title: Face verification across age progression

abstract: Human faces undergo considerable amount of variations with aging. While studies have revealed the extent to which factors such as illumination variations, pose variations, facial expression and occlusions affect face recognition, the role of natural factors such as aging effects in affecting the same are yet to be studied. How does age progression affect the similarity between two images of an individual? What is the confidence associated with establishing the identity between two age separated face images of an individual? On a database of pairs of passport images, we study similarity of faces as a function time. We propose a Bayesian age-difference classifier that is built on a probabilistic eigenspaces framework. Since age separated face images invariably differ in illumination and have facial variations due to aging, we propose a method to overcome non uniform illumination across face images. The problem discussed in this paper has direct applications in passport renewal and homeland security.


Design and Implementation of ETSI-Standard Reconfigurable Mobile Device for Heterogeneous Network


title: Forecasting where computational intelligence meets the stock market

abstract: Forecasting is an important activity in finance. Traditionally, forecasting has been done with in-depth knowledge in finance and the market. Advances in computational intelligence have created opportunities that were never there before. Computational finance techniques, machine learning in particular, can dramatically enhance our ability to forecast. They can help us to forecast ahead of our competitors and pick out scarce opportunities. This paper explains some of the opportunities offered by computational intelligence and some of the achievements so far. It also explains the underlying technologies and explores the research horizon.


title: Relational coordination promotes quality of chronic care delivery in Dutch disease-management programs

abstract: BACKGROUND ::: Previous studies have shown that relational coordination is positively associated with the delivery of hospital care, acute care, emergency care, trauma care, and nursing home care. The effect of relational coordination in primary care settings, such as disease-management programs, remains unknown. ::: ::: ::: PURPOSE ::: This study examined relational coordination between general practitioners and other professionals in disease-management programs and assessed the impact of relational coordination on the delivery of chronic illness care. ::: ::: ::: METHODOLOGY ::: Professionals (n = 188; response rate = 57%) in 19 disease-management programs located throughout the Netherlands completed surveys that assessed relational coordination and chronic care delivery. We used a cross-sectional study design. ::: ::: ::: FINDINGS ::: Our study demonstrated that the delivery of chronic illness care was positively related to relational coordination. We found positive relationships with community linkages (r = .210, p < .01), self-management support (r = .217, p < .01), decision support (r = .190, p < .01), delivery system design (r = .278, p < .001), and clinical information systems (r = .193, p < .01). Organization of the health delivery system was not significantly related to relational coordination. The regression analyses showed that even after controlling for all background variables, relational coordination still significantly affected chronic care delivery (β = .212, p ≤ .01). As expected, our findings showed a lower degree of relational coordination among general practitioners than between general practitioners and other core disease-management team members: practice nurses (M = 2.69 vs. 3.73; p < .001), dieticians (M = 2.69 vs. 3.07; p < .01), physical therapists (M = 2.69 vs. 3.06; p < .01), medical specialists (M = 2.69 vs. 3.16; p < .01), and nurse practitioners (M = 2.69 vs. 3.19; p < .001). ::: ::: ::: PRACTICE IMPLICATIONS ::: The enhancement of relational coordination among core disease-management professionals with different disciplines is expected to improve chronic illness care delivery.


title: Water, sanitation and hygiene for the prevention of diarrhoea

abstract: Background Ever since John Snow’s intervention on the Broad St pump, the effect of water quality, hygiene and sanitation in preventing diarrhoea deaths has always been debated. The evidence identified in previous reviews is of variable quality, and mostly relates to morbidity rather than mortality. ::: ::: Methods We drew on three systematic reviews, two of them for the Cochrane Collaboration, focussed on the effect of handwashing with soap on diarrhoea, of water quality improvement and of excreta disposal, respectively. The estimated effect on diarrhoea mortality was determined by applying the rules adopted for this supplement, where appropriate. ::: ::: Results The striking effect of handwashing with soap is consistent across various study designs and pathogens, though it depends on access to water. The effect of water treatment appears similarly large, but is not found in few blinded studies, suggesting that it may be partly due to the placebo effect. There is very little rigorous evidence for the health benefit of sanitation; four intervention studies were eventually identified, though they were all quasi-randomized, had morbidity as the outcome, and were in Chinese. ::: ::: Conclusion We propose diarrhoea risk reductions of 48, 17 and 36%, associated respectively, with handwashing with soap, improved water quality and excreta disposal as the estimates of effect for the LiST model. Most of the evidence is of poor quality. More trials are needed, but the evidence is nonetheless strong enough to support the provision of water supply, sanitation and hygiene for all.


title: PUMA: planning under uncertainty with macro-actions

abstract: Planning in large, partially observable domains is challenging, especially when a long-horizon lookahead is necessary to obtain a good policy. Traditional POMDP planners that plan a different potential action for each future observation can be prohibitively expensive when planning many steps ahead. An efficient solution for planning far into the future in fully observable domains is to use temporally-extended sequences of actions, or "macro-actions." In this paper, we present a POMDP algorithm for planning under uncertainty with macro-actions (PUMA) that automatically constructs and evaluates open-loop macro-actions within forward-search planning, where the planner branches on observations only at the end of each macro-action. Additionally, we show how to incrementally refine the plan over time, resulting in an anytime algorithm that provably converges to an ∊-optimal policy. In experiments on several large POMDP problems which require a long horizon lookahead, PUMA outperforms existing state-of-the art solvers.


title: Power analysis of large-scale, real-time neural networks on SpiNNaker

abstract: Simulating large spiking neural networks is non trivial: supercomputers offer great flexibility at the price of power and communication overheads; custom neuromorphic circuits are more power efficient but less flexible; while alternative approaches based on GPGPUs and FPGAs, whilst being more readily available, show similar model specialization. As well as efficiency and flexibility, real time simulation is a desirable neural network characteristic, for example in cognitive robotics where embodied agents interact with the environment using low-power, event-based neuromorphic sensors. The SpiNNaker neuromimetic architecture has been designed to address these requirements, simulating large-scale heterogeneous models of spiking neurons in real-time, offering a unique combination of flexibility, scalability and power efficiency. In this work a 48-chip board is utilised to generate a SpiNNaker power estimation model, based on numbers of neurons, synapses and their firing rates. In addition, we demonstrate simulations capable of handling up to a quarter of a million neurons, 81 million synapses and 1.8 billion synaptic events per second, with the most complex simulations consuming less than 1 Watt per SpiNNaker chip.


title: Requirements for Data Quality Metrics

abstract: Data quality and especially the assessment of data quality have been intensively discussed in research and practice alike. To support an economically oriented management of data quality and decision making under uncertainty, it is essential to assess the data quality level by means of well-founded metrics. However, if not adequately defined, these metrics can lead to wrong decisions and economic losses. Therefore, based on a decision-oriented framework, we present a set of five requirements for data quality metrics. These requirements are relevant for a metric that aims to support an economically oriented management of data quality and decision making under uncertainty. We further demonstrate the applicability and efficacy of these requirements by evaluating five data quality metrics for different data quality dimensions. Moreover, we discuss practical implications when applying the presented requirements.


title: The Promise of an Emerging Field

abstract: Exoskeletons are wearable robots exhibiting a close cognitive and physical interaction with the human user. These are rigid robotic exoskeletal structures that typically operate alongside human limbs. Scientific and technological work on exoskeletons began in the early 1960s but have only recently been applied to rehabilitation and functional substitution in patients suffering from motor disorders. Key topics for further development of exoskeletons in rehabilitation scenarios include the need for robust human-robot multimodal cognitive interaction, safe and dependable physical interaction, true wearability and portability, and user aspects such as acceptance and usability. This discussion provides an overview of these aspects and draws conclusions regarding potential future research directions in robotic exoskeletons.


Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation


Agarose Gel Electrophoresis for the Separation of DNA Fragments


title: AMADA: web data repositories in the amazon cloud

abstract: We present AMADA, a platform for storing Web data (in particular, XML documents and RDF graphs) based on the Amazon Web Services (AWS) cloud infrastructure. AMADA operates in a Software as a Service (SaaS) approach, allowing users to upload, index, store, and query large volumes of Web data. The demonstration shows (i) the step-by-step procedure for building and exploiting the warehouse (storing, indexing, querying) and (ii) the monitoring tools enabling one to control the expenses (monetary costs) charged by AWS for the operations involved while running AMADA.


title: Teleological reasoning in infancy: The naïve theory of rational action

abstract: Converging evidence demonstrates that one-year-olds interpret and draw inferences about other’s goaldirected actions. We contrast alternative theories about how this early competence relates to our ability to attribute mental states to others. We propose that one-year-olds apply a non-mentalistic interpretational system, the ’teleological stance’ to represent actions by relating relevant aspects of reality (action, goal-state and situational constraints) through the principle of rational action, which assumes that actions function to realize goal-states by the most efficient means available. We argue that this early inferential principle is identical to the rationality principle of the mentalistic stance ‐ a representational system that develops later to guide inferences about mental states.


title: Non-intrusive performance profiling for entire software stacks based on the flow reconstruction principle

abstract: Understanding the performance behavior of distributed server stacks at scale is non-trivial. The servicing of just a single request can trigger numerous sub-requests across heterogeneous software components; and many similar requests are serviced concurrently and in parallel. When a user experiences poor performance, it is extremely difficult to identify the root cause, as well as the software components and machines that are the culprits. ::: ::: This paper describes Stitch, a non-intrusive tool capable of profiling the performance of an entire distributed software stack solely using the unstructured logs output by heterogeneous software components. Stitch is substantially different from all prior related tools in that it is capable of constructing a system model of an entire software stack without building any domain knowledge into Stitch. Instead, it automatically reconstructs the extensive domain knowledge of the programmers who wrote the code; it does this by relying on the Flow Reconstruction Principle which states that programmers log events such that one can reliably reconstruct the execution flow a posteriori.


title: Target-Motion-Induced Radar Imaging

abstract: Imaging from ground-based (stationary) radars of moving targets is often possible by utilizing a "synthetic aperture" developed from the target motion itself. The theory and experimental results associated with such processing are addressed. An aircraft is imaged from both a straight flight and a turn with recognizable results. Analysis shows that two-phase components exist in the radar return, one being gross velocity induced, the other being interscatterer interference within the target itself. The former phase must be removed prior to imaging and techniques are developed for this task. Preprocessing, range curvature, range alignment, motion compensation, and presumming are all addressed prior to presenting the experimental results. Coherence processing intervals, range collapsing, and range realignment are all examined during the processing aspects of the paper.


Supplementary material of “H-plots for displaying non-metric dissimilarity matrices.”


MULTI-SCALE HESSIAN BASED APPROACH FOR SEGMENTATION OF LUNG VESSEL TREE IN 3-D CTA DATA


The role of smartphone usage types , emotional intelligence , social stress , self-regulation , age , and gender


title: On the Fusion of Periocular and Iris Biometrics in Non-ideal Imagery

abstract: Human recognition based on the iris biometric is severely impacted when encountering non-ideal images of the eye characterized by occluded irises, motion and spatial blur, poor contrast, and illumination artifacts. This paper discusses the use of the periocular region surrounding the iris, along with the iris texture patterns, in order to improve the overall recognition performance in such images. Periocular texture is extracted from a small, fixed region of the skin surrounding the eye. Experiments on the images extracted from the Near Infra-Red (NIR) face videos of the Multi Biometric Grand Challenge (MBGC) dataset demonstrate that valuable information is contained in the periocular region and it can be fused with the iris texture to improve the overall identification accuracy in non-ideal situations.


title: A survey of open source tools for machine learning with big data in the Hadoop ecosystem

abstract: With an ever-increasing amount of options, the task of selecting machine learning tools for big data can be difficult. The available tools have advantages and drawbacks, and many have overlapping uses. The world’s data is growing rapidly, and traditional tools for machine learning are becoming insufficient as we move towards distributed and real-time processing. This paper is intended to aid the researcher or professional who understands machine learning but is inexperienced with big data. In order to evaluate tools, one should have a thorough understanding of what to look for. To that end, this paper provides a list of criteria for making selections along with an analysis of the advantages and drawbacks of each. We do this by starting from the beginning, and looking at what exactly the term “big data” means. From there, we go on to the Hadoop ecosystem for a look at many of the projects that are part of a typical machine learning architecture and an understanding of how everything might fit together. We discuss the advantages and disadvantages of three different processing paradigms along with a comparison of engines that implement them, including MapReduce, Spark, Flink, Storm, and H2O. We then look at machine learning libraries and frameworks including Mahout, MLlib, SAMOA, and evaluate them based on criteria such as scalability, ease of use, and extensibility. There is no single toolkit that truly embodies a one-size-fits-all solution, so this paper aims to help make decisions smoother by providing as much information as possible and quantifying what the tradeoffs will be. Additionally, throughout this paper, we review recent research in the field using these tools and talk about possible future directions for toolkit-based learning.


title: Using Neo4j graph database in social network analysis

abstract: This article describes how Neo4j database capabilities can be utilized to implement measures often used in social network analysis. It gives a brief overview of the concept of Neo4j graph database. The UML class diagram of domain model is presented and discussed in details. On the basis of implementation of degree centrality and local clustering coefficient measures, several Neo4j core features are presented. In the summary, some general comments on using this database as a tool in a social network analysis are provided.


title: The architecture of subterranean ant nests: beauty and mystery underfoot

abstract: Over the 100 million years of their evolution, ants have constructed or occupied nests in a wide range of materials and situations. A large number of ant species excavate nests in the soil, and these subterranean nests have evolved into a wide range of sizes and architectures. On the basis of casts made of such nests, this variation and the patterns that govern it are described. The possible functions of architectural features are discussed, as are the behavioral “rules” through which the nests are created by worker ants. Copyright Springer Science+Business Media New York 2015


title: A Novel PCR-Based Approach for Accurate Identification of Vibrio parahaemolyticus

abstract: A PCR-based assay was developed for more accurate identification of Vibrio parahaemolyticus through targeting the bla CARB-17 like element, an intrinsic β-lactamase gene that may also be regarded as a novel species-specific genetic marker of this organism. Homologous analysis showed that bla CARB-17 like genes were more conservative than the tlh, toxR and atpA genes, the genetic markers commonly used as detection targets in identification of V. parahaemolyticus. Our data showed that this bla CARB-17-specific PCR-based detection approach consistently achieved 100% specificity, whereas PCR targeting the tlh and atpA genes occasionally produced false positive results. Furthermore, a positive result of this test is consistently associated with an intrinsic ampicillin resistance phenotype of the test organism, presumably conferred by the products of bla CARB-17 like genes. We envision that combined analysis of the unique genetic and phenotypic characteristics conferred by bla CARB-17 shall further enhance the detection specificity of this novel yet easy-to-use detection approach to a level superior to the conventional methods used in V. parahaemolyticus detection and identification.


title: Sparking Creativity: Improving Electronic Brainstorming with Individual Cognitive Priming

abstract: In recent years, there has been growing interest in the study of individual cognition in teams. Meanwhile, we learned that much of human behavior involves nonconscious cognition. Recent research has shown that it is possible to manipulate nonconscious cognition through "priming." Priming is a well-known phenomenon for individual behavior, but we do not know whether priming can be used to influence team behavior. We developed a Web-based computer game that was designed to improve creativity through supraliminal priming, a form of priming in which users are aware of the priming, but not aware of its purpose. Participants were exposed to a priming game and then worked as members of a team to generate ideas on a creativity task. Our results show that when users played the game designed to improve performance, their teams generated significantly more ideas that were of better quality than when they were exposed to neutral priming.


title: Recognition of shapes by editing their shock graphs

abstract: This paper presents a novel framework for the recognition of objects based on their silhouettes. The main idea is to measure the distance between two shapes as the minimum extent of deformation necessary for one shape to match the other. Since the space of deformations is very high-dimensional, three steps are taken to make the search practical: 1) define an equivalence class for shapes based on shock-graph topology, 2) define an equivalence class for deformation paths based on shock-graph transitions, and 3) avoid complexity-increasing deformation paths by moving toward shock-graph degeneracy. Despite these steps, which tremendously reduce the search requirement, there still remain numerous deformation paths to consider. To that end, we employ an edit-distance algorithm for shock graphs that finds the optimal deformation path in polynomial time. The proposed approach gives intuitive correspondences for a variety of shapes and is robust in the presence of a wide range of visual transformations. The recognition rates on two distinct databases of 99 and 216 shapes each indicate highly successful within category matches (100 percent in top three matches), which render the framework potentially usable in a range of shape-based recognition applications.


title: The IAM-database: an English sentence database for offline handwriting recognition

abstract: In this paper we describe a database that consists of handwritten English sentences. It is based on the Lancaster-Oslo/Bergen (LOB) corpus. This corpus is a collection of texts that comprise about one million word instances. The database includes 1,066 forms produced by approximately 400 different writers. A total of 82,227 word instances out of a vocabulary of 10,841 words occur in the collection. The database consists of full English sentences. It can serve as a basis for a variety of handwriting recognition tasks. However, it is expected that the database would be particularly useful for recognition tasks where linguistic knowledge beyond the lexicon level is used, because this knowledge can be automatically derived from the underlying corpus. The database also includes a few image-processing procedures for extracting the handwritten text from the forms and the segmentation of the text into lines and words.


title: Quality of Life, Coping Styles, Stress Levels, and Time Use in Mothers of Children with Autism Spectrum Disorders: Comparing Single Versus Coupled Households

abstract: This study aimed to examine the influence of differences in household status on the parental stress, coping, time use and quality of life (QoL) among mothers of children with autism spectrum disorders. Forty-three single and 164 coupled mothers completed the survey. Data were analysed using multivariate logistic regression. We found that single mothers were 1.05 times more likely to report lower levels of environmental QoL. Whilst they were 1.73 times more likely to use acceptance coping style, this association did not persist after adjusting for total number of children, household income and employment status. There was no difference in time use and stress between these mothers. Possible environmental issues for single mothers and implications for future research are discussed.


title: The cell biology of cell-in-cell structures

abstract: For more than a century, scientists have observed cells internalized inside other cells. These cell-in-cell structures often consist of viable rather than apoptotic cells, and can form by the invasion of one cell into another, rather than by engulfment. This review will address how cell-in-cell structures might form and what physiological roles they might have.


title: Exploiting the Structure of Bipartite Graphs for Algebraic and Spectral Graph Theory Applications

abstract: Abstract In this article, we extend several algebraic graph analysis methods to bipartite networks. In various areas of science, engineering, and commerce, many types of information can be represented as networks, and thus, the discipline of network analysis plays an important role in these domains. A powerful and widespread class of network analysis methods is based on algebraic graph theory, i.e., representing graphs as square adjacency matrices. However, many networks are of a very specific form that clashes with that representation: they are bipartite. That is, they consist of two node types, with each edge connecting a node of one type with a node of the other type. Examples of bipartite networks (also called two-mode networks) are persons and the social groups they belong to, musical artists and the musical genres they play, and text documents and the words they contain. In fact, any type of feature that can be represented by a categorical variable can be interpreted as a bipartite network. Although...


title: Spatial-Based Predictive Control and Geometric Corridor Planning for Adaptive Cruise Control Coupled With Obstacle Avoidance

abstract: This paper presents an integrated control approach for autonomous driving comprising a corridor path planner that determines constraints on vehicle position, and a linear time-varying model predictive controller combining path planning and tracking in a road-aligned coordinate frame. The capabilities of the approach are illustrated in obstacle-free curved road-profile tracking, in an application coupling adaptive cruise control (ACC) with obstacle avoidance (OA), and in a typical driving maneuver on highways. The vehicle is modeled as a nonlinear dynamic bicycle model with throttle, brake pedal position, and steering angle as control inputs. Proximity measurements are assumed to be available within a given range field surrounding the vehicle. The proposed general feedback control architecture includes an estimator design for fusion of database information (maps), exteroceptive as well as proprioceptive measurements, a geometric corridor planner based on graph theory for the avoidance of multiple, potentially dynamically moving objects, and a spatial-based predictive controller. Switching rules for transitioning between four different driving modes, i.e., ACC, OA, obstacle-free road tracking (RT), and controlled braking (Brake), are discussed. The proposed method is evaluated on test cases, including curved and highway two-lane road tracks with static as well as moving obstacles.


title: Non-Adversarial Unsupervised Word Translation

abstract: Unsupervised word translation from non-parallel inter-lingual corpora has attracted much research interest. Very recently, neural network methods trained with adversarial loss functions achieved high accuracy on this task. Despite the impressive success of the recent techniques, they suffer from the typical drawbacks of generative adversarial models: sensitivity to hyper-parameters, long training time and lack of interpretability. In this paper, we make the observation that two sufficiently similar distributions can be aligned correctly with iterative matching methods. We present a novel method that first aligns the second moment of the word distributions of the two languages and then iteratively refines the alignment. Extensive experiments on word translation of European and Non-European languages show that our method achieves better performance than recent state-of-the-art deep adversarial approaches and is competitive with the supervised baseline. It is also efficient, easy to parallelize on CPU and interpretable.


title: Teaching Children Confidence in a High Tech World: The Netherlands 1950-1962

abstract: The article addresses the question how modern technologies were represented in a widely read illustrated magazine, Panorama, and in the comic for children which appeared on its final page. The article shows how technologies such as airplanes, robots and nuclear power, which were depicted and described in the magazine, also make their appearance, in a transformed way, in the comic story at the end of the magazine. This transformation is analyzed and it is argued that the children's stories may be read as an answer to the fears and uncertainties about these modern technologies that were common among adults at the time.


title: Estimating the NDVI from SAR by Convolutional Neural Networks

abstract: Since optical remote sensing images are useless in cloudy conditions, a possible alternative is to resort to synthetic aperture radar (SAR) images. However, many conventional techniques for Earth monitoring applications require specific spectral features which are defined only for multispectral data. For this reason, in this work we propose to estimate missing spectral features through data fusion and deep learning, exploiting both temporal and cross-sensor dependencies on Sentinel-1 and Sentinel-2 time-series. The proposed approach, validated focusing on the estimation of the normalized difference vegetation index (NDVI), shows very interesting results with a large performance gain over the linear regression approach according to several accuracy indicators.


title: DIAG-NRE: A Deep Pattern Diagnosis Framework for Distant Supervision Neural Relation Extraction

abstract: Modern neural network models have achieved the state-of-the-art performance on relation extraction (RE) tasks. Although distant supervision (DS) can automatically generate training labels for RE, the effectiveness of DS highly depends on datasets and relation types, and sometimes it may introduce large labeling noises. In this paper, we propose a deep pattern diagnosis framework, DIAG-NRE, that aims to diagnose and improve neural relation extraction (NRE) models trained on DS-generated data. DIAG-NRE includes three stages: (1) The deep pattern extraction stage employs reinforcement learning to extract regular-expression-style patterns from NRE models. (2) The pattern refinement stage builds a pattern hierarchy to find the most representative patterns and lets human reviewers evaluate them quantitatively by annotating a certain number of pattern-matched examples. In this way, we minimize both the number of labels to annotate and the difficulty of writing heuristic patterns. (3) The weak label fusion stage fuses multiple weak label sources, including DS and refined patterns, to produce noise-reduced labels that can train a better NRE model. To demonstrate the broad applicability of DIAG-NRE, we use it to diagnose 14 relation types of two public datasets with one simple hyper-parameter configuration. We observe different noise behaviors and obtain significant F1 improvements on all relation types suffering from large labeling noises.


title: The Comparison And Analysis Of The Streaming Media Transport Protocol In The Transmission System

abstract: In order to solve the the problem of poor video quality in the transmission process of the streaming media which is caused by transmission delay and packet loss, this paper analyzes and compares the streaming media transport protocols from the aspect of the streaming media transmission protocols. The comparative results show that: RTP protocol based on UDP can effectively improve the performance of streaming media's transmission.


title: Topology Optimized Design, Fabrication, and Characterization of a Soft Cable-Driven Gripper

abstract: Soft-bodied robots, due to their intrinsic compliance, have shown great potential for operating within unstructured environment and interacting with unknown objects. This letter deals with automatic design and fabrication of soft robots. From a structure point of view, we synthesize a soft cable-driven gripper by recasting its mechanical design as a topology optimization problem. Building on previous work on compliant mechanism optimization, we model the interactions between the gripper and objects more practically, in form of pressure loadings and friction tractions, and furthermore, we investigate how the interaction uncertainties affect the optimization solution by varying the contact location and area. The optimized soft fingers were three-dimensionally printed and then assembled to build a gripper. The experiments show that the gripper can handle a large range of unknown objects of different shapes and weights (up to 1 kg), with different grasping modes. This letter represents an important step toward leveraging the full potential of the freeform design space to generate novel soft-bodied robots.


title: End-to-End Learning of Motion Representation for Video Understanding

abstract: Despite the recent success of end-to-end learned representations, hand-crafted optical flow features are still widely used in video analysis tasks. To fill this gap, we propose TVNet, a novel end-to-end trainable neural network, to learn optical-flow-like features from data. TVNet subsumes a specific optical flow solver, the TV-L1 method, and is initialized by unfolding its optimization iterations as neural layers. TVNet can therefore be used directly without any extra learning. Moreover, it can be naturally concatenated with other task-specific networks to formulate an end-to-end architecture, thus making our method more efficient than current multi-stage approaches by avoiding the need to pre-compute and store features on disk. Finally, the parameters of the TVNet can be further fine-tuned by end-to-end training. This enables TVNet to learn richer and task-specific patterns beyond exact optical flow. Extensive experiments on two action recognition benchmarks verify the effectiveness of the proposed approach. Our TVNet achieves better accuracies than all compared methods, while being competitive with the fastest counterpart in terms of features extraction time.


title: Children referred for possible sexual abuse: medical findings in 2384 children

abstract: OBJECTIVE: The goal of this study was to compare rates of positive medical findings in a 5-year prospective study of 2384 children, referred for evaluation of possible sexual abuse, with two decades of research. The prospective study summarizes demographic information, clinical history, relationship of perpetrators, nature of abuse, and clinical findings. The study reports on the results by patterns of referral and the medical examination. RESULTS: There were 2384 children evaluated in a tertiary referral center between 1985 and 1990 for possible sexual abuse. Children were referred after they disclosed sexual abuse, because of behavioral changes or exposure to an abusive environment, and because of possible medical conditions. A total of 96.3% of all children referred for evaluation had a normal medical examination; 95.6% of children reporting abuse were normal, 99.8% who were referred for behavioral changes or exposure to abuse were also normal. Of the 182 children referred for evaluation of medical conditions, 92% were found to be normal at the time of examination by the Child Advocacy Center. The remaining 15/182 (8%) that were found to be abnormal were diagnosed with sexually transmitted diseases, acute or healed genital injuries, and were 17% (15/88) of the total cases found to have medical findings diagnostic of abuse. Interviews of the children indicated that 68% of the girls and 70% of the boys reported severe abuse, defined as penetration of vagina or anus. Penetration was associated with a higher percentage of abnormal findings in girls (6%) compared to 1% of the boys. The relationship of the abuser impacted on the severity of the abuse. CONCLUSION: Research indicates that medical, social, and legal professionals have relied too heavily on the medical examination in diagnosing child sexual abuse. History from the child remains the single most important diagnostic feature in coming to the conclusion that a child has been sexually abused. Only 4% of all children referred for medical evaluation of sexual abuse have abnormal examinations at the time of evaluation. Even with a history of severe abuse such as vaginal or anal penetration, the rate of abnormal medical findings is only 5.5%. Biological parents are less likely to engage in severe abuse than parental substitutes, extended family members, or strangers. Language: en


title: Mosaic block detection based on HOG with SVM classifier and template matching

abstract: Digital video signal is widely used in modern society. There is increasing demand for it to be more secure and highly reliable. Focusing on this, we propose a method of detecting mosaic blocks. Our proposed method combines two algorithms: HOG with SVM classifier and template matching. We also consider characteristics of mosaic blocks other than shape. Experimental results show that our proposed method has high detection performance of mosaic blocks.


title: The Simple Micro-Economics of Public-Private Partnerships

abstract: We build on the existing literature in public-private partnerships (PPP) to analyze the main incentive issues in PPPs and the shape of optimal contracts in those contexts. We present a basic model of procurement in a multitask environment in which a risk-averse firm chooses noncontractible efforts in cost reduction and quality improvement. We first consider the effect on incentives and risk transfer of bundling building and management stages into a single contract, allowing for different assumptions on feasible contracts and information available to the government. Then we extend the model in novel directions. We study the relationship between the operator and its financiers and the impact of private finance. We discuss the trade-off between incentive and flexibility in PPP agreements and the dynamics of PPPs, including cost overruns. We also consider how institutions, and specifically the risk of regulatory opportunism, affect contract design and incentives. The conclusion summarizes policy implications on the desirability of PPPs.


title: BayesPy: Variational Bayesian Inference in Python

abstract: BayesPy is an open-source Python software package for performing variational Bayesian inference. It is based on the variational message passing framework and supports conjugate exponential family models. By removing the tedious task of implementing the variational Bayesian update equations, the user can construct models faster and in a less error-prone way. Simple syntax, flexible model construction and efficient inference make BayesPy suitable for both average and expert Bayesian users. It also supports some advanced methods such as stochastic and collapsed variational inference.


The influence of parasitic capacitors on SAR ADC characteristics


title: Modelling security market events in continuous time: Intensity based, multivariate point process models

abstract: A continuous time econometric modelling framework for multivariate financial market event (or `transactions') data is developed in which the model is specified via the vector stochastic intensity. This has the advantage that the conditioning sigma-field is updated continuously in time as new information arrives. The class of generalised Hawkes models is introduced which allows the estimation of the dependence of the intensity on the events of previous trading days. Analytic likelihoods are available and it is shown how to construct diagnostic tests based on the transformation of non-Poisson processes into standard Poisson processes using random changes of time. A proof of the validity of the diagnostic testing procedures is given that imposes only a very weak condition on the point process model, thus establishing their widespread applicability. A continuous time, bivariate point process model of the timing of trades and mid-quote changes is presented for a New York Stock Exchange stock and the empirical findings are related to the theoretical and empirical market microstructure literature. The two-way interaction of trades and quote changes is found to be important empirically.


title: Autonomous Visual Mapping and Exploration With a Micro Aerial Vehicle

abstract: Cameras are a natural fit for micro aerial vehicles MAVs due to their low weight, low power consumption, and two-dimensional field of view. However, computationally-intensive algorithms are required to infer the 3D structure of the environment from 2D image data. This requirement is made more difficult with the MAV's limited payload which only allows for one CPU board. Hence, we have to design efficient algorithms for state estimation, mapping, planning, and exploration. We implement a set of algorithms on two different vision-based MAV systems such that these algorithms enable the MAVs to map and explore unknown environments. By using both self-built and off-the-shelf systems, we show that our algorithms can be used on different platforms. All algorithms necessary for autonomous mapping and exploration run on-board the MAV. Using a front-looking stereo camera as the main sensor, we maintain a tiled octree-based 3D occupancy map. The MAV uses this map for local navigation and frontier-based exploration. In addition, we use a wall-following algorithm as an alternative exploration algorithm in open areas where frontier-based exploration under-performs. During the exploration, data is transmitted to the ground station which runs large-scale visual SLAM. We estimate the MAV's state with inertial data from an IMU together with metric velocity measurements from a custom-built optical flow sensor and pose estimates from visual odometry. We verify our approaches with experimental results, which to the best of our knowledge, demonstrate our MAVs to be the first vision-based MAVs to autonomously explore both indoor and outdoor environments.


title: Fault Detection and Diagnosis Using Support Vector Machines - A SVC and SVR Comparison

abstract: This paper presents the use of Support Vector Machines (SVM) methodology for fault detection and diagnosis. Two approaches are addressed: the SVM for classification (Support Vector Classification - SVC) and SVM for regression (Support Vector Regression - SVR). A comparison was made between the two techniques through the study of a reactor of cyclopentenol production. In the case studied, different fault scenarios were introduced and it was evaluated which technique was able to detect and diagnose them. Finally, a comparison was made between the fault detection methodologies based on SVM and Dynamic Principal Component Analysis (DPCA) based detection techniques for a jacketed CSTR.


title: Enhancing rule-based scheduling in wafer fabrication facilities by evolutionary algorithms: Review and opportunity

abstract: Scheduling is a critical and challenging task in manufacturing systems, especially in large-scale complex systems like wafer fabrication facilities. Although evolutionary algorithms (EAs) have demonstrated many successful applications in the field of manufacturing scheduling, there are very few studies on scheduling of wafer fabs using EAs. Dispatching rules are one of the most common techniques for fab scheduling. In this paper, we present six ways of applying EAs for enhancing the rule-based scheduling system. We provide potential EA-based solutions and review relevant literature. Many of the mentioned viewpoints can serve as new research topics for both researchers in the fields of scheduling and evolutionary computation (EC). Several general EC techniques including multiobjective optimization, expensive optimization, and parallelization are also introduced and shown to be helpful to fab scheduling.


title: Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment

abstract: Automatically detecting verbal irony (roughly, sarcasm) in online content is important for many practical applications (e.g., sentiment detection), but it is difficult. Previous approaches have relied predominantly on signal gleaned from word counts and grammatical cues. But such approaches fail to exploit the context in which comments are embedded. We thus propose a novel strategy for verbal irony classification that exploits contextual features, specifically by combining noun phrases and sentiment extracted from comments with the forum type (e.g., conservative or liberal) to which they were posted. We show that this approach improves verbal irony classification performance. Furthermore, because this method generates a very large feature space (and we expect predictive contextual features to be strong but few), we propose a mixed regularization strategy that places a sparsity-inducing `1 penalty on the contextual feature weights on top of the `2 penalty applied to all model coefficients. This increases model sparsity and reduces the variance of model performance.


title: Do Convnets Learn Correspondence?

abstract: Convolutional neural nets (convnets) trained from massive labeled datasets [1] have substantially improved the state-of-the-art in image classification [2] and object detection [3]. However, visual understanding requires establishing correspondence on a finer level than object category. Given their large pooling regions and training from whole-image labels, it is not clear that convnets derive their success from an accurate correspondence model which could be used for precise localization. In this paper, we study the effectiveness of convnet activation features for tasks requiring correspondence. We present evidence that convnet features localize at a much finer scale than their receptive field sizes, that they can be used to perform intraclass aligment as well as conventional hand-engineered features, and that they outperform conventional features in keypoint prediction on objects from PASCAL VOC 2011 [4].


title: A study of some e-Government activities in South Africa

abstract: This paper examines the nature and extent of e-government activities in South Africa. E-Government refers to the practice of leveraging ICT to deliver services to employees (G2E), to citizens (G2C), to business (G2B) and to other government departments (G2G). The research method used was through direct personal engagement through semi-structured interviews with 10 stakeholders at local, provincial and national government level. The sample comprised two senior provincial management, two senior municipal managers, two academic government researchers, one national government official and three businessmen connected with government. A literature review was also conducted with data sourced from journals, books, websites and popular media for evidence of e-Government not known to the respondent or a provincial (state) or national initiative. It finds although e-Government activities is being provided by, inter alia, government, citizens and business, the service is few, far between and expensive. Although government has committed to e-services, there are, with few exceptions, such services available. On the other hand citizens are rising in anger as they demand improved service levels. It determines some typically government-centric services have already perhaps out of necessity been usurped by citizen led initiatives. Business is also providing innovative e-services. This comes however with skewed costs that may agitate the digital divide. There is an increasingly pervasive broadband infrastructure from both public and private enterprise that represents an opportunity to roll e-services. The ubiquity of mobile devices and ICT may make-services possible. This is an enticing opportunity, particularly given the increasing and sometimes violent calls for service delivery. To achieve traction the authors argue a stronger advocacy role is needed to promote e-Government activities by all stakeholders namely government, business or civil society. This paper uses the Prossler-Krimmer Model to analyze egovernment activities in South Africa including KwaZulu-Natal. It examines the technological, political, legislative and societal(sectors) respectively in South Africa and concludes eGovernment is a major opportunities for all stakeholders through a combination or research, cross training, advocacy and a re-contextualization of e-government.


title: Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis

abstract: In this paper we present an approach to extract sentiments associated with a phrase or sentence. Sentiment analysis has been attempted mostly for documents typically a review or a news item. Conjunctions have a substantial impact on the overall sentiment of a sentence, so here we present how atomic sentiments of individual phrases combine together in the presence of conjuncts to decide the overall sentiment of a sentence. We used word dependencies and dependency trees to analyze the sentence constructs and were able to get results close to 80%. We have also analyzed the effect of WordNet on the accuracy of the results over General Inquirer.


title: Hume: A Domain-Specific Language for Real-Time Embedded Systems

abstract: This paper describes Hume: a novel domain-specific language whose purpose is to explore the expressibility/costability spectrum in resource-constrained systems, such as real-time embedded or control systems. Hume provides a number of high level features including higher-order functions, polymorphic types, arbitrary but sized user-defined data structures, asynchronous processes, lightweight exception handling, automatic memory management and domain-specific metaprogramming features, whilst seeking to guarantee strong space/time behaviour and maintaining overall determinacy.


title: A 77 GHz 90 nm CMOS Transceiver for FMCW Radar Applications

abstract: The first 77 GHz frequency modulated continuous wave (FMCW) radar transceiver IC with an accurate FMCW chirp signal generator using a 90 nm CMOS process is presented. To realize accurate FMCW radar system in CMOS, a PLL synthesizer based FMCW generator with chirp smoothing technique that is able to output linear FMCW frequency chirp using a nonlinear reference chirp signal supplied from a low spec/cost digital-oriented frequency reference is applied. The fabricated IC consists of an LNA, a down-conversion mixer with an output buffer, a driver amplifier, a power amplifier with power combiner, an LO distributer and an FMCW synthesizer. The measured FMCW signal from the proposed FMCW generator achieves 93 kHz frequency error (nonlinearity) at the 77 GHz chip signal. Radar performance of the IC has less than 1% ranging error from 1 m to 8 m distance from the measurement in a laboratory. These results show the transceiver achieves a fundamental function for radar applications with 520 mW power consumptions.


Learning Dota 2 Team Compositions


title: Multilingual Bottle-Neck Features and its Application for Under-Resourced Languages

abstract: In this paper we present our latest investigation on multilingual bottle-neck (BN) features and its application to rapid language adaptation to new languages. We show that the overall performance of a Multilayer Perceptron (MLP) network improves significantly by initializing it with a multilingual MLP. Furthermore, ASR performance increases on both, on those languages which were used for multilingual MLP training, and on a new language. We propose a new strategy called “open target language” MLP to train more flexible models for language adaptation, which is particularly suited for small amounts of training data. The final results on the Vietnamese GlobalPhone database gave 15.8% relative improvement in terms of Syllable Error Rate (SyllER) for the ASR system trained with 22.5h data and 16.9% relative gains for the system trained with only 2h data.


title: Bike sharing systems: Solving the static rebalancing problem

abstract: Abstract This paper deals with a new problem that is a generalization of the many to many pickup and delivery problem and which is motivated by operating self-service bike sharing systems. There is only one commodity, initially distributed among the vertices of a graph, and a capacitated single vehicle aims to redistribute the commodity in order to reach a target distribution. Each vertex can be visited several times and also can be used as a buffer in which the commodity is stored for a later visit. This problem is NP-hard, since it contains several NP-hard problems as special cases (the TSP being maybe the most obvious one). Even finding a tractable exact formulation remains problematic. This paper presents efficient algorithms for solving instances of reasonable size, and contains several theoretical results related to these algorithms. A branch-and-cut algorithm is proposed for solving a relaxation of the problem. An upper bound of the optimal solution of the problem is obtained by a tabu search, which is based on some theoretical properties of the solution, once fixed the sequence of the visited vertices. The possibility of using the information provided by the relaxation receives a special attention, both from a theoretical and a practical point of view. It is proven that to build a feasible solution of the problem by using the one obtained by the relaxation is an NP-hard problem. Nevertheless, a tabu search initialized with the optimal solution of the relaxation often shows that it is the optimal one. The algorithms have been tested on a set of instances coming from the literature, proving their effectiveness.


title: A Multi-Ontology Approach for Personal Information Management

abstract: The increasingly huge volume of personal information stored in a desktop computer is characterized by disparate models, unstructured contents, and implicit knowledge. Aiming at a semantic rich environment, a number of Semantic Desktop frameworks have been proposed, concentrating on different aspects, including organization, manipulation, and visualization of the data. In this paper, we propose a layered and semantic ontology-based framework for personal information management, and we discuss its annotations, associations, and navigation. We also discuss query processing in two cases: query rewriting in a single personal information application, PIA, and that between two PIAs.


title: Resource Allocation with Population Dynamics

abstract: We study the problem of provisioning of information about the state of a system of shared resources to its users, assuming the state of the system is a result of the actions of the users and that the information about the state, in turn, drive the actions of the users. We extend the formulation of Marecek et al. [Int. J. Control 88(10), 2015], such that the evolution of a heterogeneous population of users is governed by a Markov chain. Still, we are able to show that the distribution of users across the resources converges in distribution, for suitable means of provisioning the information about the system, under certain assumptions. The model and proof techniques may have wider applicability.


title: Artificial intelligence and pediatrics: A synthetic mini review

abstract: The use of artificial intelligence intelligencein medicine can be traced back to 1968 when Paycha published his paper Le diagnostic a l'aide d'intelligences artificielle, presentation de la premiere machine diagnostri. Few years later Shortliffe et al. presented an expert system named Mycin which was able to identify bacteria causing severe blood infections and to recommend antibiotics. Despite the fact that Mycin outperformed members of the Stanford medical school in the reliability of diagnosis it was never used in practice due to a legal issue who do you sue if it gives a wrong diagnosis?. However only in 2016 when the artificial intelligence software built into the IBM Watson AI platform correctly diagnosed and proposed an effective treatment for a 60-year-old womans rare form of leukemia the AI use in medicine become really popular.On of first papers presenting the use of AI in paediatrics was published in 1984. The paper introduced a computer-assisted medical decision making system called SHELP.


title: Scalable SQL and NoSQL data stores

abstract: In this paper, we examine a number of SQL and socalled "NoSQL" data stores designed to scale simple OLTP-style application loads over many servers. Originally motivated by Web 2.0 applications, these systems are designed to scale to thousands or millions of users doing updates as well as reads, in contrast to traditional DBMSs and data warehouses. We contrast the new systems on their data model, consistency mechanisms, storage mechanisms, durability guarantees, availability, query support, and other dimensions. These systems typically sacrifice some of these dimensions, e.g. database-wide transaction consistency, in order to achieve others, e.g. higher availability and scalability.


title: Complete Blood Count Reference Intervals for Healthy Han Chinese Adults

abstract: BACKGROUND ::: Complete blood count (CBC) reference intervals are important to diagnose diseases, screen blood donors, and assess overall health. However, current reference intervals established by older instruments and technologies and those from American and European populations are not suitable for Chinese samples due to ethnic, dietary, and lifestyle differences. The aim of this multicenter collaborative study was to establish CBC reference intervals for healthy Han Chinese adults. ::: ::: ::: METHODS ::: A total of 4,642 healthy individuals (2,136 males and 2,506 females) were recruited from six clinical centers in China (Shenyang, Beijing, Shanghai, Guangzhou, Chengdu, and Xi'an). Blood samples collected in K2EDTA anticoagulant tubes were analyzed. Analysis of variance was performed to determine differences in consensus intervals according to the use of data from the combined sample and selected samples. ::: ::: ::: RESULTS ::: Median and mean platelet counts from the Chengdu center were significantly lower than those from other centers. Red blood cell count (RBC), hemoglobin (HGB), and hematocrit (HCT) values were higher in males than in females at all ages. Other CBC parameters showed no significant instrument-, region-, age-, or sex-dependent difference. Thalassemia carriers were found to affect the lower or upper limit of different RBC profiles. ::: ::: ::: CONCLUSION ::: We were able to establish consensus intervals for CBC parameters in healthy Han Chinese adults. RBC, HGB, and HCT intervals were established for each sex. The reference interval for platelets for the Chengdu center should be established independently.


title: A new gate driver integrated circuit for IGBT devices with advanced protections

abstract: The aim of this paper is to discuss new solutions in the design of insulated gate bipolar transistor (IGBT) gate drivers with advanced protections such as two-level turn-on to reduce peak current when turning on the device, two-level turn-off to limit over-voltage when the device is turned off, and an active Miller clamp function that acts against cross conduction phenomena. Afterwards, we describe a new circuit which includes a two-level turn-off driver and an active Miller clamp function. Tests and results for these advanced functions are discussed, with particular emphasis on the influence of an intermediate level in a two-level turn-off driver on overshoot across the IGBT.


title: Novel ultra-wideband discone antenna

abstract: A double-discone antenna for an ultra-wideband fre- quency scan is presented. An exquisite assembly of two inverse-feeding discone antennas shows a 30:1 broad bandwidth with VSWR below 2.5 and an omnidirectional radiation pattern. These features make the pro- posed antenna very suitable for both the UWB system antenna and the wideband scan antenna. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 42: 113-115, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20224


Incorporating visual features into word embeddings: A bimodal autoencoder-based approach.


title: A survey of video processing techniques for traffic applications

abstract: Video sensors become particularly important in traffic applications mainly due to their fast response, easy installation, operation and maintenance, and their ability to monitor wide areas. Research in several fields of traffic applications has resulted in a wealth of video processing and analysis methods. Two of the most demanding and widely studied applications relate to traffic monitoring and automatic vehicle guidance. In general, systems developed for these areas must integrate, amongst their other tasks, the analysis of their static environment (automatic lane finding) and the detection of static or moving obstacles (object detection) within their space of interest. In this paper we present an overview of image processing and analysis tools used in these applications and we relate these tools with complete systems developed for specific traffic applications. More specifically, we categorize processing methods based on the intrinsic organization of their input data (feature-driven, area-driven, or model-based) and the domain of processing (spatial/frame or temporal/video). Furthermore, we discriminate between the cases of static and mobile camera. Based on this categorization of processing tools, we present representative systems that have been deployed for operation. Thus, the purpose of the paper is threefold. First, to classify image-processing methods used in traffic applications. Second, to provide the advantages and disadvantages of these algorithms. Third, from this integrated consideration, to attempt an evaluation of shortcomings and general needs in this field of active research.


title: Human Gait Recognition Using Patch Distribution Feature and Locality-Constrained Group Sparse Representation

abstract: In this paper, we propose a new patch distribution feature (PDF) (i.e., referred to as Gabor-PDF) for human gait recognition. We represent each gait energy image (GEI) as a set of local augmented Gabor features, which concatenate the Gabor features extracted from different scales and different orientations together with the X-Y coordinates. We learn a global Gaussian mixture model (GMM) (i.e., referred to as the universal background model) with the local augmented Gabor features from all the gallery GEIs; then, each gallery or probe GEI is further expressed as the normalized parameters of an image-specific GMM adapted from the global GMM. Observing that one video is naturally represented as a group of GEIs, we also propose a new classification method called locality-constrained group sparse representation (LGSR) to classify each probe video by minimizing the weighted l1, 2 mixed-norm-regularized reconstruction error with respect to the gallery videos. In contrast to the standard group sparse representation method that is a special case of LGSR, the group sparsity and local smooth sparsity constraints are both enforced in LGSR. Our comprehensive experiments on the benchmark USF HumanID database demonstrate the effectiveness of the newly proposed feature Gabor-PDF and the new classification method LGSR for human gait recognition. Moreover, LGSR using the new feature Gabor-PDF achieves the best average Rank-1 and Rank-5 recognition rates on this database among all gait recognition algorithms proposed to date.


title: A New Code for Spectrometric Analysis for Environmental Radiological Surveillance on Monitors Focused on Gamma Radioactivity on Aerosols

abstract: This paper presents a new code for the analysis of gamma spectra generated by an equipment for continuous measurement and identification of gamma radioactivity in aerosols with paper filter. It is called pGamma and has been developed by the NERG group at the Technical University of Catalonia — Barcelona Tech and Raditel Serveis i Subministraments Tec-nologics, Ltd. The code is being adapted to the monitors of the Environmental Radiological Surveillance Network of the Gener-alitat de Catalunya (local Catalan Government), Spain. The code is a spectrum analysis system for identifying and determining activity concentration of gamma emitters. It generates alarms depending on the activity of emitters and then elaborates reports. Moreover, it includes a library with NORM and artificial emitters of interest. The code is being used at three stations of the Network equipped with an aerosol monitor (Asco and Vandellos (province of Tarragona)and Barcelona).


title: Writing About Emotional Experiences as a Therapeutic Process

abstract: For the past decade, an increasing number of studies have demonstrated that when individuals write about emotional experiences, significant physical and mental health improvements follow The basic paradigm and findings are summarized along with some boundary conditions Although a reduction in inhibition may contribute to the disclosure phenomenon changes in basic cognitive and linguistic processes during writing predict better health Implications for theory and treatment are discussed


title: Affective state detection via facial expression analysis within a human-computer interaction context

abstract: The advancement in technology indicates that there is an opportunity to enhance human–computer interaction by way of affective state recognition. Affective state recognition is typically based on passive stimuli such as watching video clips, which does not reflect genuine interaction. This paper presents a study on affective state recognition using active stimuli, i.e. facial expressions of users when they attempt computerised tasks, particularly across typical usage of computer systems. A data collection experiment is presented for acquiring data from normal users whilst they interact with software, attempting to complete a set of predefined tasks. In addition, a hierarchical machine learning approach is presented for facial expression-based affective state recognition, which employs an Euclidean distance-based feature representation, conjointly with a customised encoding for users’ self-reported affective states. Consequently, the aim is to find the potential relationship between the facial expressions, as defined by Paul Ekman, and the self-reported emotional states specified by users using Russells Circumplex model, in relation to the actual feelings and affective states. The main findings of this study suggest that facial expressions cannot precisely reveal the actual feelings of users whilst interacting with common computerised tasks. Moreover, during active interaction tasks more variation occurs within the facial expressions of participants than occurs within passive interaction.


title: VISTA: computational tools for comparative genomics

abstract: Comparison of DNA sequences from different species is a fundamental method for identifying functional elements in genomes. Here, we describe the VISTA family of tools created to assist biologists in carrying out this task. Our first VISTA server at http://www-gsd.lbl.gov/vista/ was launched in the summer of 2000 and was designed to align long genomic sequences and visualize these alignments with associated functional annotations. Currently the VISTA site includes multiple comparative genomics tools and provides users with rich capabilities to browse pre-computed whole-genome alignments of large vertebrate genomes and other groups of organisms with VISTA Browser, to submit their own sequences of interest to several VISTA servers for various types of comparative analysis and to obtain detailed comparative analysis results for a set of cardiovascular genes. We illustrate capabilities of the VISTA site by the analysis of a 180 kb interval on human chromosome 5 that encodes for the kinesin family member 3A (KIF3A) protein.


title: Intelligent Maze Solving Robot Based on Image Processing and Graph Theory Algorithms

abstract: The most important task for maze solving robots is the fast and reliable finding of its shortest path from its initial point to its final destination point. This paper proposes an intelligent maze solving robot that can determine its shortest path on a line maze based on image processing and artificial intelligence algorithms. The image of the line maze is captured by a camera and sent to the computer to be analyzed and processed by a program developed using Visual C++ and OpenCV libraries and based on graph theory algorithms. The developed program solves the captured maze by examining all possible paths exist in the maze that could convey the robot to the required destination point. After that, the best shortest path is determined and then the instructions that guide the car-like robot to reach its desired destination point are sent to the robot through Bluetooth. The robot follows the received guide path to reach its destination. The proposed approach works faster than the traditional methods which push the robot to move through the maze cell by cell in order to find its destination point. Moreover, the proposed method allows the maze solving robot to avoid trapping and falling in infinity loops. Applications of maze solving systems include intelligent traffic control that helps ambulances, fire fighters, or rescuing robots to find their shortest path to their destination.


title: Incremental Fixed-Rank Robust PCA for Video Background Recovery

abstract: Video background recovery is a very important task in computer vision applications. Recent research offers robust principal component analysis RPCA as a promising approach for solving video background recovery. RPCA works by decomposing a data matrix into a low-rank matrix and a sparse matrix. Our previous work shows that when the desired rank of the low-rank matrix is known, fixing the rank in the algorithm called FrALM fixed-rank ALM yields more robust and accurate results than existing RPCA algorithms. However, application of RPCA to video background recovery requires that each frame in the video is encoded as a column in the data matrix. This is impractical in real applications because the videos can be easily larger than the amount of memory in a computer. This paper presents an algorithm called iFrALM incremental fixed-rank ALM that computes fixed-rank RPCA incrementally by splitting the video frames into an initial batch and an incremental batch. Comprehensive tests show that iFrALM uses less memory and time compared to FrALM. Moreover, the initial batch size and batch quality can be carefully selected to ensure that iFrALM reduces memory and time complexity without sacrificing accuracy.


Clinical Features of Adult/Adolescent Atopic Dermatitis and Chinese Criteria for Atopic Dermatitis


title: A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data

abstract: Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph (DAG)—a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer's disease (AD) and reveal findings that could lead to advancements in AD research.


title: Evaluating and Improving Annotation Tools for Medical Forms

abstract: The annotation of entities with concepts from standardized terminologies and ontologies is of high importance in the life sciences to enhance semantic interoperability, information retrieval and meta-analysis. Unfortunately, medical documents such as clinical forms or electronic health records are still rarely annotated despite the availability of some tools to automatically determine possible annotations. In this study, we comparatively evaluate the quality of two such tools, cTAKES and MetaMap, as well as of a recently proposed annotation approach from our group for annotating medical forms. We also investigate how to improve the match quality of the tools by post-filtering computed annotations as well as by combining several annotation approaches.


title: Repeated observation of breast tumor subtypes in independent gene expression data sets.

abstract: Characteristic patterns of gene expression measured by DNA microarrays have been used to classify tumors into clinically relevant subgroups. In this study, we have refined the previously defined subtypes of breast tumors that could be distinguished by their distinct patterns of gene expression. A total of 115 malignant breast tumors were analyzed by hierarchical clustering based on patterns of expression of 534 "intrinsic" genes and shown to subdivide into one basal-like, one ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like subgroup. The genes used for classification were selected based on their similar expression levels between pairs of consecutive samples taken from the same tumor separated by 15 weeks of neoadjuvant treatment. Similar cluster analyses of two published, independent data sets representing different patient cohorts from different laboratories, uncovered some of the same breast cancer subtypes. In the one data set that included information on time to development of distant metastasis, subtypes were associated with significant differences in this clinical feature. By including a group of tumors from BRCA1 carriers in the analysis, we found that this genotype predisposes to the basal tumor subtype. Our results strongly support the idea that many of these breast tumor subtypes represent biologically distinct disease entities.


title: Analysis of agreement between measurements of continuous variables: general principles and lessons from studies of imaging of carotid stenosis

abstract: Abstract Clinical research often involves measurement of continuous variables. However, clinical measurements are seldom precise. It is frequently necessary, therefore, either for the reproducibility of measurement to be assessed (observer agreement studies), or for measurements made by different techniques to be compared (method comparison studies). There are numerous ways in which data can be analysed and reported in such studies, and several pitfalls. In order to determine which methods are commonly used in the medical literature, a systematic review of studies involving measurement of carotid stenosis was performed. A random sample of 40 studies were selected for detailed assessment. The methods of analysis of reproducibility of measurement of stenosis and/or comparison of two alternative techniques of measurement were recorded. Ten different methods were identified. The advantages and disadvantages of each of the methods are discusses in a non-technical and non-mathematical manner, and illustrated using data from a study of measurement of carotid stenosis by two observers on 1001 carotid angiograms.


title: Interpreting multimodal referring expressions in real time

abstract: Humans communicate about objects using language, gesture, and context, fusing information from multiple modalities over time. Robots need to interpret this communication in order to collaborate with humans on shared tasks. Processing communicative input incrementally has the potential to increase the speed and accuracy of a robot's reaction. It also enables the robot to incorporate the relative timing of words and gestures into the understanding process. To address this problem, we define a multimodal Bayes filter for interpreting a person's referential expressions to objects. Our approach outputs a distribution over the referent object at 14Hz, updating dynamically as it receives new observations of the person's spoken words and gestures. We collected a new dataset of people referring to one of four objects in a tabletop setting and demonstrate that our approach is able to infer the correct object with 90% accuracy. Additionally, we augment and improve our filter in a simulated home kitchen domain by learning contextual knowledge in an unsupervised manner from existing written text, increasing our maximum accuracy to 96%, even with an increase in the number of objects from four to seventy.


CAROTID-ARTERY INTIMA AND MEDIA THICKNESS AS A RISK FACTOR FOR MYOCARDIAL INFARCTION AND STROKE IN OLDER ADULTS


title: Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm

abstract: This paper presents a new, efficient method for learning task-specific word vectors using a variant of the Passive-Aggressive algorithm. Specifically, this algorithm learns a word embedding matrix in tandem with the classifier parameters in an online fashion, solving a bi-convex constrained optimization at each iteration. We provide a theoretical analysis of this new algorithm in terms of regret bounds, and evaluate it on both synthetic data and NLP classification problems, including text classification and sentiment analysis. In the latter case, we compare various pre-trained word vectors to initialize our word embedding matrix, and show that the matrix learned by our algorithm vastly outperforms the initial matrix, with performance results comparable or above the state-of-the-art on these tasks.


title: A Survey on Temporal Databases and Data mining

abstract: Temporal database is a database which captures and maintains past, present and future data. Conventional databases are not suitable for handling such time varying data. In this context temporal database has gained a significant importance in the field of databases and data mining. The major objective of this research is to perform a detailed survey on temporal databases and the various temporal data mining techniques and explore the various research issues in temporal data mining. We also throw light on the temporal association rules and temporal clustering works carried in literature.


title: Solving Marginal MAP Problems with NP Oracles and Parity Constraints

abstract: Arising from many applications at the intersection of decision making and machine learning, Marginal Maximum A Posteriori (Marginal MAP) Problems unify the two main classes of inference, namely maximization (optimization) and marginal inference (counting), and are believed to have higher complexity than both of them. We propose XOR_MMAP, a novel approach to solve the Marginal MAP Problem, which represents the intractable counting subproblem with queries to NP oracles, subject to additional parity constraints. XOR_MMAP provides a constant factor approximation to the Marginal MAP Problem, by encoding it as a single optimization in polynomial size of the original problem. We evaluate our approach in several machine learning and decision making applications, and show that our approach outperforms several state-of-the-art Marginal MAP solvers.


title: A SAR Interferometric Model for Soil Moisture

abstract: There is a need for scattering models that link quantitatively synthetic aperture radar (SAR) interferometric observables to soil moisture. In this paper, we propose a model based on plane waves and the Born approximation, deriving first the vertical complex wavenumbers in the soil as a function of geometrical and dielectric properties and successively the complex interferometric coherences. It is observed that soil moisture behaves on the phase in a similar way as tomography does, breaking the phase consistency in triplets of interferograms. The proposed model is validated with L-band airborne SAR data; preliminary inversion results based on interferogram triplets and coherence magnitudes are presented.


title: Learning Joint Multilingual Sentence Representations with Neural Machine Translation

abstract: In this paper, we use the framework of neural machine translation to learn joint sentence representations across six very different languages. Our aim is that a representation which is independent of the language, is likely to capture the underlying semantics. We define a new cross-lingual similarity measure, compare up to 1.4M sentence representations and study the characteristics of close sentences. We provide experimental evidence that sentences that are close in embedding space are indeed semantically highly related, but often have quite different structure and syntax. These relations also hold when comparing sentences in different languages.


title: Sentiment expression via emoticons on social media

abstract: Emoticons (e.g., :) and :( ) have been widely used in sentiment analysis and other NLP tasks as features to machine learning algorithms or as entries of sentiment lexicons. In this paper, we argue that while emoticons are strong and common signals of sentiment expression on social media the relationship between emoticons and sentiment polarity are not always clear. Thus, any algorithm that deals with sentiment polarity should take emoticons into account but extreme caution should be exercised in which emoticons to depend on. First, to demonstrate the prevalence of emoticons on social media, we analyzed the frequency of emoticons in a large recent Twitter data set. Then we carried out four analyses to examine the relationship between emoticons and sentiment polarity as well as the contexts in which emoticons are used. The first analysis surveyed a group of participants for their perceived sentiment polarity of the most frequent emoticons. The second analysis examined clustering of words and emoticons to better understand the meaning conveyed by the emoticons. The third analysis compared the sentiment polarity of microblog posts before and after emoticons were removed from the text. The last analysis tested the hypothesis that removing emoticons from text hurts sentiment classification by training two models with and without emoticons in the text, respectively. The results confirms the arguments that: 1) a few emoticons are strong and reliable signals of sentiment polarity and one should take advantage of them in any sentiment analysis; 2) a large group of the emoticons conveys complicated sentiment hence they should be treated with extreme caution.


title: Rapid Evaluation of B-spline Surfaces Using GPU

abstract: Many GPU based applications in 3D geometric modeling system have been proposed along with the augmented programmable performance of Graphics Processing Unit (GPU). A new method of rapid evaluation of B-spline surfaces was proposed with GPU fragment program. The efficiency of the proposed GPU-based evaluating method was tested and compared with the traditional method. The results show that the efficiency of the proposed algorithm outperforms the traditional algorithm. The proposed scheme can also be applied to the evaluation of other parametric surfaces such as Bezier and NURBS surfaces, with its usability and extensibility.


title: Joint MUD exploitation and ICI mitigation based scheduling with limited base station cooperation

abstract: In this paper, we propose a novel opportunistic scheduling algorithm in multi-cell cooperation scenario, i.e., joint multi-user diversity (MUD) exploitation and inter-cell interference (ICI) mitigation based scheduling (MEIMS). Our algorithm jointly considers the intended channel condition of the scheduled user from its serving cell and the orthogonality between that and the corresponding interference channels to concurrently scheduled users in neighboring cells so as to exploit MUD and mitigate ICI simultaneously. The performance of our algorithm is evaluated through simulation. Results show that, our scheme can significantly enhance the received signal to interference plus noise ratio (SINR) with relatively better fairness guarantee, thus achieves the largest throughput and utility comparing to several well-known scheduling algorithms.


title: Concept and design of a modular lower limb exoskeleton

abstract: Powered lower limb exoskeletons become more and more common and are used for different applications such as rehabilitation and human strength augmentation. However, many of the existing designs are limited to a very specific task. We present the concept and design of a modular, reconfigurable lower limb exoskeleton that can be adapted with respect to it's kinematics and actuation to a wide range of users and applications. The introduced modular design allows to compose lower limb exoskeletons with up to four degrees of freedom per leg: abduction/adduction of the hip and flexion/extension of the hip, knee, and ankle. Each passive joint can be extended by a module in terms of functionality, e.g. by actuation or spring stabilization. Three different modules are introduced: one actuator for active support and two spring modules that stabilize the joint around an equilibrium position. Using the presented components, three exemplary systems are composed and discussed: An active hip support exoskeleton, a fully articulated lower limb exoskeleton for gait support and a system for treadmill based gait rehabilitation. The three designs show that the concept of a modular exoskeleton is feasible and that it can be an alternative to a specialized system for environments with changing requirements, for example in rehabilitation or research.


title: A Survey on Automatic Text Summarization

abstract: The increasing availability of online information has necessitated intensive research in the area of automatic text summarization within the Natural Language Processing (NLP) community. Over the past half a century, the problem has been addressed from many dierent perspectives, in varying domains and using various paradigms. This survey intends to investigate some of the most relevant approaches both in the areas of single-document and multipledocument summarization, giving special emphasis to empirical methods and extractive techniques. Some promising approaches that concentrate on specic details of the summarization problem are also discussed. Special attention is devoted to automatic evaluation of summarization systems, as future research on summarization is strongly dependent on progress in this area.


title: High-Throughput Parallel Architecture for H.265/HEVC Deblocking Filter

abstract: A novel parallel VLSI architecture is proposed in order to improve the performance of the H.265/HEVC deblocking filter. The overall computation is pipelined, and a new parallel-zigzag processing order is introduced to achieve high throughput. The processing order of the filter is efficiently rearranged to process the horizontal edges and vertical edges at the same time. The proposed H.265/HEVC deblocking filter architecture improves the parallelism by dissolving the data dependency between the adjacent filtering operations. Our design is also compatible with H.264/AVC. Experimental results demonstrate that our architecture shows the best performance compared with other architectures known so far at the expense of the slightly increased gate count. We improve the performance by 52.3%, while the area is increased by 25.8% compared with the previously known best architecture for H.264/AVC. The operating clock frequency of our design is 226 MHz in TSMC LVT 65 process. The proposed design delivers the performance to process 1080p HD at 60 fps.


title: ESD protection for mixed-voltage I/O using NMOS transistors stacked in a cascode configuration

abstract: Abstract We demonstrate that NMOS transistors stacked in a cascode configuration provide robust ESD protection for mixed voltage I / O in both silicided and silicide-blocked technologies. Circuits for gate voltage modulation were added to ensure uniform finger triggering of the fully silicided device. Layout and circuit rules were developed to avoid parasitic breakdown paths.


title: Seizure-related vehicular crashes and falls with injuries for people with epilepsy (PWE) in northeastern Thailand

abstract: This cross-sectional study aimed to determine the number and types of falls and vehicular crashes with injuries , as well as some specific behavioral associations in people with epilepsy (PWE) in northeastern Thailand. Two hundred and three patients with epilepsy were randomly recruited from the university epilepsy clinic in Khon Kaen, who then completed an interview and a questionnaire. It was found that 84.5% of the patients were operating a vehicle on a regular basis (more than 3days a week), and 21.6% of those had been in a vehicle crash. Additionally, 25.6% of the patients had been involved in falls with injuries. Forty-three percent of the respondents had been involved in either a vehicular crash or a fall with an injury, with 39.7% of the accidents resulting in moderate to severe injuries. Medication compliance was estimated at 66%, while 59.1% said that they had little or no control over their seizures, and more than half the patients did not feel confident about their ability to take care of themselves or to take their antiepileptic drugs (AEDs) properly. In a multivariate model, the following factors significantly increased the risk of being in either a vehicular crash or a fall with an injury: being single, attaining a secondary or higher education, exercising at least three times a week, napping every day or more frequently, and having poor seizure control. The results of this study suggest that patients' poor medication adherence and lack of confidence in managing their seizures may contribute to accidents. Patients with epilepsy should be counseled to seek less risky behaviors and try to attend classes that provide education on AED management. Language: en


title: Mobile Edge Computing Potential in Making Cities Smarter

abstract: This article proposes an approach to enhance users' experience of video streaming in the context of smart cities. The proposed approach relies on the concept of MEC as a key factor in enhancing QoS. It sustains QoS by ensuring that applications/services follow the mobility of users, realizing the "Follow Me Edge" concept. The proposed scheme enforces an autonomic creation of MEC services to allow anywhere anytime data access with optimum QoE and reduced latency. Considering its application in smart city scenarios, the proposed scheme represents an important solution for reducing core network traffic and ensuring ultra-short latency through a smart MEC architecture capable of achieving the 1 ms latency dream for the upcoming 5G mobile systems.


title: CASP-DM: Context Aware Standard Process for Data Mining

abstract: We propose an extension of the Cross Industry Standard Process for Data Mining (CRISPDM) which addresses specific challenges of machine learning and data mining for context and model reuse handling. This new general context-aware process model is mapped with CRISP-DM reference model proposing some new or enhanced outputs.


title: Conical beam radial line slot antennas for 60 GHz band wireless LAN

abstract: In this paper, we fabricate a conical beam small aperture radial line slot antenna (RLSA) for a 60-GHz band wireless LAN. Measured results are also shown for demonstrating the feasibility.


title: MapFactory – Towards a mapping design pattern for big geospatial data

abstract: Abstract. With big geospatial data emerging, cartographers and geographic information scientists have to find new ways of dealing with the volume, variety, velocity, and veracity (4Vs) of the data. This requires the development of tools that allow processing, filtering, analysing, and visualising of big data through multidisciplinary collaboration. In this paper, we present the MapFactory design pattern that will be used for the creation of different maps according to the (input) design specification for big geospatial data. The design specification is based on elements from ISO19115-1:2014 Geographic information – Metadata – Part 1: Fundamentals that would guide the design and development of the map or set of maps to be produced. The results of the exploratory research suggest that the MapFactory design pattern will help with software reuse and communication. The MapFactory design pattern will aid software developers to build the tools that are required to automate map making with big geospatial data. The resulting maps would assist cartographers and others to make sense of big geospatial data.


title: Criteria for measuring KM performance outcomes in organisations

abstract: Purpose – This research attempts to comprehensively examine the criteria for measuring knowledge management (KM) performance outcomes in organisations. To date, no studies have provided a set of widely accepted measurement criteria associated with KM efforts. This paper, therefore, aims to fill the gap.Design/methodology/approach – This study was carried out by systematically reviewing the literature on KM performance outcomes. Case studies were carried out in two organisations identified to have a KM programme in place.Findings – A review of the literature indicates that there are 38 outcomes from KM implementation which have garnered impressive theoretical and empirical support. Based on this, a comprehensive set of performance outcomes is proposed and grouped into five key dimensions. The findings from the case studies indicate that this proposition is relevant.Research limitations/implications – The use of case studies limits the generalisability of the findings, but it opens up new questions to be ex...


title: Control strategy of slack enabling tendon actuator for the soft wearable robot using feedback linearization

abstract: Tendon driven mechanism has been widely used in mechanical systems because of its versatility to mechanical systems requiring complex transmission. This paper proposes a control strategy of slack enabling tendon actuator using feedback linearization. The slack enabling actuator is a newly developed tendon actuator that does not fail although the tendon slacks (non-positive tension) at the outlet of the actuator which is an essential operating phase when the tendon driven is applied to the soft wearable robots. To deal with the dynamic nonlinearities of the slack enabling actuator, input-output feedback linearization was adopted and linear disturbance observer was used to estimate the output tension of the wire and reject position following error due to the external load. The results show that the estimated tension coincide with the measured tension and a proposed method have proper characteristics to control the position of the tendon while estimating the tension of the output tendon.


title: RDF Data Management: A survey of RDBMS-Based Approaches

abstract: This paper surveys approaches and up-to-date information of RDF data management and then categorizes them into four main RDF storages. Then, the survey restricts the discussion to those methods that solve RDF data management using a RDBMS, since it gives better performance and query optimization as a result of the large quantity of work required to induce relational query efficiency and also the scalability of its storage comes into play, with respect to scalability and various characteristics of performance.


title: A proposal to evaluate ontology content

abstract: Research into ontologies has received much attention and has been the subject of study in different scientific communities. The current growth observed in ontology use and availability increases the importance of the evolution of methods to evaluate ontologies. There are formal methodologies available, but the evaluation of ontology content does not seem to be a main concern. We understand the evaluation of ontology content to be the activity of verifying whether the ontology really represents the knowledge of a domain, according to the assessment of experts. Our objective is to contribute to this sense. In order to do so, we present qualitative research about the content evaluation of an organizational ontology, developed within a large Brazilian energy utility company. The evaluation process consists of a set of questionnaires, which are based on a multidisciplinary approach, and of a prototype system. We believe that this proposal can contribute towards improvements in both modeling and evaluation processes in ontology development.


title: Food image recognition using deep convolutional network with pre-training and fine-tuning

abstract: In this paper, we examined the effectiveness of deep convolutional neural network (DCNN) for food photo recognition task. Food recognition is a kind of fine-grained visual recognition which is relatively harder problem than conventional image recognition. To tackle this problem, we sought the best combination of DCNN-related techniques such as pre-training with the large-scale ImageNet data, fine-tuning and activation features extracted from the pre-trained DCNN. From the experiments, we concluded the fine-tuned DCNN which was pre-trained with 2000 categories in the ImageNet including 1000 food-related categories was the best method, which achieved 78.77% as the top-1 accuracy for UEC-FOOD100 and 67.57% for UEC-FOOD256, both of which were the best results so far. In addition, we applied the food classifier employing the best combination of the DCNN techniques to Twitter photo data. We have achieved the great improvements on food photo mining in terms of both the number of food photos and accuracy. In addition to its high classification accuracy, we found that DCNN was very suitable for large-scale image data, since it takes only 0.03 seconds to classify one food photo with GPU.


FACTORS AFFECTING KNOWLEDGE SHARING IN PUBLIC ORGANIZATIONS IN MALAYSIA