title: Monte-Carlo Tree Search: A New Framework for Game AI

abstract: Classic approaches to game AI require either a high quality of domain knowledge, or a long time to generate effective AI behaviour. These two characteristics hamper the goal of establishing challenging game AI. In this paper, we put forward Monte-Carlo Tree Search as a novel, unified framework to game AI. In the framework, randomized explorations of the search space are used to predict the most promising game actions. We will demonstrate that Monte-Carlo Tree Search can be applied effectively to (1) classic board-games, (2) modern board-games, and (3) video games.


title: The eleven antenna: a compact low-profile decade bandwidth dual polarized feed for reflector antennas

abstract: A novel dual polarized ultrawide-band (UWB) feed with a decade bandwidth is presented for use in both single and dual reflector antennas. The feed has nearly constant beam width and 11 dBi directivity over at least a decade bandwidth. The feed gives an aperture efficiency of the reflector of 66% or better over a decade bandwidth when the subtended angle toward the sub or main reflector is about 53/spl deg/, and an overall efficiency better than 47% including mismatch. The return loss is better than 5 dB over a decade bandwidth. The calculated results have been verified with measurements on a linearly polarized lab model. The feed has no balun as it is intended to be integrated with an active 180/spl deg/ balun and receiver. The feed is referred to as the Eleven antenna because its basic configuration is two parallel dipoles 0.5 wavelengths apart and because it can be used over more than a decade bandwidth with 11 dBi directivity. We also believe that 11 dB return loss is achievable in the near future.


title: Writer Profiling Without the Writer’s Text

abstract: Social network users may wish to preserve their anonymity online by masking their identity and not using language associated with any particular demographics or personality. However, they have no control over the language in incoming communications. We show that linguistic cues in public comments directed at a user are sufficient for an accurate inference of that user’s gender, age, religion, diet, and even personality traits. Moreover, we show that directed communication is even more predictive of a user’s profile than the user’s own language. We then conduct a nuanced analysis of what types of social relationships are most predictive of users’ attributes, and propose new strategies on how individuals can modulate their online social relationships and incoming communications to preserve their anonymity.


title: Class Imbalance, Redux

abstract: Class imbalance (i.e., scenarios in which classes are unequally represented in the training data) occurs in many real-world learning tasks. Yet despite its practical importance, there is no established theory of class imbalance, and existing methods for handling it are therefore not well motivated. In this work, we approach the problem of imbalance from a probabilistic perspective, and from this vantage identify dataset characteristics (such as dimensionality, sparsity, etc.) that exacerbate the problem. Motivated by this theory, we advocate the approach of bagging an ensemble of classifiers induced over balanced bootstrap training samples, arguing that this strategy will often succeed where others fail. Thus in addition to providing a theoretical understanding of class imbalance, corroborated by our experiments on both simulated and real datasets, we provide practical guidance for the data mining practitioner working with imbalanced data.


title: A Primer to Single-Particle Cryo-Electron Microscopy

abstract: Cryo-electron microscopy (cryo-EM) of single-particle specimens is used to determine the structure of proteins and macromolecular complexes without the need for crystals. Recent advances in detector technology and software algorithms now allow images of unprecedented quality to be recorded and structures to be determined at near-atomic resolution. However, compared with X-ray crystallography, cryo-EM is a young technique with distinct challenges. This primer explains the different steps and considerations involved in structure determination by single-particle cryo-EM to provide an overview for scientists wishing to understand more about this technique and the interpretation of data obtained with it, as well as a starting guide for new practitioners.


Change through shared learning


title: Robust Real-Time Eye Detection and Tracking Under Variable Lighting Conditions and Various Face Orientations

abstract: Most eye trackers based on active IR illumination require distinctive bright pupil effect to work well. However, due to a variety of factors such as eye closure, eye occlusion, and external illumination interference, pupils are not bright enough for these methods to work well. This tends to significantly limit their scope of application. In this paper, we present an integrated eye tracker to overcome these limitations. By combining the latest technologies in appearance-based object recognition and tracking with active IR illumination, our eye tracker can robustly track eyes under variable and realistic lighting conditions and under various face orientations. In addition, our integrated eye tracker is able to handle occlusion, glasses, and to simultaneously track multiple people with different distances and poses to the camera. Results from an extensive experiment shows a significant improvement of our technique over existing eye tracking techniques.


title: Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach

abstract: Knee osteoarthritis (OA) is the most common musculoskeletal disorder. OA diagnosis is currently conducted by assessing symptoms and evaluating plain radiographs, but this process suffers from subjectivity. In this study, we present a new transparent computer-aided diagnosis method based on the Deep Siamese Convolutional Neural Network to automatically score knee OA severity according to the Kellgren-Lawrence grading scale. We trained our method using the data solely from the Multicenter Osteoarthritis Study and validated it on randomly selected 3,000 subjects (5,960 knees) from Osteoarthritis Initiative dataset. Our method yielded a quadratic Kappa coefficient of 0.83 and average multiclass accuracy of 66.71% compared to the annotations given by a committee of clinical experts. Here, we also report a radiological OA diagnosis area under the ROC curve of 0.93. Besides this, we present attention maps highlighting the radiological features affecting the network decision. Such information makes the decision process transparent for the practitioner, which builds better trust toward automatic methods. We believe that our model is useful for clinical decision making and for OA research; therefore, we openly release our training codes and the data set created in this study.


title: Kazhdan-Lusztig immanants III: Transition matrices between canonical bases of immanants

abstract: We study two bases of the vector space of immanants of C(x1,1,...,xn,n): the bitableaux basis of Desarmenien-Kung-Rota, and a subset of the dual canonical basis called the basis of Kazhdan-Lusztig immanants. We show that the transition matrix between these bases is unitriangular, describe new vanishing results for the Kazhdan-Lusztig immanants, and relate both bases to other immanants defined in terms of characters of Sn.


title: A Novel Domain Adaptation Framework for Medical Image Segmentation

abstract: We propose a segmentation framework that uses deep neural networks and introduce two innovations. First, we describe a biophysics-based domain adaptation method. Second, we propose an automatic method to segment white matter, gray matter, glial matter and cerebrospinal fluid, in addition to tumorous tissue. Regarding our first innovation, we use a domain adaptation framework that combines a novel multispecies biophysical tumor growth model with a generative adversarial model to create realistic looking synthetic multimodal MR images with known segmentation. These images are used for the purpose of training time data augmentation. Regarding our second innovation, we propose an automatic approach to enrich available segmentation data by computing the segmentation for healthy tissues. This segmentation, which is done using diffeomorphic image registration between the BraTS training data and a set of pre-labeled atlases, provides more information for training and reduces the class imbalance problem. Our overall approach is not specific to any particular neural network and can be used in conjunction with existing solutions. We demonstrate the performance improvement using a 2D U-Net for the BraTS’18 segmentation challenge. Our biophysics based domain adaptation achieves better results, as compared to the existing state-of-the-art GAN model used to create synthetic data for training.


title: Synergy-Driven Performance Enhancement of Vision-Based 3D Hand Pose Reconstruction

abstract: In this work we propose, for the first time, to improve the performance of a Hand Pose Reconstruction (HPR) technique from RGBD camera data, which is affected by self-occlusions, leveraging upon postural synergy information, i.e., a priori information on how human most commonly use and shape their hands in everyday life tasks. More specifically, in our approach, we ignore joint angle values estimated with low confidence through a vision-based HPR technique and fuse synergistic information with such incomplete measures. Preliminary experiments are reported showing the effectiveness of the proposed integration.


title: PayLess: A low cost network monitoring framework for Software Defined Networks

abstract: Software Defined Networking promises to simplify network management tasks by separating the control plane (a central controller) from the data plane (switches). OpenFlow has emerged as the de facto standard for communication between the controller and switches. Apart from providing flow control and communication interfaces, OpenFlow provides a flow level statistics collection mechanism from the data plane. It exposes a high level interface for per flow and aggregate statistics collection. Network applications can use this high level interface to monitor network status without being concerned about the low level details. In order to keep the switch design simple, this statistics collection mechanism is implemented as a pull-based service, i.e. network applications and in turn the controller has to periodically query the switches about flow statistics. The frequency of polling the switches determines monitoring accuracy and network overhead. In this paper, we focus on this trade-off between monitoring accuracy, timeliness and network overhead. We propose PayLess - a monitoring framework for SDN. PayLess provides a flexible RESTful API for flow statistics collection at different aggregation levels. It uses an adaptive statistics collection algorithm that delivers highly accurate information in real-time without incurring significant network overhead. We utilize the Floodlight controller's API to implement the proposed monitoring framework. The effectiveness of our solution is demonstrated through emulations in Mininet.


title: AIMQ: a methodology for information quality assessment

abstract: Information quality (IQ) is critical in organizations. Yet, despite a decade of active research and practice, the field lacks comprehensive methodologies for its assessment and improvement. Here, we develop such a methodology, which we call AIM quality (AIMQ) to form a basis for IQ assessment and benchmarking. The methodology is illustrated through its application to five major organizations. The methodology encompasses a model of IQ, a questionnaire to measure IQ, and analysis techniques for interpreting the IQ measures. We develop and validate the questionnaire and use it to collect data on the status of organizational IQ. These data are used to assess and benchmark IQ for four quadrants of the model. These analysis techniques are applied to analyze the gap between an organization and best practices. They are also applied to analyze gaps between IS professionals and information consumers. The results of the techniques are useful for determining the best area for IQ improvement activities.


title: Design of a ZCS full — Bridge DC — DC converter for PV based electric vehicle fast charging station

abstract: This paper exploits the advantages of ZCS PWM controlled Full — Bridge DC — DC Converter in Solar Power Sourced Electric Vehicle Fast Charging Stations. The wide range duty cycle of the converter makes it feasible for the Solar Energy based applications where operating voltage depends upon weather conditions. A detailed working of the converter has been explained. The circuit is simulated for a 0.5 kW 48 V system in the Simulink environment. Comparison has also been made between ZVS and ZCS converters. The prototype of the proposed system is under development in the Power Systems Laboratory of NIT Calicut.


title: The Automatic Statistician: A Relational Perspective

abstract: Gaussian Processes (GPs) provide a general and analytically tractable way of modeling complex time-varying, nonparametric functions. The Automatic Bayesian Covariance Discovery (ABCD) system constructs natural-language description of time-series data by treating unknown time-series data nonparametrically using GP with a composite covariance kernel function. Unfortunately, learning a composite covariance kernel with a single time-series data set often results in less informative kernel that may not give qualitative, distinctive descriptions of data. We address this challenge by proposing two relational kernel learning methods which can model multiple time-series data sets by finding common, shared causes of changes. We show that the relational kernel learning methods find more accurate models for regression problems on several real-world data sets; US stock data, US house price index data and currency exchange rate data.


An Introduction to Mathematical Cognition


title: Enterprise Knowledge Management and Emerging Technologies

abstract: Improving management of information and knowledge in organizations has long been a major objective, but efforts to address it often foundered. Knowledge typically resides in structured documents, informal discussions that may or may not persist online, and in tacit form. Terminology differences and dispersed contextual information hinder efforts to use formal representations. Features of dynamic emerging technologies — unstructured tagging, web-logs, and search — show strong promise in overcoming past obstacles. They exploit digital representations of less formal language and could greatly increase the value of such representations.


title: Ichneumon wasps of the Khasurty locality in Transbaikalia (Insecta, Hymenoptera, Ichneumonidae)

abstract: One new genus and four new species of ichneumon wasps are described from the Lower Cretaceous of Buryatia (Khasurty locality): Amplicella mininae, sp. nov., A. shcherbakovi, sp. nov., A. bashkuyevi, sp. nov., Khasurtella buriatica, gen. et sp. nov. The species Tanychora sinensis Zhang, 1991 is transferred to the new genus Khasurtella. Similarity of the ichneumonid faunas of Khasurty and the Chinese localities Yixian and Laiyang indicates the age similarity between these localities. Identification key to genera and species of Tanychorinae is given.


title: GRAPHiQL: A graph intuitive query language for relational databases

abstract: Graph analytics is becoming increasingly popular, driving many important business applications from social network analysis to machine learning. Since most graph data is collected in a relational database, it seems natural to attempt to perform graph analytics within the relational environment. However, SQL, the query language for relational databases, makes it difficult to express graph analytics operations. This is because SQL requires programmers to think in terms of tables and joins, rather than the more natural representation of graphs as collections of nodes and edges. As a result, even relatively simple graph operations can require very complex SQL queries. In this paper, we present GRAPHiQL, an intuitive query language for graph analytics, which allows developers to reason in terms of nodes and edges. GRAPHiQL provides key graph constructs such as looping, recursion, and neighborhood operations. At runtime, GRAPHiQL compiles graph programs into efficient SQL queries that can run on any relational database. We demonstrate the applicability of GRAPHiQL on several applications and compare the performance of GRAPHiQL queries with those of Apache Giraph (a popular ‘vertex centric’ graph programming language).


title: LipidHome: A Database of Theoretical Lipids Optimized for High Throughput Mass Spectrometry Lipidomics

abstract: Protein sequence databases are the pillar upon which modern proteomics is supported, representing a stable reference space of predicted and validated proteins. One example of such resources is UniProt, enriched with both expertly curated and automatic annotations. Taken largely for granted, similar mature resources such as UniProt are not available yet in some other “omics” fields, lipidomics being one of them. While having a seasoned community of wet lab scientists, lipidomics lies significantly behind proteomics in the adoption of data standards and other core bioinformatics concepts. This work aims to reduce the gap by developing an equivalent resource to UniProt called ‘LipidHome’, providing theoretically generated lipid molecules and useful metadata. Using the ‘FASTLipid’ Java library, a database was populated with theoretical lipids, generated from a set of community agreed upon chemical bounds. In parallel, a web application was developed to present the information and provide computational access via a web service. Designed specifically to accommodate high throughput mass spectrometry based approaches, lipids are organised into a hierarchy that reflects the variety in the structural resolution of lipid identifications. Additionally, cross-references to other lipid related resources and papers that cite specific lipids were used to annotate lipid records. The web application encompasses a browser for viewing lipid records and a ‘tools’ section where an MS1 search engine is currently implemented. LipidHome can be accessed at http://www.ebi.ac.uk/apweiler-srv/lipidhome.


title: On-Chip Millimeter Wave Antennas and Transceivers

abstract: The main mechanisms responsible for performance degradation of millimeter wave (mmWave) and terahertz (THz) on-chip antennas are reviewed. Several techniques to improve the performance of the antennas and several high efficiency antenna types are presented. In order to illustrate the effects of the chip topology on the antenna, simulations and measurements of mmWave and THz on-chip antennas are shown. Finally, different transceiver architectures are explored with emphasis on the challenges faced in a wireless multi-core environment.


title: Predict Demographic Information Using Word2vec on Spatial Trajectories

abstract: Inferring socio-demographic attributes of users is an important and challenging task that could help with personalization, recommendation, advertising, etc. Sensor data collected from mobile devices can be utilized for inferring such attributes. Previous works have focused on combining different types of sensors, such as applications, accelerometer, GPS, battery, and many others, to achieve this task. In this study, we were able to infer attributes, such as gender, age, marital status, and whether the user has children, using solely the GPS sensor. We suggest a novel inference technique, which learns an embedding representation of preprocessed spatial GPS trajectories using an adaption of the Word2vec approach. Based on the embedding representation, we later train multiple classification models to achieve the inference goals. Our empirical results indicate that the suggested embedding approach outperforms a classification approach which does not take into consideration the embedding patterns. Experiments on real datasets collected from Android devices show that the proposed method achieves over 80% accuracy for various demographic prediction tasks.


title: 283 Safe Streets, Livable Streets

abstract: Abstract Transportation safety is a highly contentious issue in the design of cities and communities. While urban designers, architects, and planners often encourage the use of aesthetic streetscape treatments to enhance the liability of urban streets, conventional transportation safety practice regards roadside features such as street trees as fixed-object hazards and strongly discourages their use. In this study, I examine the subject of livable streetscape treatments and find compelling evidence that suggests they may actually enhance the safety of urban roadways. Concerns about their safety effects do not appear to be founded on empirical observations of crash performance, but instead on a design philosophy that discounts the important relationship between driver behavior and safety. This study traces the origin and evolution of this philosophy, and proposes an alternative that may better account for the dynamic relationships between road design, driver behavior, and transportation safety. It has take...


1 Social Modeling for Requirements Engineering: An Introduction


title: FFT Based Sum-Product Algorithm for Decoding LDPC Lattices

abstract: LDPC lattices were introduced by Sadeghi et al. in [13] and have a good performance under generalized minsum and sum-product algorithms. The high complexity of these algorithms is mainly due to the search for local valid codewords in each check node process. In addition, when the dimension of such lattices is increased, these decoding algorithms are very time-consuming. In this paper, we propose an FFT based sum-product algorithm to decode LDPC lattices. In the check node process, using the FFT method reduces the check node complexity from O(dcg2) to O(dcg log g) where dc is the degree of a check equation and g is the alphabet size of an LDPC lattice. As a result, with almost the same complexity cost, we have a significant improvement over the performance of the minsum based decoding 2-level LDPC lattices with the symbol error probability smaller than 10-5 at SNR = 1.5 dB.


title: ACE: Automated CTF Estimation

abstract: In this paper we present a completely automated algorithm for estimating the parameters of the contrast transfer function (CTF) of a transmission electron microscope. The primary contribution of this paper is the determination of the astigmatism prior to the estimation of the CTF parameters. The CTF parameter estimation is then reduced to a 1D problem using elliptical averaging. We have also implemented an automated method to calculate lower and upper cutoff frequencies to eliminate regions of the power spectrum which perturb the estimation of the CTF parameters. The algorithm is comprised of three optimization subproblems, two of which are proven to be convex. Results of the CTF estimation method are presented for images of carbon support films as well as for images of single particles embedded in ice and suspended over holes in the support film. A MATLAB implementation of the algorithm, called ACE, is freely available.


title: Joint Sentiment-Topic Detection from Text Document

abstract: Automated tools are used to detect subjective information like attitudes, opinions and feelings. Such process is called as sentiment analysis. The Joint Sentiment-Detection (JST) model is the probabilistic model which is extension of Latent Dirichlet Allocation (LDA) model that detects sentiment and topic simultaneously from text. Supervised approaches to sentiment classification often fail to produce satisfactory results when applied to other domains while the JST model is weakly supervised in nature where supervision only comes from domain independent sentiment lexicon. Thus, makes JST model portable to other domains. The proposed system incorporates a small amount of domain independent prior knowledge which is sentiment lexicon to further improve the sentiment classification accuracy. It also carry out experiments and evaluates the model performance on different datasets.


title: PSI: Exact Symbolic Inference for Probabilistic Programs

abstract: Probabilistic inference is a key mechanism for reasoning about probabilistic programs. Since exact inference is theoretically expensive, most probabilistic inference systems today have adopted approximate inference techniques, which trade precision for better performance (but often without guarantees). As a result, while desirable for its ultimate precision, the practical effectiveness of exact inference for probabilistic programs is mostly unknown.


title: Effect of Foot Orthoses on Ankle and Foot Injuries in Military Service Recruits: A Randomized Controlled Trial

abstract: Lower extremity injuries are common among military recruits and leads to poor military outcome. Orthotics are shown to reduce injuries in runners. Effect of orthotics on military recruit foot injuries have not been adequately studied. We aimed to assess orthotics’ effect on ankle sprains, foot pain, tenderness and injury intensity in military recruits. 610 recruits entered this randomized, non-blinded, single centered controlled trial, 300 recruits were given a semi-rigid orthotic to put in their boots and 310 recruits who were not given anything were assigned as the control group. They were asked about history of foot pain in seven distinct parts of the lower limb and ankle sprains and their severity and were examined regarding tenderness in those foot parts for 2 times: at the beginning and the end of the two-month military trainings. Ankle sprain frequency did not differ significantly in trials (0.27±1.2) compared with controls (0.12±0.55) while ankle tenderness was significantly more common (p=0.02) and heel, foot arc and metatarsal pain and tenderness were significantly less common among trials. Pain induced inability to march or walk was less common in trials (p<0.00). Orthotics reduce foot sole pain and injury intensity and help to reduce pain induced training inability in military recruits. Further studies should assess their effect on ankle and leg pain.


title: Simple linear and multivariate regression models

abstract: Summary In biomedical research it is common to find problems in which we wish to relate a response variable to one or more variables capable of describing the behaviour of the former variable by means of mathematical models. Regression techniques are used to this effect, in which an equation is determined relating the two variables. While such equations can have different forms, linear equations are the most widely used form and are easy to interpret. The present article describes simple and multiple linear regression models, how they are calculated, and how their applicability assumptions are checked. Illustrative examples are provided, based on the use of the freely accessible R program.


title: Bearings-only tracking with fusion from heterogenous passive sensors: ESM/EO and acoustic

abstract: The performance of the conventional bearings-only tracking (BOT) from a single passive sensor largely depends on the sensor platform maneuvers. This paper presents a new BOT approach based on fusion from two heterogenous bearings-only sensors residing on the same moving or stationary platform. The two sensors are an ESM/EO with negligible propagation delay and an acoustic sensor with significant propagation delay. Since target range information is contained in the acoustic propagation delay, the problem is therefore observable even when the platform is stationary. An out-of-sequence measurement fusion from the acoustic and ESM/EO sensors (OOSM-AE) is developed to estimate the target trajectory. It consists of an unscented Kalman filter (UKF) to handle in-sequence ESM/EO measurements and an OOSM unscented Gauss-Helmert filter (OOSM-UGHF) to handle out-of-sequence acoustic measurements. Simulation tests are conducted to demonstrate the performance of this new BOT approach.


title: An Auditory Scene Analysis Approach to Monaural Speech Segregation

abstract: A human listener has the remarkable ability to segregate an acoustic mixture and attend to a target sound. This perceptual process is called auditory scene analysis (ASA). Moreover, the listener can accomplish much of auditory scene analysis with only one ear. Research in ASA has inspired many studies in computational auditory scene analysis (CASA) for sound segregation. In this chapter we introduce a CASA approach to monaural speech segregation. After a brief overview of CASA, we present in detail a CASA system that segregates both voiced and unvoiced speech. Our description covers the major stages of CASA, including feature extraction, auditory segmentation, and grouping.


title: Application of the Webqual Instrument to Three Australian B2C Websites: An Exploratory Investigation

abstract: Assessing the quality of a Website has received growing attention from academics and practitioners. The objective of this study is to apply the WebQualTM framework developed by Loiacono, Watson and Goodhue (2002) to determine whether any differences exist between three Australian industry groups across the Website Quality constructs. This study delineates the Website Quality model into its latent dimensions and examines for differences between three B2C Websites. This extends previous research on the WebQualTM instrument by presenting some preliminary findings in the Australian setting. Relevant implications for managing Website quality are highlighted, with directions for future research discussed.


title: A Strategy to Deal with Mass Small Files in HDFS

abstract: HDFS performs badly in storing and managing a great number of small files as a result of the great memory occupation of the single Namenode and massive seeks and hopping from datanode to datanode. Traditional solutions are only efficient for specific file size or file format. In this paper, we evaluate the performance of some different solutions such as Hbase and Avro. Then in order to compensate for the lack of their inefficiency for middle size small file, we implement a merging and prefetching mechanism. Finally for the purpose of reducing the influence of different file size distributions, we present a strategy of using different schemes for small files of different sizes. Through the experiments of performance comparison, it can be demonstrated that the strategy can improve the original HDFS's writing and reading performance by about 70%.


title: Accurate Image Search with Multi-Scale Contextual Evidences

abstract: This paper considers the task of image search using the Bag-of-Words (BoW) model. In this model, the precision of visual matching plays a critical role. Conventionally, local cues of a keypoint, e.g., SIFT, are employed. However, such strategy does not consider the contextual evidences of a keypoint, a problem which would lead to the prevalence of false matches. To address this problem and enable accurate visual matching, this paper proposes to integrate discriminative cues from multiple contextual levels, i.e., local, regional, and global, via probabilistic analysis. "True match" is defined as a pair of keypoints corresponding to the same scene location on all three levels (Fig. 1). Specifically, the Convolutional Neural Network (CNN) is employed to extract features from regional and global patches. We show that CNN feature is complementary to SIFT due to its semantic awareness and compares favorably to several other descriptors such as GIST, HSV, etc. To reduce memory usage, we propose to index CNN features outside the inverted file, communicated by memory-efficient pointers. Experiments on three benchmark datasets demonstrate that our method greatly promotes the search accuracy when CNN feature is integrated. We show that our method is efficient in terms of time cost compared with the BoW baseline, and yields competitive accuracy with the state-of-the-arts.


title: Chains of coupled square dielectric optical microcavities

abstract: Chains of coupled square dielectric cavities are investigated in a 2-D setting, by means of a quasi-analytical eigenmode expansion method. Resonant transfer of optical power can be achieved along quite arbitrary, moderately long rectangular paths (up to 9 coupled cavities are considered), even with individual standing-wave resonators of limited quality. We introduce an ab-initio coupled mode model, based on a simple superposition of slab mode profiles as a template for the field of individual cavities. Although no loss mechanisms are built in, the model can still help to interprete the results of the former numerical experiments.


title: Old School vs. New School: Comparing Transition-Based Parsers with and without Neural Network Enhancement

abstract: In this paper, we attempt a comparison between "new school" transition-based parsers that use neural networks and their classical "old school" coun-terpart. We carry out experiments on treebanks fr ...


title: Trans-activation of the Tetrahymena ribozyme by its P2-2.1 domains.

abstract: The Tetrahymena group I self-splicing intron contains peripheral domains P2-2.1. Mutant introns lacking these domains are hardly active. We found that if an independently prepared P2-2.1 RNA is added in trans, it efficiently enhances the catalytic activity of an intron lacking the domains. P2-2.1 RNA together with the previously identified activator, P5abc RNA, of the Tetrahymena intron can activate the intron lacking both of them. The trans-activation depends on the long-range interaction between P2.1 and P9.1 domains.


title: Factors Which Influence Girls’ Orientations To ICT Subjects In Schools. Evidence From Spain

abstract: The low representation of women in computer science and telecommunication engineering studies continues to be a topic of interest within the scientific community, and also a topic of concern to the policy community in Spain. The main aim of this paper is to review the literature in the Spanish language regarding the lack of interest and engagement of female secondary students in ICT-related subjects and careers. The content of the Spanish secondary educational system, the influence of stereotypical beliefs about men’s and women’s competences and attitudes towards ICTs and the way young women perceive their technology abilities are discussed in this paper. These aspects, together with a group of contextual factors specific to the context of Spain and the influence of parents and career advisors are tackled in this paper. The implications of the empirical findings and future research directions are discussed.


title: An Edge Computing Framework for Real-Time Monitoring in Smart Grid

abstract: Due to the ever-growing demands in modern cities, unreliable and inefficient power transportation becomes one critical issue in nowadays power grid. This makes power grid monitoring one of the key modules in power grid system and play an important role in preventing severe safety accidents. However, the traditional manual inspection cannot efficiently achieve this goal due to its low efficiency and high cost. Smart grid as a new generation of the power grid, sheds new light to construct an intelligent, reliable and efficient power grid with advanced information technology. In smart grid, automated monitoring can be realized by applying advanced deep learning algorithms on powerful cloud computing platform together with such IoT (Internet of Things) devices as smart cameras. The performance of cloud monitoring, however, can still be unsatisfactory since a large amount of data transmission over the Internet will lead to high delay and low frame rate. In this paper, we note that the edge computing paradigm can well complement the cloud and significantly reduce the delay to improve the overall performance. To this end, we propose an edge computing framework for real-time monitoring, which moves the computation away from the centralized cloud to the near-device edge servers. To maximize the benefits, we formulate a scheduling problem to further optimize the framework and propose an efficient heuristic algorithm based on the simulated annealing strategy. Both real-world experiments and simulation results show that our framework can increase the monitoring frame rate up to 10 times and reduce the detection delay up to 85% comparing to the cloud monitoring solution.


Security and Privacy Consideration for Internet of Things in Smart Home Environments


title: An Algorithm and Demand Estimation Procedure for Retail Assortment Optimization

abstract: We consider the problem of choosing, from a set of N potential SKUs in a retail category, K SKUs to be carried at each store so as to maximize revenue or profit. Assortments can vary by store, subject to a maximum number of dierent assortments. We introduce a model of substitution behavior, in case a customer’s first choice is unavailable and consider the impact of substitution in choosing assortments for the retail chain. We view a SKU as a set of attribute levels, apply maximum likelihood estimation to sales history of the SKUs currently carried by the retailer to estimate the demand for attribute levels and substitution probabilities, and from this, the demand for any potential SKU, including those not currently carried by the retailer. We specify several alternative heuristics for choosing SKUs. We apply this approach to optimize assortments for three real examples: snack cakes, tires and automotive appearance chemicals. A portion of our recommendations for tires and appearance chemicals were implemented and produced sales increases of 5.8% and 3.6% respectively, which are significant improvements relative to typical retailer annual comparable store revenue increases. We also forecast sales shares of 1, 11 and 25 new SKUs, for the snack cakes, tires and automotive appearance chemicals applications, respectively with MAPEs of 16.2%, 19.1% and 28.7%.


title: Triac Speed Control of Three-Phase Induction Motor with Phase-Locked Loop Regulation

abstract: The speed control of a three-phase squirrel cage induction motor by employing triacs in the lines which operate in the normal phase control mode is described. The inherent limitation of low circuit commutated (dv/dt), which makes the triac circuit somewhat unreliable, has been overcome by suitably designing the snubber circuit. The closed-loop speed regulation of the system is then investigated by employing digital phase-locked loop scheme. Compared to the conventional method with antiparallel SCR's and analog servo, the present scheme is somewhat simpler, more economical, and extreme precision in speed control is possible. A complete model of the speed control system has been designed and experimentally evaluated in the laboratory.


title: Network Theory Tools for RNA Modeling

abstract: An introduction into the usage of graph or network theory tools for the study of RNA molecules is presented. By using vertices and edges to define RNA secondary structures as tree and dual graphs, we can enumerate, predict, and design RNA topologies. Graph connectivity and associated Laplacian eigenvalues relate to biological properties of RNA and help understand RNA motifs as well as build, by computational design, various RNA target structures. Importantly, graph theoretical representations of RNAs reduce drastically the conformational space size and therefore simplify modeling and prediction tasks. Ongoing challenges remain regarding general RNA design, representation of RNA pseudoknots, and tertiary structure prediction. Thus, developments in network theory may help advance RNA biology.


P-Tour: A Personal Navigation System for Tourism


title: Informative Path Planning and Mapping with Multiple UAVs in Wind Fields

abstract: Informative path planning (IPP) is used to design paths for robotic sensor platforms to extract the best/maximum possible information about a quantity of interest while operating under a set of constraints, such as dynamic feasibility of vehicles. The key challenges of IPP are the strong coupling in multiple layers of decisions: the selection of locations to visit, the allocation of sensor platforms to those locations; and the processing of the gathered information along the paths. This paper presents an systematic procedure for IPP and environmental mapping using multiple UAV sensor platforms. It (a) selects the best locations to observe, (b) calculates the cost and finds the best paths for each UAV, and (c) estimates the measurement value within a given region using the Gaussian process (GP) regression framework. An illustrative example of RF intensity field mapping is presented to demonstrate the validity and applicability of the proposed approach.


title: Serum beta-endorphin increase after intravenous histamine treatment of chronic daily headache.

abstract: Histamine is able to induce spontaneous-like headache attacks in migraine and cluster headache subjects. Therefore, it has been considered as a possible agent in the pathogenesis of headache. Histamine desensitization is used for the treatment of cluster and other chronic headaches like migrains with interparoxysmal headache. However, it is unknown whether desensitization plays a role in headache improvement. Since a disfunction of the opioid system has been considered responsible for idiopathic headache and since low beta-endorphin levels have been demonstrated in some idiopathic headaches, particularly in migraine with interparoxysmal headache, we planned this study to verify if histamine therapy is able to modify serum beta-endorphin concentrations. For this purpose, we studied 24 healthy control subjects and 36 patients suffering from migraine with interparoxysmal headache refractory to conventional therapies. Patients showed baseline serum beta-endorphin levels significantly lower than healthy control subjects and treatment with histamine for 15 days increased their beta-endorphin concentrations. We believe that histamine treatment can activate the opioid endogenous system. However, the therapeutic effect of histamine remains to be verified by evaluating the correlation between beta-endorphin levels and headache improvement.


title: Validation of a model of the GAL regulatory system via robustness analysis of its bistability characteristics

abstract: BackgroundIn Saccharomyces cerevisiæ, structural bistability generates a bimodal expression of the galactose uptake genes (GAL) when exposed to low and high glucose concentrations. This indicates that yeast cells can decide between using either the limited amount of glucose or growing on galactose under changing environmental conditions. A crucial requirement for any plausible mechanistic model of this system is that it reproduces the robustness of the bistable response observed in vivo against inter-individual parametric variability and fluctuating environmental conditions.ResultsWe show how a control-theoretic analysis of the robustness of a model of the GAL regulatory network may be used to establish the model’s plausibility in characterizing the persistent memory of different carbon sources, without the need for extensive simulations. Chemical Reaction Network Theory is used to establish that the proposed network model is compatible with structural bistability. The robustness of each of the two operative conditions against fluctuations of the species concentrations is demonstrated by studying the Domains of Attraction of the corresponding equilibrium points. Finally, we use a global robustness analysis method based on Semi-Definite Programming to evaluate the modification of the bistable steady states induced by multiple parametric variations throughout bounded regions of the parameter space.ConclusionsOur analysis provides convincing evidence for the robustness, and hence plausibility, of the GAL regulatory network model. The proposed workflow also demonstrates the power of analytical methods from control theory to provide a direct quantitative characterization of the dynamics of multistable biomolecular regulatory systems without recourse to extensive computer simulations.


title: Differential Effects of Early Hippocampal Pathology on Episodic and Semantic Memory

abstract: Global anterograde amnesia is described in three patients with brain injuries that occurred in one case at birth, in another by age 4, and in the third at age 9. Magnetic resonance techniques revealed bilateral hippocampal pathology in all three cases. Remarkably, despite their pronounced amnesia for the episodes of everyday life, all three patients attended mainstream schools and attained levels of speech and language competence, literacy, and factual knowledge that are within the low average to average range. The findings provide support for the view that the episodic and semantic components of cognitive memory are partly dissociable, with only the episodic component being fully dependent on the hippocampus.


title: Practical Tooling for Serverless Computing

abstract: Cloud applications are increasingly built from a mixture of runtime technologies. Hosted functions and service-oriented web hooks are among the most recent ones which are natively supported by cloud platforms. They are collectively referred to as serverless computing by application engineers due to the transparent on-demand instance activation and microbilling without the need to provision infrastructure explicitly. This half-day tutorial explains the use cases for serverless computing and the drivers and existing software solutions behind the programming and deployment model also known as Function-as-a-Service in the overall cloud computing stack. Furthermore, it presents practical open source tools for deriving functions from legacy code and for the management and execution of functions in private and public clouds.


title: Full-Duplex Aided User Virtualization for Mobile Edge Computing in 5G Networks

abstract: Driven by the increasing demand of intensive computing services and the resource limitation of mobile devices at the edge of mobile networks, mobile edge computing (MEC) is concerned with being an emerging paradigm towards the 5G communications. A main issue of MEC is the coordination of communication, computation, and storage. In this paper, we propose a novel MEC framework with a user virtualization scheme in the software-defined network virtualization cellular network, in which radio resources are virtualized along with computation and storage resources to cooperatively finish MEC services. Besides, we introduce the user virtualization assisted by the full-duplex communication which can extend the radio resource of wireless networks and provide the potential to increase system performance. Moreover, enabled by user virtualization, users can offload the edge computation tasks directly to the MEC server implemented at infrastructure providers or via the virtualized mobile device attributed to different mobile virtual network operators (MVNOs). Under this MEC framework, we formulate the virtual resource allocation as a joint optimization problem. A distributed resource allocation algorithm based on an alternating direction method of multipliers is proposed which can reduce computational complexity and signaling overhead. We evaluate the proposed algorithm through extensive simulations, and the results show that the total utility of MVNOs can be improved significantly which benefited from user virtualization.


title: Novel Statistical Post Processing to Improve Blood Pressure Estimation from Smartphone Photoplethysmogram

abstract: Blood pressure (BP) is considered to be an important biomarker for cardiac risk estimation. This paper deals with a non-conventional way of estimating BP using smartphone captured Photoplethysmogram (PPG) that enables unobtrusive health monitoring at home for possible alert generation. We have proposed a set of features that are independent to the inbuilt sensor of the capturing device. It is also observed that, BP estimated from a typical smartphone PPG signal fluctuates in successive cardiac cycles due to poor signal quality compared to a medical grade device. Hence, a novel post processing block is introduced, that rejects data depending on the BP distribution over all cardiac cycles in a session. Finally, Half Range Mode is used as a statistical average for the accepted sessions. This post processing methodology outperforms standard statistical averages in providing a better representative BP per session. The methodology yields mean absolute errors of 7.4% and 9.1% for predicting systolic and diastolic pressure respectively when validated over a dataset with a wide variation of BP.


title: Deep feature matching for dense correspondence

abstract: Image matching is a challenging problem as different views often undergo significant appearance changes caused by deformation, abrupt motion, and occlusion. In this paper, we explore features extracted from convolutional neural networks to help the estimation of image matching so that dense pixel correspondence can be built. As the deep features are able to describe the image structures, the matching method based on these features is able to match across different scenes and/or object appearances. We analyze the deep features and compare them with other robust features, e.g., SIFT. Extensive experiments on 5 datasets demonstrate the proposed algorithm performs favorably against the state-of-the-art methods in terms of visually matching quality and accuracy.


title: Divide-and-Conquer: Why Android Malware Cannot Be Stopped

abstract: In this paper, we demonstrate that Android malware can bypass all automated analysis systems, including AV solutions, mobile sandboxes, and the Google Bouncer. We propose a tool called Sand-Finger for the fingerprinting of Android-based analysis systems. By analyzing the fingerprints of ten unique analysis environments from different vendors, we were able to find characteristics in which all tested environments differ from actual hardware. Depending on the availability of an analysis system, malware can either behave benignly or load malicious code at runtime. We classify this group of malware as Divide-and-Conquer attacks that are efficiently obfuscated by a combination of fingerprinting and dynamic code loading. In this group, we aggregate attacks that work against dynamic as well as static analysis. To demonstrate our approach, we create proof-of-concept malware that surpasses up-to-date malware scanners for Android. We also prove that known malware samples can enter the Google Play Store by modifying them only slightly. Due to Android's lack of an API for malware scanning at runtime, it is impossible for AV solutions to secure Android devices against these attacks.


title: GPU-Aware MPI on RDMA-Enabled Clusters: Design, Implementation and Evaluation

abstract: Designing high-performance and scalable applications on GPU clusters requires tackling several challenges. The key challenge is the separate host memory and device memory, which requires programmers to use multiple programming models, such as CUDA and MPI, to operate on data in different memory spaces. This challenge becomes more difficult to tackle when non-contiguous data in multidimensional structures is used by real-world applications. These challenges limit the programming productivity and the application performance. We propose the GPU-Aware MPI to support data communication from GPU to GPU using standard MPI. It unifies the separate memory spaces, and avoids explicit CPU-GPU data movement and CPU/GPU buffer management. It supports all MPI datatypes on device memory with two algorithms: a GPU datatype vectorization algorithm and a vector based GPU kernel data pack and unpack algorithm. A pipeline is designed to overlap the non-contiguous data packing and unpacking on GPUs, the data movement on the PCIe, and the RDMA data transfer on the network. We incorporate our design with the open-source MPI library MVAPICH2 and optimize a production application: the multiphase 3D LBM. Besides the increase of programming productivity, we observe up to 19.9 percent improvement in application-level performance on 64 GPUs of the Oakley supercomputer.


title: Extracting and matching authors and affiliations in scholarly documents

abstract: We introduce Enlil, an information extraction system that discovers the institutional affiliations of authors in scholarly papers. Enlil consists of two steps: one that first identifies authors and affiliations using a conditional random field; and a second support vector machine that connects authors to their affiliations. We benchmark Enlil in three separate experiments drawn from three different sources: the ACL Anthology Corpus, the ACM Digital Library, and a set of cross-disciplinary scientific journal articles acquired by querying Google Scholar. Against a state-of-the-art production baseline, Enlil reports a statistically significant improvement in F_1 of nearly 10% (p << 0.01). In the case of multidisciplinary articles from Google Scholar, Enlil is benchmarked over both clean input (F_1 > 90%) and automatically-acquired input (F_1 > 80%). ::: We have deployed Enlil in a case study involving Asian genomics research publication patterns to understand how government sponsored collaborative links evolve. Enlil has enabled our team to construct and validate new metrics to quantify the facilitation of research as opposed to direct publication.


title: Adapting Golog for Composition of Semantic Web Services

abstract: Motivated by the problem of automatically composing network accessible services, such as those on the World Wide Web, this paper proposes an approach to building agent technology based on the notion of generic procedures and customizing user constraint. We argue that an augmented version of the logic programming language Golog provides a natural formalism for automatically composing services on the Semantic Web. To this end, we adapt and extend the Golog language to enable programs that are generic, customizable and usable in the context of the Web. Further, we propose logical criteria for these generic procedures that define when they are knowledge self-sufficient and physically selfsufficient. To support information gathering combined with search, we propose a middle-ground Golog interpreter that operates under an assumption of reasonable persistence of certain information. These contributions are realized in our augmentation of a ConGolog interpreter that combines online execution of information-providing Web services with offline simulation of worldaltering Web services, to determine a sequence of Web Services for subsequent execution. Our implemented system is currently interacting with services on the Web.


title: Online Supervised Hashing for Ever-Growing Datasets

abstract: Supervised hashing methods are widely-used for nearest neighbor search in computer vision applications. Most state-of-the-art supervised hashing approaches employ batch-learners. Unfortunately, batch-learning strategies can be inefficient when confronted with large training datasets. Moreover, with batch-learners, it is unclear how to adapt the hash functions as a dataset continues to grow and diversify over time. Yet, in many practical scenarios the dataset grows and diversifies; thus, both the hash functions and the indexing must swiftly accommodate these changes. To address these issues, we propose an online hashing method that is amenable to changes and expansions of the datasets. Since it is an online algorithm, our approach offers linear complexity with the dataset size. Our solution is supervised, in that we incorporate available label information to preserve the semantic neighborhood. Such an adaptive hashing method is attractive; but it requires recomputing the hash table as the hash functions are updated. If the frequency of update is high, then recomputing the hash table entries may cause inefficiencies in the system, especially for large indexes. Thus, we also propose a framework to reduce hash table updates. We compare our method to state-of-the-art solutions on two benchmarks and demonstrate significant improvements over previous work.


title: Enhancing product search by best-selling prediction in e-commerce

abstract: With the rapid growth of E-Commerce on the Internet, online product search service has emerged as a popular and effective paradigm for customers to find desired products and select transactions. Most product search engines today are based on adaptations of relevance models devised for information retrieval. However, there is still a big gap between the mechanism of finding products that customers really desire to purchase and that of retrieving products of high relevance to customers' query. In this paper, we address this problem by proposing a new ranking framework for enhancing product search based on dynamic best-selling prediction in E-Commerce. Specifically, we first develop an effective algorithm to predict the dynamic best-selling, i.e. the volume of sales, for each product item based on its transaction history. By incorporating such best-selling prediction with relevance, we propose a new ranking model for product search, in which we rank higher the product items that are not only relevant to the customer's need but with higher probability to be purchased by the customer. Results of a large scale evaluation, conducted over the dataset from a commercial product search engine, demonstrate that our new ranking method is more effective for locating those product items that customers really desire to buy at higher rank positions without hurting the search relevance.


title: Robinhood: Towards Efficient Work-Stealing in Virtualized Environments

abstract: Work-stealing, as a common user-level task scheduler for managing and scheduling tasks of multithreaded applications, suffers from inefficiency in virtualized environments, because the steal attempts of thief threads may waste CPU cycles that could be otherwise used by busy threads. This paper contributes a novel scheduling framework named Robinhood. The basic idea of Robinhood is to use the time slices of thieves to accelerate busy threads with no available tasks (referred to as poor workers) at both the guest Operating System (OS) level and Virtual Machine Monitor (VMM) level. In this way, Robinhood can reduce the cost of steal attempts and accelerate the threads doing useful work, so as to put the CPU cycles to better use. We implement Robinhood based on BWS, Linux and Xen. Our evaluation with various benchmarks demonstrates that Robinhood paves a way to efficiently run work-stealing applications in virtualized environments. Compared to Cilk++ and BWS, Robinhood can reduce up to 90 and 72 percent execution time of work-stealing applications, respectively.


title: From Latency-Insensitive Design to Communication-Based System-Level Design

abstract: By the end of the 20th century, the continuous progress of the semiconductor industry brought a major transformation in the design of integrated circuits: as the speed of global wires could not keep up with the speed of ever-smaller transistors, the digital chip became a distributed system. This fact broke the synchronous paradigm assumption, i.e., the foundation of those computer-aided design (CAD) flows which had made possible three decades of unique technology progress: from chips with thousands of transistors to systems on chips (SoCs) with over a billion transistors. Latency-insensitive design (LID) is a correct-by-construction design methodology that was originally developed to address this challenge while preserving as much as possible the synchronous assumption. A broad new approach that transforms the fundamentals of how complex digital systems are assembled, LID introduces the protocols and shells paradigm, which offers several main benefits: modularity (by reconciling the synchronous paradigm with the dominant impact of global interconnect delays that characterizes nanometer technologies), scalability (by making key properties of the design be correct by construction through interface synthesis), flexibility (by simplifying the design and validation of a system through the separation of communication from computation), and efficiency (by enabling the reuse of predesigned components, thus reducing the overall design time). This paper overviews the principles and practice of LID, offers a retrospective on related research over the past decade, and looks ahead in proposing the protocols and shells paradigm as the foundation to bridge the gap between system-level and logic/physical design, a requisite to cope with the complexity of engineering future SoC platforms.


title: High performance communication architecture for smart distribution power grid in developing nations

abstract: In a smart distribution power grid, cost efficient and reliable communication architecture plays a crucial role in achieving complete functionality. There are different sets of Quality of Services (QoS) requirements for different data packets transmitting inside the microgrid (a regionally limited smart distribution grid), making it challenging to derive optimal communication architecture. The objective of this research work is to determine the optimal communication technologies for each data packet based on its QoS requirement. In this paper, we have proposed an architecture for a smart distribution power grid with Cyber Physical System enabled microgrids, which accommodate almost all functional requirements of a smart distribution power grid. For easy transition towards optimal communication architecture, we have presented a six-tier communication topology, which is derived from the architecture for a smart distribution power grid. The optimization formulations for each packet structure presented in this paper minimize the overall cost and consider the QoS requirements for each packet. Based on the simulation results, we have made recommendations for optimal communication technologies for each packet and thereby developed a heterogeneous communication architecture for a microgrid.


Chapter 13 Nostalgia Conceptual Issues and Existential Functions


title: Experimental evidence of a template aging effect in iris biometrics

abstract: It has been widely accepted that iris biometric systems are not subject to a template aging effect. Baker et al. [1] recently presented the first published evidence of a template aging effect, using images acquired from 2004 through 2008 with an LG 2200 iris imaging system, representing a total of 13 subjects (26 irises). We report on a template aging study involving two different iris recognition algorithms, a larger number of subjects (43), a more modern imaging system (LG 4000), and over a shorter time-lapse (2 years). We also investigate the degree to which the template aging effect may be related to pupil dilation and/or contact lenses. We find evidence of a template aging effect, resulting in an increase in match hamming distance and false reject rate.


title: An inertial-based human motion tracking system with twists and exponential maps

abstract: Wearable inertial tracking is well accepted due to its convenience for free-style motion tracking with high accuracy. Traditionally, complicated high-order calculations for human kinematic modeling and inaccurate estimation of sensor placement are interfering the efficiency of real-time tracking. In order to tackle the challenges, a wearable human motion tracking system is developed by applying twists and exponential maps techniques. When the body segments are articulated by product of exponential maps, joint positions are continuously updated based on these techniques and their rotational angles are represented individually within the global frame. It is more efficient to achieve real-time motion tracking with low-order calculations. Meanwhile by applying the well-designed calibration procedure, it is more convenient to estimate a sensor's position and orientation regardless of knowing its placement. This paper presents our approach and exemplifies the assessment of proposed motion tracking system by several tests of limb and full body motion tracking. The comparisons with Vicon and OptiTrack motion capture systems verify satisfactorily high accuracy.


title: Occam's Gates

abstract: We present a complimentary objective for training recurrent neural networks (RNN) with gating units that helps with regularization and interpretability of the trained model. Attention-based RNN models have shown success in many difficult sequence to sequence classification problems with long and short term dependencies, however these models are prone to overfitting. In this paper, we describe how to regularize these models through an L1 penalty on the activation of the gating units, and show that this technique reduces overfitting on a variety of tasks while also providing to us a human-interpretable visualization of the inputs used by the network. These tasks include sentiment analysis, paraphrase recognition, and question answering.


title: Characteristics of external genitalia in pre- and postmenopausal women.

abstract: OBJECTIVE ::: To determine those objective measurements that characterize the differences between the external genital organs of pre- and postmenopausal women. ::: ::: ::: METHODS ::: During the study period, 50 premenopausal and 50 postmenopausal patients were recruited. Only women who were admitted for routine control examinations were consecutively included in the study. Exclusion criteria were previous history of pelvic surgery including external and internal genital organs, presence of diseases that may change the anatomy of external genital organs, Mullerian anomalies, previous vaginal birth with mediolateral episiotomy, and use of hormone replacement therapy. The following measurements were performed: length and width of clitoris, labium majus, and labium minus, the distance between the clitoris and urethra, perineal length, and length of vagina. ::: ::: ::: RESULTS ::: The length of the vagina and the width of the labium minus were significantly different between the two groups. Mean vaginal length was significantly longer in premenopausal women compared to postmenopausal women (90.3 +/- 14.8 mm vs. 82.3 +/- 11.2 mm, respectively). The labia minora were wider in premenopausal women than in postmenopausal women (17.9 +/- 4.1 mm vs. 15.4 +/- 4.7 mm). ::: ::: ::: CONCLUSIONS ::: Characterization of the anatomical changes and relationships of external genitalia in postmenopausal women is important for functional and perioperative evaluation. In addition to reconstructive surgical procedures, determination of the objective measurements of anatomical landmarks in postmenopausal external genitalia might also be useful for assessing the results of treatment of 'atrophic' changes in women.


title: Connected Vehicles: Solutions and Challenges

abstract: Providing various wireless connectivities for vehicles enables the communication between vehicles and their internal and external environments. Such a connected vehicle solution is expected to be the next frontier for automotive revolution and the key to the evolution to next generation intelligent transportation systems (ITSs). Moreover, connected vehicles are also the building blocks of emerging Internet of Vehicles (IoV). Extensive research activities and numerous industrial initiatives have paved the way for the coming era of connected vehicles. In this paper, we focus on wireless technologies and potential challenges to provide vehicle-to-x connectivity. In particular, we discuss the challenges and review the state-of-the-art wireless solutions for vehicle-to-sensor, vehicle-to-vehicle, vehicle-to-Internet, and vehicle-to-road infrastructure connectivities. We also identify future research issues for building connected vehicles.


title: Modeling of TURBOFLO - A novel biofilm reactor for wastewater treatment

abstract: A mathematical model of a novel industrial-scale biofilm reactor - TURBOFLO - was developed. The reactor is an internal circulating airlift with rectangular cross-section. High density polyethylene granules with a density of less than 1 g/cm3 were used to support bacterial growth. The reactor was used for several months for nitrification of secondary effluent. The mathematical model consisted of three differential mass balance equations for ammonia, dissolved oxygen (DO) and biomass. Description of gas-liquid mass transfer was the critical part of the model. The most successful approach was based on Higbie's penetration theory and Kolmogorov's isotropic turbulence.


title: Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking

abstract: Saliency detection has been a hot topic in recent years, and many efforts have been devoted in this area. Unfortunately, the results of saliency detection can hardly be utilized in general applications. The primary reason, we think, is unspecific definition of salient objects, which makes that the previously published methods cannot extend to practical applications. To solve this problem, we claim that saliency should be defined in a context and the salient band selection in hyperspectral image (HSI) is introduced as an example. Unfortunately, the traditional salient band selection methods suffer from the problem of inappropriate measurement of band difference. To tackle this problem, we propose to eliminate the drawbacks of traditional salient band selection methods by manifold ranking. It puts the band vectors in the more accurate manifold space and treats the saliency problem from a novel ranking perspective, which is considered to be the main contributions of this paper. To justify the effectiveness of the proposed method, experiments are conducted on three HSIs, and our method is compared with the six existing competitors. Results show that the proposed method is very effective and can achieve the best performance among the competitors.


title: Automatic detection of cyber-recruitment by violent extremists

abstract: Growing use of the Internet as a major means of communication has led to the formation of cyber-communities, which have become increasingly appealing to terrorist groups due to the unregulated nature of Internet communication. Online communities enable violent extremists to increase recruitment by allowing them to build personal relationships with a worldwide audience capable of accessing uncensored content. This article presents methods for identifying the recruitment activities of violent groups within extremist social media websites. Specifically, these methods apply known techniques within supervised learning and natural language processing to the untested task of automatically identifying forum posts intended to recruit new violent extremist members. We used data from the western jihadist website Ansar AlJihad Network, which was compiled by the University of Arizona’s Dark Web Project. Multiple judges manually annotated a sample of these data, marking 192 randomly sampled posts as recruiting (Yes) or non-recruiting (No). We observed significant agreement between the judges’ labels; Cohen’s κ=(0.5,0.9) at p=0.01. We tested the feasibility of using naive Bayes models, logistic regression, classification trees, boosting, and support vector machines (SVM) to classify the forum posts. Evaluation with receiver operating characteristic (ROC) curves shows that our SVM classifier achieves an 89% area under the curve (AUC), a significant improvement over the 63% AUC performance achieved by our simplest naive Bayes model (Tukey’s test at p=0.05). To our knowledge, this is the first result reported on this task, and our analysis indicates that automatic detection of online terrorist recruitment is a feasible task. We also identify a number of important areas of future work including classifying non-English posts and measuring how recruitment posts and current events change membership numbers over time.


title: Incentives for Procrastinators

abstract: We examine how principals should design incentives to induce time-inconsistent procrastinating agents to complete tasks efficiently. Delay is costly to the principal, but the agent faces stochastic costs of completing the task, and efficiency requires waiting when costs are high. If the principal knows the task-cost distribution, she can always achieve first-best efficiency. If the agent has private information, the principal can induce first-best efficiency for time-consistent agents, but often cannot for procrastinators. We show that second-best optimal incentives for procrastinators typically involve an increasing punishment for delay as time passes.


title: Orienting to social stimuli differentiates social cognitive impairment in autism and schizophrenia

abstract: Both autism and schizophrenia feature deficits in aspects of social cognition that may be related to amygdala dysfunction, but it is unclear whether these are similar or different patterns of impairment. We compared the visual scanning patterns and emotion judgments of individuals with autism, individuals with schizophrenia and controls on a task well characterized with respect to amygdala functioning. On this task, eye movements of participants are recorded as they assess emotional content within a series of complex social scenes where faces are either included or digitally erased. Results indicated marked abnormalities in visual scanning for both disorders. Controls increased their gaze on face regions when faces were present to a significantly greater degree than both the autism or schizophrenia groups. While the control and the schizophrenia groups oriented to face regions faster when faces were present compared to when they were absent, the autism group oriented at the same rate in both conditions. The schizophrenia group, meanwhile, exhibited a delay in orienting to face regions across both conditions, although whether anti-psychotic medication contributed to this effect is unclear. These findings suggest that while processing emotional information in social scenes, both individuals with autism and individuals with schizophrenia fixate faces less than controls, although only those with autism fail to orient to faces more rapidly based on the presence of facial information. Autism and schizophrenia may therefore share an abnormality in utilizing facial information for assessing emotional content in social scenes, but differ in the ability to seek out socially relevant cues from complex stimuli. Impairments in social orienting are discussed within the context of evidence suggesting the role of the amygdala in orienting to emotionally meaningful information.


title: Adaptive Gesture Recognition with Variation Estimation for Interactive Systems

abstract: This article presents a gesture recognition/adaptation system for human--computer interaction applications that goes beyond activity classification and that, as a complement to gesture labeling, characterizes the movement execution. We describe a template-based recognition method that simultaneously aligns the input gesture to the templates using a Sequential Monte Carlo inference technique. Contrary to standard template-based methods based on dynamic programming, such as Dynamic Time Warping, the algorithm has an adaptation process that tracks gesture variation in real time. The method continuously updates, during execution of the gesture, the estimated parameters and recognition results, which offers key advantages for continuous human--machine interaction. The technique is evaluated in several different ways: Recognition and early recognition are evaluated on 2D onscreen pen gestures; adaptation is assessed on synthetic data; and both early recognition and adaptation are evaluated in a user study involving 3D free-space gestures. The method is robust to noise, and successfully adapts to parameter variation. Moreover, it performs recognition as well as or better than nonadapting offline template-based methods.


title: Analysis and characterization of inherent application resilience for approximate computing

abstract: Approximate computing is an emerging design paradigm that enables highly efficient hardware and software implementations by exploiting the inherent resilience of applications to in-exactness in their computations. Previous work in this area has demonstrated the potential for significant energy and performance improvements, but largely consists of ad hoc techniques that have been applied to a small number of applications. Taking approximate computing closer to mainstream adoption requires (i) a deeper understanding of inherent application resilience across a broader range of applications (ii) tools that can quantitatively establish the inherent resilience of an application, and (iii) methods to quickly assess the potential of various approximate computing techniques for a given application. We make two key contributions in this direction. Our primary contribution is the analysis and characterization of inherent application resilience present in a suite of 12 widely used applications from the domains of recognition, data mining, and search. Based on this analysis, we present several new insights into the nature of resilience and its relationship to various key application characteristics. To facilitate our analysis, we propose a systematic framework for Application Resilience Characterization (ARC) that (a) partitions an application into resilient and sensitive parts and (b) characterizes the resilient parts using approximation models that abstract a wide range of approximate computing techniques. We believe that the key insights that we present can help shape further research in the area of approximate computing, while automatic resilience characterization frameworks such as ARC can greatly aid designers in the adoption approximate computing.


title: Precise flow-insensitive may-alias analysis is NP-hard

abstract: Determining aliases is one of the foundamental static analysis problems, in part because the precision with which this problem is solved can affect the precision of other analyses such as live variables, available expressions, and constant propagation. Previous work has investigated the complexity of flow-sensitive alias analysis. In this article we show that precise flow- insensitive may-alias analysis is NP-hard given arbitrary levels of pointers and arbitrary pointer dereferencing.


title: A calibration method for structured light systems based on a virtual camera

abstract: Structured light systems are a simple and efficient tool in the field of 3D reconstruction. Existing calibration methods of structured light systems are either not precise or elastic. In this paper, an easy to use method is proposed. The calibration of a structured light system can accomplished with a good accuracy. The projector projects a checkerboard pattern rendered by OpenGL functions onto a planar board, and the coordinates of corner points in the projector image plane are adjusted to the target position with an iterative algorithm. Thus, the projector can "view" the printed chessboard pattern. The calibration of a projector becomes essentially the same as that of a monocular camera. Meanwhile, the projector calibration doesn't depend on a pre-calibrated camera, which makes errors of the camera calibration cannot effect the projector calibration. Experimental results show that the mean re-projection error of the projector is less than 1 pixel.


title: Homicides due to positional asphyxia: two case reports

abstract: Homicides by positional asphyxia do not come up very often within forensic literature in contrast to other ways of asphyxia have been frequently described. This article describes two cases in which the autopsy findings of the external and internal examination, may suggest patterns for future studies and/or reference points for studying this kind of death. In addition, it could open discussion about an unfrequented topic in forensics. The analysis of this case report aims to understand positional asphyxia as a homicide method in order to shed light on such a complex and rare kind of death.


title: Model Checking Interlocking Control Tables

abstract: A challenging problem for model checking is represented by railway interlocking systems. It is a well known fact that interlocking systems, due to their inherent complexity related to the high number of variables involved, are not amenable to automatic verification, typically incurring in state space explosion problems. The literature is however quite scarce on data concerning the size of interlocking systems that have been successfully proved with model checking techniques. In this paper we attempt a systematic study of the applicability bounds for general purpose model checkers on this class of systems, by studying the typical characteristics of control tables and their size parameters. The results confirm that, although small scale interlocking systems can be addressed by model checking, interlockings that control medium or large railway yards can not, asking for specialized verification techniques.


title: The Influence of Texting Language on Grammar and Executive Functions in Primary School Children

abstract: When sending text messages on their mobile phone to friends, children often use a special type of register, which is called textese. This register allows the omission of words and the use of textisms: instances of non-standard written language such as 4ever (forever). Previous studies have shown that textese has a positive effect on children’s literacy abilities. In addition, it is possible that children’s grammar system is affected by textese as well, as grammar rules are often transgressed in this register. Therefore, the main aim of this study was to investigate whether the use of textese influences children’s grammar performance, and whether this effect is specific to grammar or language in general. Additionally, studies have not yet investigated the influence of textese on children’s cognitive abilities. Consequently, the secondary aim of this study was to find out whether textese affects children’s executive functions. To investigate this, 55 children between 10 and 13 years old were tested on a receptive vocabulary and grammar performance (sentence repetition) task and various tasks measuring executive functioning. In addition, text messages were elicited and the number of omissions and textisms in children’s messages were calculated. Regression analyses showed that omissions were a significant predictor of children’s grammar performance after various other variables were controlled for: the more words children omitted in their text messages, the better their performance on the grammar task. Although textisms correlated (marginally) significantly with vocabulary, grammar and selective attention scores and omissions marginally significantly with vocabulary scores, no other significant effects were obtained for measures of textese in the regression analyses: neither for the language outcomes, nor for the executive function tasks. Hence, our results show that textese is positively related to children’s grammar performance. On the other hand, use of textese does not affect—positively nor negatively—children’s executive functions.


title: Music listening after stroke: beneficial effects and potential neural mechanisms.

abstract: Music is an enjoyable leisure activity that also engages many emotional, cognitive, and motor processes in the brain. Here, we will first review previous literature on the emotional and cognitive effects of music listening in healthy persons and various clinical groups. Then we will present findings about the short- and long-term effects of music listening on the recovery of cognitive function in stroke patients and the underlying neural mechanisms of these music effects. First, our results indicate that listening to pleasant music can have a short-term facilitating effect on visual awareness in patients with visual neglect, which is associated with functional coupling between emotional and attentional brain regions. Second, daily music listening can improve auditory and verbal memory, focused attention, and mood as well as induce structural gray matter changes in the early poststroke stage. The psychological and neural mechanisms potentially underlying the rehabilitating effect of music after stroke are discussed.


Statistical Arbitrage Stock Trading using Time Delay Neural Networks


title: Convolutional CRFs for Semantic Segmentation

abstract: For the challenging semantic image segmentation task the most efficient models have traditionally combined the structured modelling capabilities of Conditional Random Fields (CRFs) with the feature extraction power of CNNs. In more recent works however, CRF post-processing has fallen out of favour. We argue that this is mainly due to the slow training and inference speeds of CRFs, as well as the difficulty of learning the internal CRF parameters. To overcome both issues we propose to add the assumption of conditional independence to the framework of fully-connected CRFs. This allows us to reformulate the inference in terms of convolutions, which can be implemented highly efficiently on GPUs. Doing so speeds up inference and training by a factor of more then 100. All parameters of the convolutional CRFs can easily be optimized using backpropagation. To facilitating further CRF research we make our implementation publicly available. Please visit: this https URL


title: Transforaminal Epidural Steroid Injection for Lumbosacral Radiculopathy: Preganglionic versus Conventional Approach

abstract: Objective: The present study was undertaken to evaluate the effectiveness of transforaminal epidural steroid injection (TFESI) with using a preganglionic approach for treating lumbar radiculopathy when the nerve root compression was located at the level of the supra-adjacent intervertebral disc.


title: Marhaba, how may i help you?: effects of politeness and culture on robot acceptance and anthropomorphization

abstract: How do politeness strategies and cultural aspects affect robot acceptance and anthropomorphization across native speakers of English and Arabic? Previous work in cross-cultural HRI studies has mostly focused on Western and East Asian cultures. In contrast, Middle Eastern attitudes and perceptions of robot assistants are a barely researched topic. We investigated culture-specific determinants of robot acceptance and anthropomorphization by conducting a between-subjects study in Qatar. A total of 92 native speakers of either English or Arabic interacted with a receptionist robot in two different interaction tasks. We further manipulated the robot’s verbal behavior in experimental sub-groups to explore different politeness strategies. Our results suggest that Arab participants perceived the robot more positively and anthropomorphized it more than English speaking participants. In addition, the use of positive politeness strategies and the change of interaction task had an effect on participants’ HRI experience. Our findings complement the existing body of cross-cultural HRI research with a Middle Eastern perspective that will help to inform the design of robots intended for use in cross-cultural, multi-lingual settings.


title: A high boost ratio bidirectional isolated DC-DC converter for wide range low voltage high current applications

abstract: This paper introduces a high boost ratio, bidirectional, isolated, dc/dc converter designed as an interface between low voltage, high current dc sources and 24 V dc systems. This converter realizes bidirectional power flow with two main switches and operates under a wide input voltage range, from 1.5 V to 12 V, with an input current as high as 300 A. An active snubber circuit is utilized to recycle the energy stored in the leakage inductor of transformer and realize zero voltage switching (ZVS) off for one main switch. The zero current switching (ZCS) on of this switch is achieved by using transformer leakage inductor, thus no additional components are needed. Furthermore, the power loss of another main switch is minimized by operating it in the Synchronous Rectification mode. Complete circuit description and operation principles are provided in this paper. A 1.2 kW lab prototype has been built and tested with full power rating. Both simulation results and experimental results are presented to verify proposed functions.


title: Refining Event Extraction through Cross-Document Inference

abstract: We apply the hypothesis of “One Sense Per Discourse” (Yarowsky, 1995) to information extraction (IE), and extend the scope of “discourse” from one single document to a cluster of topically-related documents. We employ a similar approach to propagate consistent event arguments across sentences and documents. Combining global evidence from related documents with local decisions, we design a simple scheme to conduct cross-document inference for improving the ACE event extraction task 1 . Without using any additional labeled data this new approach obtained 7.6% higher F-Measure in trigger labeling and 6% higher F-Measure in argument labeling over a state-of-the-art IE system which extracts events independently for each sentence.


title: MorusDB: a resource for mulberry genomics and genome biology

abstract: Mulberry is an important cultivated plant that has received the attention of biologists interested in sericulture and plant–insect interaction. Morus notabilis, a wild mulberry species with a minimal chromosome number is an ideal material for whole-genome sequencing and assembly. The genome and transcriptome of M. notabilis were sequenced and analyzed. In this article, a web-based and open-access database, the Morus Genome Database (MorusDB), was developed to enable easy-to-access and data mining. The MorusDB provides an integrated data source and an easy accession of mulberry large-scale genomic sequencing and assembly, predicted genes and functional annotations, expressed sequence tags (ESTs), transposable elements (TEs), Gene Ontology (GO) terms, horizontal gene transfers between mulberry and silkworm and ortholog and paralog groups. Transcriptome sequencing data for M. notabilis root, leaf, bark, winter bud and male flower can also be searched and downloaded. Furthermore, MorusDB provides an analytical workbench with some built-in tools and pipelines, such as BLAST, Search GO, Mulberry GO and Mulberry GBrowse, to facilitate genomic studies and comparative genomics. The MorusDB provides important genomic resources for scientists working with mulberry and other Moraceae species, which include many important fruit crops. Designed as a basic platform and accompanied by the SilkDB, MorusDB strives to be a comprehensive platform for the silkworm–mulberry interaction studies. Database URL: http://morus.swu.edu.cn/morusdb.


title: Clustering and Bayesian network for image of faces classification

abstract: In a content based image classification system, target images are sorted by feature similarities with respect to the query (CBIR). In this paper, we propose to use new approach combining distance tangent, k-means algorithm and Bayesian network for image classification. First, we use the technique of tangent distance to calculate several tangent spaces representing the same image. The objective is to reduce the error in the classification phase. Second, we cut the image in a whole of blocks. For each block, we compute a vector of descriptors. Then, we use K-means to cluster the low-level features including color and texture information to build a vector of labels for each image. Finally, we apply five variants of Bayesian networks classifiers (Na\"ive Bayes, Global Tree Augmented Na\"ive Bayes (GTAN), Global Forest Augmented Na\"ive Bayes (GFAN), Tree Augmented Na\"ive Bayes for each class (TAN), and Forest Augmented Na\"ive Bayes for each class (FAN) to classify the image of faces using the vector of labels. In order to validate the feasibility and effectively, we compare the results of GFAN to FAN and to the others classifiers (NB, GTAN, TAN). The results demonstrate FAN outperforms than GFAN, NB, GTAN and TAN in the overall classification accuracy.


title: Reflections on the dimensions of trust and trustworthiness among online consumers

abstract: Trust is emerging as a potentially central aspect leading to IT acceptance. It is especially needed in the case of gaining and later retaining consumers of online vendors. This has been shown by previous research using single dimensional constructs. But trust can also be viewed as a multi-dimensional construct combining specific beliefs (sometimes labeled as trustworthiness) that either directly or through an overall assessment of trust influence relevant behavioral intentions. To show the need to examine trust in a multi-dimensional perspective, this study proposes a three-dimensional scale of trustworthiness dealing with integrity, benevolence, and ability in the unique case of online consumer trust, and then shows the importance of examining the effects of each dimension individually. The data show that trustworthiness and trust should not be regarded as a single construct with a single effect. Rather, different beliefs influence different consumer activity intentions. Window-shopping intentions was affected by the ability-trustworthiness of the vendor, while overall trust in the vendor and its integrity-trustworthiness affected purchase intentions. The importance of differentiating among online customer activities and what dimensions of trustworthiness affect each is discussed.


title: Catchment classification by runoff behaviour with self-organizing maps (SOM)

abstract: Abstract. Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation. In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in Rhineland-Palatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment. Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments. As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67% between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.


title: Interactions between auditory and dorsal premotor cortex during synchronization to musical rhythms

abstract: Abstract When listening to music, we often spontaneously synchronize our body movements to a rhythm's beat (e.g. tapping our feet). The goals of this study were to determine how features of a rhythm such as metric structure, can facilitate motor responses, and to elucidate the neural correlates of these auditory–motor interactions using fMRI. Five variants of an isochronous rhythm were created by increasing the contrast in sound amplitude between accented and unaccented tones, progressively highlighting the rhythm's metric structure. Subjects tapped in synchrony to these rhythms, and as metric saliency increased across the five levels, louder tones evoked longer tap durations with concomitant increases in the BOLD response at auditory and dorsal premotor cortices. The functional connectivity between these regions was also modulated by the stimulus manipulation. These results show that metric organization, as manipulated via intensity accentuation, modulates motor behavior and neural responses in auditory and dorsal premotor cortex. Auditory–motor interactions may take place at these regions with the dorsal premotor cortex interfacing sensory cues with temporally organized movement.


title: Simulated annealing, its parameter settings and the longest common subsequence problem

abstract: Simulated Annealing is a probabilistic search heuristic for solving optimization problems and is used with great success on real life problems. In its standard form Simulated Annealing has two parameters, namely the initial temperature and the cooldown factor. In literature there are only rules of the thumb for choosing appropriate parameter values. This paper investigates the influence of different values for these two parameters on the optimization process from a theoretical point of view and presents some criteria for problem specific adjusting of these parameters. With these results the performance of the Simulated Annealing algorithm on solving the Longest Common Subsequence Problem is analysed using different values for the two parameters mentioned above. For all these parameter settings it is proved that even rather simple input instances of the Longest Common Subsequence Problem can neither be solved to optimality nor approximately up to an approximation factor arbitrarily close to 2 efficiently.


title: Do-it-Yourself Digital Agriculture applications with semantically enhanced IoT platform

abstract: Internet of Things (IoT) enables various applications (crop growth monitoring and selection, irrigation decision support, etc) in Digital Agriculture domain. Semantic enhancements to IoT platforms address challenges of interoperability, data fusion, integration of heterogeneous IoT silos, annotation of data streams, just to name a few. This paper discusses the recently developed OpenIoT platform which demonstrated its applicability and efficiency in a number of use cases, including a Digital Agriculture use case (Phenonet). An ontology to represent Phenonet domain concepts in order to facilitate smart collection, annotation, validation, processing and storing of data streams from sensors in the field has been proposed and the results of experimental study, related semantic queries and reasoning using the ontology are presented. A Do-It-Yourself principle-driven zero-programming enabling Phenonet user interface demonstrates benefits, novelty and efficiency of the approach.