on measurement error. For objects with known inertial properties, the average relative error of the mass and the CM coordinates (x and y) were 0.76%, 0.89%, and 1.99%, respectively. The resolution of the moment of inertia calculation depends on the ratio of test piece inertia to system inertia, such that the higher the ratio, the more accurate the measurements. We conducted a Gage Repeatability and Repro- ducibility (Gage RR the results showed that over 90% of the variance in inertia was caused by differ- ences in the wheelchairs being measured. Gage R&R analysis indicated that measurement system operation was acceptable using criteria from the Automobile Industry Action Group for both inertia and mass measurements.


title: Active sampling for entity matching

abstract: In entity matching, a fundamental issue while training a classifier to label pairs of entities as either duplicates or non-duplicates is the one of selecting informative training examples. Although active learning presents an attractive solution to this problem, previous approaches minimize the misclassification rate (0-1 loss) of the classifier, which is an unsuitable metric for entity matching due to class imbalance (i.e., many more non-duplicate pairs than duplicate pairs). To address this, a recent paper [1] proposes to maximize recall of the classifier under the constraint that its precision should be greater than a specified threshold. However, the proposed technique requires the labels of all n input pairs in the worst-case. Our main result is an active learning algorithm that approximately maximizes recall of the classifier while respecting a precision constraint with provably sub-linear label complexity (under certain distributional assumptions). Our algorithm uses as a black-box any active learning module that minimizes 0-1 loss. We show that label complexity of our algorithm is at most log n times the label complexity of the black-box, and also bound the difference in the recall of classifier learnt by our algorithm and the recall of the optimal classifier satisfying the precision constraint. We provide an empirical evaluation of our algorithm on several real-world matching data sets that demonstrates the effectiveness of our approach.


title: Regional gray matter variation in male-to-female transsexualism

abstract: Gender identity—one's sense of being a man or a woman—is a fundamental perception experienced by all individuals that extends beyond biological sex. Yet, what contributes to our sense of gender remains uncertain. Since individuals who identify as transsexual report strong feelings of being the opposite sex and a belief that their sexual characteristics do not reflect their true gender, they constitute an invaluable model to understand the biological underpinnings of gender identity. We analyzed MRI data of 24 male-to-female (MTF) transsexuals not yet treated with cross-sex hormones in order to determine whether gray matter volumes in MTF transsexuals more closely resemble people who share their biological sex (30 control men), or people who share their gender identity (30 control women). Results revealed that regional gray matter variation in MTF transsexuals is more similar to the pattern found in men than in women. However, MTF transsexuals show a significantly larger volume of regional gray matter in the right putamen compared to men. These findings provide new evidence that transsexualism is associated with distinct cerebral pattern, which supports the assumption that brain anatomy plays a role in gender identity.


title: A data mining based spam detection system for YouTube

abstract: People now feel more comfortable socializing over the internet through popular social networking and media websites than face to face. Thus, the social media websites are thriving more and more nowadays. Like others YouTube is a vastly popular social media site which is expanding at very fast pace. YouTube depends mostly on user created contents and sharing and spreading. Business entities and public figures are taking advantage of this popularity by creating their own page and shared information among the large number of visitors. However, due to this popularity, YouTube has become more susceptible to different types of unwanted and malicious spammer. Currently, YouTube does not have any way to handle its video spammers. It only considers mass comments or messages to be part of spamming. To increase the popularity of a video, malicious users post video response spam, where the video content is not related to the topic being discussed in the particular video or does not contain the media it is supposed to. In this research, we explore different attributes that could lead to video spammers. We first collect data of YouTube videos and manually classify them as either legitimate videos or spams. We then devise a number of attributes of videos which could potentially be used to detect spams. We apply Microsoft SQL Server Data Mining Tools (SSDT) to provide a heuristic for classifying an arbitrary video as either spam or legitimate. Our result demonstrates that in the long run we could successfully classify videos as spam or legitimate videos for most of the cases.


title: Through-the-Wall Sensing of Personnel Using Passive Bistatic WiFi Radar at Standoff Distances

abstract: In this paper, we investigate the feasibility of uncooperatively and covertly detecting people moving behind walls using passive bistatic WiFi radar at standoff distances. A series of experiments was conducted which involved personnel targets moving inside a building within the coverage area of a WiFi access point. These targets were monitored from outside the building using a 2.4-GHz passive multistatic receiver, and the data were processed offline to yield range and Doppler information. The results presented show the first through-the-wall (TTW) detections of moving personnel using passive WiFi radar. The measured Doppler shifts agree with those predicted by bistatic theory. Further analysis of the data revealed that the system is limited by the signal-to-interference ratio (SIR), and not the signal-to-noise ratio. We have also shown that a new interference suppression technique based on the CLEAN algorithm can improve the SIR by approximately 19 dB. These encouraging initial findings demonstrate the potential for using passive WiFi radar as a low-cost TTW detection sensor with widespread applicability.


title: Forwarding redundancy in opportunistic mobile networks: Investigation and elimination

abstract: Opportunistic mobile networks consist of mobile devices which are intermittently connected via short-range radios. Forwarding in such networks relies on selecting relays to carry and deliver data to destinations upon opportunistic contacts. Due to the intermittent network connectivity, relays in current for- warding schemes are selected separately in a distributed manner. The contact capabilities of relays hence may overlap when they contact the same nodes and cause forwarding redundancy .T his redundancy reduces the efficiency of resource utilization in the network, and may impair the forwarding performance if being ignored. In this paper, based on experimental investigations on the characteristics of forwarding redundancy in realistic mobile networks, we propose methods to eliminate unnecessary for- warding redundancy and ensure efficient utilization of network resources. We first develop techniques to eliminate forwarding redundancy with global network information, and then improve these techniques to be operable in a fully distributed manner with limited network information.


title: Performance-energy efficiency model of heterogeneous parallel multicore system

abstract: Energy-efficiency is one of the most challenges of designing future heterogeneous multicore system, beyond performance, hereby we propose a performance-energy efficiency analytical model for integrated heterogeneous parallel multicore system which is promising to be used for big data applications. The model extends the traditional computing-centric model by considering the overhead of data preparation which can not be neglected in heterogenous multicore processor system any more. The analysis clearly shows that higher parallelism gained from either computation or data preparation brings greater energy-efficiency. Improving the performance-energy efficiency of data preparation is another promising approach to affect power consumption. Therefore, more informed tradeoffs should be taken when we design a modern heterogeneous processor system within limited budget of energy(power).


title: CiteSpace II: Visualization and Knowledge Discovery in Bibliographic Databases

abstract: This article presents a description and case study of CiteSpace II, a Java application which supports visual exploration with knowledge discovery in bibliographic databases. Highly cited and pivotal documents, areas of specialization within a knowledge domain, and emergence of research topics are visually mapped through a progressive knowledge domain visualization approach to detecting and visualizing trends and patterns in scientific literature. The test case in this study is progressive knowledge domain visualization of the field of medical informatics. Datasets based on publications from twelve journals in the medical informatics field covering the time period from 1964–2004 were extracted from PubMed and Web of Science (WOS) and developed as testbeds for evaluation of the CiteSpace system. Two resulting document-term co-citation and MeSH term co-occurrence visualizations are qualitatively evaluated for identification of pivotal documents, areas of specialization, and research trends. Practical applications in bio-medical research settings are discussed.


title: Integrating Internet of Things (IoT) into STEM undergraduate education: Case study of a modern technology infused courseware for embedded system course

abstract: Internet of Things (IoT) is rapidly emerging as the next generation of communication infrastructure, where myriad of multi-scale sensors and devices are seamlessly blended for ubiquitous computing and communication. The rapid growth of IoT applications has increased the demand for experienced professionals in the area. Since few, if any, dedicated IoT courses are currently offered, most Science, Technology, Engineering, and Mathematics (STEM) students will have limited or no exposure to IoT development until after graduation and entrance into the workforce. Moreover, there is a little room for adding additional courses into existing STEM curriculum. Therefore, we propose to transform STEM core courses by integrating IoT-based learning framework into their corresponding lab projects. The design challenges of the new learning framework is summarized in the paper. Subsequently, we propose the effective learning approaches to address those challenges. Moreover, in this paper, we present a case study by incorporating IoT-based learning framework into a Software Engineering (SWE) embedded system analysis & design course. Specifically, we introduce a lab development kit composed of Raspberry Pi/Arduino boards and a set of sensors with Zigbee supporting to provide wireless communication in the class lab section. We adopt module design method to design the course labware. Well-developed modules are presented and one sample module is illustrated in the paper. The labware is evaluated through survey questions. The majority of the students provided positive feedback and enjoyed the IoT-based lab development kit.


The Use of Blood Flow Restriction Therapy in the Treatment of a Professional Baseball Player Status Post Meniscectomy: A Case Report


title: Design Science Research as Research Approach in Doctoral Studies

abstract: Since the use of design science research (DSR) gained momentum as a research approach in information systems (IS), the adoption of a DSR approach in postgraduate studies became more acceptable. This paper reflects on a study to investigate how a selection of IS doctoral students conducted their research and why they used DSR. The study focussed in particular on the topics of the theses, the artefacts produced, the research designs followed, the motivation for selecting DSR and the students’ experience in using DSR. The study found that the cyclic nature and the relevance aspects were DSR’s strengths when developing artefacts, but that discourse is still needed on the philosophical underpinnings. An alignment between the motivation to use DSR and the benefits of applying the approach was also found. This paper contributes mainly towards our understanding of the challenges and advantages when using DSR as a research approach for postgraduate studies.


Using deep learning on X-ray images to detect threats


title: Reputation-based incentive protocols in crowdsourcing applications

abstract: Crowdsourcing websites (e.g. Yahoo! Answers, Amazon Mechanical Turk, and etc.) emerged in recent years that allow requesters from all around the world to post tasks and seek help from an equally global pool of workers. However, intrinsic incentive problems reside in crowdsourcing applications as workers and requester are selfish and aim to strategically maximize their own benefit. In this paper, we propose to provide incentives for workers to exert effort using a novel game-theoretic model based on repeated games. As there is always a gap in the social welfare between the non-cooperative equilibria emerging when workers pursue their self-interests and the desirable Pareto efficient outcome, we propose a novel class of incentive protocols based on social norms which integrates reputation mechanisms into the existing pricing schemes currently implemented on crowdsourcing websites, in order to improve the performance of the non-cooperative equilibria emerging in such applications. We first formulate the exchanges on a crowdsourcing website as a two-sided market where requesters and workers are matched and play gift-giving games repeatedly. Subsequently, we study the protocol designer's problem of finding an optimal and sustainable (equilibrium) protocol which achieves the highest social welfare for that website. We prove that the proposed incentives protocol can make the website operate close to Pareto efficiency. Moreover, we also examine an alternative scenario, where the protocol designer aims at maximizing the revenue of the website and evaluate the performance of the optimal protocol.


title: Design Exploration of Hybrid CMOS and Memristor Circuit by New Modified Nodal Analysis

abstract: Design of hybrid circuits and systems based on CMOS and nano-device requires rethinking of fundamental circuit analysis to aid design exploration. Conventional circuit analysis with modified nodal analysis (MNA) cannot consider new nano-devices such as memristor together with the traditional CMOS devices. This paper has introduced a new MNA method with magnetic flux (Φ) as new state variable. New SPICE-like circuit simulator is thereby developed for the design of hybrid CMOS and memristor circuits. A number of CMOS and memristor-based designs are explored, such as oscillator, chaotic circuit, programmable logic, analog-learning circuit, and crossbar memory, where their functionality, performance, reliability and power can be efficiently verified by the newly developed simulator. Specifically, one new 3-D-crossbar architecture with diode-added memristor is also proposed to improve integration density and to avoid sneak path during read-write operation.


title: STAIR Actions: A Video Dataset of Everyday Home Actions

abstract: A new large-scale video dataset for human action recognition, called STAIR Actions is introduced. STAIR Actions contains 100 categories of action labels representing fine-grained everyday home actions so that it can be applied to research in various home tasks such as nursing, caring, and security. In STAIR Actions, each video has a single action label. Moreover, for each action category, there are around 1,000 videos that were obtained from YouTube or produced by crowdsource workers. The duration of each video is mostly five to six seconds. The total number of videos is 102,462. We explain how we constructed STAIR Actions and show the characteristics of STAIR Actions compared to existing datasets for human action recognition. Experiments with three major models for action recognition show that STAIR Actions can train large models and achieve good performance. STAIR Actions can be downloaded from this http URL


Visual Question Answering Dataset for Bilingual Image Understanding: A Study of Cross-Lingual Transfer Using Attention Maps


title: Intellectual Property Protection of 3D Print Supply Chain with Blockchain Technology

abstract: Within “Industrie 4.0” approach 3D printing technology is characterized as one of the disruptive innovations. Conventional supply chains are replaced by value-added networks. The spatially distributed development of printed components, e.g. for the rapid delivery of spare parts, creates a new challenge when differentiating between “original part”, “copy” or “counterfeit” becomes necessary. This is especially true for safety-critical products. Based on these changes classic branded products adopt the characteristics of licensing models as we know them in the areas of software and digital media. This paper describes the use of digital rights management as a key technology for the successful transition to Additive Manufacturing methods and a key for its commercial implementation and the prevention of intellectual property theft. Risks will be identified along the process chain and solution concepts are presented. These are currently being developed by an 8-partner project named SAMPL (Secure Additive Manufacturing Platform).


title: On the Convergence of Learning-based Iterative Methods for Nonconvex Inverse Problems

abstract: Numerous tasks at the core of statistics, learning and vision areas are specific cases of ill-posed inverse problems. Recently, learning-based (e.g., deep) iterative methods have been empirically shown to be useful for these problems. Nevertheless, integrating learnable structures into iterations is still a laborious process, which can only be guided by intuitions or empirical insights. Moreover, there is a lack of rigorous analysis about the convergence behaviors of these reimplemented iterations, and thus the significance of such methods is a little bit vague. This paper moves beyond these limits and proposes Flexible Iterative Modularization Algorithm (FIMA), a generic and provable paradigm for nonconvex inverse problems. Our theoretical analysis reveals that FIMA allows us to generate globally convergent trajectories for learning-based iterative methods. Meanwhile, the devised scheduling policies on flexible modules should also be beneficial for classical numerical methods in the nonconvex scenario. Extensive experiments on real applications verify the superiority of FIMA.


abstract: In this paper, multimodal learning for facial expression recognition (FER) is proposed. The multimodal learning method makes the first attempt to learn the joint representation by considering the texture and landmark modality of facial images, which are complementary with each other. In order to learn the representation of each modality and the correlation and interaction between different modalities, the structured regularization (SR) is employed to enforce and learn the modality-specific sparsity and density of each modality, respectively. By introducing SR, the comprehensiveness of the facial expression is fully taken into consideration, which can not only handle the subtle expression but also perform robustly to different input of facial images. With the proposed multimodal learning network, the joint representation learning from multimodal inputs will be more suitable for FER. Experimental results on the CK+ and NVIE databases demonstrate the superiority of our proposed method. HighlightsMultimodal learning for facial expression recognition (FER) is proposed.The first attempt to do FER from the joint representation of texture and landmarks.The multimodal structure combines feature extraction and classification together.Structured regularization is used to enforce the sparsity of different modalities.


title: A Cantilever-Based NEM Nonvolatile Memory Utilizing Electrostatic Actuation and Vibrational Deactuation for High-Temperature Operation

abstract: This paper proposes a cantilever-based nanoelectro-mechanical (NEM) nonvolatile memory (NVM) with a novel write scheme for reliable memory operation at very high-operating temperature (up to 300 $^{\circ}{\rm C}$ ) in rugged electronics. The memory bit (0/1) is formed by the opening/closing of a cantilever beam. Permanent retention is obtained by adhesive force between two smooth surfaces in contact, eliminating leakage observed in all types of storage-layer-based NVMs. This allows the proposed NEM memory structure to be implemented using a simple bilayer design and easily integrated with the CMOS platform with leakage of 144 pA, which is significantly less compared with SRAM. The experimental analysis of vibrational reset is reported for the first time in this paper. An array structure using the proposed NEM memory device and CMOS devices is presented. Each bit cell consists of one NEM memory device and one nMOS transistor for realizing full random-access operation.


title: Optimization of a spherical mechanism for a minimally invasive surgical robot: theoretical and experimental approaches

abstract: With a focus on design methodology for developing a compact and lightweight minimally invasive surgery (MIS) robot manipulator, the goal of this study is progress toward a next-generation surgical robot system that will help surgeons deliver healthcare more effectively. Based on an extensive database of in-vivo surgical measurements, the workspace requirements were clearly defined. The pivot point constraint in MIS makes the spherical manipulator a natural candidate. An experimental evaluation process helped to more clearly understand the application and limitations of the spherical mechanism as an MIS robot manipulator. The best configuration consists of two serial manipulators in order to avoid collision problems. A complete kinematic analysis and optimization incorporating the requirements for MIS was performed to find the optimal link lengths of the manipulator. The results show that for the serial spherical 2-link manipulator used to guide the surgical tool, the optimal link lengths (angles) are (60/spl deg/, 50/spl deg/). A prototype 6-DOF surgical robot has been developed and will be the subject of further study.


title: Efficiency and effectiveness analysis of a new direct drive miniature quadruped robot

abstract: This paper introduces a new, 50g miniature robot (Figure 1 and attached video) that uses four direct-drive hybrid wheel/legs and has been clocked at speeds exceeding 30 body lengths/s. The lowest recorded cost of transport (defined as total energy to move the robot mass a given distance) is 0.90. We discuss hardware considerations for design of robots at this scale, and the benefits of a direct-drive system over coupled transmissions. We develop a simple simulation model to examine the effects of drive speed and mechanical properties of the legs, and compare the results with experimental data on efficiency and effectiveness of locomotion with various leg designs.


title: Modeling and Simulation of BLDC motor in Electric Power Steering

abstract: This paper briefly describes the composition of electric power steering (EPS) system and the development advantages of brushless DC (BLDC) motor. Based on the mathematical model of the BLDC motor and the requirements of the EPS system for assist motor, a control strategy of the BLDC motor used in EPS system is proposed, then a simulation model of control system using in BLDC motor is set up, and its simulation analysis is made. The feasibility, high reliability of the system and the validity of the control method are proved by the fruitful simulation results which serve a design method for a further research of BLDC motor in EPS.


title: Optimal Power Flow by Black Hole Optimization Algorithm

abstract: In this paper, a black hole optimization algorithm (BH) is utilized to solve the optimal power flow problem considering the generation fuel cost, reduction of voltage deviation and improvement of voltage stability as an objective functions. The black hole algorithm simulate the black hole phenomenon which relay on tow operations, the star absorption and star sucking. The IEEE 30-Bus and IEEE 57-Bus systems are used to illustrate performance of the proposed algorithm and results are compared with those in literatures.


title: Extinct and Vanishing Animals

abstract: The formal study of embryology has been severely curtailed by the increasing encroachment of time available for teaching anatomy. This is regrettable in view of its increasing importance to modern medicine. Tuchmann-Duplessis and his associates succeed in giving us three outstanding books on human embryology. Volumes 1 and 2 deal with embryogenesis and organogenesis. This third volume covers the development of the nervous system, the sense organs, and those endocrine glands specifically related to the nervous system. In clear and concise terms, we receive a vivid and conclusive account of the development of the nervous system. One can easily read the book in one evening, and the fantastic illustrations prove that "a picture is worth a thousand words." Perfect photomicrographs and matching colored drawings of a "Michaelangelo" quality lend clarity and simplicity to the developing organism so that the reader does not have to rely on his memory to organize a mass of material. Many will enjoy this work because there are no hypothetical ventures-it simply "tells it like it is." The discussion of the central nervous system development in man summarizes the complex stages undergone by neural structures during the course of evolution. In an attempt to make embryological information more integrated and meaningful, the authors present developmental concepts on a comparative basis with some zoological overtones. The sense organs and endocrine glands are treated as functional systems organized around the central nervous system. The authors present the synthesized currently accepted viewpoint, but footnotes point out areas of controversy. Each section ends with a strong summary on human malformations. The usefulness of this volume loses nothing in the translation from French to English. Writing a book of this size on human embryology is a big job requiring a great deal of courage. I congratulate the authors on a great contribution to medical education. Robert H. Davis Hahnemann Medical College Philadelphia Extinct and Vanishing Animals By Vinzenz Ziswiler Translated from the German by F. & P. Bunnell


title: Modeling in Event-B: System and Software Engineering

abstract: A practical text suitable for an introductory or advanced course in formal methods, this book presents a mathematical approach to modelling and designing systems using an extension of the B formal method: Event-B. Based on the idea of refinement, the author's systematic approach allows the user to construct models gradually and to facilitate a systematic reasoning method by means of proofs. Readers will learn how to build models of programs and, more generally, discrete systems, but this is all done with practice in mind. The numerous examples provided arise from various sources of computer system developments, including sequential programs, concurrent programs and electronic circuits. The book also contains a large number of exercises and projects ranging in difficulty. Each of the examples included in the book has been proved using the Rodin Platform tool set, which is available free for download at www.event-b.org.


title: Differential effects of mindful breathing, progressive muscle relaxation, and loving kindness meditation on decentering and negative reactions to repetitive thoughts

abstract: Abstract Decentering has been proposed as a potential mechanism of mindfulness-based interventions but has received limited empirical examination to date in experimental studies comparing mindfulness meditation to active comparison conditions. In the present study, we compared the immediate effects of mindful breathing (MB) to two alternative stress-management techniques: progressive muscle relaxation (PMR) and loving-kindness meditation (LKM) to test whether decentering is unique to mindfulness meditation or common across approaches. Novice meditators (190 female undergraduates) were randomly assigned to complete one of three 15-min stress-management exercises (MB, PMR, or LKM) presented by audio recording. Immediately after the exercise, participants completed measures of decentering, frequency of repetitive thoughts during the exercise, and degree of negative reaction to thoughts. As predicted, participants in the MB condition reported greater decentering relative to the other two conditions. The association between frequency of repetitive thought and negative reactions to thoughts was relatively weaker in the MB condition than in the PMR and LKM conditions, in which these two variables were strongly and positively correlated. Consistent with the construct of decentering, the relative independence between these two variables in the MB condition suggests that mindful breathing may help to reduce reactivity to repetitive thoughts. Taken together, results help to provide further evidence of decentering as a potential mechanism that distinguishes mindfulness practice from other credible stress-management approaches.


title: An Analysis of Temporal-Difference Learning with Function Approximation

abstract: We present new results about the temporal-difference learning algorithm, as applied to approximating the cost-to-go function of a Markov chain using linear function approximators. The algorithm we analyze performs on-line updating of a parameter vector during a single endless trajectory of an aperiodic irreducible finite state Markov chain. Results include convergence (with probability 1), a characterization of the limit of convergence, and a bound on the resulting approximation error. In addition to establishing new and stronger results than those previously available, our analysis is based on a new line of reasoning that provides new intuition about the dynamics of temporal-difference learning. Furthermore, we discuss the implications of two counter-examples with regards to the Significance of on-line updating and linearly parameterized function approximators.


title: Classification of DNA microarrays using artificial neural networks and ABC algorithm

abstract: Graphical abstractDisplay Omitted HighlightsABC algorithm discovered the best set of genes to classify correctly cancer samples.With less than 1% of information is possible to classify with an accuracy of 93.2%.Multilayer perceptron performs better than radial basis function network. DNA microarray is an efficient new technology that allows to analyze, at the same time, the expression level of millions of genes. The gene expression level indicates the synthesis of different messenger ribonucleic acid (mRNA) molecule in a cell. Using this gene expression level, it is possible to diagnose diseases, identify tumors, select the best treatment to resist illness, detect mutations among other processes. In order to achieve that purpose, several computational techniques such as pattern classification approaches can be applied. The classification problem consists in identifying different classes or groups associated with a particular disease (e.g., various types of cancer, in terms of the gene expression level). However, the enormous quantity of genes and the few samples available, make difficult the processes of learning and recognition of any classification technique. Artificial neural networks (ANN) are computational models in artificial intelligence used for classifying, predicting and approximating functions. Among the most popular ones, we could mention the multilayer perceptron (MLP), the radial basis function neural network (RBF) and support vector machine (SVM). The aim of this research is to propose a methodology for classifying DNA microarray. The proposed method performs a feature selection process based on a swarm intelligence algorithm to find a subset of genes that best describe a disease. After that, different ANN are trained using the subset of genes. Finally, four different datasets were used to validate the accuracy of the proposal and test the relevance of genes to correctly classify the samples of the disease.


title: Likelihood-Based Data Association for Extended Object Tracking Using Sampling Methods

abstract: Environment perception is a key enabling technology in autonomous vehicles, and multiple object tracking is an important part of this. The use of high resolution sensors, such as automotive radar and lidar, leads to the extended object tracking problem, with multiple detections per tracked object. For computationally feasible multiple extended object tracking, the data association problem must be handled. Previous work has relied on a two-step approach, using clustering algorithms, together with assignment algorithms, to achieve this. In this paper, we show that it is possible to handle the data association in a single step that works directly on the desired likelihood function. Single step data association is beneficial, because it enables better use of the measurement model and the predicted multiobject density. For single step data association, we use algorithms based on stochastic sampling, and integrate them into a Poisson Multi-Bernoulli Mixture filter. In a simulation study, and in an experiment with Velodyne data acquired in an urban environment, four sampling algorithms are compared to clustering and assignment. The results from the simulations and the experiment show that single-step likelihood-based data association achieves better performance than two-step clustering and assignment data association does.


title: Graph Classification with 2D Convolutional Neural Networks

abstract: Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations. Convolutional Neural Networks (CNNs) offer a very appealing alternative, but processing graphs with CNNs is not trivial. To address this challenge, many sophisticated extensions of CNNs have recently been introduced. In this paper, we reverse the problem: rather than proposing yet another graph CNN model, we introduce a novel way to represent graphs as multi-channel image-like structures that allows them to be handled by vanilla 2D CNNs. Experiments reveal that our method is more accurate than state-of-the-art graph kernels and graph CNNs on 4 out of 6 real-world datasets (with and without continuous node attributes), and close elsewhere. Our approach is also preferable to graph kernels in terms of time complexity. Code and data are publicly available.


title: Segmentation of retinal blood vessels based on analysis of the hessian matrix and Clustering Algorithm

abstract: In this paper, a novel unsupervised method to segment retinal blood vessels from colour fundus images is proposed. A new vesselness measure is introduced which is based on detecting vessel centerlines and orientation in scale space. Based on this vesselness measure a generated ground truth (GGT) image is obtained by thresholding and removing segments of small sizes. The segmentation is obtained by using this GGT image in conjunction with a RAdius-based Clustering Algorithm (RACAL). A dataset of 20 images publicly available is used to evaluate the performance of our proposed method. Experimental results show that a true positive rate (TPR) of 81% at false positive rate (FPR) of 4.5% is achieved compared with TPR of 76% at the same FPR from the piecewise threshold probing method [1].


title: Systematic literature reviews in software engineering - A tertiary study

abstract: Context: In a previous study, we reported on a systematic literature review (SLR), based on a manual search of 13 journals and conferences undertaken in the period 1st January 2004 to 30th June 2007. Objective: The aim of this on-going research is to provide an annotated catalogue of SLRs available to software engineering researchers and practitioners. This study updates our previous study using a broad automated search. Method: We performed a broad automated search to find SLRs published in the time period 1st January 2004 to 30th June 2008. We contrast the number, quality and source of these SLRs with SLRs found in the original study. Results: Our broad search found an additional 35 SLRs corresponding to 33 unique studies. Of these papers, 17 appeared relevant to the undergraduate educational curriculum and 12 appeared of possible interest to practitioners. The number of SLRs being published is increasing. The quality of papers in conferences and workshops has improved as more researchers use SLR guidelines. Conclusion: SLRs appear to have gone past the stage of being used solely by innovators but cannot yet be considered a main stream software engineering research methodology. They are addressing a wide range of topics but still have limitations, such as often failing to assess primary study quality.


title: Knowledge Representation Issues in Musical Instrument Ontology Design

abstract: This paper presents preliminary work on musical instruments ontology design, and investigates heterogeneity and limitations in existing instrument classification schemes. Numerous research to date aims at representing information about musical instruments. The works we examined are based on the well known Hornbostel and Sach’s classification scheme. We developed representations using the Ontology Web Language (OWL), and compared terminological and conceptual heterogeneity using SPARQL queries. We found evidence to support that traditional designs based on taxonomy trees lead to ill-defined knowledge representation, especially in the context of an ontology for the Semantic Web. In order to overcome this issue, it is desirable to have an instrument ontology that exhibits a semantically rich structure.


title: Topic-Oriented Exploratory Search Based on an Indexing Network

abstract: An exploratory search may be driven by a user’s curiosity or desire for specific information. When users investigate unfamiliar fields, they may want to learn more about a particular subject area to increase their knowledge rather than solve a specific problem. This work proposes a topic-oriented exploratory search method that provides browse guidance to users. It allows them to discover new associations and knowledge, and helps them find their interested information and knowledge. Since an exploratory search needs to judge the ability to discover new knowledge, the existing commonly used metrics fail to capture it. This paper thus defines a new set of criteria containing clarity, relevance, novelty, and diversity to analyze the effectiveness of an exploratory search. Experiments are designed to compare results from the proposed method and Google’s “search related to ….” The results show that the proposed one is more suitable for learning new associations and discovering new knowledge with highly likely relevance to a query. This work concludes that it is more suitable than Google for an exploratory search.


title: Protecting Your Software Updates

abstract: As described in many blog posts and the scientific literature, exploits for software vulnerabilities are often engineered on the basis of patches, which often involves the manual or automated identification of vulnerable code. The authors evaluate how this identification can be automated with the most frequently referenced diffing tools, demonstrating that for certain types of patches, these tools are indeed effective attacker tools. But they also demonstrate that by using binary code diversification, the effectiveness of the tools can be diminished severely, thus severely closing the attacker's window of opportunity.


title: Design of radome-covered substrate integrated waveguide slot antenna array

abstract: A design of a radome-covered slot antenna array based on Substrate Integrated Waveguide (SIW) technology is presented in this paper. The design method consists of the analysis of an isolated radiating element, the synthesis of a linear array, the optimization of a planar array, and the development of a power divider with a transition from a feeding metal waveguide to an SIW. The antenna array is designed using a full-wave electromagnetic solver (CST Microwave Studio) for the operating frequency band 26.5-27.5 GHz. The paper describes simulation results of the antenna array consisting of 10×10 longitudinal slots and simulated and measured results of a fabricated antenna array consisting of 4×4 slots.


title: ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge

abstract: This paper describes Luminoso's participation in SemEval 2017 Task 2, "Multilingual and Cross-lingual Semantic Word Similarity", with a system based on ConceptNet. ConceptNet is an open, multilingual knowledge graph that focuses on general knowledge that relates the meanings of words and phrases. Our submission to SemEval was an update of previous work that builds high-quality, multilingual word embeddings from a combination of ConceptNet and distributional semantics. Our system took first place in both subtasks. It ranked first in 4 out of 5 of the separate languages, and also ranked first in all 10 of the cross-lingual language pairs.


ulvo-vaginal atrophy : A new treatment modality using hermo-ablative fractional CO 2 laser


title: Adolescent Onset of the Gender Difference in Lifetime Rates of Major Depression: A Theoretical Model

abstract: Prepubescent boys are, if anything, more likely than girls to be depressed. During adolescence, however, a dramatic shift occurs: between the ages of 11 and 13 years, this trend in depression rates is reversed. By 15 years of age, females are approximately twice as likely as males to have experienced an episode of depression, and this gender gap persists for the next 35 to 40 years. We offer a theoretical framework that addresses the timing of this phenomenon. First, we discuss the social and hormonal mechanisms that stimulate affiliative needs for females at puberty. Next, we describe how heightened affiliative need can interact with adolescent transition difficulties to create a depressogenic diathesis as at-risk females reach puberty. This gender-linked vulnerability explains why adolescent females are more likely than males to become depressed when faced with negative life events and, particularly, life events with interpersonal consequences.


title: Network Structure Inference, A Survey: Motivations, Methods, and Applications

abstract: Networks represent relationships between entities in many complex systems, spanning from online social interactions to biological cell development and brain connectivity. In many cases, relationships between entities are unambiguously known: are two users 'friends' in a social network? Do two researchers collaborate on a published paper? Do two road segments in a transportation system intersect? These are directly observable in the system in question. In most cases, relationship between nodes are not directly observable and must be inferred: does one gene regulate the expression of another? Do two animals who physically co-locate have a social bond? Who infected whom in a disease outbreak in a population? Existing approaches for inferring networks from data are found across many application domains, and use specialized knowledge to infer and measure the quality of inferred network for a specific task or hypothesis. However, current research lacks a rigorous methodology which employs standard statistical validation on inferred models. In this survey, we examine (1) how network representations are constructed from underlying data, (2) the variety of questions and tasks on these representations over several domains, and (3) validation strategies for measuring the inferred network's capability of answering questions on the system of interest.


title: Survey on crowd behaviour evacuation modelling during emergency event

abstract: In the event of natural disasters, catastrophes or war, efficient tools are needed to produce evacuation plans to evacuate the crowd safely. Disaster is an unplanned situation that can happen anywhere and anytime. The worst case is when disasters happen on large gatherings which can increase risk of death during egress evacuation. When an emergency arises, crowds will evacuate to the nearest exit they know of. There are many risks involved during an emergency when it is unplanned. Research on crowd evacuation pay less attention to the realism of crowd behavioural factors which influence the evacuation result. As such this has prompted a novel crowd evacuation model as a potential solution for realistic crowd behavioural evacuation modelling through a combination of crowd behaviour, evacuation planning and path finding.


title: PEMOGEN: automatic adaptive performance modeling during program runtime

abstract: Traditional means of gathering performance data are tracing, which is limited by the available storage, and profiling, which has limited accuracy. Performance modeling is often used to interpret the tracing data and generate performance predictions. We aim to complement the traditional data collection mechanisms with online performance modeling, a method that generates performance models while the application is running. This allows us to greatly reduce the storage overhead while still producing accurate predictions. We present PEMOGEN, our compilation and modeling framework that automatically instruments applications to generate performance models during program execution. We demonstrate the ability of PEMOGEN to both reduce storage cost and improve the prediction accuracy compared to traditional techniques such as least squares fitting. With our tool, we automatically detect 3,370 kernels from fifteen NAS and Mantevo applications and model their execution time with a median coefficient of variation (R2) of 0.81. These automatically generated performance models can be used to quickly assess the scaling and potential bottlenecks with regards to any input parameter and the number of processes of a parallel application.


title: Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces

abstract: Dry and noncontact electroencephalographic (EEG) electrodes, which do not require gel or even direct scalp coupling, have been considered as an enabler of practical, real-world, brain-computer interface (BCI) platforms. This study compares wet electrodes to dry and through hair, noncontact electrodes within a steady state visual evoked potential (SSVEP) BCI paradigm. The construction of a dry contact electrode, featuring fingered contact posts and active buffering circuitry is presented. Additionally, the development of a new, noncontact, capacitive electrode that utilizes a custom integrated, high-impedance analog front-end is introduced. Offline tests on 10 subjects characterize the signal quality from the different electrodes and demonstrate that acquisition of small amplitude, SSVEP signals is possible, even through hair using the new integrated noncontact sensor. Online BCI experiments demonstrate that the information transfer rate (ITR) with the dry electrodes is comparable to that of wet electrodes, completely without the need for gel or other conductive media. In addition, data from the noncontact electrode, operating on the top of hair, show a maximum ITR in excess of 19 bits/min at 100% accuracy (versus 29.2 bits/min for wet electrodes and 34.4 bits/min for dry electrodes), a level that has never been demonstrated before. The results of these experiments show that both dry and noncontact electrodes, with further development, may become a viable tool for both future mobile BCI and general EEG applications.


title: A Software-Defined GPS and Galileo Receiver: Single-Frequency Approach BIOGRAPHIES Kai Borre is a Professor of Geodesy at the Aalborg University

abstract: Preface List of Figures List of Tables Abbreviations Signals and Systems GPS Signal Galileo Signal GNSS Antennas and Front Ends GNSS Receiver Operation Overview Acquisition Carrier and Code Tracking Data Processing for Positioning Problems Appendix A. MATLAB Code Appendix B. GNSS Signal Simulation Bibliography Index


title: EXPLAINED VARIATION FOR LOGISTIC REGRESSION

abstract: SUMMARY Different measures of the proportion of variation in a dependent variable explained by covariates are reported by different standard programs for logistic regression. We review twelve measures that have been suggested or might be useful to measure explained variation in logistic regression models. The definitions and properties of these measures are discussed and their performance is compared in an empirical study. Two of the measures (squared Pearson correlation between the binary outcome and the predictor, and the proportional reduction of squared Pearson residuals by the use of covariates) give almost identical results, agree very well with the multiple R2 of the general linear model, have an intuitively clear interpretation and perform satisfactorily in our study. For all measures the explained variation for the given sample and also the one expected in future samples can be obtained easily. For small samples an adjustment analogous to RZdj in the general linear model is suggested. We discuss some aspects of application and recommend the routine use of a suitable measure of explained variation for logistic models.


title: Winning Florida: How the Bush Team Fought the Battle

abstract: With a reporter's keen eye for detail, award-winning journalist Zelnick conveys every emotion of the key players in this battle, presenting a rich, colorful tale that reads more like a fictional political thriller than the bizarre real-life drama it was-from election night through the U.S. Supreme Court's historic decision.


title: Making a “Completely Blind” Image Quality Analyzer

abstract: An important aim of research on the blind image quality assessment (IQA) problem is to devise perceptual models that can predict the quality of distorted images with as little prior knowledge of the images or their distortions as possible. Current state-of-the-art “general purpose” no reference (NR) IQA algorithms require knowledge about anticipated distortions in the form of training examples and corresponding human opinion scores. However we have recently derived a blind IQA model that only makes use of measurable deviations from statistical regularities observed in natural images, without training on human-rated distorted images, and, indeed without any exposure to distorted images. Thus, it is “completely blind.” The new IQA model, which we call the Natural Image Quality Evaluator (NIQE) is based on the construction of a “quality aware” collection of statistical features based on a simple and successful space domain natural scene statistic (NSS) model. These features are derived from a corpus of natural, undistorted images. Experimental results show that the new index delivers performance comparable to top performing NR IQA models that require training on large databases of human opinions of distorted images. A software release is available at http://live.ece.utexas.edu/research/quality/niqe_release.zip.


title: Aspect-oriented programming and modular reasoning

abstract: Aspects cut new interfaces through the primary decomposition of a system. This implies that in the presence of aspects, the complete interface of a module can only be determined once the complete configuration of modules in the system is known. While this may seem anti-modular, it is an inherent property of crosscutting concerns, and using aspect-oriented programming enables modular reasoning in the presence of such concerns.


title: Mate preferences in Brazil: Evolved desires and cultural evolution over three decades

abstract: Abstract Mate preferences provide unique windows into evolved mating psychology and extant cultural values. The current study used two research instruments—one ranking and one rating—to examine mate preferences in Brazil. We compared modern Brazilians (n = 1186) with a Brazilian sample studied three decades earlier, in 1984 (n = 630). Mate preferences for mutual attraction and love, kindness, and intelligence remained important and relatively invariant over time. Sex differences in mate preferences for cues to fertility (relative youth, physical attractiveness) and resources (earning capacity, financial prospects, social status) also remained relatively invariant over time. Several changes in mate preferences emerged over time for both men and women, including a stronger preference for mates who have good financial prospects and a dramatic decline in the desire for children. Discussion highlights limitations of the study, and stresses the importance of mate preferences as windows into evolved mating psychology and both the expression and reflection of cultural values.


title: Curiosity-driven optimization

abstract: The principle of artificial curiosity directs active exploration towards the most informative or most interesting data. We show its usefulness for global black box optimization when data point evaluations are expensive. Gaussian process regression is used to model the fitness function based on all available observations so far. For each candidate point this model estimates expected fitness reduction, and yields a novel closed-form expression of expected information gain. A new type of Pareto-front algorithm continually pushes the boundary of candidates not dominated by any other known data according to both criteria, using multi-objective evolutionary search. This makes the exploration-exploitation trade-off explicit, and permits maximally informed data selection. We illustrate the robustness of our approach in a number of experimental scenarios.


title: A 12-bit 50-MS/s 3.3-mW SAR ADC with background digital calibration

abstract: This paper describes a background digital calibration technique based on bitwise correlation (BWC) to correct the capacitive digital-to-analog converter (DAC) mismatch error in successive-approximation-register (SAR) analog-to-digital converters (ADC's). Aided by a single-bit pseudorandom noise (PN) injected to the ADC input, the calibration engine extracts all bit weights simultaneously to facilitate a digital-domain correction. The analog overhead associated with this technique is negligible and the conversion speed is fully retained (in contrast to [1] in which the ADC throughput is halved). A prototype 12bit 50-MS/s SAR ADC fabricated in 90-nm CMOS measured a 66.5-dB peak SNDR and an 86.0-dB peak SFDR with calibration, while occupying 0.046 mm2 and dissipating 3.3 mW from a 1.2-V supply. The calibration logic is estimated to occupy 0.072 mm2 with a power consumption of 1.4 mW in the same process.


title: Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds

abstract: In this paper, we initiate a systematic investigation of differentially private algorithms for convex empirical risk minimization. Various instantiations of this problem have been studied before. We provide new algorithms and matching lower bounds for private ERM assuming only that each data point's contribution to the loss function is Lipschitz bounded and that the domain of optimization is bounded. We provide a separate set of algorithms and matching lower bounds for the setting in which the loss functions are known to also be strongly convex. Our algorithms run in polynomial time, and in some cases even match the optimal non-private running time (as measured by oracle complexity). We give separate algorithms (and lower bounds) for $(\epsilon,0)$- and $(\epsilon,\delta)$-differential privacy; perhaps surprisingly, the techniques used for designing optimal algorithms in the two cases are completely different. Our lower bounds apply even to very simple, smooth function families, such as linear and quadratic functions. This implies that algorithms from previous work can be used to obtain optimal error rates, under the additional assumption that the contributions of each data point to the loss function is smooth. We show that simple approaches to smoothing arbitrary loss functions (in order to apply previous techniques) do not yield optimal error rates. In particular, optimal algorithms were not previously known for problems such as training support vector machines and the high-dimensional median.


A Neural Model for Part-of-Speech Tagging in Historical Texts


title: Comparative Study of Capital Structure Determinants in Selected Stock Exchanges of Developing Countries and Tehran Stock Exchange

abstract: This paper studies determinants of capital structure in listed firms of selected stock exchanges of developing countries and Tehran stock exchange, and comprises effects of these determinants on selected stock exchanges with Iran. In this study, determinants of capital structure are studied in firm and country levels. In firm-level profitability, distance from bankruptcy, size and tangible assets, and in country level stock market development and GDP growth are studied. Data are collected from Compustat Global Vantage database, World Bank databases and Tadbirpardaz software. Panel Regression is used for analysis and Excel and EViews 6 and F and t test statistics are used. Results of study in level of developing countries show that except development of stock market, GDP growth and distance from bankruptcy, all variables have a significant relationship with capital structure; distance from bankruptcy and tangible assets on Tehran Stock Exchange have a significant relationship with capital structure. Impact of distance from of bankruptcy, size and tangible assets in Tehran Stock Exchange and selected developing countries stock exchanges capital structure are different. This paper enables to comparison, benchmarking, identifying strengths and weaknesses and its results can be used to determine strategies and objectives of firms financing.


title: Type D Personality Parents of Children With Leukemia Tend to Experience Anxiety: The Mediating Effects of Social Support and Coping Style

abstract: AbstractThe aims were to access anxiety and type D personality (TDP) in parents of children with leukemia, and to determine the mediating effect of social support and coping style on the relationship between TDP and anxiety.A cross-sectional study was conducted among 231 parents of children with leukemia and 261 parents of children with acute diseases in hospitals. Parents completed questionnaires on anxiety, TDP, social support, coping styles, children's clinical characteristics, and demographic characteristics.Parents of children with leukemia showed higher prevalence of anxiety (64.5% vs 40.2%, P < 0.01) and TDP (44.2% vs 24.1%, P < 0.01) compared with controls. TDP (odds ratio [OR] = 4.34, P < 0.01), lower social support (OR = 1.92, P = 0.02), and less positive coping (OR = 1.87, P = 0.02) were independently associated with anxiety. Parents with TDP showed lower social support and less positive coping, but more negative coping compared with those without. Moreover, multiple mediation analyses revealed that the significant effect of TDP on anxiety was partially mediated by social support and positive coping.In conclusion, anxiety and TDP were highly prevalent in parents of children with leukemia. The predictive factors could be used to identify those parents who are at high risk of anxiety and may also be targets for prevention and intervention.


title: Hierarchical face parsing via deep learning

abstract: This paper investigates how to parse (segment) facial components from face images which may be partially occluded. We propose a novel face parser, which recasts segmentation of face components as a cross-modality data transformation problem, i.e., transforming an image patch to a label map. Specifically, a face is represented hierarchically by parts, components, and pixel-wise labels. With this representation, our approach first detects faces at both the part- and component-levels, and then computes the pixel-wise label maps (Fig.1). Our part-based and component-based detectors are generatively trained with the deep belief network (DBN), and are discriminatively tuned by logistic regression. The segmentators transform the detected face components to label maps, which are obtained by learning a highly nonlinear mapping with the deep autoencoder. The proposed hierarchical face parsing is not only robust to partial occlusions but also provide richer information for face analysis and face synthesis compared with face keypoint detection and face alignment. The effectiveness of our algorithm is shown through several tasks on 2, 239 images selected from three datasets (e.g., LFW [12], BioID [13] and CUFSF [29]).


title: Correcting forecasts with multifactor neural attention

abstract: Automatic forecasting of time series data is a challenging problem in many industries. Current forecast models adopted by businesses do not provide adequate means for including data representing external factors that may have a significant impact on the time series, such as weather, national events, local events, social media trends, promotions, etc. This paper introduces a novel neural network attention mechanism that naturally incorporates data from multiple external sources without the feature engineering needed to get other techniques to work. We demonstrate empirically that the proposed model achieves superior performance for predicting the demand of 20 commodities across 107 stores of one of America's largest retailers when compared to other baseline models, including neural networks, linear models, certain kernel methods, Bayesian regression, and decision trees. Our method ultimately accounts for a 23.9% relative improvement as a result of the incorporation of external data sources, and provides an unprecedented level of descriptive ability for a neural network forecasting model.


title: Principles of IEEE 802.11s

abstract: In 2003, interests in the Institute of Electronics and Electrical Engineering (IEEE) 802.11 Working Group (WG) led to the formation of Task Group (TG) "S". 802.11s develops a Wireless Mesh Network (WMN) amendment. Unlike existing Mesh products, 802.11s forms a transparent 802 broadcast domain that supports any higher layer protocols. Therefore, 802.11s provides frame forwarding and path selection at layer-2. 802.11i describes a security concept for stations that associate with an Access Point (AP). However, in a Mesh Basic Service Set (BSS) devices need to mutually authenticate to provide integrity of the network. Thus, 802.11s adds additional elements to the concepts of 802.11L While traditional Wireless Local Area Networks (WLANs) are AP centred an 802.11 Mesh is fully distributed. Hence, 802.11s considers extensions to the Medium Access Control (MAC) too. The authors have contributed to the standardization of 802.11s since 2003. As constant participants we give insight to draft 1.02 of TG "s" and provide an outlook to future evolution of 802.11's first Mesh standard.


Elastography of the Abdomen


title: Where do bright ideas occur in our brain? Meta-analytic evidence from neuroimaging studies of domain-specific creativity

abstract: Many studies have assessed the neural underpinnings of creativity, failing to find a clear anatomical localization. We aimed to provide evidence for a multi-componential neural system for creativity. We applied a general activation likelihood estimation (ALE) meta-analysis to 45 fMRI studies. Three individual ALE analyses were performed to assess creativity in different cognitive domains (Musical, Verbal, and Visuo-spatial). The general ALE revealed that creativity relies on clusters of activations in the bilateral occipital, parietal, frontal, and temporal lobes. The individual ALE revealed different maximal activation in different domains. Musical creativity yields activations in the bilateral medial frontal gyrus, in the left cingulate gyrus, middle frontal gyrus, and inferior parietal lobule and in the right postcentral and fusiform gyri. Verbal creativity yields activations mainly located in the left hemisphere, in the prefrontal cortex, middle and superior temporal gyri, inferior parietal lobule, postcentral and supramarginal gyri, middle occipital gyrus, and insula. The right inferior frontal gyrus and the lingual gyrus were also activated. Visuo-spatial creativity activates the right middle and inferior frontal gyri, the bilateral thalamus and the left precentral gyrus. This evidence suggests that creativity relies on multi-componential neural networks and that different creativity domains depend on different brain regions.


title: A Data Model to Ease Analysis and Mining of Educational Data

abstract: Learning software is not designed for data analysis and mining. Because usage data is not stored in a systematic way, its thorough analysis requires long and tedious preprocessing. In this contribution we first present a data model to structure data stored by Learning Management Systems (LMS). Then we give an overview of the system architecture that performs the structure/ export functionality and of its implementation for the Moodle LMS. Finally, we show first results using this data model for analysing usage data.


title: An Algorithm for Calculation of Range in a Multiple PRF Radar

abstract: A simple algorithm is described for the calculation of range in a multiple pulse repitition frequency (PRF) radar. The computational logic requires only a few multiplications and a number of logical operations. The method is described for two systems, one with two PRF's using medium PRF ranging and the other for three PRF's using high PRF ranging. The method described is considerably simpler and more understandable than the currently used methods for similar types of computations [1].


title: GOD'S ANSWER TO JOB

abstract: climax of the book, God answers Job out of a 'whirlwind5, displaying the wonders of creation and putting Job firmly back in his place. Apparently satisfied by God's answer, Job 'repents in dust and ashes'. [42: 6] Here, in the answer from the whirlwind, and in Job's humbled response, the poet's own deepest thinking about the problem of unfair suffering finds expression. But it is notoriously difficult to see how the two whirlwind speeches provide an answer to Job's complaint, and interpretations vary wildly, depending on the propensities and emphases of the reader. In this paper, I am especially concerned with the following questions. What under? lying problem is the poet wrestling with? How is God's answer to Job supposed to be relevant to this problem? And why is Job satisfied by it? I critically consider what seem to me to be two of the most important interpre? tations. Neither of them turns out to be completely satisfying. I then conclude by suggesting that the book of Job itself oscillates back and forth between two quite different conceptions of God's relation to the world. 1 Unless otherwise noted, all biblical quotations are taken from the New Revised Standard Version.


title: Automated Multi-factor Analytics for Cheat-Proofing Attendance-taking

abstract: A potential application of smartphone is as a tool to prevent attendance cheating. This paper proposes an automated multi-factor analytics using common smartphone features to identify cheating. The first factor is using Quick Response (QR) code as a unique token for validation. The second factor checks for the phone's unique International Mobile Equipment Identity (IMEI) number. The third factor checks attendance time using server time. The fourth factor is geo-location of the student. Algorithm analyzes geolocation statistical variance after passing the first two factors. The algorithm is implemented as a proof of concept in a typical university lecture and lab attendance taking. The proposed algorithm has shown promising efficiency and feasibility of implementation. User survey has indicated reasonable acceptance and potential issues.


title: Privacy Preserving Social Network Data Publication

abstract: The introduction of online social networks (OSN) has transformed the way people connect and interact with each other as well as share information. OSN have led to a tremendous explosion of network-centric data that could be harvested for better understanding of interesting phenomena such as sociological and behavioural aspects of individuals or groups. As a result, online social network service operators are compelled to publish the social network data for use by third party consumers such as researchers and advertisers. As social network data publication is vulnerable to a wide variety of reidentification and disclosure attacks, developing privacy preserving mechanisms are an active research area. This paper presents a comprehensive survey of the recent developments in social networks data publishing privacy risks, attacks, and privacy-preserving techniques. We survey and present various types of privacy attacks and information exploited by adversaries to perpetrate privacy attacks on anonymized social network data. We present an in-depth survey of the state-of-the-art privacy preserving techniques for social network data publishing, metrics for quantifying the anonymity level provided, and information loss as well as challenges and new research directions. The survey helps readers understand the threats, various privacy preserving mechanisms, and their vulnerabilities to privacy breach attacks in social network data publishing as well as observe common themes and future directions.


title: VocaListener and VocaWatcher: Imitating a human singer by using signal processing

abstract: In this paper, we describe three singing information processing systems, VocaListener, VocaListener2, and VocaWatcher, that imitate singing expressions of the voice and face of a human singer. VocaListener can synthesize natural singing voices by analyzing and imitating the pitch and dynamics of the human singing. VocaListener2 imitates temporal timbre changes in addition to the pitch and dynamics. In synchronization with the synthesized singing voices, VocaWatcher can generate realistic facial motions of a humanoid robot, the HRP-4C, by analyzing and imitating facial motions of a human singing that are recorded by a single video camera. These systems that focus on “imitation” are not only promising for representing human-like naturalness, but also useful for providing intuitive control means.


title: Real-time highlight removal using intensity ratio

abstract: In this paper, we propose an efficient method to separate the diffuse and specular reflection components from a single image. The method is built on the observation that, for diffuse pixels, the intensity ratios between the maximum values and range values (maximums minus minimums) are independent of surface geometry. The specular fractions of the image pixels can then be computed by using the intensity ratio. For textured surfaces, image pixels are classified into clusters by constructing a pseudo-chromaticity space, and the intensity ratio of each cluster is robustly estimated. Unlike existing techniques, the proposed method works in a pixel-wise manner, without specular pixel identification and any local interaction. Experimental results show that the proposed method runs 4× faster than the state of the art and produces improved accuracy in specular highlight removal.


title: Machine Learning in Complex Networks

abstract: This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas.


title: An End-to-End TextSpotter with Explicit Alignment and Attention

abstract: Text detection and recognition in natural images have long been considered as two separate tasks that are processed sequentially. Training of two tasks in a unified framework is non-trivial due to significant dif- ferences in optimisation difficulties. In this work, we present a conceptually simple yet efficient framework that simultaneously processes the two tasks in one shot. Our main contributions are three-fold: 1) we propose a novel text-alignment layer that allows it to precisely compute convolutional features of a text instance in ar- bitrary orientation, which is the key to boost the per- formance; 2) a character attention mechanism is introduced by using character spatial information as explicit supervision, leading to large improvements in recognition; 3) two technologies, together with a new RNN branch for word recognition, are integrated seamlessly into a single model which is end-to-end trainable. This allows the two tasks to work collaboratively by shar- ing convolutional features, which is critical to identify challenging text instances. Our model achieves impressive results in end-to-end recognition on the ICDAR2015 dataset, significantly advancing most recent results, with improvements of F-measure from (0.54, 0.51, 0.47) to (0.82, 0.77, 0.63), by using a strong, weak and generic lexicon respectively. Thanks to joint training, our method can also serve as a good detec- tor by achieving a new state-of-the-art detection performance on two datasets.


RECONCILING EFFICIENT MARKETS WITH BEHAVIORAL FINANCE:


title: Transition pattern of Indian states across different categories of growth and development: Post economic liberalisation experience

abstract: This paper analyses the emerging trends in twenty eight major states in India in respect of few key parameters which have an intrinsic bearing on social and economic development and establishes the economic growth (EG) - human development (HD) nexus in the post economic reforms era (1990s to 2010s). Data for the period 1990 to 2011/12 is used to analyse the relative performance of Indian states on human development and growth indicators and determine if inter-state disparity has increased or decreased over time. The two way relationship between EG and HD is empirically estimated using cross section pooled data. Further, states are classified into four different categories of growth and development: (i) vicious cycle (low EG - low HD), (ii) virtuous cycle (high EG - high HD), (iii) lopsided-EG (high EG - low HD) and (iv) lopsided-HD (low EG - high HD), for the years 1993, 1999-2000 and 2011-12. The pattern of transition of states across different categories over the two decades is traced. Such categorization and the shifts therein have obvious policy implications. The paper highlights the need for more focused regional planning and a simultaneous policy thrust on EG and HD to enable states escape the vicious cycle of low growth and development.This paper analyses the emerging trends in twenty eight major states in India in respect of few key parameters which have an intrinsic bearing on social and economic development and establishes the economic growth (EG) - human development (HD) nexus in the post economic reforms era (1990s to 2010s). Data for the period 1990 to 2011/12 is used to analyse the relative performance of Indian states on human development and growth indicators and determine if inter-state disparity has increased or decreased over time. The two way relationship between EG and HD is empirically estimated using cross section pooled data. Further, states are classified into four different categories of growth and development: (i) vicious cycle (low EG - low HD), (ii) virtuous cycle (high EG – high HD), (iii) lopsided-EG (high EG – low HD) and (iv) lopsided-HD (low EG – high HD), for the years 1993, 1999-2000 and 2011-12. The pattern of transition of states across different categories over the two decades is traced. Such categorization and the shifts therein have obvious policy implications. The paper highlights the need for more focused regional planning and a simultaneous policy thrust on EG and HD to enable states escape the vicious cycle of low growth and development.


Influence of weed control regulation on herbicide efficacy and grain yield of maize.


title: Smart Virtual eNB (SVeNB) for 5G mobile communication

abstract: In this paper, we proposed a novel locally semi-stand alone Smart Virtual evolved Base Station (SVeNB), for 5G mobile communication. Where some functions of system Evolved Packet System (EPS) elements can be done with SVeNB. According to the type of function or service and the device, which is used to fulfil that function or service. These elements can be virtualized and programmed in one Virtual Machine (VM) or more to achieve those functions and services. Our proposal gives SVeNB more sharing with baseband unit (BBU) pool and Core Network (CN), even in making decisions through its vision about its users. This will lead to reducing the burden on BBU pool, CN, decrease the latency and increase the data rate requested by users. i.e., the SVeNB can locally make the decision about all of its permanent local subscribers to connect to each other or to the internet. SVeNB performance exceeds the C-RAN with 68% in user profiles treatment. The end to end delay is reduced by SVeNB to 62%.


Sustainable entrepreneurship and cleantech. A Swedish perspective


title: An energy-efficient level shifter for low-power applications

abstract: One effective way to reduce the power consumption of biomedical implantable devices is to employ different supply voltages for different parts of the system depending on the processing speed of each part. This, however, necessitates the use of voltage level converters. This paper presents a power-efficient voltage level-shifting architecture that is capable of converting low levels of input voltages (even sub-threshold levels) to high levels of output voltages. In order to reduce the transition time of the output signal and consequently power consumption, the proposed circuit utilizes a diode-connected transistor to reduce the swing of the critical node. Moreover, the pull-down network is also improved. Post-layout simulation results of the proposed structure in a 0.18-μm CMOS technology show that at the target design of input low supply voltage of 0.4 V and high supply voltage of 1.8 V, the level-shifter has a propagation delay of 31ns, a static power dissipation of 1.16nW, and a power dissipation of 0.68μW for a 1-MHz input signal.


title: Ionic liquid as a precursor to synthesize nitrogen- and sulfur-co-doped carbon dots for detection of copper(II) ions

abstract: A facile approach was reported for the synthesis of nitrogen- and sulfur-co-doped carbon dots(N/S-CDs) by the sulfuric acid carbonization of 1-butyl-3-methylimidazolium 2-amino-3-mercaptopropionic acid(L-cysteine) salt([C4mim][Cys]) ionic liquid. The as-prepared N/S-CDs exhibited a good solubility, excitation-dependent emission behavior, and high resistance to photobleaching. Furthermore, the N/S-CDs were employed as a fluorescent probe for detecting Cu2+ in real water samples with satisfactory recovery.


title: Dreaming without REM sleep

abstract: Abstract To test whether mental activities collected from non-REM sleep are influenced by REM sleep, we suppressed REM sleep using clomipramine 50 mg (an antidepressant) or placebo in the evening, in a double blind cross-over design, in 11 healthy young men. Subjects were awakened every hour and asked about their mental activity. The marked (81%, range 39–98%) REM-sleep suppression induced by clomipramine did not substantially affect any aspects of dream recall (report length, complexity, bizarreness, pleasantness and self-perception of dream or thought-like mentation). Since long, complex and bizarre dreams persist even after suppressing REM sleep either partially or totally, it suggests that the generation of mental activity during sleep is independent of sleep stage.


Strategic human resource practices and innovation performance: the mediating role of knowledge management capacity


title: Representing smooth functions as compositions of near-identity functions with implications for deep network optimization

abstract: We show that any smooth bi-Lipschitz $h$ can be represented exactly as a composition $h_m \circ ... \circ h_1$ of functions $h_1,...,h_m$ that are close to the identity in the sense that each $\left(h_i-\mathrm{Id}\right)$ is Lipschitz, and the Lipschitz constant decreases inversely with the number $m$ of functions composed. This implies that $h$ can be represented to any accuracy by a deep residual network whose nonlinear layers compute functions with a small Lipschitz constant. Next, we consider nonlinear regression with a composition of near-identity nonlinear maps. We show that, regarding Fr\'echet derivatives with respect to the $h_1,...,h_m$, any critical point of a quadratic criterion in this near-identity region must be a global minimizer. In contrast, if we consider derivatives with respect to parameters of a fixed-size residual network with sigmoid activation functions, we show that there are near-identity critical points that are suboptimal, even in the realizable case. Informally, this means that functional gradient methods for residual networks cannot get stuck at suboptimal critical points corresponding to near-identity layers, whereas parametric gradient methods for sigmoidal residual networks suffer from suboptimal critical points in the near-identity region.


Comparing Speech Recognition Systems (Microsoft API, Google API And CMU Sphinx)


title: International Happiness: A New View on the Measure of Performance

abstract: Executive Overview This paper describes the findings from a new, and intrinsically interdisciplinary, literature on happiness and well-being. The paper focuses on international evidence. We report the patterns in modern data, discuss what has been persuasively established and what has not, and suggest paths for future research. Looking ahead, our instinct is that this social science research avenue will gradually merge with a related literature—from the medical, epidemiological, and biological sciences—on biomarkers and health. Nevertheless, we expect that intellectual convergence to happen slowly.


title: CPS: stateful policy enforcement for control system device usage

abstract: Networked control systems used in energy, manufacturing, and transportation combine large, vulnerable attack surfaces with far overprovisioned privileges. Often, compromising a single computer or user account is sufficient to give an attacker free reign over physical machinery. Significant reduction of attack surface size is an ongoing problem, so we shift our focus to reducing the privileges granted to system operators and embedded controllers. To this end, we introduce C2, an enforcement mechanism for policies governing the usage of electromechanical devices. In presenting C2, we address two basic problems: (i.) How should a policy for physical device usage be expressed and enforced? This is a challenging question, as the safe usage of physical devices is dependent on mechanical limitations and the behavior of nearby devices. (ii.) What actions should be taken if a physical machine is issued an operation that violates the policy? C2 takes measures to ensure unsafe behaviors are not caused when denying slightly erroneous yet legitimate operations. We evaluate C2 against six representative control systems, and show that it can efficiently perform policy checks with less than 3.7% overhead, while not introducing new unsafe behaviors into a control system.


title: Explain Black-box Image Classifications Using Superpixel-based Interpretation

abstract: How to best understand and interpret the decisions of deep neural networks is a crucial topic, as the impact of intelligent deep network systems is prevalent in many applications. We propose a superpixel based method to interpret and explain the results of black-box deep networks in the widely-applied image classification tasks. We perform probabilistic prediction difference analysis upon one or more superpixels clustered from image pixels. Our method generates a superpixel score map visualization that can provide rich interpretation regarding image components. Such interpretation provides supportive/unsupportive likelihood of image regions upon the decisions performed by the black-box classifier. We compare our method against state-of-art pixelwise interpretation methods over the latest deep neural network classifiers on the ImageNet dataset. Results show that our method produces more consistent interpretations in less computation time. Our method also supports interactive interpretation, where users can acquire explanations on specified regions through a convenient interface for a prompt reaction.


title: Knowledge Graph Representation with Jointly Structural and Textual Encoding

abstract: The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces. Previously, most works focused on symbolic representation of knowledge graph with structure information, which can not handle new entities or entities with few facts well. In this paper, we propose a novel deep architecture to utilize both structural and textual information of entities. Specifically, we introduce three neural models to encode the valuable information from text description of entity, among which an attentive model can select related information as needed. Then, a gating mechanism is applied to integrate representations of structure and text into a unified architecture. Experiments show that our models outperform baseline and obtain state-of-the-art results on link prediction and triplet classification tasks.


title: EFFICIENT CYCLIC LEARNING RATE SCHEDULES AND THEIR EVALUATIONS FOR NEURAL NETWORK ENSEMBLE

abstract: Warm restart techniques on training deep neural networks often achieve better recognition accuracies and can be regarded as easy methods to obtain multiple neural networks with no additional training cost from a single training process. Ensembles of intermediate neural networks obtained by warm restart techniques can provide higher accuracy than a single neural network obtained finally by a whole training process. However, existing methods on both of warm restart and its ensemble techniques use fixed cyclic schedules and have little degree of parameter adaption. This paper extends a class of possible schedule strategies of warm restart, and clarifies their effectiveness for recognition performance. Specifically, we propose parameterized functions and various cycle schedules to improve recognition accuracies by the use of deep neural networks with no additional training cost. Experiments on CIFAR-10 and CIFAR-100 show that our methods can achieve more accurate rates than the existing cyclic training and ensemble methods.


title: Advanced partitioning techniques for massively distributed computation

abstract: An increasing number of companies rely on distributed data storage and processing over large clusters of commodity machines for critical business decisions. Although plain MapReduce systems provide several benefits, they carry certain limitations that impact developer productivity and optimization opportunities. Higher level programming languages plus conceptual data models have recently emerged to address such limitations. These languages offer a single machine programming abstraction and are able to perform sophisticated query optimization and apply efficient execution strategies. In massively distributed computation, data shuffling is typically the most expensive operation and can lead to serious performance bottlenecks if not done properly. An important optimization opportunity in this environment is that of judicious placement of repartitioning operators and choice of alternative implementations. In this paper we discuss advanced partitioning strategies, their implementation, and how they are integrated in the Microsoft Scope system. We show experimentally that our approach significantly improves performance for a large class of real-world jobs.