title: Developing and Evaluating Quilts for the Depiction of Large Layered Graphs

abstract: Traditional layered graph depictions such as flow charts are in wide use. Yet as graphs grow more complex, these depictions can become difficult to understand. Quilts are matrix-based depictions for layered graphs designed to address this problem. In this research, we first improve Quilts by developing three design alternatives, and then compare the best of these alternatives to better-known node-link and matrix depictions. A primary weakness in Quilts is their depiction of skip links, links that do not simply connect to a succeeding layer. Therefore in our first study, we compare Quilts using color-only, text-only, and mixed (color and text) skip link depictions, finding that path finding with the color-only depiction is significantly slower and less accurate, and that in certain cases, the mixed depiction offers an advantage over the text-only depiction. In our second study, we compare Quilts using the mixed depiction to node-link diagrams and centered matrices. Overall results show that users can find paths through graphs significantly faster with Quilts (46.6 secs) than with node-link (58.3 secs) or matrix (71.2 secs) diagrams. This speed advantage is still greater in large graphs (e.g. in 200 node graphs, 55.4 secs vs. 71.1 secs for node-link and 84.2 secs for matrix depictions).


title: WAVE PARAMETER HINDCASTING IN A LAKE USING THE SWAN MODEL

abstract: Wind-induced wave characteristics are one of the most important factors in the design of coastal and marine structures. Therefore, accurate estimation of wave parameters is of considerable importance. The wave climate study can be conducted by eld measurements, empirical studies, physical modeling and numerical simulations. In this paper, the skill of a third-generation spectral model called SWAN has been evaluated in the prediction of wave parameters. The varying wind and wave climate of Lake Erie in the year 2002 has been used for evaluation of the model. The signi cant wave height (Hs) and the peak spectral wave period (Tp) were the parameters employed in the study and the model has been executed in a nonstationary mode. The linear and exponential growth from wind input, four-wave nonlinear interaction, whitecapping and bottom friction have been considered in the simulation. The results of this study show that in the investigated case, the average scatter index of SWAN is about 19 percent for signi cant wave height and 23 percent for the peak period. The error of the SWAN model in prediction of the wave height and period reduced about 3 percent after elimination of wave heights less than 0.5 meters. It was also found that using the cumulative steepness method for whitecapping dissipation yields worse results for signi cant wave height and better results for peak spectral period estimation. After using this method, the average scatter index for the prediction of Hs increased about 5 percent and decreased more than 4 percent for Tp. It should be mentioned that the computational time required by using this method is approximately more than twice that of the Komen option.


title: The social brain in psychiatric and neurological disorders

abstract: Psychiatric and neurological disorders have historically provided key insights into the structure-function relationships that subserve human social cognition and behavior, informing the concept of the ‘social brain’. In this review, we take stock of the current status of this concept, retaining a focus on disorders that impact social behavior. We discuss how the social brain, social cognition, and social behavior are interdependent, and emphasize the important role of development and compensation. We suggest that the social brain, and its dysfunction and recovery, must be understood not in terms of specific structures, but rather in terms of their interaction in large-scale networks.


title: Design considerations for a 10 GHz CMOS transmit-receive switch

abstract: A transmit-receive (T/R) switch is designed to operate at 10 GHz in a triple-well CMOS process. The transmit switch includes multiple parallel resonant networks to improve loss and isolation characteristics and the receive switch includes an impedance matching network and DC bias circuitry to increase linearity. A simple simulation model is proposed for linearity analysis of the receive switch. Finally, a figure-of-merit for T/R switches is proposed and used to compare previous published results.


title: Imaging of nanoparticle dynamics in live and apoptotic cells using temporally-modulated polarization

abstract: Gold nanoparticles are widely exploited in phototherapy. Owing to their biocompatibility and their strong visible-light surface plasmonic resonance, these particles also serve as contrast agents for cell image enhancement and super-resolved imaging. Yet, their optical signal is still insufficiently strong for many important real-life applications. Also, the differentiation between adjacent nanoparticles is usually limited by the optical resolution and the orientations of non-spherical particles are unknown. These limitations hamper the progress in cell research by direct optical microscopy and narrow the range of phototherapy applications. Here we demonstrate exploiting the optical anisotropy of non-spherical nanoparticles to achieve super-resolution in live cell imaging and to resolve the intracellular nanoparticle orientations. In particular, by modulating the light polarization and taking advantage of the polarization-dependence of gold nanorod optical properties, we realize the ‘lock-in amplification’, widely-used in electronic engineering, to achieve image enhancement in live cells and in cells that undergo apoptotic changes.


title: A Method of Retrieving Tropospheric Refractivity Above Ocean Surface using GNSS

abstract: This paper proposed a method of retrieving tropospheric refractivity above ocean surface using Global Navigation Satellite System, which takes the Final Analysis Data of Global Forecast System as background data, and uses intelligent optimization algorithm to train the inversion-network. the method can get tropospheric refractivity profile in any sea area after the zenith tropospheric delay and the atmospheric temperature, humidity, pressure were measured, and it can be used in refraction error correction applications such as radar detection and space tracking. It has less cost than radiosonde but higher precision than traditional empirical model. In addition, it can be used in real time and has higher temporal and spatial resolution.


title: Vision-Based Navigation With Language-Based Assistance via Imitation Learning With Indirect Intervention

abstract: We present Vision-based Navigation with Language-based Assistance (VNLA), a grounded vision-language task where an agent with visual perception is guided via language to find objects in photorealistic indoor environments. The task emulates a real-world scenario in that (a) the requester may not know how to navigate to the target objects and thus makes requests by only specifying high-level end-goals, and (b) the agent is capable of sensing when it is lost and querying an advisor, who is more qualified at the task, to obtain language subgoals to make progress. To model language-based assistance, we develop a general framework termed Imitation Learning with Indirect Intervention (I3L), and propose a solution that is effective on the VNLA task. Empirical results show that this approach significantly improves the success rate of the learning agent over other baselines on both seen and unseen environments. Our code and data are publicly available at https://github.com/debadeepta/vnla .


title: Using social data for resume job matching

abstract: Bright has built an automated system for ranking job candidates against job descriptions. The candidate's resume and social media profiles are interwoven to build an augmented user profile. Similarly, the job description is augmented by external databases and user-generated content to build an enhanced job profile. These augmented user and job profiles are then analyzed in order to develop numerical overlap features each with strong discriminating power, and in sum with maximal coverage. The resulting feature scores are then combined into a single Bright Score using a custom algorithm, where the feature weights are derived from a nation-wide and controlled study in which we collected a large sample of human judgments on real resume-job pairings. We demonstrate that the addition of social media profile data and external data improves the classification accuracy dramatically in terms of identifying the most qualified candidates.


title: The Case for Data Visualization Management Systems

abstract: Most visualizations today are produced by retrieving data from a database and using a specialized visualization tool to render it. This decoupled approach results in significant duplication of functionality, such as aggregation and filters, and misses tremendous opportunities for cross-layer optimizations. In this paper, we present the case for an integrated Data Visualization Management System (DVMS) based on a declarative visualization language that fully compiles the end-to-end visualization pipeline into a set of relational algebra queries. Thus the DVMS can be both expressive via the visualization language, and performant by lever-aging traditional and visualization-specific optimizations to scale interactive visualizations to massive datasets.


title: EEG Transient Event Detection and Classification Using Association Rules

abstract: In this paper, a methodology for the automated detection and classification of transient events in electroencephalographic (EEG) recordings is presented. It is based on association rule mining and classifies transient events into four categories: epileptic spikes, muscle activity, eye blinking activity, and sharp alpha activity. The methodology involves four stages: 1) transient event detection; 2) clustering of transient events and feature extraction; 3) feature discretization and feature subset selection; and 4) association rule mining and classification of transient events. The methodology is evaluated using 25 EEG recordings, and the best obtained accuracy was 87.38%. The proposed approach combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules


title: Automatic emotion recognition in video

abstract: Automatic recognition of emotion is becoming increasingly important component in Human-Machine Interaction (HMI) systems. Recognizing human facial emotion by computer is interesting and challenging problem. Emotions are the key semantic component of human communication. This paper presents the system for recognizing emotions through facial expression displayed in the video. Audio-visual emotion recognition can be carried out with the video sequence. The video sequence is a mixture of both the audio and video information. But in this paper, we are dealing only with the video information to recognize the emotion of a character. This process is carried out by the selection of the target frame followed by the segmentation of the video sequence. Facial feature tracking is performed to extract features. The face is classified in to different Action Units. Face emotions are recognized by using Dynamic Bayesian Network. The results so far shows the approach to have a promising success rate.


title: Planning Smooth and Obstacle-Avoiding B-Spline Paths for Autonomous Mining Vehicles

abstract: We study the problem of automatic generation of smooth and obstacle-avoiding planar paths for efficient guidance of autonomous mining vehicles. Fast traversal of a path is of special interest. We consider fourwheel four-gear articulated vehicles and assume that we have an a priori knowledge of the mine wall environment in the form of polygonal chains. Computing quartic uniform B-spline curves, minimizing curvature variation, staying at least at a proposed safety margin distance from the mine walls, we plan high speed paths. We present a study where our implementations are successfully applied on eight path-planning cases arising from real-world mining data provided by the Swedish mining company Luossavaara-Kiirunavaara AB (LKAB). The results from the study indicate that our proposed methods for computing obstacle-avoiding minimum curvature variation B-splines yield paths that are substantially better than the ones used by LKAB today. Our simulations show that, with an average 32.13%, the new paths are faster to travel along than the paths currently in use. Preliminary results from the production at LKAB show an overall 5%-10% decrease in the total time for an entire mining cycle. Such a cycle includes both traveling, ore loading, and unloading.


title: An Improved Single-Phase Direct PWM Inverting Buck–Boost AC–AC Converter

abstract: A single-phase inverting buck–boost ac–ac converter is proposed in this paper. The proposed converter has no shoot-through and dead-time problems and, like conventional dc–dc converters, it can be operated with simple pulse width modulation (PWM) control. It offers high frequency and high efficiency operation because high speed mosfet can be used as switching device without the reverse recovery issues and losses of its body diode. The proposed converter features quasi-continuous input and output currents, high input power factor, low total harmonic distortion of input and output currents, and high efficiency. A 460 W laboratory prototype was constructed and tested with interleaved and complementary PWM controls using a resistive and inductive load. Detailed experimental results are provided to show the novelty of the proposed converter. Experimental results confirmed that the proposed converter can obtain 93.8% efficiency at 60 kHz switching frequency.


title: Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network

abstract: The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary support device is of great significance for operation safety and cost reduction. Nowadays, the catenary support devices are periodically captured by the cameras mounted on the inspection vehicles during the night, but the inspection still mostly relies on human visual interpretation. To reduce the human involvement, this paper proposes a novel vision-based method that applies the deep convolutional neural networks (DCNNs) in the defect detection of the fasteners. Our system cascades three DCNN-based detection stages in a coarse-to-fine manner, including two detectors to sequentially localize the cantilever joints and their fasteners and a classifier to diagnose the fasteners’ defects. Extensive experiments and comparisons of the defect detection of catenary support devices along the Wuhan–Guangzhou high-speed railway line indicate that the system can achieve a high detection rate with good adaptation and robustness in complex environments.


title: Systematic Review of the Efficacy of Meditation Techniques as Treatments for Medical Illness

abstract: BACKGROUND ::: Meditative techniques are sought frequently by patients coping with medical and psychological problems. Because of their increasingly widespread appeal and use, and the potential for use as medical therapies, a concise and thorough review of the current state of scientific knowledge of these practices as medical interventions was conducted. ::: ::: ::: PURPOSE ::: To systematically review the evidence supporting efficacy and safety of meditative practices in treating illnesses, and examine areas warranting further study. Studies on normal healthy populations are not included. ::: ::: ::: METHODS ::: Searches were performed using PubMed, PsycInfo, and the Cochrane Database. Keywords were Meditation, Meditative Prayer, Yoga, Relaxation Response. Qualifying studies were reviewed and independently rated based on quality by two reviewers. Mid-to-high-quality studies (those scoring above 0.65 or 65% on a validated research quality scale) were included. ::: ::: ::: RESULTS ::: From a total of 82 identified studies, 20 randomized controlled trials met our criteria. The studies included 958 subjects total (397 experimentally treated, 561 controls). No serious adverse events were reported in any of the included or excluded clinical trials. Serious adverse events are reported in the medical literature, though rare. The strongest evidence for efficacy was found for epilepsy, symptoms of the premenstrual syndrome and menopausal symptoms. Benefit was also demonstrated for mood and anxiety disorders, autoimmune illness, and emotional disturbance in neoplastic disease. ::: ::: ::: CONCLUSIONS ::: The results support the safety and potential efficacy of meditative practices for treating certain illnesses, particularly in nonpsychotic mood and anxiety disorders. Clear and reproducible evidence supporting efficacy from large, methodologically sound studies is lacking.


title: A 2-port 6T SRAM bitcell design with multi-port capabilities at reduced area overhead

abstract: Low power, minimum transistor count and fast access static random access memory (SRAM) is essential for embedded multimedia and communication applications realized using system on a chip (SoC) technology. Hence, simultaneous or parallel read/write (R/W) access multi-port SRAM bitcells are widely employed in such embedded systems. In this paper, we present a 2-port 6T SRAM bitcell with multi-port capabilities and a reduced area overhead compared to existing 2-port 7-transistor (7T) and 8T SRAM bitcells. The proposed 2-port bitcell has six transistors (6T) and single-ended read and write bitlines (RBL/WBL). We compare the stability, simultaneous read/write disturbance, SNM sensitivity and misread current from the read bitline with the 7T and 8T bitcells. The static noise margin (SNM) of the 6T bitcells around the write disturbed bitcell is 53% to 61% higher than that of the 7T bitcell. The average active power dissipation under the different read/write operations of the 6T bitcells is 28% lower than the 8T and equal to 7T bitcell. Hence, the proposed 2-port 6T-SRAM is a potential candidate in terms of process variability, stability, area, and power dissipation.


title: The Influences of Humorous Advertising on Brand Popularity and Advertising Effects in the Tourism Industry

abstract: With a diversity of promotional channels and ever-increasing numbers of participants, the tourism industry in Taiwan faces keen competition. Along with the direct cross-strait flights policy, groups like Eastern Multimedia Group, Tsann Kuen Enterprise Company Limited and Want Want China Times Group have founded travel agencies for the purpose of increasing their market share in the tourism industry. Therefore, the formulation of marketing strategies has become a critical issue for all travel agencies that wish to ensure their prominence in the market. The frequent use of humorous materials and appeals has been the main tendency in advertising. However, no consistent conclusions regarding the actual effects of humorous advertising have been determined. The subjects of the current study were users of the Lion Travel website. Online questionnaires were sent to and collected from random respondents. One hundred questionnaires were delivered, and 512 valid questionnaires were collected. Each collected questionnaire represented a valid sample. The study generated the following five results: (1) humorous advertisements may partially influence brand awareness; (2) humorous elements may partially influence advertising effects; (3) brand awareness has significant positive effects on the advertising attitude of advertising effects; (4) brand awareness has significant positive effects on the brand attitude of advertising effects; (5) brand awareness has significant positive effects on the purchasing intention of advertising effects. Eventually, it is anticipated that the results of this study can serve as a reference and provide suggestions for humorous advertising strategies in the tourism industry.


title: Beyond money: toward an economy of well-being

abstract: Policy decisions at the organizational, corporate, and governmental levels should be more heavily influenced by issues related to well-being—people’s evaluations and feelings about their lives. Domestic policy currently focuses heavily on economic outcomes, although economic indicators omit, and even mislead about, much of what society values. We show that economic indicators have many shortcomings, and that measures of well-being point to important conclusions that are not apparent from economic indicators alone. For example, although economic output has risen steeply over the past decades, there has been no rise in life satisfaction during this period, and there has been a substantial increase in depression and distrust. We argue that economic indicators were extremely important in the early stages of economic development, when the fulfillment of basic needs was the main issue. As societies grow wealthy, however, differences in well-being are less frequently due to income, and are more frequently due to factors such as social relationships and enjoyment at work.


title: Enhancing digital libraries with TechLens+

abstract: The number of research papers available is growing at a staggering rate. Researchers need tools to help them find the papers they should read among all the papers published each year. In this paper, we present and experiment with hybrid recommender algorithms that combine Collaborative Filtering and Content-based. Filtering to recommend research papers to users. Our hybrid algorithms combine the strengths of each filtering approach to address their individual weaknesses. We evaluated our algorithms through offline experiments on a database of 102, 000 research papers, and through an online experiment with 110 users. For both experiments we used a dataset created from the CiteSeer repository of computer science research papers. We developed separate English and Portuguese versions of the interface and specifically recruited American and Brazilian users to test for cross-cultural effects. Our results show that users value paper recommendations, that the hybrid algorithms can be successfully combined, that different algorithms are more suitable for recommending different kinds of papers, and that users with different levels of experience perceive recommendations differently These results can be applied to develop recommender systems for other types of digital libraries.


title: Experimental Demonstration of the Capacity Gain of Plane Spiral OAM-Based MIMO System

abstract: A plane spiral orbital angular momentum (OAM) waves-based $4 \times 4$ multiple-in-multiple-out (MIMO) communication system for line-of-sight channel is proposed and experimentally demonstrated in comparison with conventional MIMO. The transmitting OAM waves are propagating along the transverse direction; hence there is no singularity or divergence problem. Compared with the conventional MIMO, this system could achieve lower spatial correlations and a higher capacity gain while the OAMantennas as the elements of the transmitting array are sorted in an ordered sequence. The measured bit-error-rate performance clearly demonstrates the feasibility of the proposed system, and the causation of the improved performance is also detailedly discussed. This technology can be applied in near-field communications or short-range MIMO systems to realize higher capacity communications.


title: Equivalence of graphical lasso and thresholding for sparse graphs

abstract: This paper is concerned with the problem of finding a sparse graph capturing the conditional dependence between the entries of a Gaussian random vector, where the only available information is a sample correlation matrix. A popular approach is to solve a graphical lasso problem with a sparsity-promoting regularization term. This paper derives a simple condition under which the computationally-expensive graphical lasso behaves the same as the simple heuristic method of thresholding. This condition depends only on the solution of graphical lasso and makes no direct use of the sample correlation matrix or the regularization coefficient. It is also proved that this condition is always satisfied if the solution of graphical lasso is replaced by its first-order Taylor approximation. The condition is tested on several random problems and it is shown that graphical lasso and the thresholding method (based on the correlation matrix) lead to a similar result (if not equivalent), provided the regularization term is high enough to seek a sparse graph.


title: Bioinformatics analysis of the structural and evolutionary characteristics for toll-like receptor 15

abstract: Toll-like receptors (TLRs) play important role in the innate immune system. TLR15 is reported to have a unique role in defense against pathogens, but its structural and evolution characterizations are still poorly understood. In this study, we identified 57 completed TLR15 genes from avian and reptilian genomes. TLR15 clustered into an individual clade and was closely related to family 1 on the phylogenetic tree. Unlike the TLRs in family 1 with the broken asparagine ladders in the middle, TLR15 ectodomain had an intact asparagine ladder that is critical to maintain the overall shape of ectodomain. The conservation analysis found that TLR15 ectodomain had a highly evolutionarily conserved region on the convex surface of LRR11 module, which is probably involved in TLR15 activation process. Furthermore, the protein–protein docking analysis indicated that TLR15 TIR domains have the potential to form homodimers, the predicted interaction interface of TIR dimer was formed mainly by residues from the BB-loops and αC-helixes. Although TLR15 mainly underwent purifying selection, we detected 27 sites under positive selection for TLR15, 24 of which are located on its ectodomain. Our observations suggest the structural features of TLR15 which may be relevant to its function, but which requires further experimental validation.


title: UKP-WSI: UKP Lab Semeval-2013 Task 11 System Description

abstract: In this paper, we describe the UKP Lab system participating in the Semeval-2013 task “Word Sense Induction and Disambiguation within an End-User Application”. Our approach uses preprocessing, co-occurrence extraction, graph clustering, and a state-of-theart word sense disambiguation system. We developed a configurable pipeline which can be used to integrate and evaluate other components for the various steps of the complex task.


title: Non-Maximum Suppression for Object Detection by Passing Messages between Windows

abstract: Non-maximum suppression (NMS) is a key post-processing step in many computer vision applications. In the context of object detection, it is used to transform a smooth response map that triggers many imprecise object window hypotheses in, ideally, a single bounding-box for each detected object. The most common approach for NMS for object detection is a greedy, locally optimal strategy with several hand-designed components (e.g., thresholds). Such a strategy inherently suffers from several shortcomings, such as the inability to detect nearby objects. In this paper, we try to alleviate these problems and explore a novel formulation of NMS as a well-defined clustering problem. Our method builds on the recent Affinity Propagation Clustering algorithm, which passes messages between data points to identify cluster exemplars. Contrary to the greedy approach, our method is solved globally and its parameters can be automatically learned from training data. In experiments, we show in two contexts – object class and generic object detection – that it provides a promising solution to the shortcomings of the greedy NMS.


title: Everyday Memory Compensation: The Impact of Cognitive Reserve, Subjective Memory, and Stress

abstract: To determine the potential importance of several unexplored covariates of everyday memory compensation, the authors examined relations between responses on the Memory Compensation Questionnaire (a self-report measure of everyday memory compensation) and cognitive reserve (education and verbal IQ), subjective memory, and life stress in 66 older adults (mean age 70.55 years). Key results indicated that compensation occurred in people (a) whose IQ level was greater than their education level (representing cognitive reserve “discordance”) but not in people whose IQ was commensurate with their education (representing cognitive reserve “concordance”); (b) who had greater perceived memory errors; and (c) who experienced heightened stress. Further, high-stress older adults compensated whether perceived memory errors were low or high, but low-stress older adults compensated only if they perceived high memory errors. Bootstrapped confidence intervals around model betas provided further support for estimate reliability. These results suggest boundary conditions for the concept of cognitive reserve, and highlight the importance of subjective memory and life stress for defining contexts in which compensation may occur.


title: Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach

abstract: The robustness of neural networks to adversarial examples has received great attention due to security implications. Despite various attack approaches to crafting visually imperceptible adversarial examples, little has been developed towards a comprehensive measure of robustness. In this paper, we provide a theoretical justification for converting robustness analysis into a local Lipschitz constant estimation problem, and propose to use the Extreme Value Theory for efficient evaluation. Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness. The proposed CLEVER score is attack-agnostic and computationally feasible for large neural networks. Experimental results on various networks, including ResNet, Inception-v3 and MobileNet, show that (i) CLEVER is aligned with the robustness indication measured by the $\ell_2$ and $\ell_\infty$ norms of adversarial examples from powerful attacks, and (ii) defended networks using defensive distillation or bounded ReLU indeed achieve better CLEVER scores. To the best of our knowledge, CLEVER is the first attack-independent robustness metric that can be applied to any neural network classifier.


title: Attitude control of a fixed-wing UAV using thrust vectoring system

abstract: This paper describes the development of a fixed-wing UAV with thrust vectoring system for improvement of fault tolerance. Generally, a flxed-wing UAV is controlled by using throttle and three moving surfaces that are aileron, elevator and ladder. If one of these actuators is damaged, fault tolerance can be guaranteed by designing a complicated flight control system that would requires processing of high calculation load. A thrust vectoring system treated in this paper can control the UAV without using any moving surfaces. Therefore, it is possible to control the UAV with a thrust vectoring system by applying simple control method. The thrust vectoring system consists of a double gimbal structure and two servo motors. Moreover, fault tolerance is realized by a simple structure and extremely lightweight. A nonlinear flight control system is designed by using extended state observer (ESO). The effectiveness of the proposed thrust vectoring system is verified by numerical simulation and experiment. We also show the developed thrust vectoring system.


title: Neologisms, Nonces and Word Formation

abstract: Neologism dictionaries celebrate the influence of science and technology on language - how scientists ::: and technologists add to the stock of words, and occasionally, add new grammatical structures. There ::: are two claims made in the literature on neologisms: First, that borrowings, from classical languages and ::: "prestige" languages are widespread in the science and technology literature. Second that affixation is an ::: ephemeral mechanism for forming new words. Bold claims? Let us look at some of the evidence for and ::: against these claims.


Diversity-aware Multi-Video Summarization ( Supplementary Material )


title: Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI.

abstract: In children with attention deficit hyperactivity disorder (ADHD), functional neuroimaging studies have revealed abnormalities in various brain regions, including prefrontal-striatal circuit, cerebellum, and brainstem. In the current study, we used a new marker of functional magnetic resonance imaging (fMRI), amplitude of low-frequency (0.01-0.08Hz) fluctuation (ALFF) to investigate the baseline brain function of this disorder. Thirteen boys with ADHD (13.0+/-1.4 years) were examined by resting-state fMRI and compared with age-matched controls. As a result, we found that patients with ADHD had decreased ALFF in the right inferior frontal cortex, [corrected] and bilateral cerebellum and the vermis as well as increased ALFF in the right anterior cingulated cortex, left sensorimotor cortex, and bilateral brainstem. This resting-state fMRI study suggests that the changed spontaneous neuronal activity of these regions may be implicated in the underlying pathophysiology in children with ADHD.


title: On channel estimation and optimal training design for amplify and forward relay networks

abstract: In this paper, we provide a complete study on the training based channel estimation issues for relay networks that employ the amplify-and-forward (AF) transmission scheme. We first point out that separately estimating the channel from source to relay and relay to destination suffers from many drawbacks. Then we provide a new estimation scheme that directly estimates the overall channels from the source to the destination. The proposed channel estimation well serves the AF based space time coding (STC) that was recently developed. There exists many differences between the proposed channel estimation and that in the traditional single input single out (SISO) and multiple input single output (MISO) systems. For example, a relay must linearly precode its received training sequence by a sophisticatedly designed matrix in order to minimize the channel estimation error. Besides, each relay node is individually constrained by a different power requirement because of the non-cooperation among all relay nodes. We study both the linear least-square (LS) estimator and the minimum mean-square-error (MMSE) estimator. The corresponding optimal training sequences, as well as the optimal preceding matrices are derived from an efficient convex optimization process.


title: Level of Immersion in Virtual Environments Impacts the Ability to Assess and Teach Social Skills in Autism Spectrum Disorder

abstract: Abstract Virtual environments (VEs) may be useful for delivering social skills interventions to individuals with autism spectrum disorder (ASD). Immersive VEs provide opportunities for individuals with ASD to learn and practice skills in a controlled replicable setting. However, not all VEs are delivered using the same technology, and the level of immersion differs across settings. We group studies into low-, moderate-, and high-immersion categories by examining five aspects of immersion. In doing so, we draw conclusions regarding the influence of this technical manipulation on the efficacy of VEs as a tool for assessing and teaching social skills. We also highlight ways in which future studies can advance our understanding of how manipulating aspects of immersion may impact intervention success.


title: The efficacy of prototyping under time constraints

abstract: Iterative prototyping helps designers refine their ideas and discover previously unknown issues and opportunities. However, the time constraints of production schedules can discourage iteration in favor of realization. Is this tradeoff prudent? This paper investigates if - under tight time constraints - iterating multiple times provides more benefit than a single iteration. A between-subjects study manipulates participants' ability to iterate on a design task. Participants in the iteration condition outperformed those in the non-iteration condition. Participants with prior experience with the task performed better. Notably, participants in the iteration condition without prior task experience performed as well as non-iterating participants with prior task experience.


title: Memory Networks for Recommendation

abstract: Memory networks are a recently introduced model that combines reasoning, attention and memory for solving tasks in the areas of language understanding and dialogue -- where one exciting direction is the use of these models for dialogue-based recommendation. In this talk we describe these models and how they can learn to discuss, answer questions about, and recommend sets of items to a user. The ultimate goal of this research is to produce a full dialogue-based recommendation assistant. We will discuss recent datasets and evaluation tasks that have been built to assess these models abilities to see how far we have come.


title: Parametric estimate of intensity inhomogeneities applied to MRI

abstract: This paper presents a new approach to the correction of intensity inhomogeneities in magnetic resonance imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentation. The new correction method called parametric bias field correction (PABIC) is based on a simplified model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inhomogeneity field. We assume that the image is composed of pixels assigned to a small number of categories with a priori known statistics. Further we assume that the image is corrupted by noise and a low-frequency inhomogeneity field. The estimation of the parametric bias field is formulated as a nonlinear energy minimization problem using an evolution strategy (ES). The resulting bias field is independent of the image region configurations and thus overcomes limitations of methods based on homomorphic filtering. Furthermore, PABIC can correct bias distortions much larger than the image contrast. Input parameters are the intensity statistics of the classes and the degree of the polynomial function. The polynomial approach combines bias correction with histogram adjustment, making it well suited for normalizing the intensity histogram of datasets from serial studies. We present simulations and a quantitative validation with phantom and test images. A large number of MR image data acquired with breast, surface, and head coils, both in two dimensions and three dimensions, have been processed and demonstrate the versatility and robustness of this new bias correction scheme.


title: A High-Speed and Ultra Low-Power Subthreshold Signal Level Shifter

abstract: In this paper, we present a novel level shifter circuit converting subthreshold signal levels to super-threshold signal levels at high-speed using ultra low-power and a small silicon area, making it well-suited for low-power applications such as wireless sensor networks and implantable medical devices. The proposed circuit introduces a new voltage level shifter topology employing a level-shifting capacitor contributing to increase the range of conversion voltages, while significantly reducing the conversion delay. Such a level-shifting capacitor is quickly charged, whenever the input signal detects a low-to-high transition, in order to boost internal voltage nodes, and quickly reach a high output voltage level. The proposed circuit achieves a shorter propagation delay and a smaller silicon area for a given operating frequency and power consumption compared to other circuit solutions. Measurement results are presented for the proposed circuit fabricated in a 0.18- $\mu \text {m}$ TSMC technology. The proposed circuit can convert a wide range of the input voltages from 330 mV to 1.8 V, and operate over a frequency range of 100 Hz to 100 MHz. It has a propagation delay of 29 ns and a power consumption of 61.5 nW for input signals 0.4 V, at a frequency of 500-kHz, outperforming previous designs.


title: Real-Time Photogrammetry

abstract: Today’s speed of image processing tools as well as the availability of robust techniques for extracting geometric and basic thematic information from image streams makes real-time photogrammetry possible. The paper discusses the basic tools for fully automatic calibration, orientation and surface recontruction as well as for tracking, ego-motion determination and behaviour analysis. Examples demonstrate today’s potential for future applications.


title: Assessing binary classifiers using only positive and unlabeled data

abstract: Assessing the performance of a learned model is a crucial part of machine learning. Most evaluation metrics can only be computed with labeled data. Unfortunately, in many domains we have many more unlabeled than labeled examples. Furthermore, in some domains only positive and unlabeled examples are available, in which case most standard metrics cannot be computed at all. In this paper, we propose an approach that is able to estimate several widely used metrics including ROC and PR curves using only positive and unlabeled data. We provide theoretical bounds on the quality of our estimates. Empirically, we demonstrate that even given only a small number of positive examples and unlabeled data, we are able to reliable estimate both ROC and PR curves.


title: Agreeableness as a moderator of interpersonal conflict.

abstract: This multimethod research linked the Big Five personality dimensions to interpersonal conflicts. Agreeableness was the focus because this dimension is associated with motives to maintain positive interpersonal relations. Converging responses to both hypothetical conflicts and to diary records of actual daily interpersonal conflicts across a two-week period were assessed. Agreeableness was expected to moderate affective responses and tactical choices during conflicts. Patterns of daily conflict were related to self-reported reactions to hypothetical conflicts and to teacher-rated adjustment in adolescents. As predicted, Agreeableness was related to responsiveness to conflict. Agreeableness differences and use of destructive tactics in conflict were significantly related to evaluations of the individual’s adjustment by knowledgeable raters. Among the Big Five dimensions, Agreeableness was most closely associated with processes and outcomes during interpersonal conflict.


title: Modeling and simulation of an unmanned ground vehicle power system

abstract: Long-duration missions challenge ground robot systems with respect to energy storage and efficient conversion to power on demand. Ground robot systems can contain multiple power sources such as fuel cell, battery and/or ultra-capacitor. This paper presents a hybrid systems framework for collectively modeling the dynamics and switching between these different power components. The hybrid system allows modeling power source on/off switching and different regimes of operation, together with continuous parameters such as state of charge, temperature, and power output. We apply this modeling framework to a fuel cell/battery power system applicable to unmanned ground vehicles such as Packbot or TALON. A simulation comparison of different control strategies is presented. These strategies are compared based on maximizing energy efficiency and meeting thermal constraints.


title: Stereovision-Based Multiple Object Tracking in Traffic Scenarios Using Free-Form Obstacle Delimiters and Particle Filters

abstract: In this paper we present a stereovision-based approach for tracking multiple objects in crowded environments where, typically, the road lane markings are not visible and the surrounding infrastructure is not known. The proposed technique relies on measurement data provided by an intermediate occupancy grid derived from processing a stereovision-based elevation map and on free-form object delimiters extracted from this grid. Unlike other existing methods that track rigid objects using also rigid representations, we present a particle filter-based solution for tracking visual appearance-based free-form obstacle representations. At each step, the particle state is described by two components, i.e., the object's dynamic parameters and its estimated geometry. In order to solve the high-dimensionality state–space problem, a Rao–Blackwellized particle filter is used. By accurately modeling the object geometry using the polygonal lines instead of a 3-D box and, at the same time, separating the position and speed tracking from the geometry tracking at the estimator level, the proposed solution combines the efficiency of the rigid model with the benefits of a flexible object model.


Best Practices in the Systematic Direct Observation of Student Behavior 63


title: An Agent Based Anti-Money Laundering System Architecture for Financial Supervision

abstract: Anti-money laundering is a hot issue nowadays. How to establish an automatically technology to extract SARs (suspicious activity report) is the core problem for financial institutions. This paper aims to establish system architecture based on agent technology financial supervision in financial institution taking into consideration of not only transaction information database in financial institutions, but also customer profiles information and external information. The capability of self-learning and self-adaptive makes it difficult for those customers to avoid supervision through simple learning from regulations.


title: A new vector evaluated PBIL algorithm for reinsurance analytics

abstract: The purpose of this paper is to evaluate the performance of a new multiobjective algorithm called Vector Evaluated Population Based Incremental Learning (VEPBIL). The new algorithm was applied in solving a real world application named Reinsurance Contract Optimization (RCO), which is a multiobjective problem consisting of maximizing two conflicting functions: expected return and risk. The VEPBIL was tested on two instances of the problem composed by 7 and 15 layers of real anonymized data. In order to evaluate the algorithm, metrics such as hyper volume, number of solutions and coverage were used. A comparisons against Vector Evaluated Differential evolution (VEDE) is also carried out. The comparison has shown that VEPBIL can dominate about 70% and 50% of solutions from VEDE using 7 and 15 layers respectively, whereas VEDE dominates about 10% and 30% of solutions in the way around.


title: Rumors Detection in Sina Weibo Based on Text and User Characteristics

abstract: M414 such as Twitter and Sina Weibo have brought convenience to people in recent years, but there are a large number of internet rumors bring serious harm to us. Because of the fuzziness and concealment of rumors, the automatic identification of microblog rumors is very difficult, which brings great challenges to microblog's supervision. In this paper, for the text information of rumors, we construct a new emotional classification method, and use this method to analyze the emotional orientation of comments, and then calculate the proportion of negative emotional comments. Combined with user information at the same time, we analyze retweet-list and comment-list, and then judge whether there are authoritative users who participated in the spread of rumors. Moreover, we extract features from the information of user and calculate the user's reputation value. Finally, we use the training classifier to detect microblog rumors. Experimental results illustrate the efficacy and efficiency of the proposed new features in microblog rumors detection.


title: Fibromyalgia and Depression

abstract: Fibromyalgia and depression might represent two manifestations of affective spectrum disorder. They share similar pathophysiology and are largely targeted by the same drugs with dual action on serotoninergic and noradrenergic systems. Here, we review evidence for genetic and environmental factors that predispose, precipitate, and perpetuate fibromyalgia and depression and include laboratory findings on the role of depression in fibromyalgia. Further, we comment on several aspects of fibromyalgia which support the development of reactive depression, substantially more so than in other chronic pain syndromes. However, while sharing many features with depression, fibromyalgia is associated with somatic comorbidities and absolutely defined by fluctuating spontaneous widespread pain. Fibromyalgia may, therefore, be more appropriately grouped together with other functional pain disorders, while psychologically distressed subgroups grouped additionally or solely with affective spectrum disorders.


title: Using Machine Learning to Detect Noisy Neighbors in 5G Networks

abstract: 5G networks are expected to be more dynamic and chaotic in their structure than current networks. With the advent of Network Function Virtualization (NFV), Network Functions (NF) will no longer be tightly coupled with the hardware they are running on, which poses new challenges in network management. Noisy neighbor is a term commonly used to describe situations in NFV infrastructure where an application experiences degradation in performance due to the fact that some of the resources it needs are occupied by other applications in the same cloud node. These situations cannot be easily identified using straightforward approaches, which calls for the use of sophisticated methods for NFV infrastructure management. In this paper we demonstrate how Machine Learning (ML) techniques can be used to identify such events. Through experiments using data collected at real NFV infrastructure, we show that standard models for automated classification can detect the noisy neighbor phenomenon with an accuracy of more than 90% in a simple scenario.


title: A cost-effective method for reducing the volume of laboratory tests in a university-associated teaching hospital.

abstract: BACKGROUND ::: Laboratory tests comprise a significant portion of hospital expenditure. Attempts to reduce their use have had mixed results. ::: ::: ::: OBJECTIVE ::: To investigate the effect of an intervention based on a simple form-based system for ordering laboratory tests by physicians, on both use of laboratory resources and diagnostic accuracy. ::: ::: ::: DESIGN ::: At Kaplan Medical Center in Rehovot , Israel , there are 4 similar Internal Medicine departments. In one department (C), the new system was initiated, whereas in the other 3 departments (A, B and D), the traditional method of ordering blood tests was continued. The intervention used was a requirement for tests to be specifically requested by residents following unbundling of test panels, with hands-on supervision by a senior physician. In addition, the residents attended a series of lectures on the economic implications of laboratory testing. The intervention study lasted for 3 years. ::: ::: ::: MEASUREMENTS ::: Total number of tests performed in each department, number of tests per admission and total cost of each test at Medicare reimbursement prices. ::: ::: ::: RESULTS ::: The number of tests per admission prior to the intervention was 1.91 +/- 0.89; it decreased for each of the next 3 years: 0.76 +/- 0.61, 0.80 +/- 0.62 and 0.78 +/- 0.63 respectively. There was a total decrease of 97,365 tests during the 3-year period, saving 1,914,149 dollars. There was no difference in the readmission rate or in the number of diagnoses of conditions based primarily on blood tests such as hypokalemia or hyponatremia, between department C and the other departments. ::: ::: ::: CONCLUSIONS ::: The intervention developed here produced significant and sustained reduction of financial savings in the number of laboratory tests ordered, without negatively impacting diagnostic capability or patient care.


Hybrid Text Mining For Finding Abbreviations And Their Definitions


title: Aircraft systems cyber security

abstract: Presents a collection of slides from the author's conference presentation. Topics covered include: The Transition of Aircraft Systems; The Difference Between Cyber Security in Aircraft Systems and Information Technology (IT); Potential Cyber Security Threats to Aircraft Systems; and FAA Aircraft Systems Cyber Security Activities.


title: Students' learning styles and their effects on the use of social media technology for learning

abstract: The participatory, collaborative, and independent learning styles were investigated.The social media acceptance model was proposed.The factors Self and Performance were found to influence students' intention of using social media.The factor Self had the greatest influence on all learning styles.Students with participatory style focused more on Self than those of the collaborative style did. Students with different learning styles approach learning differently. With the rise of social media technologies, investigating the effect of these styles on their intentions to use social media for learning has become all the more important. This study explored the factors affecting students' intentions to use social media for learning based on their learning styles (i.e., participatory, collaborative, and independent), using the social media acceptance model. By convenience sampling, 300 Malaysian students were recruited via an online survey (Nparticipatory=116; Nindependent=97; and Ncollaborative=87). The survey was prepared by drawing on the social media acceptance model. It was piloted before the final data collection step was conducted in August 2013. The demographic details of the students were analyzed using Statistical Program for Social Sciences 21, while path modeling and multivariate analyses were conducted using SmartPLS 2.0. The results revealed the significant effect of Self and Performance on students' intentions to use social media regardless of their learning styles. A pair-wise comparison revealed that Self was more significant in participatory students than in collaborative students. Effort was found to be the least significant factor, indicating the popularity of social media among students. Further insight into the different factors that drive students with different learning styles to use social media will help educators use this technology to assist learning more effectively.


Toward a Justification of Meta-learning : Is the No Free Lunch Theorem a Showstopper ?


title: Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

abstract: As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases.


title: Current practice: Antiplatelet use in interventional cardiology

abstract: Thrombosis within the target vessel is one of the most feared complications associated with coronary intervention, as it is often associated with severe adverse clinical sequelae. This thrombosis is mediated via the activation and aggregation of platelets and therefore considerable effort has been directed at ways of inhibiting platelet function. It is now mandatory to consider the use of two and often three different antiplatelet agents, particularly when intracoronary stents are inserted. Using these regimes, many of the adverse clinical outcomes associated with platelet activation can be reduced.


title: Analysis Scheme in the Ensemble Kalman Filter

abstract: This paper discusses an important issue related to the implementation and interpretation of the analysis scheme in the ensemble Kalman filter. It is shown that the observations must be treated as random variables at the analysis steps. That is, one should add random perturbations with the correct statistics to the observations and generate an ensemble of observations that then is used in updating the ensemble of model states. Traditionally, this has not been done in previous applications of the ensemble Kalman filter and, as will be shown, this has resulted in an updated ensemble with a variance that is too low. This simple modification of the analysis scheme results in a completely consistent approach if the covariance of the ensemble of model states is interpreted as the prediction error covariance, and there are no further requirements on the ensemble Kalman filter method, except for the use of an ensemble of sufficient size. Thus, there is a unique correspondence between the error statistics from the ensemble Kalman filter and the standard Kalman filter approach.


title: Printed high-directivity metamaterial MS/NRI coupled-line coupler for signal monitoring applications

abstract: Using the general coupled-mode formulation, it is shown that the interaction between a regular transmission line and a negative-refractive-index (NRI) line leads to complex coupled modes. Specifically the coupling between a regular microstrip (MS) and a NRI line is analyzed and the theory reveals conditions giving rise to high directivity. Experimental results are presented for a fully printed MS/NRI coupled-line coupler exhibiting a coupling level of -27dB, isolation of -72dB, and directivity of 45dB at the design frequency of 2.04GHz. Such low-coupling levels but high-directivity couplers are well suited for signal monitoring and instrumentation applications.


title: EMBEDDED IMAGE CAPTURING SYSTEM USING RASPBERRY PI SYSTEM

abstract: An image capture system with embedded computing can extract information from images without need for an external processing unit, and interface devices used to make results available to other devices. The choosing of an Embedded platform is very unique and easy to implement. The paper proposed an image capturing technique in an embedded system based on Raspberry Pi board. Considering the requirements of image capturing and recognition algorithm, Raspberry Pi processing module and its peripherals, implementing based on this platform, finally actualized Embedded Image Capturing using Raspberry Pi system (EICSRS). Experimental results show that the designed system is fast enough to run the image capturing, recognition algorithm, and the data stream can flow smoothly between the camera and the Raspberry Pi board.


title: Building Successful Knowledge Management Projects

abstract: A gripper of a liftable and lowerable load-carrying apparatus for a container, includes a frame placed from above onto a container and having spaced-apart cross beams. Each cross beam defines a longitudinal direction and has at least two gripper elements spaced from one another at a distance which is greater than a dimension of the gripper elements, with each gripper element depending from the cross beam for insertion through a container opening when the frame is placed on the container. The cross beam is made of two sheets which are movable relative to one another in the longitudinal direction, with one of the sheets having one of the gripper elements and the other one of the sheets having the other one of the gripper elements, with the two gripper elements movable in opposite direction to one another in the longitudinal direction of the beam between a ready position and a gripping position.


title: Normalization: A Preprocessing Stage

abstract: As we know that the normalization is a pre-processing stage of any type problem statement. Especially normalization takes important role in the field of soft computing, cloud computing etc. for manipulation of data like scale down or scale up the range of data before it becomes used for further stage. There are so many normalization techniques are there namely Min-Max normalization, Z-score normalization and Decimal scaling normalization. So by referring these normalization techniques we are going to propose one new normalization technique namely, Integer Scaling Normalization. And we are going to show our proposed normalization technique using various data sets.


Image Steganography based on a Parameterized Canny Edge Detection Algorithm


Notions of Computation as Monoids ( extended version )


title: VoxelPipe: a programmable pipeline for 3D voxelization

abstract: We present a highly flexible and efficient software pipeline for programmable triangle voxelization. The pipeline, entirely written in CUDA, supports both fully conservative and thin voxelizations, multiple boolean, floating point, vector-typed render targets, user-defined vertex and fragment shaders, and a bucketing mode which can be used to generate 3D A-buffers containing the entire list of fragments belonging to each voxel. For maximum efficiency, voxelization is implemented as a sort-middle tile-based rasterizer, while the A-buffer mode, essentially performing 3D binning of triangles over uniform grids, uses a sort-last pipeline. Despite its major flexibility, the performance of our tile-based rasterizer is always competitive with and sometimes more than an order of magnitude superior to that of state-of-the-art binary voxelizers, whereas our bucketing system is up to 4 times faster than previous implementations. In both cases the results have been achieved through the use of careful load-balancing and high performance sorting primitives.


The Cost of Email Use in the Workplace: Lower Productivity and Higher Stress


title: Visualizing Stars and Emission Nebulae

abstract: We describe the star and nebula visualization techniques used to create a 3D volumetric visualization of the Orion Nebula. The nebula’s ionization layer is modeled first as a surface model, derived from infrared and visible light observations. The model is imported into a volume scene graph-based volume visualization system to simulate the nebula’s emissive gases. Stars are rendered using Gaussian spots that are attenuated with distance.


title: The ClusTree: indexing micro-clusters for anytime stream mining

abstract: Clustering streaming data requires algorithms that are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited. Clustering has to be performed in a single pass over the incoming data and within the possibly varying inter-arrival times of the stream. Likewise, memory is limited, making it impossible to store all data. For clustering, we are faced with the challenge of maintaining a current result that can be presented to the user at any given time. In this work, we propose a parameter-free algorithm that automatically adapts to the speed of the data stream. It makes best use of the time available under the current constraints to provide a clustering of the objects seen up to that point. Our approach incorporates the age of the objects to reflect the greater importance of more recent data. For efficient and effective handling, we introduce the ClusTree, a compact and self-adaptive index structure for maintaining stream summaries. Additionally we present solutions to handle very fast streams through aggregation mechanisms and propose novel descent strategies that improve the clustering result on slower streams as long as time permits. Our experiments show that our approach is capable of handling a multitude of different stream characteristics for accurate and scalable anytime stream clustering.


Smart Home Automation Security: A Literature Review.


title: Multi-controller Based Software-Defined Networking: A Survey

abstract: Software-defined networking (SDN) is a novel network paradigm that enables flexible management for networks. As the network size increases, the single centralized controller cannot meet the increasing demand for flow processing. Thus, the promising solution for SDN with large-scale networks is the multi-controller. In this paper, we present a compressive survey for multi-controller research in SDN. First, we introduce the overview of multi-controller, including the origin of multi-controller and its challenges. Then, we classify multi-controller research into four aspects (scalability, consistency, reliability, and load balancing) depending on the process of implementing the multi-controller. Finally, we propose some relevant research issues to deal with in the future and conclude the multi-controller research.


title: Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior

abstract: Microblogging services such as Twitter are said to have the potential for increasing political participation. Given the feature of 'retweeting' as a simple yet powerful mechanism for information diffusion, Twitter is an ideal platform for users to spread not only information in general but also political opinions through their networks as Twitter may also be used to publicly agree with, as well as to reinforce, someone's political opinions or thoughts. Besides their content and intended use, Twitter messages ('tweets') also often convey pertinent information about their author's sentiment. In this paper, we seek to examine whether sentiment occurring in politically relevant tweets has an effect on their retweetability (i.e., how often these tweets will be retweeted). Based on a data set of 64,431 political tweets, we find a positive relationship between the quantity of words indicating affective dimensions, including positive and negative emotions associated with certain political parties or politicians, in a tweet and its retweet rate. Furthermore, we investigate how political discussions take place in the Twitter network during periods of political elections with a focus on the most active and most influential users. Finally, we conclude by discussing the implications of our results.


title: Neural coding of continuous speech in auditory cortex during monaural and dichotic listening

abstract: The cortical representation of the acoustic features of continuous speech is the foundation of speech perception. In this study, noninvasive magnetoencephalography (MEG) recordings are obtained from human subjects actively listening to spoken narratives, in both simple and cocktail party-like auditory scenes. By modeling how acoustic features of speech are encoded in ongoing MEG activity as a spectrotemporal response function, we demonstrate that the slow temporal modulations of speech in a broad spectral region are represented bilaterally in auditory cortex by a phase-locked temporal code. For speech presented monaurally to either ear, this phase-locked response is always more faithful in the right hemisphere, but with a shorter latency in the hemisphere contralateral to the stimulated ear. When different spoken narratives are presented to each ear simultaneously (dichotic listening), the resulting cortical neural activity precisely encodes the acoustic features of both of the spoken narratives, but slightly weakened and delayed compared with the monaural response. Critically, the early sensory response to the attended speech is considerably stronger than that to the unattended speech, demonstrating top-down attentional gain control. This attentional gain is substantial even during the subjects' very first exposure to the speech mixture and therefore largely independent of knowledge of the speech content. Together, these findings characterize how the spectrotemporal features of speech are encoded in human auditory cortex and establish a single-trial-based paradigm to study the neural basis underlying the cocktail party phenomenon.


title: Transferring a semantic representation for person re-identification and search

abstract: Learning semantic attributes for person re-identification and description-based person search has gained increasing interest due to attributes' great potential as a pose and view-invariant representation. However, existing attribute-centric approaches have thus far underperformed state-of-the-art conventional approaches. This is due to their nonscalable need for extensive domain (camera) specific annotation. In this paper we present a new semantic attribute learning approach for person re-identification and search. Our model is trained on existing fashion photography datasets - either weakly or strongly labelled. It can then be transferred and adapted to provide a powerful semantic description of surveillance person detections, without requiring any surveillance domain supervision. The resulting representation is useful for both unsupervised and supervised person re-identification, achieving state-of-the-art and near state-of-the-art performance respectively. Furthermore, as a semantic representation it allows description-based person search to be integrated within the same framework.


title: STREAMSCOPE: continuous reliable distributed processing of big data streams

abstract: STREAMSCOPE (or STREAMS) is a reliable distributed stream computation engine that has been deployed in shared 20,000-server production clusters at Microsoft. STREAMS provides a continuous temporal stream model that allows users to express complex stream processing logic naturally and declaratively. STREAMS supports business-critical streaming applications that can process tens of billions (or tens of terabytes) of input events per day continuously with complex logic involving tens of temporal joins, aggregations, and sophisticated user-defined functions, while maintaining tens of terabytes in-memory computation states on thousands of machines. ::: ::: STREAMS introduces two abstractions, rVertex and rStream, to manage the complexity in distributed stream computation systems. The abstractions allow efficient and flexible distributed execution and failure recovery, make it easy to reason about correctness even with failures, and facilitate the development, debugging, and deployment of complex multi-stage streaming applications.


Geographic and individual correlates of cervical cancer screening among HIV-infected women attending HIV Care and Treatment Programs in Kenya


title: Modeling and Control of a Multiport Power Electronic Transformer (PET) for Electric Traction Applications

abstract: This paper proposes a multiport power electronic transformer (PET) topology with multiwinding medium-frequency transformer (MW-MFT) isolation along with the associated modeling analysis and control scheme. The power balance at different ports can be controlled using the multiwinding transformer's common flux linkage. The potential applications of the proposed multiport PET are high-power traction systems for locomotives and electric multiple units, marine propulsion, wind power generation, and utility grid distribution applications. The complementary polygon equivalent circuit modeling of an MW-MFT is presented. The current and power characteristics of the virtual circuit branches and the multiports with general-phase-shift control are described. The general current and power analysis for the multiple active bridge (MAB) isolation units is investigated. Power decoupling methods, including nonlinear solution for power balancing are proposed. The zero-voltage-switching conditions for the MAB are discussed. Control strategies including soft-switching-phase-shift control and voltage balancing control based on the power decoupling calculations are described. Simulations and experiments are presented to verify the performance of the proposed topology and control algorithms.


title: A matching pursuit method for approximating overlapping ultrasonic echoes

abstract: Ultrasonic pulse-echo methods have been used extensively in measuring the thickness of layered structures as well as those of thin adhesive interface layers. When acoustically measuring thin layers, the resulting echoes from two successive interfaces overlap in time, limiting the minimum thickness that can be resolved using conventional pulse-echo techniques. In this paper, we propose a method, named support matching pursuit (SMP), for resolving the individual echoes. The method is based on the concept of sparse signal approximation in an overcomplete dictionary composed of Gabor atoms (elementary functions). Although the dictionary enables highly flexible approximations, it is also overcomplete, which implies that the approximation is not unique. We propose a method for approximation in which each ultrasonic echo is principally represented by a single atom and therefore has a physical interpretation. SMP operates similarly to the sparse matching pursuit (MP) method. It iteratively improves the approximation by adding, at each iteration, a single atom to the solution set. However, our atom selection criterion utilizes the time localization nature of ultrasonic echoes, which causes portions of a multi-echo ultrasonic signal to be composed mainly from a single echo. This leads to accurate approximations in which each echo is characterized by a set of physical parameters that represent the composing ultrasonic echoes. In the current research we compare SMP to other sparse approximation methods such as MP and basis pursuit (BP). We perform simulations and experiments on adhesively bonded structures which clearly demonstrate the superior performance of the SMP method over the MP and BP methods.


An architecture of diversity for commonsense reasoning


title: Supervised Machine Learning Applied to Link Prediction in Bipartite Social Networks

abstract: This work copes with the problem of link prediction in large-scale two-mode social networks. Two variations of the link prediction tasks are studied: predicting links in a bipartite graph and predicting links in a unimodal graph obtained by the projection of a bipartite graph over one of its node sets. For both tasks, we show in an empirical way, that taking into account the bipartite nature of the graph can enhance substantially the performances of prediction models we learn. This is achieved by introducing new variations of topological atttributes to measure the likelihood of two nodes to be connected. Our approach, for both tasks, consists in expressing the link prediction problem as a two class discrimination problem. Classical supervised machine learning approaches can then be applied in order to learn prediction models. Experimental validation of the proposed approach is carried out on two real data sets: a co-authoring network extracted from the DBLP bibliographical database and bipartite graph history of transactions on an on-line music e-commerce site.


title: Approximate fingerprint matching using kd-tree

abstract: Fast and robust fingerprint matching is a challenging task today in fingerprint-based biometric systems. A fingerprint matching algorithm compares two given fingerprints and returns either a degree of similarity or a binary decision. Minutiae-based fingerprint matching is the most well-known and widely used method. This paper reveals a new technique of fingerprint matching, using an efficient data structure, combining the minutiae representation with the individual usefulness of each minutia, to make the matching more powerful. Experimental results exhibit the strength of this method.


title: Correlation Versus Interchangeability: The Limited Robustness of Empirical Findings on Democracy Using Highly Correlated Data Sets

abstract: This article shows that highly correlated measures can produce different results. We identify a democratization model from the literature and test it in over 120 countries from 1951-1992. Then, we check whether the results are robust regarding measures of democracy, time periods, and levels of development. The findings show that measures do matter: while some of the findings are robust, most of them are not. This explains, in part, why the debates on democracy have continued rather than been resolved. More importantly, it underscores the need for more careful use of measures and further testing to increase confidence in the findings. Scholars in comparative politics increasingly are drawn to large-N statistical analyses, often using datasets collected by others. As in any field, we show how they must be careful in choosing the most appropriate measures for their study, without assuming that any correlated measure will do.


title: A 1.8 V 11.02 μW single-ended inverter-based OTA with 113.62 dB gain

abstract: This paper presents a low power 1.8 V single-ended operational trans conductance amplifier (OTA) with a very high dc gain targeted for audio applications. Cascoding and gain boosting are employed to achieve the high gain. The amplifier is implemented in UMC 180 nm CMOS technology. For a typical process corner, the amplifier provides a dc gain of 113.62 dB and a unity gain bandwidth of 2.49 MHz at 62.9° phase margin with capacitive load of 2.5 pF. The amplifier consumes only 6.12 μA of quiescent current from 1.8 V supply. The amplifier exhibits good performance across all the process corners with the use of body bias technique.


Scheduling in the Dark (Improved Result) Abstract


title: Fast Approximate Geodesic Paths on Triangle Mesh

abstract: We present a new algorithm to compute a geodesic path over a triangle mesh. Based on Novotni’s propagating wavefront method which is similar to the well known Dijkstra algorithm, we made some improvements which Novotni had missed and we also gave the method to find out the geodesic path which Novotni had not. It can handle both convex and non-convex surfaces or even with boundaries. Experiment results show that our method works very well both in efficiency and precision.


Convex Regions on the n-Dimensional Spherical Surface


title: Applied and Computational Harmonic Analysis

abstract: Article history: A popular approach to deal with the "curse of dimensionality" in relation with high- dimensional data analysis is to assume that points in these datasets lie on a low- dimensional manifold immersed in a high-dimensional ambient space. Kernel methods operate on this assumption and introduce the notion of local affinities between data points via the construction of a suitable kernel. Spectral analysis of this kernel provides a global, preferably low-dimensional, coordinate system that preserves the qualities of the manifold. In this paper, we extend the scalar relations used in this framework to matrix relations, which can encompass multidimensional similarities between local neighborhoods of points on the manifold. We utilize the diffusion maps methodology together with linear- projection operators between tangent spaces of the manifold to construct a super-kernel that represents these relations. The properties of the presented super-kernels are explored and their spectral decompositions are utilized to embed the patches of the manifold into a tensor space in which the relations between them are revealed. We present two applications that utilize the patch-to-tensor embedding framework: data classification and data clustering.


title: Reweighted random walks for graph matching

abstract: Graph matching is an essential problem in computer vision and machine learning. In this paper, we introduce a random walk view on the problem and propose a robust graph matching algorithm against outliers and deformation. Matching between two graphs is formulated as node selection on an association graph whose nodes represent candidate correspondences between the two graphs. The solution is obtained by simulating random walks with reweighting jumps enforcing the matching constraints on the association graph. Our algorithm achieves noise-robust graph matching by iteratively updating and exploiting the confidences of candidate correspondences. In a practical sense, our work is of particular importance since the real-world matching problem is made difficult by the presence of noise and outliers. Extensive and comparative experiments demonstrate that it outperforms the state-of-the-art graph matching algorithms especially in the presence of outliers and deformation.


title: Critical infrastructure interdependency modeling: Using graph models to assess the vulnerability of smart power grid and SCADA networks

abstract: We discuss a framework for quantitative vulnerability assessment of critical infrastructure systems. We focus on the smart electric power delivery systems, i.e., electricity transmission and distribution smart grids, along with SCADA and EMS systems. We introduce concepts and results from graph and social network theories, and apply them to the study of the WSCC-Smart Power Grid Network-SCADA-EMS (WSSE) System. We also calculate values of the topological characteristics of the networks and compare their error and attack tolerances, i.e., their performance when vertices are removed, randomly or in a malicious way. Also, we study the possible topology generation models such as the random graph, small-world, and scale-free models. The WSEE system was found to follow the scale-free graph model of social network theory.


title: IMU-Based Indoor Localization for Construction Applications

abstract: Localization and tracking of resources on construction jobsites is an emerging area where the location of materials, labour, and equipment is used to estimate productivity, measure project's progress and/or enhance jobsite safety. GPS has been widely used for outdoor tracking of construction operations. However, GPS is not suitable for indoor applications due to the lack of signal coverage; particularly inside tunnels or buildings. Several indoor localization research had been attempted, however such developments rely heavily on extensive external communication network infrastructures. These developments also are susceptible to electromagnetic interference in noisy construction jobsites. This paper presents indoor localization system using a microcontroller equipped with an inertial measurement unit (IMU). The IMU contains a cluster of sensors: accelerometer, gyroscope and magnetometer. The microcontroller uses a direct cosine matrix algorithm to fuse sensors data and calculate non-gravitational acceleration using ninedegrees-of-freedom motion equations. Current position is calculated based on measured acceleration and heading, while accounting for growing error in speed estimation utilizing jerk integration algorithm. Experimental results are presented to illustrate the relative effectiveness of the developed system, which is able to operate independently of any external aids and visibility conditions.


title: Wireless sensor network protocol developed for microcontroller based Wireless Sensor units, and data processing with visualization by LabVIEW

abstract: The sensor networks are established by the connection of numerous sensors which are located at different places. In our paper we introduce the general structure of Wireless Sensor Networks as well as different wireless standards and network components. Furthermore, we give an overview about the construction of the Wireless Sensor Unit and the operation of LabVIEW as a web-server and its network setup options. Finally, we highlight the developed Sensor Network Protocol.


A Twitter Corpus and Benchmark Resources for German Sentiment Analysis


Deep Learning in Paralinguistic Recognition Tasks: Are Hand-crafted Features Still Relevant?


title: Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization

abstract: Semantic specialization is the process of fine-tuning pre-trained distributional word vectors using external lexical knowledge (e.g., WordNet) to accentuate a particular semantic relation in the specialized vector space. While post-processing specialization methods are applicable to arbitrary distributional vectors, they are limited to updating only the vectors of words occurring in external lexicons (i.e., seen words), leaving the vectors of all other words unchanged. We propose a novel approach to specializing the full distributional vocabulary. Our adversarial post-specialization method propagates the external lexical knowledge to the full distributional space. We exploit words seen in the resources as training examples for learning a global specialization function. This function is learned by combining a standard L2-distance loss with an adversarial loss: the adversarial component produces more realistic output vectors. We show the effectiveness and robustness of the proposed method across three languages and on three tasks: word similarity, dialog state tracking, and lexical simplification. We report consistent improvements over distributional word vectors and vectors specialized by other state-of-the-art specialization frameworks. Finally, we also propose a cross-lingual transfer method for zero-shot specialization which successfully specializes a full target distributional space without any lexical knowledge in the target language and without any bilingual data.


title: Statistical Zero-Knowledge Proofs with Efficient Provers: Lattice Problems and More

abstract: We construct several new statistical zero-knowledge proofs with efficient provers, i.e. ones where the prover strategy runs in probabilistic polynomial time given an NP witness for the input string. Our first proof systems are for approximate versions of the SHORTEST VECTOR PROBLEM (SVP) and CLOSEST VECTOR PROBLEM (CVP), where the witness is simply a short vector in the lattice or a lattice vector close to the target, respectively. Our proof systems are in fact proofs of knowledge, and as a result, we immediately obtain efficient lattice-based identification schemes which can be implemented with arbitrary families of lattices in which the approximate SVP or CVP are hard. We then turn to the general question of whether all problems in SZKnNP admit statistical zero-knowledge proofs with efficient provers. Towards this end, we give a statistical zero-knowledge proof system with an efficient prover for a natural restriction of STATISTICAL DIFFERENCE, a complete problem for SZK. We also suggest a plausible approach to resolving the general question in the positive.


title: Turbulent Touch: Touchscreen Input for Cockpit Flight Displays

abstract: Touchscreen input in commercial aircraft cockpits offers potential advantages, including ease of use, modifiability, and reduced weight. However, tolerance to turbulence is a challenge for their deployment. To better understand the impact of turbulence on cockpit input methods we conducted a comparative study of user performance with three input methods -- touch, trackball (as currently used in commercial aircraft), and a touchscreen stencil overlay designed to assist finger stabilization. These input methods were compared across a variety of interactive tasks and at three levels of simulated turbulence (none, low, and high). Results showed that performance degrades and subjective workload increases as vibration increases. Touch-based interaction was faster than the trackball when precision requirements were low (at all vibrations), but it was slower and less accurate for more precise pointing, particularly at high vibrations. The stencil did not improve touch selection times, although it did reduce errors on small targets at high vibrations, but only when finger lift-off errors had been eliminated by a timeout. Our work provides new information on the types of tasks affected by turbulence and the input mechanisms that perform best under different levels of vibration.


title: CLAMI: Defect Prediction on Unlabeled Datasets (T)

abstract: Defect prediction on new projects or projects with limited historical data is an interesting problem in software engineering. This is largely because it is difficult to collect defect information to label a dataset for training a prediction model. Cross-project defect prediction (CPDP) has tried to address this problem by reusing prediction models built by other projects that have enough historical data. However, CPDP does not always build a strong prediction model because of the different distributions among datasets. Approaches for defect prediction on unlabeled datasets have also tried to address the problem by adopting unsupervised learning but it has one major limitation, the necessity for manual effort. In this study, we propose novel approaches, CLA and CLAMI, that show the potential for defect prediction on unlabeled datasets in an automated manner without need for manual effort. The key idea of the CLA and CLAMI approaches is to label an unlabeled dataset by using the magnitude of metric values. In our empirical study on seven open-source projects, the CLAMI approach led to the promising prediction performances, 0.636 and 0.723 in average f-measure and AUC, that are comparable to those of defect prediction based on supervised learning.


title: Acquisition of Triples of Knowledge from Lecture Notes: A Natural Langauge Processing Approach

abstract: Automated acquisition of knowledge from text has been utilised across several research areas including domain modeling of knowledge-based systems and semantic web. Primarily, knowledge is decomposed as fragments in the form of entities and relations called triples (or triplets). Although empirical studies have already been developed to extract entities (or concepts), relation extraction is still considered as a challenging task and hence, performed semi-automatically or manually in educational applications such as Intelligent Tutoring Systems. This paper presents Natural Language Processing (NLP) techniques to identify subject-verb-object (SVO) in lecture notes, supporting the creation of concept-relation-concept triple for visualisation in concept map activities. Domain experts have already been invested in producing legible slides. However, automated knowledge acquisition is challenging due to potential issues such as the use of sentence fragments, ambiguity and confusing use of idioms. Our work integrates the naturally-structured layout of presentation environments to solve semantically, syntactically missing or ambiguous elements. We evaluate our approach using a corpus of Computer Science lecture notes and discuss further uses of our technique in the educational context.


Future Perspectives on Next Generation e-Sports Infrastructure and Exploring Their Benefits


title: Genital Beautification: A Concept That Offers More Than Reduction of the Labia Minora

abstract: BACKGROUND ::: The interest and demand for female genital rejuvenation surgery are steadily increasing. This report presents a concept of genital beautification consisting of labia minora reduction, labia majora augmentation by autologous fat transplantation, labial brightening by laser, mons pubis reduction by liposuction, and vaginal tightening if desired. ::: ::: ::: METHODS ::: Genital beautification was performed for 124 patients between May 2009 and January 2012 and followed up for 1 year to obtain data about satisfaction with the surgery. ::: ::: ::: RESULTS ::: Of the 124 female patients included in the study, 118 (95.2 %) were happy and 4 (3.2 %) were very happy with their postoperative appearance. In terms of postoperative functionality, 84 patients (67.7 %) were happy and 40 (32.3 %) were very happy. Only 2 patients (1.6 %) were not satisfied with the aesthetic result of their genital beautification procedures, and 10 patients (8.1 %) experienced wound dehiscence. ::: ::: ::: CONCLUSION ::: The described technique of genital beautification combines different aesthetic female genital surgery techniques. Like other aesthetic surgeries, these procedures are designed for the subjective improvement of the appearance and feelings of the patients. The effects of the operation are functional and psychological. They offer the opportunity for sexual stimulation and satisfaction. The complication rate is low. Superior aesthetic results and patient satisfaction can be achieved by applying this technique. ::: ::: ::: LEVEL OF EVIDENCE IV ::: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .


Classification Algorithms in Human Activity Recognition using Smartphones


title: Pavlik Harness Disease Revisited: Does Prolonged Treatment of a Dislocated Hip in a Harness Adversely Affect the &agr; Angle?

abstract: Background: Current dogma contends that prolonged treatment of a dislocated hip in Pavlik harness beyond 3 weeks will cause “Pavlik harness disease.” To our knowledge, however, no previous studies have documented objective morphologic changes to the acetabulum from continued treatment of a persistently dislocated hip. Methods: We retrospectively reviewed a consecutive series of infants with developmental dysplasia of the hip, below 6 months old, who failed Pavlik treatment from a single, tertiary-care pediatric hospital and a multicenter, international study group. Inclusion criteria were dislocated hips confirmed by ultrasound (both initially and at Pavlik termination) and a minimum of 2 ultrasounds during harness treatment at least 3 weeks apart. As a global measure of acetabular morphology, &agr; angle (AA) was compared between initial and final ultrasound. The final means of obtaining successful hip reduction was recorded from the medical records. Results: Forty-nine hips in 38 patients were identified. Median age at Pavlik initiation was 4 weeks (range, 0 to 18 wk); median time in harness was 6 weeks (range, 3 to 14 wk). Surprisingly, a mean of 4 degrees improvement in AA (95% CI, 2-6 degrees; P=0.001) was observed between first and final ultrasound. We found no difference in AA change between those in harness 3 to 5 weeks and those with prolonged wear >5 weeks (P=0.817). There was no significant association between change in AA and time in harness (P=0.545), age at Pavlik initiation (P=0.199), clinical reducibility of the hip (P=0.202), or initial percent femoral head coverage (P=0.956). Following harness failure, 22/49 hips (45%) were successfully treated with rigid abduction bracing, 16 (33%) by closed reduction/spica casting, and 10 (20%) by open reduction; 1 hip (2%) spontaneously reduced and required no further treatment. Conclusions: On the basis of the lengths of harness treatment in our series, most hips did not exhibit negative changes in the acetabular AA in response to prolonged treatment of a dislocated hip in harness. Furthermore, 80% of hips failing Pavlik treatment were successfully reduced through closed means, indicating that subsequent treatment was not compromised. Level of Evidence: Level IV—retrospective case series.


title: Exploiting deep semantics and compositionality of natural language for Human-Robot-Interaction

abstract: We develop a natural language interface for human robot interaction that implements reasoning about deep semantics in natural language. To realize the required deep analysis, we employ methods from cognitive linguistics, namely the modular and compositional framework of Embodied Construction Grammar (ECG) [Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference resolution problems and other issues related to deep semantics and compositionality of natural language. This also includes verbal interaction with humans to clarify commands and queries that are too ambiguous to be executed safely. We implement our NLU framework as a ROS package and present proof-of-concept scenarios with different robots, as well as a survey on the state of the art.


title: Socialization in an Open Source Software Community: A Socio-Technical Analysis

abstract: Open Source Software (OSS) development is often characterized as a fundamentally new way to develop software. Past analyses and discussions, however, have treated OSS projects and their organization mostly as a static phenomenon. Consequently, we do not know how these communities of software developers are sustained and reproduced over time through the progressive integration of new members. To shed light on this issue I report on my analyses of socialization in a particular OSS community. In particular, I document the relationships OSS newcomers develop over time with both the social and material aspects of a project. To do so, I combine two mutually informing activities: ethnography and the use of software specially designed to visualize and explore the interacting networks of human and material resources incorporated in the email and code databases of OSS. Socialization in this community is analyzed from two perspectives: as an individual learning process and as a political process. From these analyses it appears that successful participants progressively construct identities as software craftsmen, and that this process is punctuated by specific rites of passage. Successful participants also understand the political nature of software development and progressively enroll a network of human and material allies to support their efforts. I conclude by discussing how these results could inform the design of software to support socialization in OSS projects, as well as practical implications for the future of these projects.