title: Spatiotemporal GMM for Background Subtraction with Superpixel Hierarchy

abstract: We propose a background subtraction algorithm using hierarchical superpixel segmentation, spanning trees and optical flow. First, we generate superpixel segmentation trees using a number of Gaussian Mixture Models (GMMs) by treating each GMM as one vertex to construct spanning trees. Next, we use the $M$ -smoother to enhance the spatial consistency on the spanning trees and estimate optical flow to extend the $M$ -smoother to the temporal domain. Experimental results on synthetic and real-world benchmark datasets show that the proposed algorithm performs favorably for background subtraction in videos against the state-of-the-art methods in spite of frequent and sudden changes of pixel values.


title: Packaging: past, present and future

abstract: In the past, microsystems packaging played two roles: 1) It provided I/O connections to and from devices, referred to as IC or wafer level packaging, and 2) It interconnected both active and passive components on system level boards, referred to as systems packaging. Both were accomplished by interconnections or multilayer wiring at the package or board level. More recently, the IC devices have begun to integrate not only more and more transistors but also active and passive components on an individual chip, leading the community to believe that someday there may be a single-chip system, referred to as SOC or system-on-chip. This can be called horizontal or 2D integration of IC blocks toward systems. The community began to realize, however, that such an approach presents fundamental, engineering and investment limits and computing and integration limits for wireless and wired communication systems over the long run. This led to 3-D packaging approaches, often referred to as SIP or system-in-package. The SIP, while providing major opportunities in both miniaturization and integration for advanced and portable electronic products, is limited by the CMOS process just like the SOC. Some existing and emerging applications, however, include sensors, memory modules and embedded processors with DRAMs. More recent 3-D solutions, which incorporate stacked package approaches, offer solutions towards faster time-to-market and business impediments that have plagued MCM deployment for the past decade. There is a new concept which is called SOP or system-on-package. With SOP, Wit package, not the board, is the system. As such, SOP is beginning to address the shortcomings of both SOC and SIP, as well as traditional packaging which is bulky, costly, and lower in performance and reliability than ICs, in two ways: It uses CMOS-based Si for what it is good for, namely, for transistor integration, and the package, for what it is good for, namely, RF, optical and digital integration by means of IC-package-system co-design. The SOP package, therefore, overcomes both the computing limitations and integration limitations of SOC, SIP, MCM and traditional system packaging. It does this by having global wiring as well as RF, digital and optical component integration in the package, not in the chip. SOP, therefore, includes both active and passive components including embedded digital, RF and


An agent-oriented programming approach for C + +-based simulation models


title: Termination of psychotherapy: the journey of 10 psychoanalytic and psychodynamic therapists.

abstract: Objectives: Literature on termination originates mainly from clinical and theoretical accounts as well as practitioners' autobiographical reports. There is, however, a paucity of psychological research on termination, and studies of therapists' experiences are particularly rare. The purpose of this study is to examine the process of termination of therapy based on therapists' narratives of experiences of endings with patients. Design: Grounded Theory methodology has been applied in this study in order to conceptualise the process of termination from the therapist’s perspective. Methods: Ten psychoanalytic and psychodynamic therapists were interviewed for this study. Results: Grounded Theory analysis of the data revealed five central categories: therapist as a person, therapist's awareness of termination, development of therapeutic relationship, working through termination, and the aftermath (post-termination phase). Conclusions: The results offer a Grounded Theory model of the therapist's journey through termination of therapy with patients. Subcategories and their relationships will be explored. Implications for clinical practice, limitations and suggestions for further research will be discussed.


title: Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms

abstract: We describe a global optimization technique using “basin-hopping” in which the potential energy surface is transformed into a collection of interpenetrating staircases. This method has been designed to exploit the features that recent work suggests must be present in an energy landscape for efficient relaxation to the global minimum. The transformation associates any point in configuration space with the local minimum obtained by a geometry optimization started from that point, effectively removing transition state regions from the problem. However, unlike other methods based upon hypersurface deformation, this transformation does not change the global minimum. The lowest known structures are located for all Lennard-Jones clusters up to 110 atoms, including a number that have never been found before in unbiased searches.


title: Understanding cultures and implicit leadership theories across the globe: an introduction to project GLOBE

abstract: GLOBE (Global Leadership and Organizational Behavior Effectiveness) is a research program focusing on culture and leadership in 61 nations. National cultures are examined in terms of nine dimensions: performance orientation, future orientation, assertiveness, power distance, humane orientation, institutional collectivism, in-group collectivism, uncertainty avoidance, and gender egalitarianism. In a survey of thousands of middle managers in food processing, finance, and telecommunications industries in these countries, GLOBE compares their cultures and attributes of effective leadership. Six global leadership attributes are identified and discussed.


title: End-to-End Joint Semantic Segmentation of Actors and Actions in Video

abstract: Traditional video understanding tasks include human action recognition and actor/object semantic segmentation. However, the combined task of providing semantic segmentation for different actor classes simultaneously with their action class remains a challenging but necessary task for many applications. In this work, we propose a new end-to-end architecture for tackling this task in videos. Our model effectively leverages multiple input modalities, contextual information, and multitask learning in the video to directly output semantic segmentations in a single unified framework. We train and benchmark our model on the Actor-Action Dataset (A2D) for joint actor-action semantic segmentation, and demonstrate state-of-the-art performance for both segmentation and detection. We also perform experiments verifying our approach improves performance for zero-shot recognition, indicating generalizability of our jointly learned feature space.


title: Business Model Analysis Applied to Mobile Business

abstract: Mobile business is a young promising industry created by the emergence of wireless data networks.. Similar to other emerging industries, it is characterized by a large number of uncertainties at different levels concerning technology, demand and strategy. This paper focuses on the strategic uncertainties, where a large number of actors are trying a number of strategic approaches to position themselves in the most favourable position in the value system. As a consequence, they are experimenting with a number of innovating business models. We argue that successful business models are likely to be the ones that best address the economic peculiarities underlying this industry, like mobility, network effects and natural monopolies. The paper presents the principal classes of actors that will participate in the mobile business industry and give an overview of their business models based on a formalized ontology.


title: Model predictive control of the interleaved DC-DC boost converter with coupled inductors

abstract: This paper proposes a model predictive control (MPC) scheme for the interleaved dc-dc boost converter with coupled inductors. The main control objectives are the regulation of the output voltage to its reference value, despite changes in the input voltage and the load, and the equal sharing of the load current by the two circuit inductors. An inner control loop, using MPC, regulates the input current to its reference that is provided by the outer loop, which is based on a load observer. Simulation results are provided to highlight the performance of the proposed control scheme.


title: Model-Based Influences on Humans' Choices and Striatal Prediction Errors

abstract: The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors, and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making.


title: Sensor Data Fusion for Full Arm Tracking Using Myo Armband and Leap Motion

abstract: The Myo Armband (Myo) is a sensor which can be attached to the forearm, and allows to send to devices equipped with Bluetooth muscular patterns of arm movements triggered by some gestures made by the hand. Also, it produces rotation data based on a gyroscope, accelerometer and magnetometer embedded on it. The Leap Motion (LM) is a sensor-based cameras and infrared lights, which allows accurately to track the hands motion, including fingers. Therefore, this work describes the use of sensor data fusion techniques to combine the data from both sensors in order to create a 3D virtual simulation in real-time of an arm motion, including the forearm, hand and fingers. The purpose of this combination is also improve the performance, aiming to overcome the main sensors' limitations when they are used individually, as occurs with Myo, which is limited to produce only forearm motion data and the LM, which has a limited field of view and range tracking. The experiment results shows that the studied algorithm is able to combines the data from both sensors achieving a considerable gain in the precision and tracking of the arm.


title: Internet self-efficacy and electronic service acceptance

abstract: Internet self-efficacy (ISE), or the beliefs in one's capabilities to organize and execute courses of Internet actions required to produce given attainments, is a potentially important factor to explain the consumers' decisions in e-commerce use, such as e-service. In this study, we introduce two types of ISE (i.e., general Internet self-efficacy and Web-specific self-efficacy) as new factors that reflect the user's behavioral control beliefs in e-service acceptance. Using these two constructs as behavioral control factors, we extend and empirically validate the Theory of Planned Behavior (TPB) for the World Wide Web (WWW) context.


Cooperative Education, Experiential Learning, and Personal Knowledge.


title: The role of context in perceptions of the aesthetics of web pages over time

abstract: An important aspect of the empirical study of user experience is the process by which users form aesthetic and other judgements of interactive products. The current study extends previous research by presenting test users with a context (mode of use) in which to make their judgements, using sets of web pages from specific domains rather than unrelated pages, studying the congruence of perceptions of aesthetic value over time, including judgements after use of a web site, manipulating the aesthetic design of web pages and studying the relationship between usability and aesthetic value. The results from two experiments demonstrate that context increases the stability of judgements from perceptions after brief exposure to those after self-paced exposure and from perceptions after self-paced exposure to those of after site use. Experiment 1 shows that relatively attractive pages are preferred over relatively unattractive pages after brief exposure, but only if no context is provided. Experiment 2 shows that after brief exposure, classically aesthetic pages that are information oriented are rated as more attractive than expressively aesthetic pages. Perceptions are not correlated with measures of task performance or mental effort. We conclude that context is a pivotal factor influencing the stability of users' perceptions, which must be explicitly addressed in the study of users' product experience. Furthermore, the type of aesthetics that is relevant to users' perceptions appears to depend on the application domain. The principle 'what is beautiful is usable' is not confirmed.


title: Automatic Labeling of Semantic Role on Chinese FrameNet Using Conditional Random Fields

abstract: Given an input sentence and a target word and its frame, automatic semantic role labeling on the Chinese FrameNet (CFN) database can be divided into two subtasks. One is identification of the boundaries of semantic roles, and the other is the classification of semantic roles. In this paper, the tasks are regarded as a sequential tagging problem in the sentence at word-level, so the model of conditional random fields was adopted. The best feature templates of the model were chosen by applying orthogonal arrays. The training and testing data sets consist of the sample sentences of 25 frames selected from the current CFN corpus. The experimental results in our test data set show that the F-measure for identifying boundaries of semantic roles reached 70.42\%, and the accuracy in classifying semantic roles achieved 80.4\%, but when the two subtasks were sequential automatically processed, the result was 59.48\% F-measure.


title: A longitudinal investigation of personal computers in homes: adoption determinants and emerging challenges

abstract: While technology adoption in the workplace has been studied extensively, drivers of adoption in homes have been largely overlooked. This paper presents the results of a nation-wide, two-wave, longitudinal investigation of the factors driving personal computer (PC) adoption in American homes. The findings revealed that the decisions driving adoption and non-adoption were significantly different. Adopters were driven by utilitarian outcomes, hedonic outcomes (i.e., fun), and social outcomes (i.e., status) from adoption. Non-adopters, on the other hand, were influenced primarily by rapid changes in technology and the consequent fear of obsolescence. A second wave of data collection conducted six months after the initial survey indicated an asymmetrical relationship between intent and behavior, with those who did not intend to adopt a PC following more closely with their intent than those who intended to adopt one. We present important implications for research on adoption of technologies in homes and the workplace, and also discuss challenges facing the PC industry.


Fortran to Python Interface Generator with an Application to


title: Analysis of Magnetic Interaction in Remotely Controlled Magnetic Devices and its Application to a Capsule Robot for Drug Delivery

abstract: In this paper, we present the 3-D analysis of the torque transmitted to a small permanent magnet that is embedded in a capsule robot to achieve targeted drug delivery. This analysis is carried out by using analytical models and experimental results for the magnetic field created by multiple arc-shaped permanent magnets (ASMs), and the torque imparted to a magnetic capsule robot that has an arbitrary position and orientation. Our experimental results, which are in agreement with the analytical results, show that the on-board drug release mechanism can be actuated at any position and with large inclinations of the capsule robot without having to make positional adjustments in the external magnetic system. We have established analytical expressions describing the relationship between the rotating magnetic field created by the ASMs and the transmitted torque. Such relationships are essential to establish real-time control strategies for the actuation of magnetically articulated devices such as drug delivery in capsule robots.


Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters


title: Deep Bidirectional Cross-Triplet Embedding for Online Clothing Shopping

abstract: In this article, we address the cross-domain (i.e., street and shop) clothing retrieval problem and investigate its real-world applications for online clothing shopping. It is a challenging problem due to the large discrepancy between street and shop domain images. We focus on learning an effective feature-embedding model to generate robust and discriminative feature representation across domains. Existing triplet embedding models achieve promising results by finding an embedding metric in which the distance between negative pairs is larger than the distance between positive pairs plus a margin. However, existing methods do not address the challenges in the cross-domain clothing retrieval scenario sufficiently. First, the intradomain and cross-domain data relationships need to be considered simultaneously. Second, the number of matched and nonmatched cross-domain pairs are unbalanced. To address these challenges, we propose a deep cross-triplet embedding algorithm together with a cross-triplet sampling strategy. The extensive experimental evaluations demonstrate the effectiveness of the proposed algorithms well. Furthermore, we investigate two novel online shopping applications, clothing trying on and accessories recommendation, based on a unified cross-domain clothing retrieval framework.


title: Embedded wafer level ball grid array (eWLB) technology for high-frequency system-in-package applications

abstract: The embedded wafer level ball grid array (eWLB) is a novel system integration platform introduced recently. The eWLB technology is an attractive solution for high-frequency system-in-package (SiP) integration due to the capability to design high-quality (high-Q) embedded passives in the fan-out region and side-by-side multichip integration possibilities. In this paper, we show examples of using the fan-out region and the thin-film redistribution layer (RDL) advantageous for integration of inductors and antennas into an eWLB package. In addition, the use of the through encapsulant via (TEV) technology can extend the integration capabilities to 3D. We present measurement and simulation results of vertical interconnections realized using the RDL and TEVs of the eWLB. We demonstrate that the fan-out area of the eWLB can be used for the design of passive devices using the combination of TEV and RDL structures. We show examples of 3D inductors and transformers integrated in the eWLB. We present a fully integrated single-chip 60-GHz transceiver integrated in the eWLB package together with two dipole antennas as an example of mm-wave system integration.


title: T.: Polynomial eigenvalue solutions to the 5-pt and 6-pt relative pose problems

abstract: In this paper we provide new fast and simple solutions to two important minimal problems in computer vision, the five-point relative pose problem and the six-point focal length problem. We show that these two problems can easily be formulated as polynomial eigenvalue problems of degree three and two and solved using standard efficient numerical algorithms. Our solutions are somewhat more stable than state-of-the-art solutions by Nister and Stewenius and are in some sense more straightforward and easier to implement since polynomial eigenvalue problems are well studied with many efficient and robust algorithms available. The quality of the solvers is demonstrated in experiments 1.


title: A multiclass classification model for stock news based on structured data

abstract: Stock news plays an important role on market considering the timely dissemination of news will likely to affect a stock's price changes. Classifying stock news is very helpful for investors to make investment decisions. In this paper, we design and implement a structured data based classification model, which could be effectively applied to the field of stock news classification problem. The model firstly calculates the news correlation by using structured data, and then makes use of the parameter fitting method along with the output value of softmax regression model, after that, the final probability sequence is presented. The multi-label classification is realized by the method of selecting multi-labels. Finally, we compare the novel algorithm with a general algorithm, verifying the availability and accuracy of our classification method.


title: Comparison Analysis: Granger Causality and New Causality and Their Applications to Motor Imagery

abstract: In this paper we first point out a fatal drawback that the widely used Granger causality (GC) needs to estimate the autoregressive model, which is equivalent to taking a series of backward recursive operations which are infeasible in many irreversible chemical reaction models. Thus, new causality (NC) proposed by Hu et al. (2011) is theoretically shown to be more sensitive to reveal true causality than GC. We then apply GC and NC to motor imagery (MI) which is an important mental process in cognitive neuroscience and psychology and has received growing attention for a long time. We study causality flow during MI using scalp electroencephalograms from nine subjects in Brain–computer interface competition IV held in 2008. We are interested in three regions: Cz (central area of the cerebral cortex), C3 (left area of the cerebral cortex), and C4 (right area of the cerebral cortex) which are considered to be optimal locations for recognizing MI states in the literature. Our results show that: 1) there is strong directional connectivity from Cz to C3/C4 during left- and right-hand MIs based on GC and NC; 2) during left-hand MI, there is directional connectivity from C4 to C3 based on GC and NC; 3) during right-hand MI, there is strong directional connectivity from C3 to C4 which is much clearly revealed by NC than by GC, i.e., NC largely improves the classification rate; and 4) NC is demonstrated to be much more sensitive to reveal causal influence between different brain regions than GC.


title: TUI, GUI, HUI: is a bimodal interface truly worth the sum of its parts?

abstract: This study aims to explore the relative differences in efficacy of three different computer programming interfaces for controlling robots designed for early childhood education. A sample of N=36 kindergarten students from 3 different classrooms participated in this research. Each classroom was randomly assigned to one of the following three conditions: a tangible user interface, a graphical user interface, and a hybrid user interface. Comparisons between the three conditions focus on which interface yields better understanding of the programming concepts taught. Implications for designing developmentally appropriate computer programming interfaces for early childhood education are discussed.


title: Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors

abstract: We present a new approach to population health, in which data-driven predictive models are learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from readily available electronic claims data on large populations, without additional screening cost. Proposed model uncovers early and late-stage risk factors. Using administrative claims, pharmacy records, healthcare utilization, and laboratory results of 4.1 million individuals between 2005 and 2009, an initial set of 42,000 variables were derived that together describe the full health status and history of every individual. Machine learning was then used to methodically enhance predictive variable set and fit models predicting onset of type 2 diabetes in 2009-2011, 2010-2012, and 2011-2013. We compared the enhanced model with a parsimonious model consisting of known diabetes risk factors in a real-world environment, where missing values are common and prevalent. Furthermore, we analyzed novel and known risk factors emerging from the model at different age groups at different stages before the onset. Parsimonious model using 21 classic diabetes risk factors resulted in area under ROC curve (AUC) of 0.75 for diabetes prediction within a 2-year window following the baseline. The enhanced model increased the AUC to 0.80, with about 900 variables selected as predictive (p < 0.0001 for differences between AUCs). Similar improvements were observed for models predicting diabetes onset 1-3 years and 2-4 years after baseline. The enhanced model improved positive predictive value by at least 50% and identified novel surrogate risk factors for type 2 diabetes, such as chronic liver disease (odds ratio [OR] 3.71), high alanine aminotransferase (OR 2.26), esophageal reflux (OR 1.85), and history of acute bronchitis (OR 1.45). Liver risk factors emerge later in the process of diabetes development compared with obesity-related factors such as hypertension and high hemoglobin A1c. In conclusion, population-level risk prediction for type 2 diabetes using readily available administrative data is feasible and has better prediction performance than classical diabetes risk prediction algorithms on very large populations with missing data. The new model enables intervention allocation at national scale quickly and accurately and recovers potentially novel risk factors at different stages before the disease onset.


Bach 10 Dataset---- A Versatile Polyphonic Music Dataset


title: Simultaneously Combining Multi-view Multi-label Learning with Maximum Margin Classification

abstract: Multiple feature views arise in various important data classification scenarios. However, finding a consensus feature view from multiple feature views for a classifier is still a challenging task. We present a new classification framework using the multi-label correlation information to address the problem of simultaneously combining multiple feature views and maximum margin classification. Under this framework, we propose a novel algorithm that iteratively computes the multiple view feature mapping matrices, the consensus feature view representation, and the coefficients of the classifier. Extensive experimental evaluations demonstrate the effectiveness and promise of this framework as well as the algorithm for discovering a consensus view from multiple feature views.


title: Humanoid robotics platforms developed in HRP

abstract: Abstract This paper presents humanoid robotics platform that consists of a humanoid robot and an open architecture software platform developed in METI’s Humanoid Robotics Project (HRP). The final version of the robot, called HRP-2, has 1540 mm height, 58 kg weight and 30 degrees of the freedom. The software platform includes a dynamics simulator and motion controllers of the robot for biped locomotion, falling and getting up motions. The platform has been used to develop various applications and is expected to initiate more humanoid robotics research.


title: How and what university students learn through online and face-to-face discussion: conceptions, intentions and approaches

abstract: This paper reports a phenomenographic investigation into students' experiences of learning through discussion - both online and face to face (F2F). The study context was a second-year undergraduate course in psychology for social work in which the teacher had designed discussion tasks to begin in F2F mode and to continue online. A combination of open-ended questionnaires and semi-structured interviews was used to investigate students' conceptions of what they were learning, their intentions and their approaches to learning through discussion. Analysis of the interview and open-ended questionnaire data identified a number of qualitatively different conceptions, intentions and approaches to learning through discussion. Associations were found between what students thought they were learning through discussions, their approaches to learning through discussion and their course grade. Students with a cohesive conception and students adopting a deep approach (to learning through online discussion) got better course grades. There was no significant difference between deep and surface approaches to F2F discussion and course grade. The outcomes of this study have implications for the design of online and F2F discussion tasks and in particular for helping students adopt richer conceptions of what they stand to gain through discussion.


title: Prefrontal Cortex Organization: Dissociating Effects of Temporal Abstraction, Relational Abstraction, and Integration with fMRI

abstract: The functions of the prefrontal cortex (PFC) underlie higher-level cognition. Varying proposals suggest that the PFC is organized along a rostral-caudal gradient of abstraction with more abstract representations/processes associated with more rostral areas. However, the operational definition of abstraction is unclear. Here, we contrasted 2 prominent theories of abstraction—temporal and relational—using fMRI. We further examined whether integrating abstract rules—a function common to each theory—recruited the PFC independently of other abstraction effects. While robust effects of relational abstraction were present in the PFC, temporal abstraction effects were absent. Instead, we found activations specific to the integration of relational rules in areas previously shown to be associated with temporal abstraction. We suggest that previous effects of temporal abstraction were due to confounds with integration demands. We propose an integration framework to understand the functions of the PFC that resolves discrepancies in prior data.


title: Energy-Balance Control of PV Cascaded Multilevel Grid-Connected Inverters Under Level-Shifted and Phase-Shifted PWMs

abstract: This paper presents an energy-balance control strategy for a cascaded single-phase grid-connected H-bridge multilevel inverter linking n independent photovoltaic (PV) arrays to the grid. The control scheme is based on an energy-sampled data model of the PV system and enables the design of a voltage loop linear discrete controller for each array, ensuring the stability of the system for the whole range of PV array operating conditions. The control design is adapted to phase-shifted and level-shifted carrier pulsewidth modulations to share the control action among the cascade-connected bridges in order to concurrently synthesize a multilevel waveform and to keep each of the PV arrays at its maximum power operating point. Experimental results carried out on a seven-level inverter are included to validate the proposed approach.


title: A security framework for MQTT

abstract: In this paper, we propose a simple security framework for MQTT (for short, AugMQTT) by incorporating the AugPAKE protocol [16]. As a distinguishing feature, AugMQTT does not require any certificate validation checks and certificate revocation checks on both publishers/subscribers and broker sides. Also, we discuss several aspects of AugMQTT followed by performance overhead of the AugPAKE protocol. Finally, we explain implementation details of AugMQTT that makes use of MQTT open source project Mosquitto 1.4.9 [8] in order to incorporate the AugPAKE protocol.


title: Management of Fibromyalgia Syndrome: Review of Evidence

abstract: Fibromyalgia syndrome (FMS) is a common chronic musculoskeletal pain disorder of unknown etiology and characterized by generalized body pain, hyperalgesia, and other functional and emotional comorbidities. Despite extensive research, no treatment modality is effective for all FMS patients. In this paper, we briefly review the history of FMS and diagnostic criteria, and potential pathophysiological mechanisms including central pain modulation, neurotransmitters, sympatho-adrenal and hypothalamic–pituitary–adrenal systems and peripheral muscle issues. The primary focus of the paper is to review treatment options for managing fibromyalgia symptoms. We will discuss FDA-approved medications and other pharmacologic agents, and non-pharmacologic treatments that have shown promising effects.


title: Optimization Methods for Large-Scale Machine Learning

abstract: This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in machine learning and what makes them challenging. A major theme of our study is that large-scale machine learning represents a distinctive setting in which the stochastic gradient (SG) method has traditionally played a central role while conventional gradient-based nonlinear optimization techniques typically falter. Based on this viewpoint, we present a comprehensive theory of a straightforward, yet versatile SG algorithm, discuss its practical behavior, and highlight opportunities for designing algorithms with improved performance. This leads to a discussion about the next generation of optimization methods for large-scale machine learning, including an investigation of two main streams of research on techniques that diminish noise in the stochastic directions and methods that make use of second-order derivative approximations.


title: Deep motion features for visual tracking

abstract: Robust visual tracking is a challenging computer vision problem, with many real-world applications. Most existing approaches employ hand-crafted appearance features, such as HOG or Color Names. Recently, deep RGB features extracted from convolutional neural networks have been successfully applied for tracking. Despite their success, these features only capture appearance information. On the other hand, motion cues provide discriminative and complementary information that can improve tracking performance. Contrary to visual tracking, deep motion features have been successfully applied for action recognition and video classification tasks. Typically, the motion features are learned by training a CNN on optical flow images extracted from large amounts of labeled videos. This paper presents an investigation of the impact of deep motion features in a tracking-by-detection framework. We further show that hand-crafted, deep RGB, and deep motion features contain complementary information. To the best of our knowledge, we are the first to propose fusing appearance information with deep motion features for visual tracking. Comprehensive experiments clearly suggest that our fusion approach with deep motion features outperforms standard methods relying on appearance information alone.


title: Never-ending learning

abstract: We will never really understand learning or intelligence until we can build machines that learn many different things, over years, and become better learners over time. ::: ::: This talk describes our research to build a Never-Ending Language Learner (NELL) that runs 24 hours per day, forever, learning to read the web. Each day NELL extracts (reads) more facts from the web, and integrates these into its growing knowledge base of beliefs. Each day NELL also learns to read better than yesterday, enabling it to go back to the text it read yesterday, and extract more facts, more accurately. NELL has been running 24 hours/day for over two years now. The result so far is a collection of 15 million interconnected beliefs (e.g., servedWtih(coffee, applePie), isA(applePie, bakedGood)), that NELL is considering at different levels of confidence, along with hundreds of thousands of learned phrasings, morphoogical features, and web page structures that NELL uses to extract beliefs from the web. Track NELL's progress at http://rtw.ml.cmu.edu.


title: Factorizing LambdaMART for cold start recommendations

abstract: Recommendation systems often rely on point-wise loss metrics such as the mean squared error. However, in real recommendation settings only few items are presented to a user. This observation has recently encouraged the use of rank-based metrics. LambdaMART is the state-of-the-art algorithm in learning to rank which relies on such a metric. Despite its success it does not have a principled regularization mechanism relying in empirical approaches to control model complexity leaving it thus prone to overfitting. Motivated by the fact that very often the users' and items' descriptions as well as the preference behavior can be well summarized by a small number of hidden factors, we propose a novel algorithm, LambdaMART Matrix Factorization (LambdaMART-MF), that learns a low rank latent representation of users and items using gradient boosted trees. The algorithm factorizes lambdaMART by defining relevance scores as the inner product of the learned representations of the users and items. The low rank is essentially a model complexity controller; on top of it we propose additional regularizers to constraint the learned latent representations that reflect the user and item manifolds as these are defined by their original feature based descriptors and the preference behavior. Finally we also propose to use a weighted variant of NDCG to reduce the penalty for similar items with large rating discrepancy. We experiment on two very different recommendation datasets, meta-mining and movies-users, and evaluate the performance of LambdaMART-MF, with and without regularization, in the cold start setting as well as in the simpler matrix completion setting. In both cases it outperforms in a significant manner current state of the art algorithms.


title: Unsupervised feature selection using nonnegative spectral analysis

abstract: In this paper, a new unsupervised learning algorithm, namely Nonnegative Discriminative Feature Selection (NDFS), is proposed. To exploit the discriminative information in unsupervised scenarios, we perform spectral clustering to learn the cluster labels of the input samples, during which the feature selection is performed simultaneously. The joint learning of the cluster labels and feature selection matrix enables NDFS to select the most discriminative features. To learn more accurate cluster labels, a nonnegative constraint is explicitly imposed to the class indicators. To reduce the redundant or even noisy features, l2,1-norm minimization constraint is added into the objective function, which guarantees the feature selection matrix sparse in rows. Our algorithm exploits the discriminative information and feature correlation simultaneously to select a better feature subset. A simple yet efficient iterative algorithm is designed to optimize the proposed objective function. Experimental results on different real world datasets demonstrate the encouraging performance of our algorithm over the state-of-the-arts.


Integrating reactive and scripted behaviors in a life-like presentation agent


title: Learning Attitudes and Attributes from Multi-aspect Reviews

abstract: Most online reviews consist of plain-text feedback together with a single numeric score. However, understanding the multiple `aspects' that contribute to users' ratings may help us to better understand their individual preferences. For example, a user's impression of an audio book presumably depends on aspects such as the story and the narrator, and knowing their opinions on these aspects may help us to recommend better products. In this paper, we build models for rating systems in which such dimensions are explicit, in the sense that users leave separate ratings for each aspect of a product. By introducing new corpora consisting of five million reviews, rated with between three and six aspects, we evaluate our models on three prediction tasks: First, we uncover which parts of a review discuss which of the rated aspects. Second, we summarize reviews by finding the sentences that best explain a user's rating. Finally, since aspect ratings are optional in many of the datasets we consider, we recover ratings that are missing from a user's evaluation. Our model matches state-of-the-art approaches on existing small-scale datasets, while scaling to the real-world datasets we introduce. Moreover, our model is able to `disentangle' content and sentiment words: we automatically learn content words that are indicative of a particular aspect as well as the aspect-specific sentiment words that are indicative of a particular rating.


title: Epidemiology of anxiety disorders in the 21st century

abstract: Anxiety disorders, including panic disorder with or without agoraphobia, generalized anxiety disorder, social anxiety disorder, specific phobias, and separation anxiety disorder, are the most prevalent mental disorders and are associated with immense health care costs and a high burden of disease. According to large population-based surveys, up to 33.7% of the population are affected by an anxiety disorder during their lifetime. Substantial underrecognition and undertreatment of these disorders have been demonstrated. There is no evidence that the prevalence rates of anxiety disorders have changed in the past years. In cross-cultural comparisons, prevalence rates are highly variable. It is more likely that this heterogeneity is due to differences in methodology than to cultural influences. Anxiety disorders follow a chronic course; however, there is a natural decrease in prevalence rates with older age. Anxiety disorders are highly comorbid with other anxiety disorders and other mental disorders.


title: Communist and non-communist Europe: Functional differentiation, 1970–1985

abstract: Examining four variables related to quality of life in the respective societies — the value of international trade in U.S. dollars, air passenger mileage, total school enrollments and life expectancy — this study distinguishes several Communist Eastern European states from several Non-Communist Western European states over four time intervals: 1970, 1975, 1980 and 1985. The method employed is discriminant analysis. The degree of distinction achieved with the four variables is remarkably clearcut and does not appear to be the simple consequence of different levels of economic development.


title: A Theory of Turbulent Flame Development and Nitric Oxide Formation in Stratified Charge Internal Combustion Engines

abstract: A theoretical model of turbulent flame propagation in a reciprocating stratified charge engine is developed and calculations of temperature and species concentrations as functions of space and time are made. Also pressure is calculated as a function of time. A spacial variation of mixture ratio is considered such that there is a fuel-rich region in the center of the cylinder and a fuel-lean region near the cylinder walls. The spark in the fuel-rich region results in a premixed-type of flame propagating toward the walls. A diffusion-type flame results in the wake of the other flame due to the mixing of the excess air and fuel.


title: Recent Advances in Conversational Intelligent Tutoring Systems

abstract: We report recent advances in intelligent tutoring systems with conversational dialogue. We highlight progress in terms of macro and microadaptivity. Macroadaptivity refers to a system’s capability to select appropriate instructional tasks for the learner to work on. Microadaptivity refers to a system’s capability to adapt its scaffolding while the learner is working on a particular task. The advances in macro and microadaptivity that are presented here were made possible by the use of learning progressions, deeper dialogue and natural language processing techniques, and by the use of affect-enabled components. Learning progressions and deeper dialogue and natural language processing techniques are key features of DeepTutor, the first intelligent tutoring system based on learning progressions. These improvements extend the bandwidth of possibilities for tailoring instruction to each individual student which is needed for maximizing engagement and ultimately learning.


title: Photogrammetry procedures applied to anthropometry.

abstract: This study aims to evaluate the reliability and establish procedures for the use of digital photogrammetry in anthropometric measurements of the human hand. The methodology included the construction of a platform to allow the placement of the hand always equivalent to a distance of the camera lens and to annul the effects of parallax. We developed a software to perform the measurements from the images and built up a subject of proof in a cast from a negative mold, this object was subjected to measurements with digital photogrammetry using the data collection platform in caliper and the Coordinate Measuring Machine (MMC). The results of the application of photogrammetry in the data collection segment hand, allow us to conclude that photogrammetry is an effective presenting precision coefficient below 0.940. Within normal and acceptable values, given the magnitude of the data used in anthropometry. It was concluded photogrammetry then be reliable, accurate and efficient for carrying out anthropometric surveys of population, and presents less difficulty to collect in-place.


title: Specific antigens released into sea water by contracting anemones (Coelenterata).

abstract: Abstract The anemones Rhodactis howesii and Anthopleura elegantissima release species-specific antigens during contractions. This was demonstrated with the anemones in their natural habitat as well as in the laboratory. Possible ecologic implications are discussed.


title: The Dark Side of Ethical Robots

abstract: Concerns over the risks associated with advances in Artificial Intelligence have prompted calls for greater efforts toward robust and beneficial AI, including machine ethics. Recently, roboticists have responded by initiating the development of so-called ethical robots. These robots would, ideally, evaluate the consequences of their actions and morally justify their choices. This emerging field promises to develop extensively over the next years. However, in this paper, we point out an inherent limitation of the emerging field of ethical robots. We show that building ethical robots also necessarily facilitates the construction of unethical robots. In three experiments, we show that it is remarkably easy to modify an ethical robot so that it behaves competitively, or even aggressively. The reason for this is that the specific AI, required to make an ethical robot, can always be exploited to make unethical robots. Hence, the development of ethical robots will not guarantee the responsible deployment of AI. While advocating for ethical robots, we conclude that preventing the misuse of robots is beyond the scope of engineering, and requires instead governance frameworks underpinned by legislation. Without this, the development of ethical robots will serve to increase the risks of robotic malpractice instead of diminishing it.


title: Soccer video summarization using enhanced logo detection

abstract: We propose an automatic soccer video summarization engine which relies on an improved algorithm for the detection of replay shots which delineate interesting events. Video shots are first detected using dominant color and histogram intersection methods. Replay shots are detected using an improved technique, then processed through a set of mid-level descriptors (goal-mouth and score board with other cinematic features) and finally fed into a rule-based classifier. The proposed summarization has been experimentally tested on 6 hours from 10 videos in total. The proposed logo-based replay detection technique achieves 100% recall with 96.3% precision. Interesting events such as goals are detected with 100% recall and 100% precision, attacks with 91.7% recall and 86.7% precision) and other events such as (fouls, free kicks etc) with 90.8% recall and 95% precision.


title: A 4GHz Fractional-N synthesizer for IEEE 802.11a

abstract: Phase noise of the Fractional-N synthesizer depends critically on the linearity of its building blocks. In this research, new design methods are shown to directly improve its linearity. This includes a new re-timing scheme that effectively reduces phase noise for multi-modulus dividers. Further reductions in phase noise result from introduction of a high linearity CMOS charge pump. Measurement results verify the concept and demonstrate low phase noise performance at 4GHz.


title: A Compact Wideband SIW-Fed Dielectric Antenna With End-Fire Radiation Pattern

abstract: In this communication, a triangular-shaped dielectric antenna fed by substrate-integrated waveguide (SIW) is proposed and researched. The effect of the extended substrate’s length on performance of the proposed antenna is first researched. In order to reduce sidelobe level (SLL) at high frequency as well as increase the adjustability of the proposed antenna while maintaining its planar structure, air vias are periodically perforated into the loaded substrate to modify the permittivity. The variation trend of modifying permittivity with changing performance of the proposed antenna is studied, followed by analyzing function of the transition stage(s) of the extended substrate. Through optimizing the dielectric length and modifying the diameters of air vias to change the permittivity, the proposed antenna with wide operating bandwidth and low SLL can be realized. Measured results indicate that the proposed antenna works from 17.6 to 26.7 GHz, which almost covers the whole $K$ band. Besides, stable end-fire radiation patterns in the whole operating band are obtained. Moreover, at least 8.3-dBi peak gains with low SLL are achieved as well.


title: Mechanical model of the recovery from stumbling

abstract: Several strategies have been described as a reaction to a stumble during gait. The elevating strategy, which tries to proceed with the perturbed step, was executed as a response to a perturbation during early swing. The lowering strategy, bringing the perturbed leg to the ground and overtaking the obstacle with the contralateral limb, was executed more frequently when the perturbation appeared at mid or late swing. The goal of this paper is to analyze which mechanical factors determine the most advantageous strategy. In order to determine these factors, a mechanical model of the recovery was developed and used to analyze a series of perturbation experiments. It was assumed that the goal of the recovery reaction was to control the trunk as an inverted pendulum during the double-stance phase. In order to be able to control the trunk angle, one foot should be up front and one foot should be behind the hips; otherwise it would be impossible to generate the required trunk torques. The trunk dynamics were expressed in terms of the ground reaction forces and their application point. A larger step (elevation strategy) gives the opportunity to dissolve the perturbation in one step. A small step (lowering strategy) necessarily results in a second quick step, after which the perturbation energy can be dissipated in the second double-stance phase. If a recovery step is too slow, it becomes impossible to counteract the forward flexion of the trunk. It is suggested that a measure of the ability to recover from a stumble could be based on the ability to perform quick steps.


Combined General Catalogue of Variable Stars


title: Multi-Task Label Embedding for Text Classification

abstract: Multi-task learning in text classification leverages implicit correlations among related tasks to extract common features and yield performance gains. However, most previous works treat labels of each task as independent and meaningless one-hot vectors, which cause a loss of potential information and makes it difficult for these models to jointly learn three or more tasks. In this paper, we propose Multi-Task Label Embedding to convert labels in text classification into semantic vectors, thereby turning the original tasks into vector matching tasks. We implement unsupervised, supervised and semi-supervised models of Multi-Task Label Embedding, all utilizing semantic correlations among tasks and making it particularly convenient to scale and transfer as more tasks are involved. Extensive experiments on five benchmark datasets for text classification show that our models can effectively improve performances of related tasks with semantic representations of labels and additional information from each other.


Serverless Cloud Computing : A Comparison Between "Function as a Service" Platforms


title: Chess Game System on Smart Mobile Terminal

abstract: Under the background that traditional Chinese culture needs to be carried forward, the author of this paper, combined with Unity3D game engine, introduces a self-developing Chinese chess game based on Android mobile platform and discusses the basic structure and core mechanics of this game system. Technologies like data transmission in the LAN, Shader of Unity3D, and touch-screen control are adopted in the game design. Through program optimization and compatible design, this game combines 3D modeling tools like Autodesk 3ds Max, utilizes network module of Unity3D and connects two mobile terminals in the LAN, thus finally realizing on-line VS play. Through the Network View component of Unity3D, LAN connection mode suitable for C/S system of Android can be created in Unity3D to achieve data and image synchronization. Test results turn out to prove that with this technology, this game system displays high performance in terms of operation and visual effects.


title: Effect of Antidepressants and Psychological Therapies, Including Hypnotherapy, in Irritable Bowel Syndrome: Systematic Review and Meta-Analysis

abstract: OBJECTIVES ::: Irritable bowel syndrome (IBS) is a chronic functional gastrointestinal disorder. Evidence relating to the treatment of this condition with antidepressants and psychological therapies continues to accumulate. ::: ::: ::: METHODS ::: We performed an updated systematic review and meta-analysis of randomized controlled trials (RCTs). MEDLINE, EMBASE, and the Cochrane Controlled Trials Register were searched (up to December 2013). Trials recruiting adults with IBS, which compared antidepressants with placebo, or psychological therapies with control therapy or "usual management," were eligible. Dichotomous symptom data were pooled to obtain a relative risk (RR) of remaining symptomatic after therapy, with a 95% confidence interval (CI). ::: ::: ::: RESULTS ::: The search strategy identified 3,788 citations. Forty-eight RCTs were eligible for inclusion: thirty-one compared psychological therapies with control therapy or "usual management," sixteen compared antidepressants with placebo, and one compared both psychological therapy and antidepressants with placebo. Ten of the trials of psychological therapies, and four of the RCTs of antidepressants, had been published since our previous meta-analysis. The RR of IBS symptom not improving with antidepressants vs. placebo was 0.67 (95% CI=0.58-0.77), with similar treatment effects for both tricyclic antidepressants and selective serotonin reuptake inhibitors. The RR of symptoms not improving with psychological therapies was 0.68 (95% CI=0.61-0.76). Cognitive behavioral therapy, hypnotherapy, multicomponent psychological therapy, and dynamic psychotherapy were all beneficial. ::: ::: ::: CONCLUSIONS ::: Antidepressants and some psychological therapies are effective treatments for IBS. Despite the considerable number of studies published in the intervening 5 years since we last examined this issue, the overall summary estimates of treatment effect have remained remarkably stable.


title: Multi-document summarization using cluster-based link analysis

abstract: The Markov Random Walk model has been recently exploited for multi-document summarization by making use of the link relationships between sentences in the document set, under the assumption that all the sentences are indistinguishable from each other. However, a given document set usually covers a few topic themes with each theme represented by a cluster of sentences. The topic themes are usually not equally important and the sentences in an important theme cluster are deemed more salient than the sentences in a trivial theme cluster. This paper proposes the Cluster-based Conditional Markov Random Walk Model (ClusterCMRW) and the Cluster-based HITS Model (ClusterHITS) to fully leverage the cluster-level information. Experimental results on the DUC2001 and DUC2002 datasets demonstrate the good effectiveness of our proposed summarization models. The results also demonstrate that the ClusterCMRW model is more robust than the ClusterHITS model, with respect to different cluster numbers.


title: 4D Generic Video Object Proposals

abstract: Many high-level video understanding methods require input in the form of object proposals. Currently, such proposals are predominantly generated with the help of networks that were trained for detecting and segmenting a set of known object classes, which limits their applicability to cases where all objects of interest are represented in the training set. This is a restriction for automotive scenarios, where unknown objects can frequently occur. We propose an approach that can reliably extract spatio-temporal object proposals for both known and unknown object categories from stereo video. Our 4D Generic Video Tubes (4D-GVT) method leverages motion cues, stereo data, and object instance segmentation to compute a compact set of video-object proposals that precisely localizes object candidates and their contours in 3D space and time. We show that given only a small amount of labeled data, our 4D-GVT proposal generator generalizes well to real-world scenarios, in which unknown categories appear. It outperforms other approaches that try to detect as many objects as possible by increasing the number of classes in the training set to several thousand.


title: A 60 GHz 64-element phased-array beam-pointing communication system for 5G 100 meter links up to 2 Gbps

abstract: This paper presents a 60 GHz communication link system and measurements using a 64-element phased array transmitter. The transmit array includes high-efficiency on-wafer antennas, 3-bits amplitude and 5-bits phase control on each element, a measured saturated EIRP of 38 dBm at 60 GHz and scans to +/− 55° in the E- and H-planes with near-ideal patterns and low sidelobes. The phased-array transmitter is used in a 60 GHz communication link with an external up-conversion mixers and a Keysight 802.11ad waveform generator. A standard gain horn with a gain of 20 dB is used as the receiver, coupled to a Keysight high-speed digital demodulation scope. The communication link achieves a 16-QAM modulation with 3.85 Gbps at 4 m (full 802.11ad channel) and a QPSK modulation with 1.54 GBps over 100 m while scanning to +/−45° in both planes.


title: Low power AMOLED driving scheme based on reduced data supply voltage

abstract: This paper demonstrates a new driving scheme which allows reducing the supply voltage of data drivers for low power active matrix organic light emitting diode (AMOLED) displays. The proposed technique drives down the data voltage range by 50%, which subsequently diminishes in the peak power consumption of data drivers at the full white pattern by 75%. Because the gate voltage of a driving thin film transistor (TFT) covers the same range as a conventional one by means of a level-shifting scheme, the low data supply scheme achieves the equivalent dynamic range of OLED currents. The normalized power consumptions have been kept at the lower level than 0.25.


title: The independent effects of gravity and inertia on running mechanics

abstract: It is difficult to distinguish the independent effects of gravity from those of inertia on a running animal. Simply adding mass proportionally changes both the weight (gravitational force) and mass (inertial force) of the animal. We measured ground reaction forces for eight male humans running normally at 3 m s -1 and under three experimental treatments: added gravitational and inertial forces, added inertial forces and reduced gravitational forces. Subjects ran at 110, 120 and 130 % of normal weight and mass, at 110, 120 and 130 % of normal mass while maintaining 100 % normal weight, and at 25, 50 and 75 % of normal weight while maintaining 100 % normal mass. The peak active vertical forces generated changed with weight, but did not change with mass. Surprisingly, horizontal impulses changed substantially more with weight than with mass. Gravity exerted a greater influence than inertia on both vertical and horizontal forces generated against the ground during running. Subjects changed vertical and horizontal forces proportionately at corresponding times in the step cycle to maintain the orientation of the resultant vector despite a nearly threefold change in magnitude across treatments. Maintaining the orientation of the resultant vector during periods of high force generation aligns the vector with the leg to minimize muscle forces. Summary


title: Parallel sentence generation from comparable corpora for improved SMT

abstract: A parallel corpus is an essential resource for statistical machine translation (SMT) but is often not available in the required amounts for all domains and languages. An approach is presented here which aims at producing parallel corpora from available comparable corpora. An SMT system is used to translate the source-language part of a comparable corpus and the translations are used as queries to conduct information retrieval from the target-language side of the comparable corpus. Simple filters are then used to score the SMT output and the IR-returned sentence with the filter score defining the degree of similarity between the two. Using SMT system output gives the benefit of trying to correct one of the common errors by sentence tail removal. The approach was applied to Arabic---English and French---English systems using comparable news corpora and considerable improvements were achieved in the BLEU score. We show that our approach is independent of the quality of the SMT system used to make the queries, strengthening the claim of applicability of the approach for languages and domains with limited parallel corpora available to start with. We compare our approach with one of the earlier approaches and show that our approach is easier to implement and gives equally good improvements.


title: Cyber Attack Vulnerabilities Analysis for Unmanned Aerial Vehicles.

abstract: As the technological capabilities of automated systems have increased, the use of unmanned aerial vehicles (UAVs) for traditionally exhausting and dangerous manned missions has become more feasible. The United States Army, Air Force, and Navy have released plans for the increased use of UAVs, but have only recently shown interest in the cyber security aspect of UAVs. As a result, current autopilot systems were not built with cyber security considerations taken into account, and are thus vulnerable to cyber attack. Since UAVs rely heavily on their on-board autopilots to function, it is important to develop an autopilot system that is robust to possible cyber attacks. In order to develop a cyber-secure autopilot architecture, we have run a study on potential cyber threats and vulnerabilities of the current autopilot systems. This study involved a literature review on general cyber attack methods and on networked systems, which we used to identify the possible threats and vulnerabilities of the current autopilot system. We then studied the identi ed threats and vulnerabilities in order to analyze the post-attack behavior of the autopilot system through simulation. The uses of UAVs are increasing in many applications other than the traditional military use. We describe several example scenarios involving cyber attacks that demonstrate the vulnerabilities of current autopilot systems.


title: Knowledge Transfer for Melanoma Screening with Deep Learning

abstract: Knowledge transfer impacts the performance of deep learning -- the state of the art for image classification tasks, including automated melanoma screening. Deep learning's greed for large amounts of training data poses a challenge for medical tasks, which we can alleviate by recycling knowledge from models trained on different tasks, in a scheme called transfer learning. Although much of the best art on automated melanoma screening employs some form of transfer learning, a systematic evaluation was missing. Here we investigate the presence of transfer, from which task the transfer is sourced, and the application of fine tuning (i.e., retraining of the deep learning model after transfer). We also test the impact of picking deeper (and more expensive) models. Our results favor deeper models, pre-trained over ImageNet, with fine-tuning, reaching an AUC of 80.7% and 84.5% for the two skin-lesion datasets evaluated.


title: Learning Disentangled Representations of Texts with Application to Biomedical Abstracts

abstract: We propose a method for learning disentangled sets of vector representations of texts that capture distinct aspects. We argue that such representations afford model transfer and interpretability. To induce disentangled embeddings, we propose an adversarial objective based on the (dis)similarity between triplets of documents w.r.t. specific aspects. Our motivating application concerns embedding abstracts describing clinical trials in a manner that disentangles the populations, interventions, and outcomes in a given trial. We show that the induced representations indeed encode these targeted clinically salient aspects and that they can be effectively used to perform aspect-specific retrieval. We demonstrate that the approach generalizes beyond this motivating example via experiments on two multi-aspect review corpora.


title: Band-pass Reservoir Computing

abstract: Many applications of Reservoir Computing (and other signal processing techniques) have to deal with information processing of signals with multiple time-scales. Classical Reservoir Computing approaches can only cope with multiple frequencies to a limited degree. In this work we investigate reservoirs build of band-pass filter neurons which can be made sensitive to a specified frequency band. We demonstrate that many currently difficult tasks for reservoirs can be handled much better by a band-pass filter reservoir.


title: Increased overbite and craniomandibular disorders--a clinical approach.

abstract: This study investigated the effect of a maxillary fixed lingual arch with anterior bite plane on adult patients with craniomandibular disorders (CMD) and increased overbite. The sample comprised 11 patients with an increased overbite (greater than 5 mm) and a normal or Class II molar relationship. The main CMD symptoms were daily tension headache in the region of anterior temporal muscles and/or pain or clicking in the temporomandibular joint. Previous treatment with stabilization splints, removal bite plates, or occlusal grinding had not given satisfactory results. When the maxillary lingual arch with anterior bite plane was fitted, molar separation was approximately 4 mm, and occlusal contact occurred only between the acrylic bite plane and the lower six anterior teeth. The permanent appliance could be removed only by the orthodontist. All patients reported relief of CMD symptoms 1 to 2 weeks after initiation of treatment. After a mean time of 3 months, a flatter curve of Spee, molar contact, and reduced overbite could be seen in all cases. The excessive overbite had decreased approximately 3.4 mm. Subsequent treatment involved orthodontic or prosthetic therapy to normalize and stabilize the sagittal and vertical dimensions. After an average posttreatment observation period of 2 years, all patients remained free of CMD pain.


title: A review of morphogenetic engineering

abstract: Generally, phenomena of spontaneous pattern formation are random and repetitive, whereas elaborate devices are the deterministic product of human design. Yet, biological organisms and collective insect constructions are exceptional examples of complex systems (CS) that are both architectured and self-organized. Can we understand their precise self-formation capabilities and integrate them with technological planning? Can physical systems be endowed with information, or informational systems be embedded in physics, to create autonomous morphologies and functions? To answer these questions, we have launched in 2009, and developed through a series of workshops and a collective book, a new field of research called morphogenetic engineering. It is the first initiative of its kind to rally and promote models and implementations of complex self-architecturing systems. Particular emphasis is set on the programmability and computational abilities of self-organization, properties that are often underappreciated in CS science--while, conversely, the benefits of self-organization are often underappreciated in engineering methodologies. [This paper is an extended version of Doursat, Sayama and Michel (2012b) (Chapter 1, in Doursat R et al. (eds.) Morphogenetic engineering: toward programmable complex systems. Understanding complex systems. Springer, 2012a).]


title: An Introduction to Evaluating

abstract: On the basis of media hype alone, you might conclude that biometric passwords will soon replace their alphanumeric counterparts with versions that cannot be stolen, forgotten, lost, or given to another person. But what if the actual performance of these systems falls short of the estimates? The authors designed this article to provide sufficient information to know what questions to ask when evaluating a biometric system, and to assist in determining whether performance levels meet the requirements of an application. For example, a low-performance biometric is probably sufficient for reducing-as opposed to eliminating-fraud. Likewise, completely replacing an existing security system with a biometric-based one may require a high-performance biometric system, or the required performance may be beyond what current technology can provide. Of the biometrics that give the user some control over data acquisition, voice, face, and fingerprint systems have undergone the most study and testing-and therefore occupy the bulk of this discussion. This article also covers the tools and techniques of biometric testing.


title: CATS Design: A Context-Aware Test Suite Design Process

abstract: Context-awareness is the ability of a system to gather information from its context and adapt its behaviors. This feature makes the testing of Context-aware software systems more challenging. Based on this assumption, this research proposes an approach to design functional test cases for context-aware software systems, the CATS Design. In its development, ideas from other domains presenting similar issues were tailored to the problem of testing context-aware software systems. This paper presents the design process of CATS Design, and its two empirical evaluation stages: 1) through a proof of concept, and 2) with an observational study involving eight undergraduate students in Uruguay. The results suggest that it is feasible to apply CATS Design for designing test cases for context-aware software systems.


title: Building Interoperable and Cross-Domain Semantic Web of Things Applications

abstract: The Web of Things (WoT) is rapidly growing in popularity getting the interest of not only technologist and scientific communities but industrial, system integrators and solution providers. The key aspect of the WoT to succeed is the relatively, easy-to-build ecosystems nature inherited from the web and the capacity for building end-to-end solutions. At the WoT connecting physical devices such as sensors, RFID tags or any devices that can send data through the Internet using the Web is almost automatic. The WoT shared data can be used to build smarter solutions that offer business services in the form of IoT applications. In this chapter, we review the main WoT challenges, with particular interest on highlighting those that rely on combining heterogeneous IoT data for the design of smarter services and applications and that benefit from data interoperability. Semantic web technologies help for overcoming with such challenges by addressing, among other ones the following objectives: 1) semantically annotating and unifying heterogeneous data, 2) enriching semantic WoT datasets with external knowledge graphs, and 3) providing an analysis of data by means of reasoning mechanisms to infer meaningful information. To overcome the challenge of building interoperable semantics-based IoT applications, the Machine-to-Machine Measurement (M3) semantic engine has been designed to semantically annotate WoT data, build the logic of smarter services and deduce meaningful knowledge by linking it to the external knowledge graphs available on the web. M3 assists application and business developers in designing interoperable Semantic Web of Things applications. Contributions in the context of European semantic-based WoT projects are discussed and a particular use case within FIESTA-IoT project is presented.


title: Is half strength of 0.05 % betamethasone valerate cream still effective in the treatment of phimosis in young children?

abstract: INTRODUCTION ::: 0.05 % betamethasone valerate cream is generally used as an alternative to circumcision for the treatment of phimosis in boys. The aim of this study is to determine whether the half-strength formula (0.025 %) of betamethasone is as effective as 0.05 % betamethasone. ::: ::: ::: METHOD ::: All boys with phimosis seen at our institution between 2010 and 2012, whose parents complained that their children had problems of micturition, i.e., crying and ballooning, and sought for some instructions or treatments, were instructed to apply betamethasone valerate cream. Two strengths, 0.05 and 0.025 %, were randomly applied to each patient twice a day for 2 months. The patients whose parents were not willing to the conservative treatment underwent circumcision. ::: ::: ::: RESULTS ::: Of the 47 patients, 23 boys with an average age of 16.65 ± 4.052 months (range 11-24 months) were given 0.025 % betamethasone cream, whereas the remaining 24 boys in control group with an average age of 18.42 ± 5.030 months (range 10-24 months) were instructed to apply with 0.05 % betamethasone valerate cream. Using unpaired t test, the age in both groups were comparable (p = 0.1932). There was a decrease in phimosis grade by the end of the therapeutic course in both groups. Further analysis using Mann-Whitney test revealed that the phimosis grade in the half-strength group (0.025 % strength) was significantly lower to the phimosis grade in the control (0.05 % betamethasone) group (p = 0.0003). There was no diversion from steroid application to circumcision or any side effects in the both groups. ::: ::: ::: CONCLUSIONS ::: 0.025 % betamethasone valerate cream produced a clinical improvement. However, the half-strength formula was not effective as the conventional formula of 0.05 % betamethasone valerate cream.


title: Automatic network traffic classification

abstract: The thesis addresses a number of critical problems in regard to fully automating the process of network traffic classification and protocol identification. Several effective solutions based on statistical analysis and machine learning techniques are proposed, which significantly reduce the requirements for human interventions in network traffic classification systems.


title: Tensor Completion Algorithms in Big Data Analytics

abstract: Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors. Due to the multidimensional character of tensors in describing complex datasets, tensor completion algorithms and their applications have received wide attention and achievement in areas like data mining, computer vision, signal processing, and neuroscience. In this survey, we provide a modern overview of recent advances in tensor completion algorithms from the perspective of big data analytics characterized by diverse variety, large volume, and high velocity. We characterize these advances from the following four perspectives: general tensor completion algorithms, tensor completion with auxiliary information (variety), scalable tensor completion algorithms (volume), and dynamic tensor completion algorithms (velocity). Further, we identify several tensor completion applications on real-world data-driven problems and present some common experimental frameworks popularized in the literature along with several available software repositories. Our goal is to summarize these popular methods and introduce them to researchers and practitioners for promoting future research and applications. We conclude with a discussion of key challenges and promising research directions in this community for future exploration.


title: Volume electron microscopy for neuronal circuit reconstruction

abstract: The last decade has seen a rapid increase in the number of tools to acquire volume electron microscopy (EM) data. Several new scanning EM (SEM) imaging methods have emerged, and classical transmission EM (TEM) methods are being scaled up and automated. Here we summarize the new methods for acquiring large EM volumes, and discuss the tradeoffs in terms of resolution, acquisition speed, and reliability. We then assess each method's applicability to the problem of reconstructing anatomical connectivity between neurons, considering both the current capabilities and future prospects of the method. Finally, we argue that neuronal ‘wiring diagrams’ are likely necessary, but not sufficient, to understand the operation of most neuronal circuits: volume EM imaging will likely find its best application in combination with other methods in neuroscience, such as molecular biology, optogenetics, and physiology.


title: The multifold relationship between memory and decision making: An individual-differences study.

abstract: Several judgment and decision-making tasks are assumed to involve memory functions, but significant knowledge gaps on the memory processes underlying these tasks remain. In a study on 568 adults between 25 and 80 years of age, hypotheses were tested on the specific relationships between individual differences in working memory, episodic memory, and semantic memory, respectively, and 6 main components of decision-making competence. In line with the hypotheses, working memory was positively related with the more cognitively demanding tasks (Resistance to Framing, Applying Decision Rules, and Under/Overconfidence), whereas episodic memory was positively associated with a more experience-based judgment task (Recognizing Social Norms). Furthermore, semantic memory was positively related with 2 more knowledge-based decision-making tasks (Consistency in Risk Perception and Resistance to Sunk Costs). Finally, the age-related decline observed in some of the decision-making tasks was (partially or totally) mediated by the age-related decline in working memory or episodic memory. These findings are discussed in relation to the functional roles fulfilled by different memory processes in judgment and decision-making tasks.


title: Perspectives on human population structure at the cusp of the sequencing era.

abstract: Human groups show structured levels of genetic similarity as a consequence of factors such as geographical subdivision and genetic drift. Surveying this structure gives us a scientific perspective on human origins, sheds light on evolutionary processes that shape both human adaptation and disease, and is integral to effectively carrying out the mission of global medical genetics and personalized medicine. Surveys of population structure have been ongoing for decades, but in the past three years, single-nucleotide-polymorphism (SNP) array technology has provided unprecedented detail on human population structure at global and regional scales. These studies have confirmed well-known relationships between distantly related populations and uncovered previously unresolvable relationships among closely related human groups. SNPs represent the first dense genome-wide markers, and as such, their analysis has raised many challenges and insights relevant to the study of population genetics with whole-genome sequences. Here we draw on the lessons from these studies to anticipate the directions that will be most fruitful to pursue during the emerging whole-genome sequencing era.


title: IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion

abstract: The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods have been suggested to extract various heuristic and high-level features from gait motion data to identify discriminative gait signatures and distinguish the target individual from others. However, the manual and hand crafted feature extraction is error prone and subjective. Furthermore, the motion data collected from inertial sensors have complex structure and the detachment between manual feature extraction module and the predictive learning models might limit the generalization capabilities. In this paper, we propose a novel approach for human gait identification using time-frequency (TF) expansion of human gait cycles in order to capture joint 2 dimensional (2D) spectral and temporal patterns of gait cycles. Then, we design a deep convolutional neural network (DCNN) learning to extract discriminative features from the 2D expanded gait cycles and jointly optimize the identification model and the spectro-temporal features in a discriminative fashion. We collect raw motion data from five inertial sensors placed at the chest, lower-back, right hand wrist, right knee, and right ankle of each human subject synchronously in order to investigate the impact of sensor location on the gait identification performance. We then present two methods for early (input level) and late (decision score level) multi-sensor fusion to improve the gait identification generalization performance. We specifically propose the minimum error score fusion (MESF) method that discriminatively learns the linear fusion weights of individual DCNN scores at the decision level by minimizing the error rate on the training data in an iterative manner. 10 subjects participated in this study and hence, the problem is a 10-class identification task. Based on our experimental results, 91% subject identification accuracy was achieved using the best individual IMU and 2DTF-DCNN. We then investigated our proposed early and late sensor fusion approaches, which improved the gait identification accuracy of the system to 93.36% and 97.06%, respectively.


An extension of the Rao-Rubin characterization of the Poisson distribution


title: Feature Selection and Intrusion Detection using Hybrid Flexible Neural Tree

abstract: Current Intrusion Detection Systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this study is to identify important input features in building an IDS that is computationally efficient and effective. This paper proposes an IDS model based on general and enhanced Flexible Neural Tree (FNT). Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using an evolutionary algorithm and the parameters are optimized by particle swarm optimization algorithm. Empirical results indicate that the proposed method is efficient.


title: Efficient FPGA hardware implementation of secure hash function SHA-256/Blake-256

abstract: : Since the beginning of study of the Secure Hash function (SHA), it has been thoroughly studied by designers with the goal of reducing the area, frequency, and throughput of the hardware implementation of this cryptosystem. The Secure Hash function algorithm has become the default choice for security services in numerous applications. In this paper, we proposed a new design for the SHA-256 and blake-256 functions. Moreover, the proposed design has been implemented on Xilinx Virtex-5 FPGA. Its area, frequency and throughput have been compared and it is shown that the proposed design achieve good performance in term of area, frequency and throughput.


title: Coooolll: A Deep Learning System for Twitter Sentiment Classification

abstract: In this paper, we develop a deep learning system for message-level Twitter sentiment classification. Among the 45 submitted systems including the SemEval 2013 participants, our system (Coooolll) is ranked 2nd on the Twitter2014 test set of SemEval 2014 Task 9. Coooolll is built in a supervised learning framework by concatenating the sentiment-specific word embedding (SSWE) features with the state-of-the-art hand-crafted features. We develop a neural network with hybrid loss function 1 to learn SSWE, which encodes the sentiment information of tweets in the continuous representation of words. To obtain large-scale training corpora, we train SSWE from 10M tweets collected by positive and negative emoticons, without any manual annotation. Our system can be easily re-implemented with the publicly available sentiment-specific word embedding.


title: Escaping or connecting ? Characteristics of youth who form close online relationships

abstract: We used data from a US national sample of Internet users, ages 10–17 (N=1501), to explore the characteristics of youth who had formed close relationships with people they met on the Internet (n=210). Girls who had high levels of conflict with parents or were highly troubled were more likely than other girls to have close online relationships, as were boys who had low levels of communication with parents or were highly troubled, compared to other boys. Age, race and aspects of Internet use were also related. We know little about the nature or quality of the close online relationships, but youth with these sorts of problems may be more vulnerable to online exploitation and to other possible ill effects of online relationships. At the same time, these relationships may have helpful aspects.


title: Prevalence of physical inactivity in Saudi Arabia: a brief review.

abstract: Major lifestyle changes in recent years in Saudi Arabia may be leading to physical inactivity and a low level of physical fitness. This paper reviews the current literature about physical inactivity in the Saudi Arabian population and discusses its implications for health. Available data from a small number of studies suggests a high prevalence (43.3%-99.5%) of physical inactivity among Saudi children and adults alike. Furthermore, the proportion of Saudi children and adults who are at risk due to inactivity is much higher than for any other coronary heart disease risk factor. It is recommended that a national policy encouraging activity in daily life be established and more studies are carried out to address physical activity patterns with representative samples of the Saudi Arabian population.


title: LocalitySensitive Binary Codes from Shift-Invariant Kernels,” Advances in neural information processing systems

abstract: This paper addresses the problem of designing binary codes for high-dimensional data such that vectors that are similar in the original space map to similar binary strings. We introduce a simple distribution-free encoding scheme based on random projections, such that the expected Hamming distance between the binary codes of two vectors is related to the value of a shift-invariant kernel (e.g., a Gaussian kernel) between the vectors. We present a full theoretical analysis of the convergence properties of the proposed scheme, and report favorable experimental performance as compared to a recent state-of-the-art method, spectral hashing.


title: Methods for computing state similarity in Markov Decision Processes

abstract: A popular approach to solving large probabilistic systems relies on aggregating states based on a measure of similarity. Many approaches in the literature are heuristic. A number of recent methods rely instead on metrics based on the notion of bisimulation, or behavioral equivalence between states (Givan et al, 2001, 2003; Ferns et al, 2004). An integral component of such metrics is the Kantorovich metric between probability distributions. However, while this metric enables many satisfying theoretical properties, it is costly to compute in practice. In this paper, we use techniques from network optimization and statistical sampling to overcome this problem. We obtain in this manner a variety of distance functions for MDP state aggregation, which differ in the tradeoff between time and space complexity, as well as the quality of the aggregation. We provide an empirical evaluation of these trade-offs.


title: A New Orthodontic Appliance with a Mini Screw for Upper Molar Distalization

abstract: The aim of this study is to present a new upper molar distalization appliance called Cise distalizer designed as intraoral device supported with orthodontic mini screw for upper permanent molar distalization. The new appliance consists of eight main components. In order to understand the optimum force level, the appliance under static loading is tested by using strain gage measurement techniques. Results show that one of the open coils produces approximately 300 gr distalization force. Cise distalizer can provide totally 600 gr distalization force. This range of force level is enough for distalization of upper first and second molar teeth.


title: A low phase-noise CMOS VCO with harmonic tuned LC tank

abstract: This paper presents a phase-noise reduction technique for voltage-controlled oscillators (VCOs) using a harmonic tuned (HT) LC tank. The phase-noise suppression is achieved through almost rectangular-shaped voltage at the switching differential cell, which effectively maximizes the slope of the switching cell output voltage at a zero crossing point. In addition, the proposed technique also suppresses the down-conversion of the noise around the second harmonic frequency by the second harmonic short of the tank. One second HT VCO and two third HT VCOs are designed and implemented to evaluate the concept using a 0.35- and 0.13-/spl mu/m CMOS process. The figure-of-merit (FOM) of the second HT VCO, third HT VCO1, and third HT VCO2 are -180.7, -183.7, and -189.5, respectively. The best FOM performance of the VCO has phase noises of -100.4, -132.0, and -140.8dBc/Hz at 100-kHz, 1-MHz, and 3-MHz offset frequencies at the 2-GHz carrier, respectively. This VCO consumes 3.29 mA from a 1.8-V supply with the silicon area of 500 /spl mu/m/spl times/750 /spl mu/m.


Dual Polarized Omnidirectional Antenna


title: Mobile phone dependence: Secondary school pupils’ attitudes

abstract: This paper regards a study aiming to investigate secondary school pupils’ attitudes regarding mobile phone possible dependence. A 26-item questionnaire was administered to 504 pupils aged 12–18 years old, in Greece. Around half of the sample reported perceptions linked to the social motivational aspect of the mobile phones (peer dependence), phone use in order to feel better, as well as some negative consequences (loss of control). Three factors were extracted, “negative consequences”, “withdrawal” and “affect”. Little self-perceived mobile phone dependence seems to exist among adolescents (slight levels of craving, withdrawal and loss of control were revealed). Girls reported higher dependence in comparison to boys, while grade (or age) had no effect on any factor. This questionnaire provides a reliable tool that can be used to study various dimensions of addiction to mobile phones. It is suggested to be used with other adolescent populations of different countries, in order to reveal possible similarities and differences. Implications for educational policy makers, teachers and pupils are discussed.


title: Graph Neural Networks and Boolean Satisfiability

abstract: In this paper we explore whether or not deep neural architectures can learn to classify Boolean satisfiability (SAT). We devote considerable time to discussing the theoretical properties of SAT. Then, we define a graph representation for Boolean formulas in conjunctive normal form, and train neural classifiers over general graph structures called Graph Neural Networks, or GNNs, to recognize features of satisfiability. To the best of our knowledge this has never been tried before. Our preliminary findings are potentially profound. In a weakly-supervised setting, that is, without problem specific feature engineering, Graph Neural Networks can learn features of satisfiability.


title: An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation

abstract: In a previous paper [Pearl and Verma, 1991] we presented an algorithm for extracting causal influences from independence information, where a causal influence was defined as the existence of a directed arc in all minimal causal models consistent with the data. In this paper we address the question of deciding whether there exists a causal model that explains ALL the observed dependencies and independencies. Formally, given a list M of conditional independence statements, it is required to decide whether there exists a directed acyclic graph (dag) D that is perfectly consistent with M, namely, every statement in M, and no other, is reflected via dseparation in D. We present and analyze an effective algorithm that tests for the existence of such a day, and produces one, if it exists.


title: Collective nominal semantic role labeling for tweets

abstract: Tweets have become an increasingly popular source of fresh information. We investigate the task of Nominal Semantic Role Labeling (NSRL) for tweets, which aims to identify predicate-argument structures defined by nominals in tweets. Studies of this task can help fine-grained information extraction and retrieval from tweets. There are two main challenges in this task: 1) The lack of information in a single tweet, rooted in the short and noisy nature of tweets; and 2) recovery of implicit arguments. We propose jointly conducting NSRL on multiple similar tweets using a graphical model, leveraging the redundancy in tweets to tackle these challenges. Extensive evaluations on a human annotated data set demonstrate that our method outperforms two baselines with an absolute gain of 2.7% in F1.


Mulligan’s mobilisation with movement: a review of the tenets and prescription of MWMs


title: Fast Scene Understanding for Autonomous Driving

abstract: Most approaches for instance-aware semantic labeling traditionally focus on accuracy. Other aspects like runtime and memory footprint are arguably as important for real-time applications such as autonomous driving. Motivated by this observation and inspired by recent works that tackle multiple tasks with a single integrated architecture, in this paper we present a real-time efficient implementation based on ENet that solves three autonomous driving related tasks at once: semantic scene segmentation, instance segmentation and monocular depth estimation. Our approach builds upon a branched ENet architecture with a shared encoder but different decoder branches for each of the three tasks. The presented method can run at 21 fps at a resolution of 1024x512 on the Cityscapes dataset without sacrificing accuracy compared to running each task separately.


title: Noninvasive Monitoring by Photoplethysmography

abstract: The photoplethysmogram (PPG) is a noninvasive circulatory signal related to the pulsatile volume in tissue and is displayed by many pulse oximeters. The PPG is similar in appearance to the invasive arterial waveform, but is noninvasive and ubiquitous in hospitals. There is increasing interest in seeking circulatory information from the PPG and developing techniques for a wide variety of novel applications. This article addresses the basic physics of photoplethysmography, physiologic principles behind pulse oximetry operation, and recent technological advances in the usefulness of the PPG waveform to assess microcirculation and intravascular fluid volume monitoring during intensive care.


title: Joint Learning of Sentence Embeddings for Relevance and Entailment

abstract: We consider the problem of Recognizing Textual Entailment within an Information Retrieval context, where we must simultaneously determine the relevancy as well as degree of entailment for individual pieces of evidence to determine a yes/no answer to a binary natural language question. ::: We compare several variants of neural networks for sentence embeddings in a setting of decision-making based on evidence of varying relevance. We propose a basic model to integrate evidence for entailment, show that joint training of the sentence embeddings to model relevance and entailment is feasible even with no explicit per-evidence supervision, and show the importance of evaluating strong baselines. We also demonstrate the benefit of carrying over text comprehension model trained on an unrelated task for our small datasets. ::: Our research is motivated primarily by a new open dataset we introduce, consisting of binary questions and news-based evidence snippets. We also apply the proposed relevance-entailment model on a similar task of ranking multiple-choice test answers, evaluating it on a preliminary dataset of school test questions as well as the standard MCTest dataset, where we improve the neural model state-of-art.