title: Evaluating the impact of a computerized surveillance algorithm and decision support system on sepsis mortality

abstract: OBJECTIVE ::: We created a system using a triad of change management, electronic surveillance, and algorithms to detect sepsis and deliver highly sensitive and specific decision support to the point of care using a mobile application. The investigators hypothesized that this system would result in a reduction in sepsis mortality. ::: ::: ::: METHODS ::: A before-and-after model was used to study the impact of the interventions on sepsis-related mortality. All patients admitted to the study units were screened per the Institute for Healthcare Improvement Surviving Sepsis Guidelines using real-time electronic surveillance. Sepsis surveillance algorithms that adjusted clinical parameters based on comorbid medical conditions were deployed for improved sensitivity and specificity. Nurses received mobile alerts for all positive sepsis screenings as well as severe sepsis and shock alerts over a period of 10 months. Advice was given for early goal-directed therapy. Sepsis mortality during a control period from January 1, 2011 to September 30, 2013 was used as baseline for comparison. ::: ::: ::: RESULTS ::: The primary outcome, sepsis mortality, decreased by 53% (P = 0.03; 95% CI, 1.06-5.25). The 30-day readmission rate reduced from 19.08% during the control period to 13.21% during the study period (P = 0.05; 95% CI, 0.97-2.52). No significant change in length of hospital stay was noted. The system had observed sensitivity of 95% and specificity of 82% for detecting sepsis compared to gold-standard physician chart review. ::: ::: ::: CONCLUSION ::: A program consisting of change management and electronic surveillance with highly sensitive and specific decision support delivered to the point of care resulted in significant reduction in deaths from sepsis.


title: Order embeddings and character-level convolutions for multimodal alignment

abstract: With the novel and fast advances in the area of deep neural networks, several challenging image-based tasks have been recently approached by researchers in pattern recognition and computer vision. In this paper, we address one of these tasks, which is to match image content with natural language descriptions, sometimes referred as multimodal content retrieval. Such a task is particularly challenging considering that we must find a semantic correspondence between captions and the respective image, a challenge for both computer vision and natural language processing areas. For such, we propose a novel multimodal approach based solely on convolutional neural networks for aligning images with their captions by directly convolving raw characters. Our proposed character-based textual embeddings allow the replacement of both word-embeddings and recurrent neural networks for text understanding, saving processing time and requiring fewer learnable parameters. Our method is based on the idea of projecting both visual and textual information into a common embedding space. For training such embeddings we optimize a contrastive loss function that is computed to minimize order-violations between images and their respective descriptions. We achieve state-of-the-art performance in the largest and most well-known image-text alignment dataset, namely Microsoft COCO, with a method that is conceptually much simpler and that possesses considerably fewer parameters than current approaches.


title: Learning Sparse Metrics, One Feature at a Time

abstract: Learning distance metrics from data is a fundamental problem in machine learning and useful way to extract data-driven features by using the matrix root of a distance matrix. Finding a proper metric amounts to optimization over the cone of positive definite (PD) matrices. This optimization is difficult since restricting optimization to remain within the PD cone or repeatedly projecting to the cone is prohibitively costly. Here we describe COMET, a block-coordinate descent procedure, which efficiently keeps the search within the PD cone, avoiding both costly projections and unnecessary computation of full gradients. COMET also continuously maintains the Cholesky root of the matrix, providing feature extraction and embedding of samples in a metric space. We further develop a structurally sparse variant of COMET, where only a small number of features interacts with other features. SparseCOMET significantly accelerates both training and inference while improving interpretability. As a block-coordinate descent procedure, COMET has fast convergence bounds showing linear convergence with high probability. When tested on benchmark datasets in a task of retrieving similar images and similar text documents, COMET has significantly better precision than competing projection-free methods. Furthermore, sparse-COMET achieves almost identical precision as dense-COMET in document classification, while running◊4.5 faster, maintaining a 0.5% sparsity level, and outperforming competing methods both in precision and in run time.


Comparison of Data Mining Techniques for Insurance Claim Prediction


title: Segmenting Human From Photo Images Based on a Coarse-to-Fine Scheme

abstract: Human segmentation in photo images is a challenging and important problem that finds numerous applications ranging from album making and photo classification to image retrieval. Previous works on human segmentation usually demand a time-consuming training phase for complex shape-matching processes. In this paper, we propose a straightforward framework to automatically recover human bodies from color photos. Employing a coarse-to-fine strategy, we first detect a coarse torso (CT) using the multicue CT detection algorithm and then extract the accurate region of the upper body. Then, an iterative multiple oblique histogram algorithm is presented to accurately recover the lower body based on human kinematics. The performance of our algorithm is evaluated on our own data set (contains 197 images with human body region ground truth data), VOC 2006, and the 2010 data set. Experimental results demonstrate the merits of the proposed method in segmenting a person with various poses.


title: Bilingual dictionaries for all EU languages

abstract: Bilingual dictionaries can be automatically generated using the GIZA++ tool. However, these dictionaries contain a lot of noise, because of which the qualities of outputs of tools relying on the dictionaries are negatively affected. In this work, we present three different methods for cleaning noise from automatically generated bilingual dictionaries: LLR, pivot and transliteration based approach. We have applied these approaches on the GIZA++ dictionaries – dictionaries covering official EU languages – in order to remove noise. Our evaluation showed that all methods help to reduce noise. However, the best performance is achieved using the transliteration based approach. We provide all bilingual dictionaries (the original GIZA++ dictionaries and the cleaned ones) free for download. We also provide the cleaning tools and scripts for free download.


title: Application of CBR in Developing a Prototype Swine Flu Medical Recommender

abstract: Case Based Reasoning (CBR) has become a successful methodology for problem solving, reasoning and learning. It is applied for diagnosis and decision support in the medical field in several areas. This is because CBR methodology is analogous to the process of human reasoning and natural problem solving. In this paper, a Swine Flu Medical Recommender (SFMR) prototype is applied using CBR that assists in diagnosis of Swine Flu. Knowledge is represented as cases of Swine Flu and stored in case base (CB). The system receives a problem case as a Query, compares it with previous cases for similarity in the CB using an algorithm for Similarity, and returns a fixed set of most similar cases that may be directly used to solve the problem or may be adapted with modifications within the restrictions of the Query.


title: A Classification Framework for Predicting Components' Remaining Useful Life Based on Discrete-Event Diagnostic Data

abstract: In this paper, we propose to define the problem of predicting the remaining useful life of a component as a binary classification task. This approach is particularly useful for problems in which the evolution of the system condition is described by a combination of a large number of discrete-event diagnostic data, and for which alternative approaches are either not applicable, or are only applicable with significant limitations or with a large computational burden. The proposed approach is demonstrated with a case study of real discrete-event data for predicting the occurrence of railway operation disruptions. For the classification task, Extreme Learning Machine (ELM) has been chosen because of its good generalization ability, computational efficiency, and low requirements on parameter tuning.


title: PROBING THE PARETO FRONTIER FOR BASIS PURSUIT SOLUTIONS

abstract: The basis pursuit problem seeks a minimum one-norm solution of an underdetermined least-squares problem. Basis pursuit denoise (BPDN) fits the least-squares problem only approximately, and a single parameter determines a curve that traces the optimal trade-off between the least-squares fit and the one-norm of the solution. We prove that this curve is convex and continuously differentiable over all points of interest, and show that it gives an explicit relationship to two other optimization problems closely related to BPDN. We describe a root-finding algorithm for finding arbitrary points on this curve; the algorithm is suitable for problems that are large scale and for those that are in the complex domain. At each iteration, a spectral gradient-projection method approximately minimizes a least-squares problem with an explicit one-norm constraint. Only matrix-vector operations are required. The primal-dual solution of this problem gives function and derivative information needed for the root-finding method. Numerical experiments on a comprehensive set of test problems demonstrate that the method scales well to large problems.


title: Multiobjective optimization of a quadruped robot gait

abstract: This paper presents the methodology used for finding the optimal set of foot trajectories for a quadruped robot using multiobjective genetic algorithm optimization. The optimization evaluates the energy per distance and average speed criteria on a robot simulation model. Robot locomotion is achieved by open-loop execution of foot trajectories generated in the local leg coordinate system. Foot trajectory is formulated as a sum of harmonics which enabled great flexibility in determining the final trajectory shape. A multiobjective optimization is introduced to tune the foot trajectory parameters in order to achieve energy optimal and fast robot locomotion. The obtained Pareto frontier showed that the bound gait is optimal for lower speeds while the trot gait enabled the robot to reach its maximum speed. The paper identifies the correlation between the stride frequency and robot speed for each identified gait laying on the Pareto frontier. Finally we discuss the trajectory shape of solutions obtained using multiobjective optimization.


title: Letter case and text legibility in normal and low vision

abstract: It is thought by cognitive scientists and typographers alike, that lower-case text is more legible than upper-case. Yet lower-case letters are, on average, smaller in height and width than upper-case characters, which suggests an upper-case advantage. Using a single unaltered font and all upper-, all lower-, and mixed-case text, we assessed size thresholds for words and random strings, and reading speeds for text with normal and visually impaired participants. Lower-case thresholds were roughly 0.1 log unit higher than upper. Reading speeds were higher for upper- than for mixed-case text at sizes twice acuity size; at larger sizes, the upper-case advantage disappeared. Results suggest that upper-case is more legible than the other case styles, especially for visually-impaired readers, because smaller letter sizes can be used than with the other case styles, with no diminution of legibility.


One-time programs


title: Ultra-wideband Vivaldi arrays for see-through-wall imaging radar applications

abstract: Two Vivaldi antenna arrays have been presented. First is an 8-element tapered slot array covering 1.2 to 4 GHz band for STW applications for brick/concrete wall imaging. Second is a 16-element antipodal array operating at 8 to 10.6 GHz for high resolution imaging when penetrating through dry wall. Based on the two designs, and utilizing a smooth wide band slot to microstrip transition to feed the Vivaldi antenna array, a 1–10 GHz frequency band can be covered. Alternatively, the design can be used in a reconfigurable structure to cover either a 1–3 GHz or 8–10 GHz band. Experimental and measured results have been completed and will be discussed in detail. The designs will significantly impact the development of compact reconfigurable and portable systems.


title: A 24-GHz transformer-based single-in differential-out CMOS low-noise amplifier

abstract: This paper presents a 24-GHz single-in differential-out (SIDO) CMOS low-noise amplifier (LNA) for wireless communication applications. The SIDO LNA utilizes a trifilar transformer inserted between the first and the second stages for the single-to-differential signal conversion. The LNA, fabricated in a 0.13 μm RF CMOS technology, exhibits a differential gain of 14.7 dB, a 3-dB bandwidth of 3.5 GHz (from 22.9 to 26.4 GHz), a noise figure of 4.3 dB, and an input 1-dB compression point (P 1dB ) of −13 dBm at 24 GHz. The gain and phase difference are 0.6 dB and 0.47°, respectively, which demonstrates well balanced characteristics.


title: EmpaTweet: Annotating and Detecting Emotions on Twitter

abstract: The rise of micro-blogging in recent years has resulted in significant access to emotion-laden text. Unlike emotion expressed in other textual sources (e.g., blogs, quotes in newswire, email, product reviews, or even clinical text), micro-blogs differ by (1) placing a strict limit on length, resulting radically in new forms of emotional expression, and (2) encouraging users to express their daily thoughts in real-time, often resulting in far more emotion statements than might normally occur. In this paper, we introduce a corpus collected from Twitter with annotated micro-blog posts (or “tweets”) annotated at the tweet-level with seven emotions: ANGER, DISGUST, FEAR, JOY, LOVE, SADNESS, and SURPRISE. We analyze how emotions are distributed in the data we annotated and compare it to the distributions in other emotion-annotated corpora. We also used the annotated corpus to train a classifier that automatically discovers the emotions in tweets. In addition, we present an analysis of the linguistic style used for expressing emotions our corpus. We hope that these observations will lead to the design of novel emotion detection techniques that account for linguistic style and psycholinguistic theories.


title: Effects of in-season short-term plyometric training program on leg power, jump- and sprint performance of soccer players.

abstract: Our hypothesis was that the addition of an 8-week lower limb plyometric training program (hurdle and depth jumping) to normal in-season conditioning would enhance measures of competitive potential (peak power output [PP], jump force, jump height, and lower limb muscle volume) in junior soccer players. The subjects (23 men, age 19 ± 0.7 years, body mass 70.5 ± 4.7 kg, height 1.75 ± 0.06 m, body fat 14.7 ± 2.6%) were randomly assigned to a control (normal training) group (Gc; n = 11) and an experimental group (Gex, n = 12) that also performed biweekly plyometric training. A force-velocity ergometer test determined PP. Characteristics of the squat jump (SJ) and the countermovement jump (CMJ) (jump height, maximal force and velocity before take-off, and average power) were determined by force platform. Video-camera kinematic analyses over a 40-m sprint yielded running velocities for the first step (VS), the first 5 m (V5m) and between 35 and 40 m (Vmax). Leg muscle volume was estimated using a standard anthropometric kit. Gex showed gains relative to controls in PP (p < 0.01); SJ (height p < 0.01; velocity p < 0.001), CMJ (height p < 0.001; velocity p < 0.001, average power p < 0.01) and all sprint velocities (p < 0.001 for V5m and Vmax, p < 0.01 for VS). There was also a significant increase (p < 0.05) in thigh muscle volume, but leg muscle volume and mean thigh cross-sectional area remain unchanged. We conclude that biweekly plyometric training of junior soccer players (including adapted hurdle and depth jumps) improved important components of athletic performance relative to standard in-season training. Accordingly, such exercises are highly recommended as part of an annual soccer training program.


title: ORIGINAL PAPER Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots

abstract: This study emphasizes the need for standardized measurement tools for human robot interaction (HRI). If we are to make progress in this field then we must be able to compare the results from different studies. A literature review has been performed on the measurements of five key concepts in HRI: anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety. The results have been distilled into five consistent questionnaires using semantic differential scales. We report reliability and validity indicators based on several empirical studies that used these questionnaires. It is our hope that these questionnaires can be used by robot developers to monitor their progress. Psychologists are invited to further develop the questionnaires by adding new concepts, and to conduct further validations where it appears necessary.


title: On the value of user preferences in search-based software engineering: a case study in software product lines

abstract: Software design is a process of trading off competing objectives. If the user objective space is rich, then we should use optimizers that can fully exploit that richness. For example, this study configures software product lines (expressed as feature maps) using various search-based software engineering methods. As we increase the number of optimization objectives, we find that methods in widespread use (e.g. NSGA-II, SPEA2) perform much worse than IBEA (Indicator-Based Evolutionary Algorithm). IBEA works best since it makes most use of user preference knowledge. Hence it does better on the standard measures (hypervolume and spread) but it also generates far more products with 0% violations of domain constraints. Our conclusion is that we need to change our methods for search-based software engineering, particularly when studying complex decision spaces.


title: Boyfriends, Babies, and a Few Good Headshots: Examining Girl Gamers' Identity Enactment on Twitter Using the Communication Theory of Identity

abstract: Girl gamers, while a substantial part of the gaming population, are often largely ignored in both the gaming industry and academic literature. In particular, there have been few investigations to date on what comprises the identity of a girl gamer, particularly outside the context of gameplay. To that end, the current study aims to investigate how girl gamers enact their identities as girl gamers on Twitter, a social network site. Eight Twitter accounts whose users self-identified as either Gamer Girls or Girl Gamers on a Twitter user directory where identified and the profiles and tweets of each collected for a two week period. This data was then analyzed according to Hecht’s communication theory of identity in order to determine how girl gamers enact their identity in an online context. INDEX WORDS: Girl gamers, Gamer girls, Communication Theory of Identity, Twitter, Social network sites, Online identity BOYFRIENDS, BABIES, AND A FEW GOOD HEADSHOTS: EXAMINING GIRL GAMERS’ IDENTITY ENACTMENT ON TWITTER USING THE COMMUNICATION THEORY OF IDENTITY


title: Examination of the Implementation Method of the Cloud Type Virtual Policy Based Network Management Scheme for the Common Use between Plural Organizations

abstract: In the current Internet system, there are many problems using anonymity of the network communication such as personal information leaks and crimes using the Internet system. This is why TCP/IP protocol used in Internet system does not have the user identification information on the communication data, and it is difficult to supervise the user performing the above acts immediately. As a study for solving the above problem, there is the study of Policy Based Network Management (PBNM). This is the scheme for managing a whole Local Area Network (LAN) through communication control for every user. In this PBNM, two types of schemes exist. The first is the scheme for managing the whole LAN by locating the communication control mechanisms on the course between network servers and clients. The second is the scheme of managing the whole LAN by locating the communication control mechanisms on clients. As the second scheme, we have studied theoretically about the Destination Addressing Control System (DACS) Scheme. By applying this DACS Scheme to Internet system management, we will realize the policy-based Internet system management. As the second step of the second phase for the last goal, we examine the implementation method of the cloud type virtual PBNM which can be used by plural organizations in this paper.


title: Designing robot learners that ask good questions

abstract: Programming new skills on a robot should take minimal time and effort. One approach to achieve this goal is to allow the robot to ask questions. This idea, called Active Learning, has recently caught a lot of attention in the robotics community. However, it has not been explored from a human-robot interaction perspective. In this paper, we identify three types of questions (label, demonstration and feature queries) and discuss how a robot can use these while learning new skills. Then, we present an experiment on human question asking which characterizes the extent to which humans use these question types. Finally, we evaluate the three question types within a human-robot teaching interaction. We investigate the ease with which different types of questions are answered and whether or not there is a general preference of one type of question over another. Based on our findings from both experiments we provide guidelines for designing question asking behaviors on a robot learner.


title: A lightweight symbolic virtual machine for solver-aided host languages

abstract: Solver-aided domain-specific languages (SDSLs) are an emerging class of computer-aided programming systems. They ease the construction of programs by using satisfiability solvers to automate tasks such as verification, debugging, synthesis, and non-deterministic execution. But reducing programming tasks to satisfiability problems involves translating programs to logical constraints, which is an engineering challenge even for domain-specific languages. We have previously shown that translation to constraints can be avoided if SDSLs are implemented by (traditional) embedding into a host language that is itself solver-aided. This paper describes how to implement a symbolic virtual machine (SVM) for such a host language. Our symbolic virtual machine is lightweight because it compiles to constraints only a small subset of the host's constructs, while allowing SDSL designers to use the entire language, including constructs for DSL embedding. This lightweight compilation employs a novel symbolic execution technique with two key properties: it produces compact encodings, and it enables concrete evaluation to strip away host constructs that are outside the subset compilable to constraints. Our symbolic virtual machine architecture is at the heart of Rosette, a solver-aided language that is host to several new SDSLs.


title: TrueTop: A Sybil-Resilient System for User Influence Measurement on Twitter

abstract: Influential users have great potential for accelerating information dissemination and acquisition on Twitter. How to measure the influence of Twitter users has attracted significant academic and industrial attention. Existing influence measurement techniques are vulnerable to sybil users that are thriving on Twitter. Although sybil defenses for online social networks have been extensively investigated, they commonly assume unique mappings from human-established trust relationships to online social associations and thus do not apply to Twitter where users can freely follow each other. This paper presents TrueTop, the first sybil-resilient system to measure the influence of Twitter users. TrueTop is rooted in two observations from real Twitter datasets. First, although non-sybil users may incautiously follow strangers, they tend to be more careful and selective in retweeting, replying to, and mentioning other users. Second, influential users usually get much more retweets, replies, and mentions than non-influential users. Detailed theoretical studies and synthetic simulations show that TrueTop can generate very accurate influence measurement results with strong resilience to sybil attacks.


title: Improved Pattern Learning for Bootstrapped Entity Extraction

abstract: Bootstrapped pattern learning for entity extraction usually starts with seed entities and iteratively learns patterns and entities from unlabeled text. Patterns are scored by their ability to extract more positive entities and less negative entities. A problem is that due to the lack of labeled data, unlabeled entities are either assumed to be negative or are ignored by the existing pattern scoring measures. In this paper, we improve pattern scoring by predicting the labels of unlabeled entities. We use various unsupervised features based on contrasting domain-specific and general text, and exploiting distributional similarity and edit distances to learned entities. Our system outperforms existing pattern scoring algorithms for extracting drug-andtreatment entities from four medical forums.


title: Head-mounted display with mid-air tactile feedback

abstract: Virtual and physical worlds are merging. Currently users of head-mounted displays cannot have unobtrusive tactile feedback while touching virtual objects. We present a mid-air tactile feedback system for head-mounted displays. Our prototype uses the focus of a modulated ultrasonic phased array for unobtrusive mid-air tactile feedback generation. The array and the hand position sensor are mounted on the front surface of a head-mounted virtual reality display. The presented system can enhance 3D user interfaces and virtual reality in a new way. To evaluate the tactile feedback together with visuals on an Oculus Rift VR headset, we had 13 participants do a simple virtual keypad tapping task with and without tactile feedback. The results indicate that while the measured speed and accuracy differed only a little, the subjects were nearly unanimous in that they preferred to use the tactile feedback. The "raw" NASA TLX questionnaires conducted after use revealed that the participants felt slightly less mental, physical and temporal demand with the tactile feedback. The participants' self-assessment of their performance was also higher with the tactile feedback.


title: Assembly process and reliability assessment of TSV/RDL/IPD interposer with multi-chip-stacking for 3D IC integration SiP

abstract: In this study, a 3D IC integration system-in-package (SiP) with TSV/RDL/IPD interposer is designed and developed. Emphasis is placed on the Cu revealing, embedded stress sensors, non-destructive inspection, thermal modeling and measurement, and final assembly and reliability assessments.


title: Review of Classifier Combination Methods

abstract: Classifier combination methods have proved to be an effective tool to increase the performance of pattern recognition applications. In this chapter we review and categorize major advancements in this field. Despite a significant number of publications describing successful classifier combination implementations, the theoretical basis is still missing and achieved improvements are inconsistent. By introducing different categories of classifier combinations in this review we attempt to put forward more specific directions for future theoretical research. We also introduce a retraining effect and effects of locality based training as important properties of classifier combinations. Such effects have significant influence on the performance of combinations, and their study is necessary for complete theoretical understanding of combination algorithms.


title: Transient responses of a Vivaldi antenna and a logarithmic periodic dipole array for ultra wideband communication

abstract: Antennas are mandatory system components for UWB communication systems. The paper presents a comprehensive approach for the characterization of UWB antenna concepts. Measurements of the transient responses of a LPDA and a Vivaldi antenna prove the effectivity of the presented model.


title: Predicting Movie Genres Based on Plot Summaries

abstract: This project explores several Machine Learning methods to predict movie genres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent Neural Networks are used for text classification, while K-binary transformation, rank method and probabilistic classification with learned probability threshold are employed for the multi-label problem involved in the genre tagging task.Experiments with more than 250,000 movies show that employing the Gated Recurrent Units (GRU) neural networks for the probabilistic classification with learned probability threshold approach achieves the best result on the test set. The model attains a Jaccard Index of 50.0%, a F-score of 0.56, and a hit rate of 80.5%.


title: Snakes and Strings: New Robotic Components for Rescue Operations

abstract: Reminders applied to pocketable personal accompanyings such as lighters, pens, ballpens, or pocket calculators, etc. By equipping such objects with mercury-switch-triggered ultramicro buzzers on suitable portion insomuch that an absent-minded owner may be reminded to take them away leaving after having used such objects to avoid indiscreet loss.


title: An analysis of the Revised Olweus Bully/Victim Questionnaire using the Rasch measurement model

abstract: BACKGROUND: Bullying is a problem in schools in many countries. There would be a benefit in the availability of a psychometrically sound instrument for its measurement, for use by teachers and researchers. The Olweus Bully/Victim Questionnaire has been used in a number of studies but comprehensive evidence on its validity is not available. AIMS: To examine the conceptual design, construct validity and reliability of the Revised Olweus Bully/Victim Questionnaire (OBVQ) and to provide further evidence on the prevalence of different forms of bullying behaviour. SAMPLE: All 335 pupils (160 [47.8%] girls; 175 [52.2%]) boys, mean age 11.9 years [range 11.2-12.8 years]), in 21 classes of a stratified sample of 7 Greek Cypriot primary schools. METHOD: The OBVQ was administered to the sample. Separate scales were created comprising (a) the items of the questionnaire concerning the extent to which pupils are being victimized; and (b) those concerning the extent to which pupils express bullying behaviour. Using the Rasch model, both scales were analysed for reliability, fit to the model, meaning, and validity. Both scales were also analysed separately for each of two sample groups (i.e. boys and girls) to test their invariance. RESULTS: Analysis of the data revealed that the instrument has satisfactory psychometric properties; namely, construct validity and reliability. The conceptual design of the instrument was also confirmed. The analysis leads also to suggestions for improving the targeting of items against student measures. Support was also provided for the relative prevalence of verbal, indirect and physical bullying. As in other countries, Cypriot boys used and experienced more bullying than girls, and boys used more physical and less indirect forms of bullying than girls. CONCLUSIONS: The OBVQ is a psychometrically sound instrument that measures two separate aspects of bullying, and whose use is supported for international studies of bullying in different countries. However, improvements to the questionnaire were also identified to provide increased usefulness to teachers tackling this significant problem facing schools in many countries. Language: en


title: Effective Identification of Similar Patients Through Sequential Matching over ICD Code Embedding

abstract: Evidence-based medicine often involves the identification of patients with similar conditions, which are often captured in ICD (International Classification of Diseases (World Health Organization 2013)) code sequences. With no satisfying prior solutions for matching ICD-10 code sequences, this paper presents a method which effectively captures the clinical similarity among routine patients who have multiple comorbidities and complex care needs. Our method leverages the recent progress in representation learning of individual ICD-10 codes, and it explicitly uses the sequential order of codes for matching. Empirical evaluation on a state-wide cancer data collection shows that our proposed method achieves significantly higher matching performance compared with state-of-the-art methods ignoring the sequential order. Our method better identifies similar patients in a number of clinical outcomes including readmission and mortality outlook. Although this paper focuses on ICD-10 diagnosis code sequences, our method can be adapted to work with other codified sequence data.


title: 10.7 A 25MHz 4-phase SAW hysteretic DC-DC converter with 1-cycle APC achieving 190ns tsettle to 4A load transient and above 80% efficiency in 96.7% of the power range

abstract: Switching power converters with fast load transients are crucial for application processors (APs) to facilitate system-level power adaptability with high current slew rate. While current-mode hysteretic control has been popularly employed in switching converters for simple structure, robust operation and fast transient response [1], it still does not suffice for the unprecedented 1A/ns current slew rate required by modern APs [2]. Meanwhile, current slew rate can be improved by extending single-phase to multiphase operation. However, when a converter powers light load, power loss caused by added phases adversely affects the efficiency. Active phase count (APC) was thus proposed to manage the number of active phases judiciously to load by sensing average inductor current [3,4]. However, the inherent sensing delay drastically slows down the phase adding/dropping actions and degrades load transient response. On the other hand, phase current imbalance among phase sub-converters could cause hot spots, jeopardizing system reliability. Upgrading from conventional current-mode hysteretic control, we propose a simple synchronized adaptive window (SAW) hysteretic control which automatically adjusts the hysteretic window to speed up the transient response. Inherent clock synchronization makes it a natural fit to multiphase operation, where APC can be accomplished within one switching cycle through an internal current sensing mechanism in the control.


title: Design Challenges for Entity Linking

abstract: Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions of the EL problem and presenting a simple yet effective, modular, unsupervised system, called Vinculum, for entity linking. We conduct an extensive evaluation on nine data sets, comparing Vinculum with two state-of-the-art systems, and elucidate key aspects of the system that include mention extraction, candidate generation, entity type prediction, entity coreference, and coherence.


Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM


title: Phonological memory deficits in language disordered children : Is there a causal connection?

abstract: Abstract The phonological memory skills of a group of children with disordered language development were compared with those of two control groups, one group matched on verbal abilities and the other matched on nonverbal intelligence. The language-disordered children were poorer at repeating single nonwords and recalling word lists than even the younger children of matched verbal abilities. The language-disordered children were, however, sensitive to both the phonological similarity and word length of the memory lists, except for the longest lists. The results of two further experiments indicate that the poor memory performance of the language-disordered children is unlikely to be due to either impaired perceptual processing or to slow articulation rates. Our proposal is that a deficit of phonological storage in working memory may underpin the poor memory performance of the language-disordered children, and could play a central role in their disordered language development.


title: Visualization of test information to assist fault localization

abstract: One of the most expensive and time-consuming components of the debugging process is locating the errors or faults. To locate faults, developers must identify statements involved in failures and select suspicious statements that might contain faults. This paper presents a new technique that uses visualization to assist with these tasks. The technique uses color to visually map the participation of each program statement in the outcome of the execution of the program with a test suite, consisting of both passed and failed test cases. Based on this visual mapping, a user can inspect the statements in the program, identify statements involved in failures, and locate potentially faulty statements. The paper also describes a prototype tool that implements our technique along with a set of empirical studies that use the tool for evaluation of the technique. The empirical studies show that, for the subject we studied, the technique can be effective in helping a user locate faults in a program.


title: Corpora Preparation and Stopword List Generation for Arabic data in Social Network

abstract: This paper proposes a methodology to prepare corpora in Arabic language from online social network (OSN) and review site for Sentiment Analysis (SA) task. The paper also proposes a methodology for generating a stopword list from the prepared corpora. The aim of the paper is to investigate the effect of removing stopwords on the SA task. The problem is that the stopwords lists generated before were on Modern Standard Arabic (MSA) which is not the common language used in OSN. We have generated a stopword list of Egyptian dialect and a corpus-based list to be used with the OSN corpora. We compare the efficiency of text classification when using the generated lists along with previously generated lists of MSA and combining the Egyptian dialect list with the MSA list. The text classification was performed using Na\"ive Bayes and Decision Tree classifiers and two feature selection approaches, unigrams and bigram. The experiments show that the general lists containing the Egyptian dialects words give better performance than using lists of MSA stopwords only.


title: Text Clustering with Seeds Affinity Propagation

abstract: Based on an effective clustering algorithm-Affinity Propagation (AP)-we present in this paper a novel semisupervised text clustering algorithm, called Seeds Affinity Propagation (SAP). There are two main contributions in our approach: 1) a new similarity metric that captures the structural information of texts, and 2) a novel seed construction method to improve the semisupervised clustering process. To study the performance of the new algorithm, we applied it to the benchmark data set Reuters-21578 and compared it to two state-of-the-art clustering algorithms, namely, k-means algorithm and the original AP algorithm. Furthermore, we have analyzed the individual impact of the two proposed contributions. Results show that the proposed similarity metric is more effective in text clustering (F-measures ca. 21 percent higher than in the AP algorithm) and the proposed semisupervised strategy achieves both better clustering results and faster convergence (using only 76 percent iterations of the original AP). The complete SAP algorithm obtains higher F-measure (ca. 40 percent improvement over k-means and AP) and lower entropy (ca. 28 percent decrease over k-means and AP), improves significantly clustering execution time (20 times faster) in respect that k-means, and provides enhanced robustness compared with all other methods.


A Code-Switching Corpus of Turkish-German Conversations


title: Intelligence: Knowns and Unknowns

abstract: Ulric Neisser (Chair) Gwyneth Boodoo Thomas J. Bouchard, Jr. A. Wade Boykin Nathan Brody Stephen J. Ceci Diane E Halpern John C. Loehlin Robert Perloff Robert J. Sternberg Susana Urbina Emory University Educational Testing Service, Princeton, New Jersey University of Minnesota, Minneapolis Howard University Wesleyan University Cornell University California State University, San Bernardino University of Texas, Austin University of Pittsburgh Yale University University of North Florida


title: Ipsilateral Scapular Fracture and Contralateral Fracture-Dislocation of the Humeral Head Due to Electrocution

abstract: Scapular fractures and shoulder fracture-dislocations as a result of electrocution are rarely seen and reported. We present the case of a 31-year-old man who sustained an ipsilateral scapular fracture and contralateral shoulder fracture-dislocation after electrocution.


title: Learning to rank relational objects and its application to web search

abstract: Learning to rank is a new statistical learning technology on creating a ranking model for sorting objects. The technology has been successfully applied to web search, and is becoming one of the key machineries for building search engines. Existing approaches to learning to rank, however, did not consider the cases in which there exists relationship between the objects to be ranked, despite of the fact that such situations are very common in practice. For example, in web search, given a query certain relationships usually exist among the the retrieved documents, e.g., URL hierarchy, similarity, etc., and sometimes it is necessary to utilize the information in ranking of the documents. This paper addresses the issue and formulates it as a novel learning problem, referred to as, 'learning to rank relational objects'. In the new learning task, the ranking model is defined as a function of not only the contents (features) of objects but also the relations between objects. The paper further focuses on one setting of the learning problem in which the way of using relation information is predetermined. It formalizes the learning task as an optimization problem in the setting. The paper then proposes a new method to perform the optimization task, particularly an implementation based on SVM. Experimental results show that the proposed method outperforms the baseline methods for two ranking tasks (Pseudo Relevance Feedback and Topic Distillation) in web search, indicating that the proposed method can indeed make effective use of relation information and content information in ranking.


title: 10/20, 10/10, and 10/5 systems revisited: Their validity as relative head-surface-based positioning systems

abstract: Abstract With the advent of multi-channel EEG hardware systems and the concurrent development of topographic and tomographic signal source localization methods, the international 10/20 system, a standard system for electrode positioning with 21 electrodes, was extended to higher density electrode settings such as 10/10 and 10/5 systems, allowing more than 300 electrode positions. However, their effectiveness as relative head-surface-based positioning systems has not been examined. We previously developed a virtual 10/20 measurement algorithm that can analyze any structural MR head and brain image. Extending this method to the virtual 10/10 and 10/5 measurement algorithms, we analyzed the MR images of 17 healthy subjects. The acquired scalp positions of the 10/10 and 10/5 systems were normalized to the Montreal Neurological Institute (MNI) stereotactic coordinates and their spatial variability was assessed. We described and examined the effects of spatial variability due to the selection of positioning systems and landmark placement strategies. As long as a detailed rule for a particular system was provided, it yielded precise landmark positions on the scalp. Moreover, we evaluated the effective spatial resolution of 329 scalp landmark positions of the 10/5 system for multi-subject studies. As long as a detailed rule for landmark setting was provided, 241 scalp positions could be set effectively when there was no overlapping of two neighboring positions. Importantly, 10/10 positions could be well separated on a scalp without overlapping. This study presents a referential framework for establishing the effective spatial resolutions of 10/20, 10/10, and 10/5 systems as relative head-surface-based positioning systems.


title: Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval

abstract: Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance


title: Simulated Car Racing Championship: Competition Software Manual

abstract: This manual describes the competition software for the Simulated Car Racing Championship, an international competition held at major conferences in the field of Evolutionary Computation and in the field of Computational Intelligence and Games. It provides an overview of the architecture, the instructions to install the software and to run the simple drivers provided in the package, the description of the sensors and the actuators.


title: Patch verification via multiversion interprocedural control flow graphs

abstract: Software development is inherently incremental; however, it is challenging to correctly introduce changes on top of existing code. Recent studies show that 15%-24% of the bug fixes are incorrect, and the most important yet hard-to-acquire information for programming changes is whether this change breaks any code elsewhere. This paper presents a framework, called Hydrogen, for patch verification. Hydrogen aims to automatically determine whether a patch correctly fixes a bug, a new bug is introduced in the change, a bug can impact multiple software releases, and the patch is applicable for all the impacted releases. Hydrogen consists of a novel program representation, namely multiversion interprocedural control flow graph (MVICFG), that integrates and compares control flow of multiple versions of programs, and a demand-driven, path-sensitive symbolic analysis that traverses the MVICFG for detecting bugs related to software changes and versions. In this paper, we present the definition, construction and applications of MVICFGs. Our experimental results show that Hydrogen correctly builds desired MVICFGs and is scalable to real-life programs such as libpng, tightvnc and putty. We experimentally demonstrate that MVICFGs can enable efficient patch verification. Using the results generated by Hydrogen, we have found a few documentation errors related to patches for a set of open-source programs.


title: Touch Sensor Array With Integrated Drivers and Comparator Using a-IGZO TFTs

abstract: Integration of an amorphous indium–gallium–zinc–oxide thin-film transistor (TFT)-based self-capacitive touch sensor array with its peripheral drivers and readout circuits is reported. Each pixel consists of one touch electrode and five TFTs that enable a pixel-by-pixel driving scheme via integrated row and column drivers made of shift registers. Outputs from all sensor pixels are fed into an integrated comparator and compared with a reference voltage to generate a more readable low or high output level. A ~7 V difference between the touch and untouched states could be detected, making the proposed touch sensor a promising candidate for flexible electronics.


title: SharesSkew: An Algorithm to Handle Skew for Joins in MapReduce

abstract: In this paper, we investigate the problem of computing a multiway join in one round of MapReduce when the data may be skewed. We optimize on communication cost, i.e., the amount of data that is transferred from the mappers to the reducers. We identify join attributes values that appear very frequently, Heavy Hitters (HH). We distribute HH valued records to reducers avoiding skew by using an adaptation of the Shares~\cite{AfUl} algorithm to achieve minimum communication cost. Our algorithm is implemented for experimentation and is offered as open source software. Furthermore, we investigate a class of multiway joins for which a simpler variant of the algorithm can handle skew. We offer closed forms for computing the parameters of the algorithm for chain and symmetric joins.


title: Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition

abstract: Recognizing fine-grained categories (e.g., bird species) is difficult due to the challenges of discriminative region localization and fine-grained feature learning. Existing approaches predominantly solve these challenges independently, while neglecting the fact that region detection and fine-grained feature learning are mutually correlated and thus can reinforce each other. In this paper, we propose a novel recurrent attention convolutional neural network (RA-CNN) which recursively learns discriminative region attention and region-based feature representation at multiple scales in a mutual reinforced way. The learning at each scale consists of a classification sub-network and an attention proposal sub-network (APN). The APN starts from full images, and iteratively generates region attention from coarse to fine by taking previous prediction as a reference, while the finer scale network takes as input an amplified attended region from previous scale in a recurrent way. The proposed RA-CNN is optimized by an intra-scale classification loss and an inter-scale ranking loss, to mutually learn accurate region attention and fine-grained representation. RA-CNN does not need bounding box/part annotations and can be trained end-to-end. We conduct comprehensive experiments and show that RA-CNN achieves the best performance in three fine-grained tasks, with relative accuracy gains of 3.3%, 3.7%, 3.8%, on CUB Birds, Stanford Dogs and Stanford Cars, respectively.


title: A Broadband WR10 Turnstile Junction Orthomode Transducer

abstract: We present a broadband waveguide ortho-mode transducer for the WR10 band that was designed for CLOVER, an astrophysics experiment aiming to characterize the polarization of the cosmic microwave background radiation. The design, based on a turnstile junction, was manufactured and then tested using a millimeter-wave vector network analyzer. The average measured return loss and isolation were -22 dB and -45 dB, respectively, across the entire WR10 band


title: Triband Planar Monopole Antenna With Compact Radiator for WLAN/WiMAX Applications

abstract: A novel coplanar waveguide (CPW)-fed triband planar monopole antenna is presented for WLAN/WiMAX applications. The monopole antenna is printed on a substrate with two rectangular corners cut off. The radiator of the antenna is very compact with an area of only 3.5 × 17 mm2, on which two inverted-L slots are etched to achieve three radiating elements so as to produce three resonant modes for triband operation. With simple structure and small size, the measured and simulated results show that the proposed antenna has 10-dB impedance bandwidths of 120 MHz (2.39-2.51 GHz), 340 MHz (3.38-3.72 GHz), and 1450 MHz (4.79-6.24 GHz) to cover all the 2.4/5.2/5.8-GHz WLAN and the 3.5/5.5-GHz WiMAX bands, and good dipole-like radiation characteristics are obtained over the operating bands.


title: Tango: a framework and system for algorithm animation

abstract: Algorithm animation is the process of abstracting the data, operations, and semantics of computer programs, and then creating animated graphical views of those abstractions. Although a handful of algorithm animation systems have been developed in recent years, relatively little work has been done on the theoretical foundations of such systems. In this work we develop a conceptual framework with formal models and precise semantics for algorithm animation. The framework contains facilities for defining operations in an algorithm, designing animations, and mapping the algorithm operations to their corresponding animations. Concurrently, we develop an algorithm animation system called TANGO (Transition-based A Nimation GeveratiOn) and a WYSIWYG demonstrational animation design tool called DANCE (Demonstrational A Nimation CrEation) that are both based upon the framework.


title: Single-phase grid integrated Photovoltaic inverter for harmonic and reactive power compensation

abstract: The paper presents a single phase grid integration of a shunt connected Photovoltaic (PV) generator. The objective of the proposed system is to provide uninterrupted power supply to the load during both strong sunlight as well as at night or at cloudy conditions. The interface between the grid and the PV is carried out through a Voltage Source Inverter (VSI), eliminating the current harmonics and compensating the load reactive power. The DC voltage control of the DC bus capacitor is employed in order to maintain unit power factor operation of the system, irrespective of changes in solar radiation level or due to change in load. The control scheme implemented is based upon single phase p-q theory.


title: Algorithms for Drug Sensitivity Prediction

abstract: Precision medicine entails the design of therapies that are matched for each individual patient. Thus, predictive modeling of drug responses for specific patients constitutes a significant challenge for personalized therapy. In this article, we consider a review of approaches that have been proposed to tackle the drug sensitivity prediction problem especially with respect to personalized cancer therapy. We first discuss modeling approaches that are based on genomic characterizations alone and further the discussion by including modeling techniques that integrate both genomic and functional information. A comparative analysis of the prediction performance of four representative algorithms, elastic net, random forest, kernelized Bayesian multi-task learning and deep learning, reflecting the broad classes of regularized linear, ensemble, kernelized and neural network-based models, respectively, has been included in the paper. The review also considers the challenges that need to be addressed for successful implementation of the algorithms in clinical practice.


title: An Electrically Large Metallic Cavity Antenna With Circular Polarization for Satellite Applications

abstract: An electrically large metallic cavity antenna with circular polarization (CP) is proposed in this letter. The aim is to increase the size of satellite antennas working in the Ku-band and other higher-frequency bands through low cost and easy fabrication. A brass C-band antenna prototype is designed, fabricated, and measured for concept verification. The overall dimensions of the antenna are 0.67λ0 × 0.67λ0 × 0.83λ0 at the working frequency. The resonant mode of the proposed cavity antenna is excited by a coaxial feeding probe, and circular polarization is realized by an inserted perturbation screw. The measured impedance bandwidth for | S11| ≤ -10 dB and 3-dB axial-ratio bandwidth are 1.02 GHz (6.07-7.09 GHz) and 320 MHz (6.59-6.91 GHz), respectively. The measured gain is 8.5 dBic across the whole 3-dB axial-ratio bandwidth. The measured 3-dB axial-ratio beamwidths at the central frequency are 150° and 148° in the xoz- and the yoz-planes, respectively, which is wide enough to cover the >; 0-dBi gain beamwidths of 120° for practical engineering applications. The measured and the simulated results show good agreement.


title: RANK-SIZE DISTRIBUTION OF SETTLEMENT SYSTEMS: A STABLE ATTRACTOR IN URBAN GROWTH

abstract: A further step towards a dynamic model for the observed size distribution of a system of settlements is presented. The occurrence and temporal stability of the rank-size distribution is obtained as the result of a dynamic self-organizing process of the nested system of settlements. The inherent time scale of this self-organizing process and the obtained hierarchical structure are related to the generally low inter settlement mobility of urban population, the tendency of the population to agglomerate, distance effects and preference effects. The well-known migration model of Weidlich and Haag is used as starting point and three approaches to express the attractivity in the system are compared. The stationary state is investigated in detail under some limiting conditions.


title: Initial mental representations of design problems: Differences between experts and novices

abstract: Defining and structuring wicked design problems has a major influence on subsequent problem solving, and demands a considerable level of skill. Previous research on mental representations in design is scarce, and has been largely based on students or individual experts. This study explored the differences in the initial mental representations of real-life product development problems between advanced product development engineering students and recommended, professional experts. Expert mental representations were found to demonstrate superior extent, depth and level of detail, accommodating more interconnections and being more geared toward action. The results indicate that targeting relevancy perceptions to locate interconnections and promote proactivity can be a key factor in developing product development education to better match the requirements faced by professionals.


title: Short-Term Residential Load Forecasting Based on Resident Behaviour Learning

abstract: Residential load forecasting has been playing an increasingly important role in modern smart grids. Due to the variability of residents’ activities, individual residential loads are usually too volatile to forecast accurately. A long short-term memory-based deep-learning forecasting framework with appliance consumption sequences is proposed to address such volatile problem. It is shown that the forecasting accuracy can be notably improved by including appliance measurements in the training data. The effectiveness of the proposed method is validated through extensive comparison studies on a real-world dataset.


title: Happiness, income satiation and turning points around the world

abstract: Income is known to be associated with happiness ::: 1 ::: , but debates persist about the exact nature of this relationship ::: 2,3 ::: . Does happiness rise indefinitely with income, or is there a point at which higher incomes no longer lead to greater well-being? We examine this question using data from the Gallup World Poll, a representative sample of over 1.7 million individuals worldwide. Controlling for demographic factors, we use spline regression models to statistically identify points of ‘income satiation’. Globally, we find that satiation occurs at $95,000 for life evaluation and $60,000 to $75,000 for emotional well-being. However, there is substantial variation across world regions, with satiation occurring later in wealthier regions. We also find that in certain parts of the world, incomes beyond satiation are associated with lower life evaluations. These findings on income and happiness have practical and theoretical significance at the individual, institutional and national levels. They point to a degree of happiness adaptation ::: 4,5 ::: and that money influences happiness through the fulfilment of both needs and increasing material desires ::: 6 ::: .


title: Associations of employment status and educational levels with mortality and hospitalization in the dialysis outcomes and practice patterns study in Japan

abstract: BACKGROUND ::: Socioeconomic status (SES) factors such as employment, educational attainment, income, and marital status can affect the health and well-being of the general population and have been associated with the prevalence of chronic kidney disease (CKD). However, no studies to date in Japan have reported on the prognosis of patients with CKD with respect to SES. This study aimed to investigate the influences of employment and education level on mortality and hospitalization among maintenance hemodialysis (HD) patients in Japan. ::: ::: ::: METHODS ::: Data on 7974 HD patients enrolled in Dialysis Outcomes and Practice Patterns Study phases 1-4 (1999-2011) in Japan were analysed. Employment status, education level, demographic data, and comorbidities were abstracted at entry into DOPPS from patient records. Mortality and hospitalization events were collected during follow-up. Patients on dialysis < 120 days at study entry were excluded from the analyses. Cox regression modelled the association between employment and both mortality and hospitalization among patients < 60 years old. The association between education and outcomes was also assessed. The association between patient characteristics and employment among patients < 60 years old was assessed using logistic regression. ::: ::: ::: RESULTS ::: During a median follow-up of 24.9 months (interquartile range, 18.4-32.0), 10% of patients died and 43% of patients had an inpatient hospitalization. Unemployment was associated with mortality (hazard ratio [HR] = 1.57; 95% confidence interval [CI]: 1.05-2.36) and hospitalization (HR = 1.25; 95% CI: 1.08-1.44). Compared to patients who graduated from university, patients with less than a high school (HS) education and patients who graduated HS with some college tended to have elevated mortality (HR = 1.41; 95% CI, 1.04-1.92 and HR = 1.36; 95% CI: 1.02-1.82, respectively) but were not at risk for increased hospitalizations. Factors associated with unemployment included lower level of education, older age, female gender, longer vintage, and several comorbidities. ::: ::: ::: CONCLUSIONS ::: Employment and education status were inversely associated with mortality in patients on maintenance HD in Japan. Employment but not education was also inversely associated with hospitalizations. After adjustment for comorbidities, the associations with clinical outcomes tended to be stronger for employment than education status.


title: Large-Scale Visual Speech Recognition

abstract: This work presents a scalable solution to open-vocabulary visual speech recognition. To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3,886 hours of video). In tandem, we designed and trained an integrated lipreading system, consisting of a video processing pipeline that maps raw video to stable videos of lips and sequences of phonemes, a scalable deep neural network that maps the lip videos to sequences of phoneme distributions, and a production-level speech decoder that outputs sequences of words. The proposed system achieves a word error rate (WER) of 40.9% as measured on a held-out set. In comparison, professional lipreaders achieve either 86.4% or 92.9% WER on the same dataset when having access to additional types of contextual information. Our approach significantly improves on other lipreading approaches, including variants of LipNet and of Watch, Attend, and Spell (WAS), which are only capable of 89.8% and 76.8% WER respectively.


Wasserstein is all you need Anonymous EMNLP submission


title: Developing Haptic and Visual Perceptual Categories for Reaching and Grasping with a Humanoid Robot

abstract: Abstract Properties of the human embodiment — sensorimotor apparatus and neurological structure — participate directly in the growth and development of cognitive processes against enormous worst case complexity. It is our position that relationships between morphology and perception over time lead to increasingly comprehensive models that describe the agent’s relationship to the world. We are applying insight derived from neuroscience, neurology, and developmental psychology to the design of advanced robot architectures. To investigate developmental processes, we have begun to approximate the human sensorimotor configuration and to engage sensory and motor subsystems in developmental sequences. Many such sequences have been documented in studies of infant development, so we intend to bootstrap cognitive structures in robots by emulating some of these growth processes that bear an essential resemblance to the human morphology. In this paper, we will show two related examples in which a humanoid robot determines the models and representations that govern its behavior. The first is a model that captures the dynamics of a haptic exploration of an object with a dextrous robot hand that supports skillful grasping. The second example constructs constellations of visual features to predict relative hand/object postures that lead reliably to haptic utility. The result is a first step in a trajectory toward associative visual-haptic categories that bounds the incremental complexity of each stage of development.


Structural Connectivity Networks of Transgender People


title: Specification by Example for Educational Purposes

abstract: The Specification By Example (SBE) is a guideline for building the right software, a software that meets customer requirements. It is based on seven process patterns and enhances communication and collaboration and it usually is used in agile software development. The connection between education and agile software development sounds actually as an emergent topic. In this paper, we propose to structure a teaching approach in analogy to an agile software developement by transposing each process pattern of SBE to a corresponding one in the teaching domain. Moreover, we show that thanks to the emergence of a collective intelligence process, the students are more confident and more responsible. Such a course offers the opportunity to learn not only technical skills, but also some values in a new mindset.


title: Energy Efficient Virtual Network Embedding for Cloud Networks

abstract: Network virtualization is widely considered to be one of the main paradigms for the future Internet architecture as it provides a number of advantages including scalability, on demand allocation of network resources, and the promise of efficient use of network resources. In this paper, we propose an energy efficient virtual network embedding (EEVNE) approach for cloud computing networks, where power savings are introduced by consolidating resources in the network and data centers. We model our approach in an IP over WDM network using mixed integer linear programming (MILP). The performance of the EEVNE approach is compared with two approaches from the literature: the bandwidth cost approach (CostVNE) and the energy aware approach (VNE-EA). The CostVNE approach optimizes the use of available bandwidth, while the VNE-EA approach minimizes the power consumption by reducing the number of activated nodes and links without taking into account the granular power consumption of the data centers and the different network devices. The results show that the EEVNE model achieves a maximum power saving of 60% (average 20%) compared to the CostVNE model under an energy inefficient data center power profile. We develop a heuristic, real-time energy optimized VNE (REOViNE), with power savings approaching those of the EEVNE model. We also compare the different approaches adopting an energy efficient data center power profile. Furthermore, we study the impact of delay and node location constraints on the energy efficiency of virtual network embedding. We also show how VNE can impact the design of optimally located data centers for minimal power consumption in cloud networks. Finally, we examine the power savings and spectral efficiency benefits that VNE offers in optical orthogonal division multiplexing networks.


title: Detecting deception: the scope and limits

abstract: With the increasing interest in the neuroimaging of deception and its commercial application, there is a need to pay more attention to methodology. The weakness of studying deception in an experimental setting has been discussed intensively for over half a century. However, even though much effort has been put into their development, paradigms are still inadequate. The problems that bedevilled the old technology have not been eliminated by the new. Advances will only be possible if experiments are designed that take account of the intentions of the subject and the context in which these occur.


title: Optimal feature selection based on mutual information for face recognition

abstract: Gabor face representation has been getting popular in face recognition applications.However,it also suffers from the high dimensional data containing diverse redundancy and different random noises.To utilize the Gabor feature for efficient face recognition,a new Gabor feature selection method is proposed.Firstly,the Gabor feature differences between every two face images within a training data set are calculated and grouped into two categories: intra-individual set and extra-individual set.Then the rank of discriminating capabilities of features can be estimated by evaluating the classification error on intra-set and extra-set based on weak classifier built by single feature.The Gabor features with small errors were selected.And at the same time,the mutual information between the candidate feature and the selected features was examined.As a result,the non-effective features carrying information already captured by the selected features will be excluded.The features thus selected are both accurate and nonredundant.Finally,the selected Gabor features were classified by PCA and LDA for final face recognition.The experiments on CAS-PEAL large-scale Chinese face database show that the proposed method can greatly reduce the dimensionality of Gabor features and effectively increase the recognition accuracy.


title: Highly porous, low elastic modulus 316L stainless steel scaffold prepared by selective laser melting

abstract: Abstract Recently, porous metallic materials have been extensively studied as candidates for use in the fabrication of scaffolds and augmentations to repair trabecular bone defects, e.g. in surroundings of joint replacements. Fabricating these complex structures by using common approaches (e.g., casting and machining) is very challenging. Therefore, rapid prototyping techniques, such as selective laser melting (SLM), have been investigated for these applications. In this study, we characterized a highly porous (87 vol.%) 316L stainless steel scaffold prepared by SLM. 316L steel was chosen because it presents a biomaterial still widely used for fabrication of joint replacements and, from the practical point of view, use of the same material for fabrication of an augmentation and a joint replacement is beneficial for corrosion prevention. The results are compared to the reported properties of two representative nonporous 316L stainless steels prepared either by SLM or casting and subsequent hot forging. The microstructural and mechanical properties and the surface chemical composition and interaction with the cells were investigated. The studied material exhibited mechanical properties that were similar to those of trabecular bone (compressive modulus of elasticity ~ 0.15 GPa, compressive yield strength ~ 3 MPa) and cytocompatibility after one day that was similar to that of wrought 316L stainless steel, which is a commonly used biomaterial. Based on the obtained results, SLM is a suitable method for the fabrication of porous 316L stainless steel scaffolds with highly porous structures.


title: Salmonella carrier state in chicken: comparison of expression of immune response genes between susceptible and resistant animals.

abstract: Asymptomatic Salmonella enterica serovar Enteritidis carrier state in poultry has serious consequences on food safety and public health due to the risks of food poisoning following consumption of contaminated products. An understanding the mechanisms of persistence of Salmonella in the digestive tract of chicken can be achieved by a better knowledge of the defects in the control of infection in susceptible versus resistant animals. The gene expression of innate immune response factors including anti-microbial molecules, inflammatory and anti-infectious cytokines was studied in the caecal lymphoid tissue associated with the carrier state. Expression levels of these genes were assessed by real-time PCR and were compared in two inbred lines of chickens differing in resistance to the carrier state following oral inoculation of S. enterica serovar Enteritidis at 1 week of age. No correlation was observed between resistance/susceptibility to caecal carrier state and level of interleukin (IL)-1beta, IL-8, IL-18, inducible NO synthase (iNOS) and natural resistance associated macrophage protein 1 (NRAMP1). A high baseline level of defensin gene expression was recorded in young animals from the susceptible line. In contrast, a significantly low expression of interferon-gamma (IFN-gamma) gene was observed in these susceptible infected animals in comparison to resistant ones and healthy counterparts. IFN-gamma expression level represents a valuable indication of immunodeficiency associated with persistence of Salmonella in the chicken digestive tract, and IFN-gamma thus represents a factor to consider in the development of prophylactic measures for the reduction of Salmonella carrier state.


title: An experimental analysis on cloud-based mobile augmentation in mobile cloud computing

abstract: Recent consumer electronic technologies have created momentous ground for smartphones in various domains, particularly healthcare and education. However, smartphones' miniature nature imposes intrinsic limitations on computational capabilities and battery lifetime that encumber performing Resource-intensive Mobile Applications (RMAs). Cloud-based Mobile Augmentation (CMA) is the-state-of-the-art augmentation model that leverages proximate and distant clouds to increase, enhance, and optimize computing capabilities of mobile devices aiming at execution of RMAs, which breeds Mobile Cloud Computing (MCC). This study analyzes impacts of mobile-cloud distance and number of intermediate hops — as influential factors — on CMA performance which are not yet investigated. The results indicate the correlation between distance and intermediate hops on overall execution costs (time and energy) of RMAs. The mathematical modeling and benchmarking unveil that distance has negligible impact on latency, whereas intermediate hops increment and communication overhead significantly degrade application performance and complicate energy and time estimation in CMA system.


title: Detecting speech act types in developer question/answer conversations during bug repair

abstract: This paper targets the problem of speech act detection in conversations about bug repair. We conduct a ``Wizard of Oz'' experiment with 30 professional programmers, in which the programmers fix bugs for two hours, and use a simulated virtual assistant for help. Then, we use an open coding manual annotation procedure to identify the speech act types in the conversations. Finally, we train and evaluate a supervised learning algorithm to automatically detect the speech act types in the conversations. In 30 two-hour conversations, we made 2459 annotations and uncovered 26 speech act types. Our automated detection achieved 69% precision and 50% recall. The key application of this work is to advance the state of the art for virtual assistants in software engineering. Virtual assistant technology is growing rapidly, though applications in software engineering are behind those in other areas, largely due to a lack of relevant data and experiments. This paper targets this problem in the area of developer Q/A conversations about bug repair.


title: Design and analysis of 1-to-4 wilkinson power divider for antenna array feeding network

abstract: In this paper, A Novel 1 to 4 modified Wilkinson power divider operating over the frequency range of (3 GHz to 8 GHz) is proposed. The design perception of the proposed divider based on two different stages and printed on FR4 (Epoxy laminate material) with the thickness of 1.57mm and є r =4.3 respectively. The modified design of this power divider including curved corners instead of the sharp edges and some modification in the length of matching stubs. In addition, this paper contain the power divider with equal power split at all ports, reasonable insertion loss, acceptable return loss below −10 dB, good impedance matching at all ports and satisfactory isolation performance has been obtained over the mentioned frequency range. The design concept and optimization development is practicable through CST simulation software.


title: VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering

abstract: In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed.


title: Lung Nodule Classification With Multilevel Patch-Based Context Analysis

abstract: In this paper, we propose a novel classification method for the four types of lung nodules, i.e., well-circumscribed, vascularized, juxta-pleural, and pleural-tail, in low dose computed tomography scans. The proposed method is based on contextual analysis by combining the lung nodule and surrounding anatomical structures, and has three main stages: an adaptive patch-based division is used to construct concentric multilevel partition; then, a new feature set is designed to incorporate intensity, texture, and gradient information for image patch feature description, and then a contextual latent semantic analysis-based classifier is designed to calculate the probabilistic estimations for the relevant images. Our proposed method was evaluated on a publicly available dataset and clearly demonstrated promising classification performance.


title: Distributed virtual machine consolidation: A systematic mapping study

abstract: Abstract Background: Virtual Machine (VM) consolidation is an effective technique to improve resource utilization and reduce energy footprint in cloud data centers. It can be implemented in a centralized or a distributed fashion. Distributed VM consolidation approaches are currently gaining popularity because they are often more scalable than their centralized counterparts and they avoid a single point of failure. Objective: To present a comprehensive, unbiased overview of the state-of-the-art on distributed VM consolidation approaches. Method: A Systematic Mapping Study (SMS) of the existing distributed VM consolidation approaches. Results: 19 papers on distributed VM consolidation categorized in a variety of ways. The results show that the existing distributed VM consolidation approaches use four types of algorithms, optimize a number of different objectives, and are often evaluated with experiments involving simulations. Conclusion: There is currently an increasing amount of interest on developing and evaluating novel distributed VM consolidation approaches. A number of research gaps exist where the focus of future research may be directed.


title: [McCune-Albright syndrome. Report of a case].

abstract: A 29year old female with bone pain and history of precocious puberty was referred for bone scintigraphy. On physical examination cafe au lait macular spots were noted on her neck, buttocks and left leg. Bone scan showed multiple areas of intense increased activity which was in favour of polyostotic fibrous dysplasia. Considering the presence of polyostotic fibrous dysplasia, precocious puberty and cafe au lait macular spots, MacCuneAlbright syndrome was confirmed in this patient.


title: Improved Handover Through Dual Connectivity in 5G mmWave Mobile Networks

abstract: The millimeter wave (mmWave) bands offer the possibility of orders of magnitude greater throughput for fifth-generation (5G) cellular systems. However, since mmWave signals are highly susceptible to blockage, channel quality on any one mmWave link can be extremely intermittent. This paper implements a novel dual connectivity protocol that enables mobile user equipment devices to maintain physical layer connections to 4G and 5G cells simultaneously. A novel uplink control signaling system combined with a local coordinator enables rapid path switching in the event of failures on any one link. This paper provides the first comprehensive end-to-end evaluation of handover mechanisms in mmWave cellular systems. The simulation framework includes detailed measurement-based channel models to realistically capture spatial dynamics of blocking events, as well as the full details of Medium Access Control, Radio Link Control, and transport protocols. Compared with conventional handover mechanisms, this paper reveals significant benefits of the proposed method under several metrics.


title: Comparing the usability of library vs. language approaches to task parallelism

abstract: In this paper, we compare the usability of a library approach with a language approach to task parallelism. There are many practical advantages and disadvantages to both approaches. A key advantage of a library-based approach is that it can be deployed without requiring any change in the tool chain, including compilers and IDEs. However, the use of library APIs to express all aspects of task parallelism can lead to code that is hard to understand and modify. A key advantage of a language-based approach is that the intent of the programmer is easier to express and understand, both by other programmers and by program analysis tools. However, a language-based approach usually requires the standardization of new constructs and (possibly) of new keywords. In this paper, we compare the java.util.concurrent (j.u.c) library [14] from Java 7 and the Habanero-Java (HJ) [16] language, supported by our experiences in teaching both models at Rice University.


title: An ontology-based intelligent system for recruitment

abstract: Abstract Nowadays, employment has a huge social importance. Current tools for facilitating job searches lack in providing intelligent matching between employer advertisements and the curriculum vitae of the candidates. The objective of this research work was to develop an intelligent web portal to serve as service provider in recruitment tasks. This portal aims at helping people living in the region of the South-east of Spain to find a job. For this purpose, the knowledge of the recruitment domain has been represented by means of ontology, which has been used to guide the design of the application and to supply the system with semantic capabilities. Furthermore, the ontological component allows for defining an ontology-guided search engine which provides more intelligent matches between job offers and candidates' curricula. Finally, this work covers the design of the ontology and the development of the web portal. Both issues are discussed and some validation results are presented.


title: Complications associated with therapeutic neck radiation.

abstract: PURPOSE ::: This retrospective study of patients who underwent neck radiation as part of their treatment for squamous cell carcinoma of the oral cavity sought to identify and quantify the morbidity associated with this treatment. ::: ::: ::: PATIENTS AND METHODS ::: Thirty-five patients who received neck radiation between 1985 and 1992 were randomly recalled for examination. All patients had been treated in a standardized fashion by the Department of Radiation Oncology. The long-term effects of neck radiation on skin changes, thyroid function, and neck range of motion, of atherosclerotic carotid artery disease, xerostomia, and glottic structures were measured. ::: ::: ::: RESULTS ::: Fifty-seven percent of patients demonstrated grade 1 skin changes. No severe changes were noted, and no development of secondary neoplasia was observed. There was no linear trend observed between radiation dose and skin changes. Hypothyroidism developed in 14.3% of patients within 3.5 years posttreatment. Logistic regression demonstrated a significant association between hypothyroidism and radiation dose. Limitation of neck mobility was the most significant complication with deficits demonstrated in all ranges of motion. Carotid bruits were documented in 14.3% of patients, with a significant association between the higher neck dosage in N+ cases. Xerostomia was found in 68% of patients. There was persistent glottic erythema in 11.4% of patients. ::: ::: ::: CONCLUSIONS ::: The results of this study indicate that there are persistent complications after neck radiation, which include limitation of neck movement, diminished thyroid function, accelerated carotid artery narrowing, and skin and salivary changes.


title: Ensuring data storage security in Cloud Computing

abstract: Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. In contrast to traditional solutions, where the IT services are under proper physical, logical and personnel controls, Cloud Computing moves the application software and databases to the large data centers, where the management of the data and services may not be fully trustworthy. This unique attribute, however, poses many new security challenges which have not been well understood. In this article, we focus on cloud data storage security, which has always been an important aspect of quality of service. To ensure the correctness of users' data in the cloud, we propose an effective and flexible distributed scheme with two salient features, opposing to its predecessors. By utilizing the homomorphic token with distributed verification of erasure-coded data, our scheme achieves the integration of storage correctness insurance and data error localization, i.e., the identification of misbehaving server(s). Unlike most prior works, the new scheme further supports secure and efficient dynamic operations on data blocks, including: data update, delete and append. Extensive security and performance analysis shows that the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and even server colluding attacks.


title: Influence Maximization on Large-Scale Mobile Social Network: A Divide-and-Conquer Method

abstract: With the proliferation of mobile devices and wireless technologies, mobile social network systems are increasingly available. A mobile social network plays an essential role as the spread of information and influence in the form of “word-of-mouth”. It is a fundamental issue to find a subset of influential individuals in a mobile social network such that targeting them initially (e.g., to adopt a new product) will maximize the spread of the influence (further adoptions of the new product). The problem of finding the most influential nodes is unfortunately NP-hard. It has been shown that a Greedy algorithm with provable approximation guarantees can give good approximation; However, it is computationally expensive, if not prohibitive, to run the greedy algorithm on a large mobile social network. In this paper, a divide-and-conquer strategy with parallel computing mechanism has been adopted. We first propose an algorithm called Community-based Greedy algorithm for mining top-K influential nodes. It encompasses two components: dividing the large-scale mobile social network into several communities by taking into account information diffusion and selecting communities to find influential nodes by a dynamic programming. Then, to further improve the performance, we parallelize the influence propagation based on communities and consider the influence propagation crossing communities. Also, we give precision analysis to show approximation guarantees of our models. Experiments on real large-scale mobile social networks show that the proposed methods are much faster than previous algorithms, meanwhile, with high accuracy.


title: Wireless Network Design for Transmission Line Monitoring in Smart Grid

abstract: In this paper, we develop a real-time situational awareness framework for the electrical transmission power grid using Wireless Sensor Network (WSN). While WSNs are capable of cost efficient monitoring over vast geographical areas, several technical challenges exist. The low power, low data rate devices cause bandwidth and latency bottlenecks. In this paper, our objective is to design a wireless network capable of real-time delivery of physical measurements for ideal preventive or corrective control action. For network design, we formulate an optimization problem with the objective of minimizing the installation and operational costs while satisfying the end-to-end latency and bandwidth constraints of the data flows. We study a hybrid hierarchical network architecture composed of a combination of wired, wireless and cellular technologies that can guarantee low cost real-time data monitoring. We formulate a placement problem to find the optimal location of cellular enabled transmission towers. Further, we present evaluation results of the optimization solution for diverse scenarios. Our formulation is generic and addresses real world scenarios with asymmetric sensor data generation, unreliable wireless link behavior, non-uniform cellular coverage, etc. Our analysis shows that a transmission line monitoring framework using WSN is indeed feasible using available technologies. Our results show that wireless link bandwidth can be a limiting factor for cost optimization.


title: Algorithms for facility location problems with outliers

abstract: Facility location problems are traditionally investigated with the assumption that all the clients are to be provided service. A significant shortcoming of this formulation is that a few very distant clients, called outliers, can exert a disproportionately strong influence over the final solution. In this paper we explore a generalization of various facility location problems (K-center, K-median, uncapacitated facility location etc) to the case when only a specified fraction of the customers are to be served. What makes the problems harder is that we have to also select the subset that should get service. We provide generalizations of various approximation algorithms to deal with this added constraint.


title: Accessible Crowdwork?: Understanding the Value in and Challenge of Microtask Employment for People with Disabilities

abstract: We present the first formal study of crowdworkers who have disabilities via in-depth open-ended interviews of 17 people (disabled crowdworkers and job coaches for people with disabilities) and a survey of 631 adults with disabilities. Our findings establish that people with a variety of disabilities currently participate in the crowd labor marketplace, despite challenges such as crowdsourcing workflow designs that inadvertently prohibit participation by, and may negatively affect the worker reputations of, people with disabilities. Despite such challenges, we find that crowdwork potentially offers different opportunities for people with disabilities relative to the normative office environment, such as job flexibility and lack of a need to rely on public transit. We close by identifying several ways in which crowd labor platform operators and/or individual task requestors could improve the accessibility of this increasingly important form of employment.


title: Primordial Magnetic Fields in Cosmology

abstract: Magnetic fields have been observed in galaxies, clusters of galaxies and probably in superclusters. While mechanisms exist to generate these in the late universe, it is possible that magnetic fields have existed since very early times. This thesis is concerned with methods to predict the form of such imprints. We review in detail a standard, linearised cosmology before introducing an electromagnetic field. We then consider the intrinsic statistics of the magnetic stresses in two ways, analytically and via static realisations. We construct the power spectra, some of which we present for the first time. At the one- and three-point level we find significant intrinsic non-Gaussianities. Finally we turn to the observable impacts a primordial magnetic field. Assuming coherence, the statistics of the source can be mapped onto the CMB in a simple manner. We demonstrate that our approach is valid by reproducing the signals for Gaussian power law fields on the microwave sky. [ABRIDGED]


title: Wildlife as a tourism resource in Nunavut.

abstract: Wildlife plays a significant role in the development of tourism in Nunavut. A certain amount of tourism in the Canadian Arctic depends on the wealth and uniqueness of the wildlife in the polar region. Two different forms of tourism based on Arctic wildlife can be distinguished: wildlife viewing and sport hunting. Both of these forms of tourism question the link between the different uses of the resource and the perceptions of wildlife. Based on an analysis of multiple information sources (literature review, newspaper analysis and semi-structured interviews), this article examines the emergence of wildlife as a tourism resource for Inuit communities and questions the issues linked to the emergence of this resource use. Using the example of polar bears, the article focuses on tourism dynamics. The article concludes that the polar bear sport hunting resource has evolved along with the changing environment. This tourism resource could be reinvented, with investments targeting other forms of tourism, such as wildlife viewing, which seems to better fit Westerners' expectations, but is not necessarily a more environmentally friendly type of tourism.


title: A Domain Independent Natural Language Interface to Databases Capable of Processing Complex Queries ⋆

abstract: We present a method for creating natural language interfaces to databases (NLIDB) that allow for translating natural language queries into SQL. The method is domain independent, i.e., it avoids the tedious process of configuring the NLIDB for a given domain. We automatically generate the domain dictionary for query translation using semantic metadata of the database. Our semantic representation of a query is a graph including information from database metadata. The query is translated taking into account the parts of speech of its words (obtained with some linguistic processing). Specifically, unlike most existing NLIDBs, we take seriously auxiliary words (prepositions and conjunctions) as set theory operators, which allows for processing more complex queries. Experimental results (conducted on two Spanish databases from different domains) show that treatment of auxiliary words improves correctness of translation by 12.1%. With the developed NLIDB 82of queries were correctly translated (and thus answered). Reconfiguring the NLIDB from one domain to the other took only ten minutes.


United States (US) multi-center study to assess the validity and reliability of the Spinal Cord Independence Measure (SCIM III)


title: Nonlinear analyses of wrinkles in a film bonded to a compliant substrate

abstract: Subject to a compressive membrane force, a film bonded to a compliant substrate often forms a pattern of wrinkles. This paper studies such wrinkles in a layered structure used in several recent experiments. The structure comprises a stiff film bonded to a compliant substrate, which in turn is bonded to a rigid support. Two types of analyses are performed. First, for sinusoidal wrinkles, by minimizing energy, we obtain the wavelength and the amplitude of the wrinkles for substrates of various moduli and thicknesses. Second, we develop a method to simultaneously evolve the two-dimensional pattern in the film and the three-dimensional elastic field in the substrate. The simulations show that the wrinkles can evolve into stripes, labyrinths, or herringbones, depending on the anisotropy of the membrane forces. Statistical averages of the amplitude and wavelength of wrinkles of various patterns correlate well with the analytical solution of the sinusoidal wrinkles.


title: Vessel Trajectories Outliers

abstract: In this work we describe our first steps towards our H2020 project MARISA participation, where we intent to develop a tool-kit towards the identification of outliers in Vessel trajectories based on electronic data regarding position and time. These outliers can correspond to illegal activities that could be related with illegal immigration, drugs transshipment among others. We developed process tools that based on any electronic Vessel position systems, like Automatic Identification System (AIS) data, it is possible to extract routes in an unsupervised approach. At the same time identify non-conformities based on AIS data signal lost and to identify situation when two or more Vessels are approaching close to each other, called the rendezvous.