Efficient Multipattern Event Processing Over High-Speed Train Data Streams. Big data is becoming a key basis for productivity growth, innovation, and consumer surplus, but also bring us great challenges in its volume, velocity, variety, value, and veracity. The notion of event is an important cornerstone to manage big data. High-speed railway is one of the most typical application domains for event-based system, especially for the train onboard system. There are usually numerous complex event patterns subscribed in system sharing the same prefix, suffix, or subpattern; consequently, multipattern complex event detection often results in plenty of redundant detection operations and computations. In this paper, we propose a multipattern complex event detection model, multipattern event processing (MPEP), constructed by three parts: 1) multipattern state transition; 2) failure transition; and 3) state output. Based on MPEP, an intelligent onboard system for high-speed train is preliminarily implemented. The system logic is described using our proposed complex event description model and compiled into a multipattern event detection model. Experimental results show that MPEP can effectively optimize the complex event detection process and improve its throughput by eliminating duplicate automata states and redundant computations. This intelligent onboard system also provides better detection ability than other models when processing real-time events stored in high-speed train Juridical Recording Unit (JRU).

Question: What is the difference between a multipattern state transition and a failure transition?


Business Models for Electronic Markets. Introduction Electronic commerce can be defined loosely as "doing business electronically" (European Commission 1997). Electronic commerce includes electronic trading of physical goods and of intangibles such as information. This encompasses all the trading steps such as online marketing, ordering, payment, and support for delivery. Electronic commerce includes the electronic provision of services, such as aftersales support or online legal advice. Finally it also includes electronic support for collaboration between companies, such as collaborative design.

Question: What is the definition of electronic commerce?


A CNN for the identification of corresponding patches in SAR and optical imagery of urban scenes. In this paper we propose a convolutional neural network (CNN), which allows to identify corresponding patches of very high resolution (VHR) optical and SAR imagery of complex urban scenes. Instead of a siamese architecture as conventionally used in CNNs designed for image matching, we resort to a pseudo-siamese configuration with no interconnection between the two streams for SAR and optical imagery. The network is trained with automatically generated training data and does not resort to any hand-crafted features. First evaluations show that the network is able to predict corresponding patches with high accuracy, thus indicating great potential for further development to a generalized multi-sensor matching procedure.

Question: What is a CNN?


Extending the Definitions of Antenna Gain and Radiation Pattern into the Time Domain. Many of the classical parameters of frequency domain (CW) antennas, such as gain and radiation pattern, are defined only in the frequency domain, and currently have no meaning in the time domain. The purpose of this note is to extend their definitions into the time domain. We develop here the concept of waveform norms as a mechanism for comparing the radiated field to the driving voltage. The concept of gain that we develop then compares a norm of the radiated field to a norm of the derivative of the driving voltage. The transient gain is therefore a function of both the choice of norm, and of the choice of driving waveform. A key feature of our definitions of gain and pnttcrn factor is that they arc equivalent in both transmit and receive modes. .

Question: What is the difference between the time domain and the frequency domain?


Practical design considerations for a LLC multi-resonant DC-DC converter in battery charging applications. In this paper, resonant tank design procedure and practical design considerations are presented for a high performance LLC multi-resonant dc-dc converter in a two-stage smart battery charger for neighborhood electric vehicle applications. The multi-resonant converter has been analyzed and its performance characteristics are presented. It eliminates both low and high frequency current ripple on the battery, thus maximizing battery life without penalizing the volume of the charger. Simulation and experimental results are presented for a prototype unit converting 390 V from the input dc link to an output voltage range of 48 V to 72 V dc at 650 W. The prototype achieves a peak efficiency of 96%.

Question: How to design a LLC multi-resonant dc-dc converter?


Deep Reinforcement Learning for Optimal Control of Space Heating. Classical methods to control heating systems are often marred by suboptimal performance, inability to adapt to dynamic conditions and unreasonable assumptions e.g. existence of building models. This paper presents a novel deep reinforcement learning algorithm which can control space heating in buildings in a computationally efficient manner, and benchmarks it against other known techniques. The proposed algorithm outperforms rule based control by between 5-10% in a simulation environment for a number of price signals. We conclude that, while not optimal, the proposed algorithm offers additional practical advantages such as faster computation times and increased robustness to non-stationarities in building dynamics.

Question: What is a reinforcement learning algorithm?


Convolutional Sketch Inversion. In this paper, we use deep neural networks for inverting face sketches to synthesize photorealistic face images. We first construct a semi-simulated dataset containing a very large number of computergenerated face sketches with different styles and corresponding face images by expanding existing unconstrained face data sets. We then train models achieving state-of-the-art results on both computer-generated sketches and hand-drawn sketches by leveraging recent advances in deep learning such as batch normalization, deep residual learning, perceptual losses and stochastic optimization in combination with our new dataset. We finally demonstrate potential applications of our models in fine arts and forensic arts. In contrast to existing patch-based approaches, our deep-neuralnetwork-based approach can be used for synthesizing photorealistic face images by inverting face sketches in the wild.

Question: What is the difference between a deep neural network and a convolutional neural network?


Collective dynamics of 'small-world' networks. Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays,, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon, (popularly known as six degrees of separation). The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

Question: What is the small-world phenomenon?


A discriminatively trained, multiscale, deformable part model. This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge in ten out of twenty categories. The system relies heavily on deformable parts. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL challenge. Our system also relies heavily on new methods for discriminative training. We combine a margin-sensitive approach for data mining hard negative examples with a formalism we call latent SVM. A latent SVM, like a hidden CRF, leads to a non-convex training problem. However, a latent SVM is semi-convex and the training problem becomes convex once latent information is specified for the positive examples. We believe that our training methods will eventually make possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose.

Question: What is a discriminatively trained, multiscale, deformable part model?


HADOOP BLOCK PLACEMENT POLICY FOR DIFFERENT FILE FORMATS. Now a day's Peta-Bytes of data becomes the norm in industries. Handling, analyzing such big data is challenging task. Even frameworks like Hadoop (Open Source Implementation of MapReduce Paradigm) and NoSQL databases like Cassandra, HBase can be used to analyze and store such large data; heterogeneity of data is still an issue. Data centers usually have clusters formed using heterogeneous nodes. Ecosystem like Hadoop can be used to manage such types of cluster but it can not schedule jobs (Application) efficiently on this heterogeneous cluster when data is itself heterogeneous. Heterogeneity of data may be because of data format or because of the complexity. This paper is review of systems and algorithms for distributed management of huge size data, efficiency of these approaches.

Question: What is the difference between Hadoop and NoSQL databases?


Detection and counting of pothole using image processing techniques. Pothole are the primary cause of accidents, hence identification and classification using image processing techniques is very important. In this paper image pre-processing based on difference of Gaussian-Filtering and clustering based image segmentation methods are implemented for better results. From the results the K-Means clustering based segmentation was preferred for its fastest computing time and edge detection based segmentation is preferred for its specificity. The main goal of this paper is to identify a better method which is highly efficient and accurate compared to the conventional methods. Different image pre-processing and segmentation methods for pothole detection where reviewed using performance measures.

Question: What is the main goal of this paper?


Theano: new features and speed improvements. Theano is a linear algebra compiler that optimizes a user's symbolically-specified mathematical computations to produce efficient low-level implementations. In this paper, we present new features and efficiency improvements to Theano, and benchmarks demonstrating Theano's performance relative to Torch7, a recently introduced machine learning library, and to RNNLM, a C++ library targeted at recurrent neural networks.

Question: What is theano?


Total variation regularization of local-global optical flow. More data fidelity terms in variational optical flow methods improve the estimation's robustness. A robust and anisotropic smoother enhances the specific fill-in process. This work presents a combined local-global (CLG) approach with total variation regularization. The combination of bilateral filtering and anisotropic (image driven) regularization is used to control the propagation phenomena. The resulted method, CLG-TV, is able to compute larger displacements in a reasonable time. The numerical scheme is highly parallelizable and runs in real-time on current generation graphic processing units.

Question: What is total variation regularization?


Recurrent neural network based recommendation for time heterogeneous feedback. In recommender systems, several kinds of user feedback with time stamps are collected and used for recommendation, which is called the time heterogeneous feedback recommendation problem. The existing recommendation methods can handle only one kind of feedback or ignore the time stamps of the feedback. To solve the time heterogeneous feedback recommendation problem, in this paper, we propose a recurrent neural network model to predict the probability that the user will access an item given the time heterogeneous feedback of this user. To our best knowledge, it is the first time to solve the time heterogeneous feedback recommendation problem by deep neural network model. The proposed model is learned by back propagation algorithm and back propagation through time algorithm. The comparison results on four real-life datasets indicate that the proposed method outperforms the compared state-ofthe-art approaches. © 2016 Elsevier B.V. All rights reserved.

Question: What is the time heterogeneous feedback recommendation problem?


Crowd Geofencing. Geofencing mechanisms allow for timely message delivery to the visitors of predefined target areas. However, conventional geofencing approaches poorly support mobile data collection scenarios in which experts need in situ assistance. In this paper, we propose crowd geofencing environments, in which a large number of crowdworkers generate geofences to support mobile experts. As a first step to open up the possibilities of crowd geofencing, we have tested its feasibility by collecting more than one thousand geofences in an unfamiliar city prior to the visit to look into urban water and air quality issues. Our experience has revealed the strengths and weaknesses of crowd geofencing in terms of geofence quality and crowd-powered situated actions.

Question: What is crowd geofencing?


FROM SIMPLE ASSOCIATIONS TO SYSTEMATIC REASONING : A Connectionist representation of rules , variables , and dynamic bindings using temporal synchrony. Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency — as though these inferences are a reflex response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the results about the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuron-like elements represent a large body of systematic knowledge and perform a range of inferences with such speed? We describe a computational model that is a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables, and perform a class of inferences in a few hundred msec. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model achieves this by i) representing dynamic bindings as the synchronous firing of appropriate nodes, ii) rules as interconnection patterns that direct the propagation of rhythmic activity, iii) and long-term facts as temporal pattern-matching sub-networks. The model is consistent with recent neurophysiological findings which suggest that synchronous activity occurs in the brain and may play a representational role in neural information processing. The model also makes specific predictions about the nature of reflexive reasoning that are psychologically significant. It identifies constraints on the form of rules that may participate in such reasoning and relates the capacity of the working memory underlying reflexive reasoning to biological parameters

Question: How can a system of simple and slow neuron-like elements represent a large body of systematic knowledge and perform a range of inferences with such speed?


Deterministic compressed-sensing matrices: Where Toeplitz meets Golay. Recently, the statistical restricted isometry property (STRIP) has been formulated to analyze the performance of deterministic sampling matrices for compressed sensing. In this paper, a class of deterministic matrices which satisfy STRIP with overwhelming probability are proposed, by taking advantage of concentration inequalities using Stein's method. These matrices, called orthogonal symmetric Toeplitz matrices (OSTM), guarantee successful recovery of all but an exponentially small fraction of K-sparse signals. Such matrices are deterministic, Toeplitz, and easy to generate. We derive the STRIP performance bound by exploiting the specific properties of OSTM, and obtain the near-optimal bound by setting the underlying sign sequence of OSTM as the Golay sequence. Simulation results show that these deterministic sensing matrices can offer reconstruction performance similar to that of random matrices.

Question: What is the near-optimal bound?


Cellular planning for next generation wireless mobile network using novel energy efficient CoMP. Cellular planning also called radio network planning is a crucial stage for the deployment of a wireless network. The enormous increase in data traffic requires augmentation of coverage, capacity, throughput, quality of service and optimized cost of the network. The main goal of cellular planning is to enhance the spectral efficiency and hence the throughput of a network. A novel CoMP algorithm has been discussed with two-tier heterogeneous network. Number of clusters has been obtained using V-R (variance ratio) Criterion. The centroid of a cluster obtained using K-means algorithm provides the deployment of BS position. Application of CoMP in this network using DPS approach with sleep mode of power saving, provides higher energy efficiency, SINR and throughput as compared to nominal CoMP. CoMP basically describes a scheme in which a group of base stations (BS) dynamically co-ordinate and co-operate among themselves to convert interference into a beneficial signal. Network planning using stochastic method and Voronoi Tessellation with two-tier network has been applied to a dense region of Surat city in Gujarat state of India. The results show clear improvement in signal-to-interference plus noise ratio (SINR) by 25% and energy efficiency of the network by 28% using the proposed CoMP transmission.

Question: What is the difference between CoMP and CoMP?


Autonomous discovery of the goal space to learn a parameterized skill. A parameterized skill is a mapping from multiple goals/task parameters to the policy parameters to accomplish them. Existing works in the literature show how a parameterized skill can be learned given a task space that defines all the possible achievable goals. In this work, we focus on tasks defined in terms of final states (goals), and we face on the challenge where the agent aims to autonomously acquire a parameterized skill to manipulate an initially unknown environment. In this case, the task space is not known a priori and the agent has to autonomously discover it. The agent may posit as a task space its whole sensory space (i.e. the space of all possible sensor readings) as the achievable goals will certainly be a subset of this space. However, the space of achievable goals may be a very tiny subspace in relation to the whole sensory space, thus directly using the sensor space as task space exposes the agent to the curse of dimensionality and makes existing autonomous skill acquisition algorithms inefficient. In this work we present an algorithm that actively discovers the manifold of the achievable goals within the sensor space. We validate the algorithm by employing it in multiple different simulated scenarios where the agent actions achieve different types of goals: moving a redundant arm, pushing an object, and changing the color of an object.

Question: How to discover the goal space?


Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic. In this paper we propose a novel energy efficient approach for the recognition of human activities using smartphones as wearable sensing devices, targeting assisted living applications such as remote patient activity monitoring for the disabled and the elderly. The method exploits fixed-point arithmetic to propose a modified multiclass Support Vector Machine (SVM) learning algorithm, allowing to better preserve the smartphone battery lifetime with respect to the conventional floating-point based formulation while maintaining comparable system accuracy levels. Experiments show comparative results between this approach and the traditional SVM in terms of recognition performance and battery consumption, highlighting the advantages of the proposed method.

Question: What is the difference between the traditional SVM and the proposed method?


An evaluation of bipartitioning techniques. Logic partitioning is an important issue in VLSI CAD, and has been an area of active research for at least the last 25 years. Numerous approaches have been developed and many different techniques have been combined for a wide range of applications. In this paper, we examine many of the existing techniques for logic bipartitioning and present a methodology for determining the best mix of approaches. The result is a novel bipartitioning algorithm that includes both new and pre-existing techniques. Our algorithm produces results that are at least 16% better than the state-of-the-art while also being efficient in run-time.

Question: What is the best bipartitioning algorithm?


Morphological Smoothing and Extrapolation of Word Embeddings. Languages with rich inflectional morphology exhibit lexical data sparsity, since the word used to express a given concept will vary with the syntactic context. For instance, each count noun in Czech has 12 forms (where English uses only singular and plural). Even in large corpora, we are unlikely to observe all inflections of a given lemma. This reduces the vocabulary coverage of methods that induce continuous representations for words from distributional corpus information. We solve this problem by exploiting existing morphological resources that can enumerate a word's component morphemes. We present a latentvariable Gaussian graphical model that allows us to extrapolate continuous representations for words not observed in the training corpus, as well as smoothing the representations provided for the observed words. The latent variables represent embeddings of morphemes, which combine to create embeddings of words. Over several languages and training sizes, our model improves the embeddings for words, when evaluated on an analogy task, skip-gram predictive accuracy, and word similarity.

Question: What is the difference between a word embedding and a word2vec?


Mixture of experts for classification of gender, ethnic origin, and pose of human faces. In this paper we describe the application of mixtures of experts on gender and ethnic classification of human faces, and pose classification, and show their feasibility on the FERET database of facial images. The FERET database allows us to demonstrate performance on hundreds or thousands of images. The mixture of experts is implemented using the "divide and conquer" modularity principle with respect to the granularity and/or the locality of information. The mixture of experts consists of ensembles of radial basis functions (RBFs). Inductive decision trees (DTs) and support vector machines (SVMs) implement the "gating network" components for deciding which of the experts should be used to determine the classification output and to restrict the support of the input space. Both the ensemble of RBF's (ERBF) and SVM use the RBF kernel ("expert") for gating the inputs. Our experimental results yield an average accuracy rate of 96% on gender classification and 92% on ethnic classification using the ERBF/DT approach from frontal face images, while the SVM yield 100% on pose classification.

Question: How to classify gender and ethnic origin of human faces?


Non-linear Feedback Neural Network for Solution of Quadratic Programming Problems. This paper presents a recurrent neural circuit for solving quadratic programming problems. The objective is tominimize a quadratic cost function subject to linearconstraints. The proposed circuit employs non-linearfeedback, in the form of unipolar comparators, to introducetranscendental terms in the energy function ensuring fastconvergence to the solution. The proof of validity of the energy function is also provided. The hardware complexity of the proposed circuit comparesfavorably with other proposed circuits for the same task. PSPICE simulation results arepresented for a chosen optimization problem and are foundto agree with the algebraic solution.

Question: What is a quadratic programming problem?


Parathyroid Hormone PTH(1-34) Formulation that Enables Uniform Coating on a Novel Transdermal Microprojection Delivery System. Assess formulation parameters to enable >24-h continuous accurate and uniform coating of PTH(1-34) on a novel transdermal microprojection array delivery system. Surface activity and rheology of the liquid formulation was determined by contact angle measurement and cone-plate viscometry. The formulation's delivery performance was assessed in vivo using the hairless guinea pig model. Peptide gelation was investigated by rheological and viscoelastic behavior changes. Accurate and uniform coating was achieved by formulating the liquid formulation to a preferred contact angle range of 30–60° with a surfactant and by establishing a Newtonian fluid (defined as a fluid maintaining a constant viscosity with shear rate and time) with a viscosity of ≥20 cps via adjusting the peptide concentration and using an appropriate acidic counterion. A non-volatile acidic counterion was found critical to compensate for the loss of the volatile acetate counterion to maintain the peptide formulation's solubility upon rehydration in the skin. Finally, the 15.5% w/w PTH(1-34) concentration was found to be the most physically stable formulation (delayed gelation) in the roll-coating reservoir. With a properly designed coating reservoir for shear force reduction, the liquid formulation could last for more than 24 h without gelation. The study successfully offered scientific rationales for developing an optimal liquid formulation for a novel titanium microprojection array coating process. The resultant formulation has an enduring physical stability (>24 h) in the coating reservoir and maintained good in vivo dissolution performance.

Question: How to formulate a liquid formulation for a novel transdermal microprojection delivery system?


Use of the stair vision library within the ISPRS 2D semantic labeling benchmark (Vaihingen). This report describes experiments conducted using the multi-class image classification framework implemented in the stair vision library (SVL, (Gould et al., 2008)) in the context of the ISPRS 2D semantic labeling benchmark. The motivation was to get results from a well-established and public available software (Gould, 2014), as a kind of baseline. Besides the use of features implemented in the SVL which makes use of three channel images, assuming RGB, we also included features derived from the height model and the NDVI which is specific here, because the benchmark dataset provides surface models and CIR images. Another point of interest concerned the impact the segmentation had on the overall result. To this end a pre-study was performed where different parameters for the graph-based segmentation method introduced by Felzenszwalb and Huttelocher (2004) have been tested, in addition we only applied a simple chessboard segmentation. Other experiments focused on the question whether the conditional random field classification approach helps to enhance the overall performance. The official evaluation of all experiments described here is available at http://www2.isprs.org/vaihingen-2d-semantic-labeling-contest.html (SVL_1 to SVL_6). The normalized height models are available through the ReseachGate profile of the author (http://www.researchgate.net/profile/Markus_Gerke)

Question: What is the ISPRS 2D semantic labeling benchmark?


Design and Analysis of a 21–29-GHz Ultra-Wideband Receiver Front-End in 0.18-$\mu$ m CMOS Technology. This paper reports the design and analysis of 21-29-GHz CMOS low-noise amplifier (LNA), balun and mixer in a standard 0.18-μm CMOS process for ultra-wideband automotive radar systems. To verify the proposed LNA, balun, and mixer architectures, a simplified receiver front-end comprising an LNA, a double-balanced Gilbert-cell-based mixer, and two Marchand baluns was implemented. The wideband Marchand baluns can convert the single RF and local oscillator (LO) signals to nearly perfect differential signals over the 21-29-GHz band. The performance of the mixer is improved with the current-bleeding technique and a parallel resonant inductor at the differential outputs of the RF transconductance stage. Over the 21-29-GHz band, the receiver front-end exhibits excellent noise figure of 4.6±0.5 dB, conversion gain of 23.7±1.4 dB, RF port reflection coefficient lower than -8.8 dB, LO-IF isolation lower than -47 dB, LO-RF isolation lower than -55 dB, and RF-IF isolation lower than -35.5 dB. The circuit occupies a chip area of 1.25×1.06 mm2, including the test pads. The dc power dissipation is only 39.2 mW.

Question: What is the difference between a balun and a transformer?


TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension. We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K questionanswer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers. We also present two baseline algorithms: a featurebased classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study.1

Question: What is the size of the dataset?


Nonlinear total variation based noise removal algorithms. A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using the gradient-projection method. This amounts to solving a time dependent partial differential equation on a manifold determined by the constraints. As t → ∞ the solution converges to a steady state which is the denoised image. The numerical algorithm is simple and relatively fast. The results appear to be state-of-the-art for very noisy images. The method is noninvasive, yielding sharp edges in the image. The technique could be interpreted as a first step of moving each level set of the image normal to itself with velocity equal to the curvature of the level set divided by the magnitude of the gradient of the image, and a second step which projects the image back onto the constraint set.

Question: What is a nonlinear total variation based noise removal algorithm?


BAliBASE: a benchmark alignment database for the evaluation of multiple alignment programs. SUMMARY BAliBASE is a database of manually refined multiple sequence alignments categorized by core blocks of conservation sequence length, similarity, and the presence of insertions and N/C-terminal extensions.   AVAILABILITY From http://www-igbmc. u-strasbg.fr/BioInfo/BAliBASE/index.html

Question: What is a benchmark alignment database?


A new method for decontamination of de novo transcriptomes using a hierarchical clustering algorithm. Motivation The identification of contaminating sequences in a de novo assembly is challenging because of the absence of information on the target species. For sample types where the target organism is impossible to isolate from its matrix, such as endoparasites, endosymbionts and soil-harvested samples, contamination is unavoidable. A few post-assembly decontamination methods are currently available but are based only on alignments to databases, which can lead to poor decontamination.   Results We present a new decontamination method based on a hierarchical clustering algorithm called MCSC. This method uses frequent patterns found in sequences to create clusters. These clusters are then linked to the target species or tagged as contaminants using classic alignment tools. The main advantage of this decontamination method is that it allows sequences to be tagged correctly even if they are unknown or misaligned to a database.   Availability and Implementation Scripts and documentation about the MCSC decontamination method are available at https://github.com/Lafond-LapalmeJ/MCSC_Decontamination .   Contact : benjamin.mimee@agr.gc.ca.   Supplementary information Supplementary data are available at Bioinformatics online.

Question: How to decontaminate a de novo transcriptome?


DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map. For applications such as augmented reality, autonomous driving, self-localization/camera pose estimation and scene parsing are crucial technologies. In this paper, we propose a unified framework to tackle these two problems simultaneously. The uniqueness of our design is a sensor fusion scheme which integrates camera videos, motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robustness and efficiency of the system. Specifically, we first have an initial coarse camera pose obtained from consumer-grade GPS/IMU, based on which a label map can be rendered from the 3D semantic map. Then, the rendered label map and the RGB image are jointly fed into a pose CNN, yielding a corrected camera pose. In addition, to incorporate temporal information, a multi-layer recurrent neural network (RNN) is further deployed improve the pose accuracy. Finally, based on the pose from RNN, we render a new label map, which is fed together with the RGB image into a segment CNN which produces perpixel semantic label. In order to validate our approach, we build a dataset with registered 3D point clouds and video camera images. Both the point clouds and the images are semantically-labeled. Each video frame has ground truth pose from highly accurate motion sensors. We show that practically, pose estimation solely relying on images like PoseNet [25] may fail due to street view confusion, and it is important to fuse multiple sensors. Finally, various ablation studies are performed, which demonstrate the effectiveness of the proposed system. In particular, we show that scene parsing and pose estimation are mutually beneficial to achieve a more robust and accurate system.

Question: What is the difference between a semantic map and a 3D map?


Design of Bidirectional DC–DC Resonant Converter for Vehicle-to-Grid (V2G) Applications. In this paper, a detailed design procedure is presented for a bidirectional CLLLC-type resonant converter for a battery charging application. This converter is similar to an LLC-type resonant converter with an extra inductor and capacitor in the secondary side. Soft-switching can be ensured in all switches without additional snubber or clamp circuitry. Because of soft-switching in all switches, very high-frequency operation is possible; thus, the size of the magnetics and the filter capacitors can be made small. To reduce the size and cost of the converter, a CLLC-type resonant network is derived from the original CLLLC-type resonant network. First, in this paper, an equivalent model for the bidirectional converter is derived for the steady-state analysis. Then, the design methodology is presented for the CLLLC-type resonant converter. Design of this converter includes determining the transformer turns ratio, design of the magnetizing inductance based on zero-voltage switching condition, design of the resonant inductances and capacitances. Then, the CLLCtype resonant network is derived from the CLLLC-type resonant network. To validate the design procedure, a 3.5-kW converter was designed following the guidelines in the proposed methodology. A prototype was built and tested in the laboratory. Experimental results verified the design procedure presented.

Question: How to design a bidirectional DC–DC resonant converter for vehicle-to-grid applications?


Automatic Tongue Diagnosis Using a Smart Phone. An automatic tongue diagnosis framework is proposed to analyzing tongue images taken by smart phones. Different from conventional tongue diagnosis systems, our input tongue images are usually in low resolution and taken under unknown lighting conditions. Consequently, existing tongue diagnosis methods cannot be directly applied to give accurate results. We propose a lighting condition estimation method based on the SVM classifier to predict the color correction matrix according to color difference of images taken with and without flashlight. We also modify the state of the art work of fur and fissure detection and successfully improve the detection accuracy by taking hue information into consideration and adding a de-noising step.

Question: How to detect tongue fur and fissures?


What causes hidradenitis suppurativa?. Hidradenitis suppurativa (HS)--a rather common, very chronic and debilitating inflammatory skin appendage disorder with a notoriously underestimated burden of disease--has long been a playground for the high priests of nomenclature: Ask a bunch of eminent dermatologists and skin pathologists to publicly share their thoughts on what causes HS, and they will soon get entrenched in a heated debate on whether this historical term is a despicable misnomer. Fortunately, the recently founded Hidradenitis Suppurativa Foundation (HSF; http://www.hs-foundation.org), to which EXP DERMATOL serves as home journal, has broken with this unproductive tradition and has encouraged publication of the current CONTROVERSIES feature. This is exclusively devoted to discussing the pathobiology of this chronic neutrophilic folliculitis of unknown origin. Although traces of terminological bickering remain visible, it does the HS experts in our virtual debate room credit that they engage in a constructive and comprehensive dissection of potential pathogenesis pathways that may culminate in the clinical picture we know under the competing terms HS or acne inversa. These experts sketch more often complementary than mutually exclusive pathogenesis scenarios, and the outlines of a conceivable consensus on the many open pathobiology questions begin to emerge in these CONTROVERSIES. Hopefully, this heralds a welcome new tradition: to get to the molecular heart of HS pathogenesis, which can only be achieved by a renaissance of solid basic HS research, as the key to developing more effective HS therapy.

Question: What causes hidradenitis suppurativa?


Magnetic Field Analysis of Halbach Permanent Magnetic Synchronous Machine. A numerical study of PMSM based on finite element method for estimating the airgap magnetic field distribution considering stator slots effect is presented both for traditional radial magnet structure and Halbach magnet array topology. Magnetic field, flux, torque and the induction in the air gap are presented. The two machines structures performances were compared which put in view the modifications brought by these structures.. According to numerical calculations, and harmonic analysis, the PMSM with Halbach magnet array provides a higher torque and better performance than the classical radial PMSM.

Question: What is the difference between a radial and a Halbach magnet array?


Imaging cellular network dynamics in three dimensions using fast 3D laser scanning. Spatiotemporal activity patterns in three-dimensionally organized cellular networks are fundamental to the function of the nervous system. Despite advances in functional imaging of cell populations, a method to resolve local network activity in three dimensions has been lacking. Here we introduce a three-dimensional (3D) line-scan technology for two-photon microscopy that permits fast fluorescence measurements from several hundred cells distributed in 3D space. We combined sinusoidal vibration of the microscope objective at 10 Hz with 'smart' movements of galvanometric x-y scanners to repeatedly scan the laser focus along a closed 3D trajectory. More than 90% of cell somata were sampled by the scan line within volumes of 250 μm side length. Using bulk-loading of calcium indicator, we applied this method to reveal spatiotemporal activity patterns in neuronal and astrocytic networks in the rat neocortex in vivo. Two-photon population imaging using 3D scanning opens the field for comprehensive studies of local network dynamics in intact tissue.

Question: What is the difference between a two-photon microscope and a confocal microscope?


Shrink Globally , Act Locally : Sparse Bayesian Regularization and Prediction. We study the classic problem of choosing a prior distribution for a location parameter β = (β1, . . . , βp) as p grows large. First, we study the standard "global-local shrinkage" approach, based on scale mixtures of normals. Two theorems are presented which characterize certain desirable properties of shrinkage priors for sparse problems. Next, we review some recent results showing how Lévy processes can be used to generate infinite-dimensional versions of standard normal scale-mixture priors, along with new priors that have yet to be seriously studied in the literature. This approach provides an intuitive framework both for generating new regularization penalties and shrinkage rules, and for performing asymptotic analysis on existing models.

Question: What is a sparse problem?


Cloud Computing – A Comprehensive Definition. Cloud computing is an evolving technology that is consistently generating impact in IT industry and academia. It performs all computational tasks over Internet by using virtualization techniques and remains isolated from intricate vast hardware and software infrastructures. The aim of this paper is to glance through the background and evolutions of cloud computing, its architecture and services to develop cumulative knowledge for future research extension and evaluation.

Question: What is cloud computing?


Pregnancy-related gigantomastia. Case report. The term gigantomastia has been used to describe breast enlargement to extreme size, with sloughing, haemorrhage and infection. The condition is rare and a case of pregnancy-related gigantomastia is reported.

Question: What is gigantomastia?


Ultra-Reliable and Low-Latency 5 G Communication. Machine-to-machine communication, M2M, will make up a large portion of the new types of services and use cases that the fifth generation (5G) systems will address. On the one hand, 5G will connect a large number of low-cost and low-energy devices in the context of the Internet of things; on the other hand it will enable critical machine type communication use cases, such as smart factory, automotive, energy, and e-health – which require communication with very high reliability and availability, as well as very low end-to-end latency. In this paper, we will discuss the requirements, enablers and challenges to support these emerging mission-critical 5G use cases. Keywords— 5G, NR, M2M, MTC, URLLC, Tactile Internet, Reliability, Latency.

Question: What is the difference between 5G and 4G?


Investigation of multilingual deep neural networks for spoken term detection. The development of high-performance speech processing systems for low-resource languages is a challenging area. One approach to address the lack of resources is to make use of data from multiple languages. A popular direction in recent years is to use bottleneck features, or hybrid systems, trained on multilingual data for speech-to-text (STT) systems. This paper presents an investigation into the application of these multilingual approaches to spoken term detection. Experiments were run using the IARPA Babel limited language pack corpora (~10 hours/language) with 4 languages for initial multilingual system development and an additional held-out target language. STT gains achieved through using multilingual bottleneck features in a Tandem configuration are shown to also apply to keyword search (KWS). Further improvements in both STT and KWS were observed by incorporating language questions into the Tandem GMM-HMM decision trees for the training set languages. Adapted hybrid systems performed slightly worse on average than the adapted Tandem systems. A language independent acoustic model test on the target language showed that retraining or adapting of the acoustic models to the target language is currently minimally needed to achieve reasonable performance.

Question: What is the canadian military?


CallRank: Combating SPIT Using Call Duration, Social Networks and Global Reputation. The growing popularity of IP telephony systems has made them attractive targets for spammers. Voice call spam, also known as Spam over Internet Telephony (SPIT), is potentially a more serious problem than email spam because of the real time processing requirements of voice packets. We explore a novel mechanism that uses duration of calls between users to combat SPIT. CallRank, the scheme proposed by us, uses call duration to establish social network linkages and global reputations for callers, based on which call recipients can decide whether the caller is legitimate or not. CallRank has been implemented within a VoIP system simulation and our results show that we are able to achieve a false negative rate of 10% and a false positive rate of 3% even in the presence of a significant fraction of spammers.

Question: How does call rank combat spam?


Environmental implications of plastic debris in marine settings--entanglement, ingestion, smothering, hangers-on, hitch-hiking and alien invasions.. Over the past five or six decades, contamination and pollution of the world's enclosed seas, coastal waters and the wider open oceans by plastics and other synthetic, non-biodegradable materials (generally known as 'marine debris') has been an ever-increasing phenomenon. The sources of these polluting materials are both land- and marine-based, their origins may be local or distant, and the environmental consequences are many and varied. The more widely recognized problems are typically associated with entanglement, ingestion, suffocation and general debilitation, and are often related to stranding events and public perception. Among the less frequently recognized and recorded problems are global hazards to shipping, fisheries and other maritime activities. Today, there are rapidly developing research interests in the biota attracted to freely floating (i.e. pelagic) marine debris, commonly known as 'hangers-on and hitch-hikers' as well as material sinking to the sea floor despite being buoyant. Dispersal of aggressive alien and invasive species by these mechanisms leads one to reflect on the possibilities that ensuing invasions could endanger sensitive, or at-risk coastal environments (both marine and terrestrial) far from their native habitats.

Question: What are the environmental implications of plastic debris in marine settings?


Exploratory search: from finding to understanding. Research tools critical for exploratory search success involve the creation of new interfaces that move the process beyond predictable fact retrieval.

Question: How to find a good research tool?


Bicycle-Sharing System Analysis and Trip Prediction. Bicycle-sharing systems, which can provide shared bike usage services for the public, have been launched in many big cities. In bicycle-sharing systems, people can borrow and return bikes at any stations in the service region very conveniently. Therefore, bicycle-sharing systems are normally used as a short distance trip supplement for private vehicles as well as regular public transportation. Meanwhile, for stations located at different places in the service region, the bike usages can be quite skewed and imbalanced. Some stations have too many incoming bikes and get jammed without enough docks for upcoming bikes, while some other stations get empty quickly and lack enough bikes for people to check out. Therefore, inferring the potential destinations and arriving time of each individual trip beforehand can effectively help the service providers schedule manual bike re-dispatch in advance. In this paper, we will study the individual trip prediction problem for bicycle-sharing systems. To address the problem, we study a real-world bicycle-sharing system and analyze individuals' bike usage behaviors first. Based on the analysis results, a new trip destination prediction and trip duration inference model will be introduced. Experiments conducted on a real-world bicycle-sharing system demonstrate the effectiveness of the proposed model.

Question: What is the purpose of the paper?


Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography.. Understanding the long association pathways that convey cortical connections is a critical step in exploring the anatomic substrates of cognition in health and disease. Diffusion tensor imaging (DTI) is able to demonstrate fibre tracts non-invasively, but present approaches have been hampered by the inability to visualize fibres that have intersecting trajectories (crossing fibres), and by the lack of a detailed map of the origins, course and terminations of the white matter pathways. We therefore used diffusion spectrum imaging (DSI) that has the ability to resolve crossing fibres at the scale of single MRI voxels, and identified the long association tracts in the monkey brain. We then compared the results with available expositions of white matter pathways in the monkey using autoradiographic histological tract tracing. We identified 10 long association fibre bundles with DSI that match the observations in the isotope material: emanating from the parietal lobe, the superior longitudinal fasciculus subcomponents I, II and III; from the occipital-parietal region, the fronto-occipital fasciculus; from the temporal lobe, the middle longitudinal fasciculus and from rostral to caudal, the uncinate fasciculus, extreme capsule and arcuate fasciculus; from the occipital-temporal region, the inferior longitudinal fasciculus; and from the cingulate gyrus, the cingulum bundle. We suggest new interpretations of the putative functions of these fibre bundles based on the cortical areas that they link. These findings using DSI and validated with reference to autoradiographic tract tracing in the monkey represent a considerable advance in the understanding of the fibre pathways in the cerebral white matter. By replicating the major features of these tracts identified by histological techniques in monkey, we show that DSI has the potential to cast new light on the or

Question: What is the function of the cingulum bundle?


A flexible exoskeleton for hip assistance. This paper proposes a new concept called "a flexible exoskeleton" and presents the design of a wearable walking assistance device to provide physical gait assistance for the elderly. To overcome some limitations of previous wearable walking assistance devices, 3 novel mechanisms are proposed: 1) flexible thigh frames that withstand the designated load direction, 2) an adjustable flexible hip brace surrounding the wearer's body, and 3) slim and backdrivable actuators that minimize the maximum height and resistance. The proposed walking assistance device has reduced metabolic energy consumption by 13.2±4% compared with a free condition.

Question: What is a flexible exoskeleton?


Multiple social identities and stereotype threat: imbalance, accessibility, and working memory.. In 4 experiments, the authors showed that concurrently making positive and negative self-relevant stereotypes available about performance in the same ability domain can eliminate stereotype threat effects. Replicating past work, the authors demonstrated that introducing negative stereotypes about women's math performance activated participants' female social identity and hurt their math performance (i.e., stereotype threat) by reducing working memory. Moving beyond past work, it was also demonstrated that concomitantly presenting a positive self-relevant stereotype (e.g., college students are good at math) increased the relative accessibility of females' college student identity and inhibited their gender identity, eliminating attendant working memory deficits and contingent math performance decrements. Furthermore, subtle manipulations in questions presented in the demographic section of a math test eliminated stereotype threat effects that result from women reporting their gender before completing the test. This work identifies the motivated processes through which people's social identities became active in situations in which self-relevant stereotypes about a stigmatized group membership and a nonstigmatized group membership were available. In addition, it demonstrates the downstream consequences of this pattern of activation on working memory and performance.

Question: How does stereotype threat work?


Kilobot: A low cost robot with scalable operations designed for collective behaviors. In current robotics research there is a vast body of work on algorithms and control methods for groups of decentralized cooperating robots, called a swarm or collective. These algorithms are generally meant to control collectives of hundreds or even thousands of robots; however, for reasons of cost, time, or complexity, they are generally validated in simulation only, or on a group of a few tens of robots. To address this issue, this paper presents Kilobot, an open-source, low cost robot designed to make testing collective algorithms on hundreds or thousands of robots accessible to robotics researchers. To enable the possibility of large Kilobot collectives where the number of robots is an order of magnitude larger than the largest that exist today, each robot is made with only $14 worth of parts and takes 5 min to assemble. Furthermore, the robot design allows a single user to easily operate a large Kilobot collective, such as programming, powering on, and charging all robots, which would be difficult or impossible to do with many existing robotic systems. We demonstrate the capabilities of the Kilobot as a collective robot, by using a small robot test collective to implement four popular swarm behaviors: foraging, formation control, phototaxis, and synchronization. © 2013 Elsevier B.V. All rights reserved.

Question: What is a low cost robot?


An Anatomical Analysis of the Supratrochlear Artery: Considerations in Facial Filler Injections and Preventing Vision Loss.. BACKGROUND Embolia cutis medicamentosa (ECM) is a rare phenomenon attributed to intra-arterial drug injection. Glabellar filler injections can result in potentially devastating visual loss from inadvertent retrograde arteriolar embolization due to the extensive vasculature within the upper face. The minimum amount of filler necessary to potentiate this complication has not yet been reported.   OBJECTIVES We aim to determine the volume of filler necessary to occupy the supratrochlear artery from the glabella to the bifurcation of the ophthalmic and central retinal arteries. We specifically examine the volume of the supratrochlear artery from the glabella to orbital apex.   METHODS The study was approved by Duke University Institutional Review Board and involved surgical dissection of six fresh tissue cadaver heads (12 hemifaces). The arterial system in each cadaver head was injected with latex for visualization. The supratrochlear arteries were isolated anteriorly from the glabella to the orbital apex posteriorly. Intra-orbital vessel radius and length were measured. The vessel volume was calculated by water displacement of the intra-arterial latex.   RESULTS The vessel volumes ranged from 0.04 to 0.12 mL. The average vessel volume was calculated as 0.085 mL, the average length as 51.75 mm, and the average radius as 0.72 mm.   CONCLUSIONS Vascular occlusion from filler injections can lead to devastating visual consequences due to inadvertent retrograde intra-arterial embolization. Our findings indicate that the average entire volume of the supratrochlear artery from the glabella to the orbital apex is 0.085 mL. Injectors should be aware that a bolus of this critical volume may lead to a significant adverse outcome.

Question: What is the volume of the supratrochlear artery?


Learning Subjective Adjectives from Corpora. Subjectivity taggingis distinguishing sentences used to present opinions and evaluations from sentences used to objectively present factual information. There are numerous applications for which subjectivity tagging is relevant, including information extraction and information retrieval. This paper identifies strong clues of subjectivity using the results of a method for clustering words according to distributional similarity (Lin 1998), seeded by a small amount of detailed manual annotation. These features are then further refined with the addition of lexical semantic features of adjectives, specifically polarityandgradability(Hatzivassiloglou & McKeown 1997), which can be automatically learned from corpora. In 10-fold cross validation experiments, features based on both similarity clusters and the lexical semantic features are shown to have higher precision than features based on each alone.

Question: What is the difference between a subjectivity and an objectivity?


Getting users to pay attention to anti-phishing education: evaluation of retention and transfer. Educational materials designed to teach users not to fall for phishing attacks are widely available but are often ignored by users. In this paper, we extend an embedded training methodology using learning science principles in which phishing education is made part of a primary task for users. The goal is to motivate users to pay attention to the training materials. In embedded training, users are sent simulated phishing attacks and trained after they fall for the attacks. Prior studies tested users immediately after training and demonstrated that embedded training improved users' ability to identify phishing emails and websites. In the present study, we tested users to determine how well they retained knowledge gained through embedded training and how well they transferred this knowledge to identify other types of phishing emails. We also compared the effectiveness of the same training materials delivered via embedded training and delivered as regular email messages. In our experiments, we found that: (a) users learn more effectively when the training materials are presented after users fall for the attack (embedded) than when the same training materials are sent by email (non-embedded); (b) users retain and transfer more knowledge after embedded training than after non-embedded training; and (c) users with higher Cognitive Reflection Test (CRT) scores are more likely than users with lower CRT scores to click on the links in the phishing emails from companies with which they have no account.

Question: How do you evaluate the effectiveness of anti-phishing education?


An introduction to modern missing data analyses.. A great deal of recent methodological research has focused on two modern missing data analysis methods: maximum likelihood and multiple imputation. These approaches are advantageous to traditional techniques (e.g. deletion and mean imputation techniques) because they require less stringent assumptions and mitigate the pitfalls of traditional techniques. This article explains the theoretical underpinnings of missing data analyses, gives an overview of traditional missing data techniques, and provides accessible descriptions of maximum likelihood and multiple imputation. In particular, this article focuses on maximum likelihood estimation and presents two analysis examples from the Longitudinal Study of American Youth data. One of these examples includes a description of the use of auxiliary variables. Finally, the paper illustrates ways that researchers can use intentional, or planned, missing data to enhance their research designs.

Question: What is missing data?


Pedestrian detection and tracking with night vision. This paper presents a method for pedestrian detection and tracking using a single night-vision video camera installed on the vehicle. To deal with the nonrigid nature of human appearance on the road, a two-step detection/tracking method is proposed. The detection phase is performed by a support vector machine (SVM) with size-normalized pedestrian candidates and the tracking phase is a combination of Kalman filter prediction and mean shift tracking. The detection phase is further strengthened by information obtained by a road-detection module that provides key information for pedestrian validation. Experimental comparisons (e.g., grayscale SVM recognition versus binary SVM recognition and entire-body detection versus upper-body detection) have been carried out to illustrate the feasibility of our approach.

Question: How to detect pedestrians using night vision?


Hardware, Design and Implementation Issues on a Fpga-Based Smart Camera. Processing images to extract useful information in real-time is a complex task, dealing with large amounts of iconic data and requiring intensive computation. Smart cameras use embedded processing to save the host system from the low-level processing load and to reduce communication flows and overheads. Field programmable devices present special interest for smart cameras design: flexibility, reconfigurability and parallel processing skills are some specially important features. In this paper we present a FPGA-based smart camera research platform. The hardware architecture is described, and some design issues are discussed. Our goal is to use the possibility to reconfigure the FPGA device in order to adapt the system architecture to a given application. To that, a design methodology, based on pre-programmed processing elements, is proposed and sketched. Some implementation issues are discussed and a template tracking application is given as example, with its experimental results.

Question: What is a smart camera?


A novel high-gain quasi-Yagi antenna with a parabolic reflector. The simplicity and intuitive design of traditional planar printed quasi-Yagi antennas has led to its widespread popularity for its good directivity. In this paper, a novel quasi-Yagi antenna with a single director and a concave parabolic reflector, operating in S-band, is proposed. The impedance characteristic and radiation characteristic are simulated with CST-Microwave Studio, and the antenna is fabricated and measured. The measured results indicate that the antenna which can operate at 2.28-2.63GHz can achieve an average gain of 6.5dBi within the operating frequency range, especially a highest gain of 7.5dBi at 2.5GHz. The proposed antenna can be widely used in WLAN/TD-LTE/BD1 and so on.

Question: What is the name of the antenna?


Emotional responses to pleasant and unpleasant music correlate with activity in paralimbic brain regions. Neural correlates of the often-powerful emotional responses to music are poorly understood. Here we used positron emission tomography to examine cerebral blood flow (CBF) changes related to affective responses to music. Ten volunteers were scanned while listening to six versions of a novel musical passage varying systematically in degree of dissonance. Reciprocal CBF covariations were observed in several distinct paralimbic and neocortical regions as a function of dissonance and of perceived pleasantness/unpleasantness. The findings suggest that music may recruit neural mechanisms similar to those previously associated with pleasant/unpleasant emotional states, but different from those underlying other components of music perception, and other emotions such as fear.

Question: What is the neural basis of music?


Bend, stretch, and touch: Locating a finger on an actively deformed transparent sensor array. The development of bendable, stretchable, and transparent touch sensors is an emerging technological goal in a variety of fields, including electronic skin, wearables, and flexible handheld devices. Although transparent tactile sensors based on metal mesh, carbon nanotubes, and silver nanowires demonstrate operation in bent configurations, we present a technology that extends the operation modes to the sensing of finger proximity including light touch during active bending and even stretching. This is accomplished using stretchable and ionically conductive hydrogel electrodes, which project electric field above the sensor to couple with and sense a finger. The polyacrylamide electrodes are embedded in silicone. These two widely available, low-cost, transparent materials are combined in a three-step manufacturing technique that is amenable to large-area fabrication. The approach is demonstrated using a proof-of-concept 4 × 4 cross-grid sensor array with a 5-mm pitch. The approach of a finger hovering a few centimeters above the array is readily detectable. Light touch produces a localized decrease in capacitance of 15%. The movement of a finger can be followed across the array, and the location of multiple fingers can be detected. Touch is detectable during bending and stretch, an important feature of any wearable device. The capacitive sensor design can be made more or less sensitive to bending by shifting it relative to the neutral axis. Ultimately, the approach is adaptable to the detection of proximity, touch, pressure, and even the conformation of the sensor surface.

Question: How to detect a finger hovering a few centimeters above a sensor array?


Listening to music and physiological and psychological functioning: the mediating role of emotion regulation and stress reactivity.. Music listening has been suggested to have short-term beneficial effects. The aim of this study was to investigate the association and potential mediating mechanisms between various aspects of habitual music-listening behaviour and physiological and psychological functioning. An internet-based survey was conducted in university students, measuring habitual music-listening behaviour, emotion regulation, stress reactivity, as well as physiological and psychological functioning. A total of 1230 individuals (mean = 24.89 ± 5.34 years, 55.3% women) completed the questionnaire. Quantitative aspects of habitual music-listening behaviour, i.e. average duration of music listening and subjective relevance of music, were not associated with physiological and psychological functioning. In contrast, qualitative aspects, i.e. reasons for listening (especially 'reducing loneliness and aggression', and 'arousing or intensifying specific emotions') were significantly related to physiological and psychological functioning (all p = 0.001). These direct effects were mediated by distress-augmenting emotion regulation and individual stress reactivity. The habitual music-listening behaviour appears to be a multifaceted behaviour that is further influenced by dispositions that are usually not related to music listening. Consequently, habitual music-listening behaviour is not obviously linked to physiological and psychological functioning.

Question: What is the relationship between music listening and physiological and psychological functioning?


A review and outlook for a 'Building Information Model' (BIM): A multi-standpoint framework for technological development. 1474-0346/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.aei.2010.06.003 * Tel.: +386 1 4768 521; fax: +386 1 4250 681. E-mail address: tomo.cerovsek@fgg.uni-lj.si This study provides a review of important issues for 'Building Information Modelling' (BIM) tools and standards and comprehensive recommendations for their advancement and development that may improve BIM technologies and provide a basis for inter-operability, integration, model-based communication, and collaboration in building projects. Based on a critical review of Building Product Modelling, including the development of standards for exchange and the features of over 150 AEC/O (Architecture, Engineering, Construction, and Operation) tools and digital models, a methodological framework is proposed for improvements to both BIM tools and schemata. The features relevant to the framework were studied using a conceptual process model and a 'BIM System-of-Systems' (BIM-SoS) model. The development, implementation, and use of the BIM Schema are analysed from the standpoint of standardisation. The results embrace the requirements for a BIM research methodology, with an example of methods and procedures, an R&D review with critique, and a multi-standpoint framework for developments with concrete recommendations, supported by BIM metrics, upon which the progress of tools, models, and standards may be measured, evaluated, streamlined, and judged. It is also proposed that any BIM Schema will never be 'completed' but should be developed as evolutionary ontology by 'segmented standpoint models' to better account for evolving tools and AEC/O practices. 2010 Elsevier Ltd. All rights reserved.

Question: What is a building information model?


Service Oriented Architecture based connectivity of automotive ECUs. Modern automotive industry uses embedded systems and corresponding Electronic Control Units (ECU) for a wide variety of purposes from entertainment to vehicle control. The use of these embedded systems became crucial to an extent that the performance of these embedded systems can severely affect the performance of the automobile. So the industry tries to develop new and efficient systems periodically, to improve the performance of the whole system. As the number of ECUs in an automobile increased, new technologies were developed to interconnect these ECUs. As a result of much efficient ECUs, the data produced by these ECUs and the data to be transmitted between these ECUs increased to a very large scale. The industry tried to select new connectivity solutions which provide higher bandwidth. The automotive embedded system industry thus ended up in using the well accepted and highly standardized Ethernet connectivity. This paved the way to develop new technologies for automotive embedded system. Service Oriented Architecture (SOA) has proved to be an efficient and flexible software design paradigm for high level systems. The main factors behind its success were the amount of scalability and reusability provided by this platform. The usage of SOA over the automotive Ethernet can provide the benefit of SOA to the automotive embedded world. Such an approach needs a deep feasibility study to identify whether such integration is possible, and to discover architectures and protocols which support it.

Question: What is the difference between SOA and SOX?


tress and anxiety detection using facial cues from videos. This study develops a framework for the detection and analysis of stress/anxiety emotional states through video-recorded facial cues. A thorough experimental protocol was established to induce systematic variability in affective states (neutral, relaxed and stressed/anxious) through a variety of external and internal stressors. The analysis was focused mainly on non-voluntary and semi-voluntary facial cues in order to estimate the emotion representation more objectively. Features under investigation included eye-related events, mouth activity, head motion parameters and heart rate estimated through camerabased photoplethysmography. A feature selection procedure was employed to select the most robust features followed by classification schemes discriminating between stress/anxiety and neutral states nxiety tress link rate ead motion with reference to a relaxed state in each experimental phase. In addition, a ranking transformation was proposed utilizing self reports in order to investigate the correlation of facial parameters with a participant perceived amount of stress/anxiety. The results indicated that, specific facial cues, derived from eye activity, mouth activity, head movements and camera based heart activity achieve good accuracy and are suitable as discriminative indicators of stress and anxiety.

Question: How can we detect stress and anxiety in videos?


Accelerating computer vision algorithms using OpenCL framework on the mobile GPU - A case study. Recently, general-purpose computing on graphics processing units (GPGPU) has been enabled on mobile devices thanks to the emerging heterogeneous programming models such as OpenCL. The capability of GPGPU on mobile devices opens a new era for mobile computing and can enable many computationally demanding computer vision algorithms on mobile devices. As a case study, this paper proposes to accelerate an exemplar-based inpainting algorithm for object removal on a mobile GPU using OpenCL. We discuss the methodology of exploring the parallelism in the algorithm as well as several optimization techniques. Experimental results demonstrate that our optimization strategies for mobile GPUs have significantly reduced the processing time and make computationally intensive computer vision algorithms feasible for a mobile device. To the best of the authors' knowledge, this work is the first published implementation of general-purpose computing using OpenCL on mobile GPUs.

Question: What is the first large-scale Canadian peacekeeping mission?


Decision Trees: Theory and Algorithms. 4.

Question: What is decision trees?


Adoption and Impact of IT Governance and Management Practices: A COBIT 5 Perspective. This paper empirically investigates how adoption of IT governance and management processes, as identified in the IT governance framework COBIT 5, relates to the level of IT-related goals achievement, which in turn associates to the level of enterprise goals achievement. Simultaneously, this research project provides an international benchmark on how organizations are currently adopting the governance and management processes as identified in COBIT 5. The findings suggest that organizations are best in adopting the "IT factory" related management processes and that implementation scores drop in management and governance processes when more business and board involvement is required. Additionally, there are significant differences in perceived implementation maturity of COBIT 5 processes between SMEs and larger organizations. Also, the data offers empirical evidence that the COBIT 5 processes have a positive association with enterprise value creation. KeywORdS COBIT 5, Enterprise Goals, Enterprise Governance and Management of IT, IT Governance, SMEs

Question: What is the difference between enterprise goals and enterprise value creation?


Fine-grained change detection in structured text documents. Detecting and understanding changes between document revisions is an important task. The acquired knowledge can be used to classify the nature of a new document revision or to support a human editor in the review process. While purely textual change detection algorithms offer fine-grained results, they do not understand the syntactic meaning of a change. By representing structured text documents as XML documents we can apply tree-to-tree correction algorithms to identify the syntactic nature of a change.  Many algorithms for change detection in XML documents have been propsed but most of them focus on the intricacies of generic XML data and emphasize speed over the quality of the result. Structured text requires a change detection algorithm to pay close attention to the content in text nodes, however, recent algorithms treat text nodes as black boxes.  We present an algorithm that combines the advantages of the purely textual approach with the advantages of tree-to-tree change detection by redistributing text from non-overlapping common substrings to the nodes of the trees. This allows us to not only spot changes in the structure but also in the text itself, thus achieving higher quality and a fine-grained result in linear time on average. The algorithm is evaluated by applying it to the corpus of structured text documents that can be found in the English Wikipedia.

Question: What is the difference between a tree-to-tree and a tree-to-text algorithm?


Mining knowledge-sharing sites for viral marketing. Viral marketing takes advantage of networks of influence among customers to inexpensively achieve large changes in behavior. Our research seeks to put it on a firmer footing by mining these networks from data, building probabilistic models of them, and using these models to choose the best viral marketing plan. Knowledge-sharing sites, where customers review products and advise each other, are a fertile source for this type of data mining. In this paper we extend our previous techniques, achieving a large reduction in computational cost, and apply them to data from a knowledge-sharing site. We optimize the amount of marketing funds spent on each customer, rather than just making a binary decision on whether to market to him. We take into account the fact that knowledge of the network is partial, and that gathering that knowledge can itself have a cost. Our results show the robustness and utility of our approach.

Question: What is a knowledge-sharing site?


Bilayer Phosphorene: Effect of Stacking Order on Bandgap and Its Potential Applications in Thin-Film Solar Cells.. Phosphorene, a monolayer of black phosphorus, is promising for nanoelectronic applications not only because it is a natural p-type semiconductor but also because it possesses a layer-number-dependent direct bandgap (in the range of 0.3 to 1.5 eV). On basis of the density functional theory calculations, we investigate electronic properties of the bilayer phosphorene with different stacking orders. We find that the direct bandgap of the bilayers can vary from 0.78 to 1.04 eV with three different stacking orders. In addition, a vertical electric field can further reduce the bandgap to 0.56 eV (at the field strength 0.5 V/Å). More importantly, we find that when a monolayer of MoS2 is superimposed with the p-type AA- or AB-stacked bilayer phosphorene, the combined trilayer can be an effective solar-cell material with type-II heterojunction alignment. The power conversion efficiency is predicted to be ∼18 or 16% with AA- or AB-stacked bilayer phosphorene, higher than reported efficiencies of the state-of-the-art trilayer graphene/transition metal dichalcogenide solar cells.

Question: What is the bandgap of bilayer phosphorene?


Path-tracking for autonomous vehicles at the limit of friction. The ability to use all of the available tire force is essential to road vehicles for emergency maneuvers and racing. As the front tires of an understeering vehicle saturate while cornering at the limit of tire-road friction, steering is lost as a control input for path-tracking. Experimental data from an autonomous vehicle show that for path-tracking at the limit of friction through steering the value of friction needs to be known to within approximately 2%. This requirement exceeds the capabilities of existing real-time friction estimation algorithms. Data collected with a professional race car driver inspire a novel control framework, with a slip angle-based control strategy of maintaining the front tires at the slip angle for which maximum tire force is attained, and longitudinal speed control for path-tracking. This approach has significantly less demanding requirements on the accuracy of friction estimation. A controller is presented to explore this concept, and experimental results demonstrate successful tracking of a circular path at the friction limit without a priori friction information.

Question: How to control a car at the limit of friction?


UNFOLD: a memory-efficient speech recognizer using on-the-fly WFST composition. Accurate, real-time Automatic Speech Recognition (ASR) requires huge memory storage and computational power. The main bottleneck in state-of-the-art ASR systems is the Viterbi search on a Weighted Finite State Transducer (WFST). The WFST is a graph-based model created by composing an Acoustic Model (AM) and a Language Model (LM) offline. Offline composition simplifies the implementation of a speech recognizer as only one WFST has to be searched. However, the size of the composed WFST is huge, typically larger than a Gigabyte, resulting in a large memory footprint and memory bandwidth requirements.  In this paper, we take a completely different approach and propose a hardware accelerator for speech recognition that composes the AM and LM graphs on-the-fly. In our ASR system, the fully-composed WFST is never generated in main memory. On the contrary, only the subset required for decoding each input speech fragment is dynamically generated from the AM and LM models. In addition to the direct benefits of this on-the-fly composition, the resulting approach is more amenable to further reduction in storage requirements through compression techniques.  The resulting accelerator, called UNFOLD, performs the decoding in real-time using the compressed AM and LM models, and reduces the size of the datasets from more than one Gigabyte to less than 40 Megabytes, which can be very important in small form factor mobile and wearable devices.  Besides, UNFOLD improves energy-efficiency by orders of magnitude with respect to CPUs and GPUs. Compared to a state-of-the-art Viterbi search accelerators, the proposed ASR system outperforms by providing 31x reduction in memory footprint and 28% energy savings on average.

Question: What is the size of the canadian military?


pVM: persistent virtual memory for efficient capacity scaling and object storage. Next-generation byte-addressable nonvolatile memories (NVMs), such as phase change memory (PCM) and Memristors, promise fast data storage, and more importantly, address DRAM scalability issues. State-of-the-art OS mechanisms for NVMs have focused on improving the block-based virtual file system (VFS) to manage both persistence and the memory capacity scaling needs of applications. However, using the VFS for capacity scaling has several limitations, such as the lack of automatic memory capacity scaling across DRAM and NVM, inefficient use of the processor cache and TLB, and high page access costs. These limitations reduce application performance and also impact applications that use NVM for persistent object storage with flat namespaces, such as photo stores, NoSQL databases, and others.  To address such limitations, we propose persistent virtual memory (pVM), a system software abstraction that provides applications with (1) automatic OS-level memory capacity scaling, (2) flexible memory placement policies across NVM, and (3) fast object storage. pVM extends the OS virtual memory (VM) instead of building on the VFS and abstracts NVM as a NUMA node with support for NVM-based memory placement mechanisms. pVM inherits benefits from the cache and TLB-efficient VM subsystem and augments these further by distinguishing between persistent and nonpersistent capacity use of NVM. Additionally, pVM achieves fast persistent storage by further extending the VM subsystem with consistent and durable OS-level persistent metadata. Our evaluation of pVM with memory capacity-intensive applications shows a 2.5x speedup and up to 80% lower TLB and cache misses compared to VFS-based systems. pVM's object store provides 2x higher throughput compared to the block-based approach of the state-of-the art solution and up to a 4x reduction in the time spent in the OS.

Question: What is the difference between persistent and nonpersistent memory?


MieLog: A Highly Interactive Visual Log Browser Using Information Visualization and Statistical Analysis. System administration has become an increasingly important function, with the fundamental task being the inspection of computer log-files. It is not, however, easy to perform such tasks for two reasons. One is the high recognition load of log contents due to the massive amount of textual data. It is a tedious, time-consuming and often error-prone task to read through them. The other problem is the difficulty in extracting unusual messages from the log. If an administrator does not have the knowledge or experience, he or she cannot readily recognize unusual log messages. To help address these issues, we have developed a highly interactive visual log browser called ''MieLog.'' MieLog uses two techniques for manual log inspection tasks: information visualization and statistical analysis. Information visualization is helpful in reducing the recognition load because it provides an alternative method of interpreting textual information without reading. Statistical analysis enables the extraction of unusual log messages without domain specific knowledge. We will give three examples that illustrate the ability of the MieLog system to isolate unusual messages more easily than before.

Question: What is the name of the system?


Active SLAM using Model Predictive Control and Attractor based Exploration. Active SLAM poses the challenge for an autonomous robot to plan efficient paths simultaneous to the SLAM process. The uncertainties of the robot, map and sensor measurements, and the dynamic and motion constraints need to be considered in the planning process. In this paper, the active SLAM problem is formulated as an optimal trajectory planning problem. A novel technique is introduced that utilises an attractor combined with local planning strategies such as model predictive control (a.k.a. receding horizon) to solve this problem. An attractor provides high level task intentions and incorporates global information about the environment for the local planner, thereby eliminating the need for costly global planning with longer horizons. It is demonstrated that trajectory planning with an attractor results in improved performance over systems that have local planning alone

Question: What is the active SLAM problem?


RSSI informed phase method for distance calculations. In an attempt to find an accurate, environmentally robust, and fast process for RFID distance estimations, a method was developed called RSSI Informed Phase. This method uses both phase and RSSI measurements to estimate the distance, and potentially location, of a moving RFID tag. RSSI is initially used to find an approximate reader to tag separation distance. This distance value is applied to find an approximate slope of the phase angle vs. frequency curve. The estimated slope can inform the phase distance calculation, meaning fewer reads are required to find the actual phase angle vs. frequency slope. The reduction in the number of necessary reads accelerates the localization process and makes this method more robust for dynamic environments.

Question: What is RSSI?


Rainy weather recognition from in-vehicle camera images for driver assistance. We propose a weather recognition method from in-vehicle camera images that uses a subspace method to judge rainy weather by detecting raindrops on the windshield. "Eigendrops" represent the principal components extracted from raindrop images in the learning stage. Then the method detects raindrops by template matching. In experiments using actual video sequences, our method showed good detection ability of raindrops and promising results for rainfall judgment from detection results.

Question: How to detect raindrops from in-vehicle camera images?


Light propagation with phase discontinuities: generalized laws of reflection and refraction.. Conventional optical components rely on gradual phase shifts accumulated during light propagation to shape light beams. New degrees of freedom are attained by introducing abrupt phase changes over the scale of the wavelength. A two-dimensional array of optical resonators with spatially varying phase response and subwavelength separation can imprint such phase discontinuities on propagating light as it traverses the interface between two media. Anomalous reflection and refraction phenomena are observed in this regime in optically thin arrays of metallic antennas on silicon with a linear phase variation along the interface, which are in excellent agreement with generalized laws derived from Fermat's principle. Phase discontinuities provide great flexibility in the design of light beams, as illustrated by the generation of optical vortices through use of planar designer metallic interfaces.

Question: What is the difference between reflection and refraction?


Static secure page allocation for light-weight dynamic information flow tracking. Dynamic information flow tracking (DIFT) is an effective security countermeasure for both low-level memory corruptions and high-level semantic attacks. However, many software approaches suffer large performance degradation, and hardware approaches have high logic and storage overhead. We propose a flexible and light-weight hardware/software co-design approach to perform DIFT based on secure page allocation. Instead of associating every data with a taint tag, we aggregate data according to their taints, i.e., putting data with different attributes in separate memory pages. Our approach is a compiler-aided process with architecture support. The implementation and analysis show that the memory overhead is little, and our approach can protect critical information, including return address, indirect jump address, and system call IDs, from being overwritten by malicious users.

Question: What is the overhead of secure page allocation?


Using the h-index to rank influential information scientists. We apply a new bibliometric measure, the h-index (Hirsch, 2005), to the literature of information science. Faculty rankings based on raw citation counts are compared with those based on h-counts. There is a strong positive correlation between the two sets of rankings. We show how the h-index can be used to express the broad impact of a scholar's research output over time in more nuanced fashion than straight citation counts.

Question: How can I use the h-index to rank influential information scientists?


Towards a Self-Organized Agent-Based Simulation Model for Exploration of Human Synaptic Connections. In this paper, the early design of our selforganized agent-based simulation model for exploration of synaptic connections that faithfully generates what is observed in natural situation is given. While we take inspiration from neuroscience, our intent is not to create a veridical model of processes in neurodevelopmental biology, nor to represent a real biological system. Instead, our goal is to design a simulation model that learns acting in the same way of human nervous system by using findings on human subjects using reflex methodologies in order to estimate unknown connections.

Question: What is the purpose of the model?


Text Encryption with Huffman Compression. Communication between a sender and receiver needs security. It can be done in any form, like plain text or binary data. Changing the information to some unidentifiable form, can save it from assailants. For example, plain text can be coded using schemes so that a stranger cannot apprehend it. Cryptography is a subject or field which deals with the secret transmission of messages/ data between two parties. Cryptography is the practice and study of hiding information. It enables you to store delicate information and transmit it across insecure networks so that it cannot be read by anyone except the authorized recipient. Applications of cryptography include ATM cards, computer passwords, and electronic commerce etc. Messages, which are being sent between two parties, should be of small size so that they occupy less space. Data compression involves encoding of information using fewer bits than the original representation. Compression algorithms reduce the redundancy in data representation to decrease the storage required for that data. Symmetric key cryptography algorithms [6] are fast and mostly used type of encryption. Many types of data compression techniques do exist. Huffman Compression [5] is a lossless data compression technique and can be considered as the most efficient algorithm. In this paper, a combination of new Symmetric key algorithm and existing Huffman compression algorithm has been proposed. Proposed method works on text data. Algorithms have been provided in the paper itself.

Question: What is the difference between symmetric and asymmetric encryption?


Non-sentential Question Resolution using Sequence to Sequence Learning. An interactive Question Answering (QA) system frequently encounters non-sentential (incomplete) questions. These non-sentential questions may not make sense to the system when a user asks them without the context of conversation. The system thus needs to take into account the conversation context to process the incomplete question. In this work, we present a recurrent neural network (RNN) based encoder decoder network that can generate a complete (intended) question, given an incomplete question and conversation context. RNN encoder decoder networks have been show to work well when trained on a parallel corpus with millions of sentences, however it is extremely hard to obtain conversation data of this magnitude. We therefore propose to decompose the original problem into two separate simplified problems where each problem focuses on an abstraction. Specifically, we train a semantic sequence model to learn semantic patterns, and a syntactic sequence model to learn linguistic patterns. We further combine syntactic and semantic sequence models to generate an ensemble model. Our model achieves a BLEU score of 30.15 as compared to 18.54 using a standard RNN encoder decoder model.

Question: What is a non-sentential question?


Synchronization and cell search algorithms in 3GPP long term evolution systems (FDD mode). In this paper initial downlink synchronization (sync) and cell identification algorithms for the 3rd generation partnership project (3GPP) long term evolution (LTE) systems are presented. The frequency division duplex (FDD) mode is used in the downlink radio frame structure. A user equipment digital receiver architecture is proposed. The orthogonality of signals in orthogonal frequency division multiplexing (OFDM) may be lost due to some impairments such as frequency, time, and phase offsets. Therefore, major parts of this research are involved in restoring the orthogonality at the user equipment (UE) by using some techniques to estimate the sync parameters, and to detect cell identity among candidate cells based on sync signals. A Farrow structure interpolator is used to compensate the fractional timing offset. Both inter site synchronous and inter site asynchronous networks are presented. Computer simulations are used to demonstrate the performance of the proposed schemes with multipath Rayleigh fading channel, frequency offsets, timing offsets, and additive white Gaussian noise (AWGN). Results show high probability of cell identification in a very short time 20 ms in both multi cell model scenarios, especially when multiple input multiple output (MIMO) technique, oversampling at the UE, and high order Farrow structure interpolator are used. Key-Words: 3GPP LTE, OFDM, MIMO, Frequency and Time Synchronization, Cell Search

Question: How to synchronize and cell search in 3GPP LTE?


Composite Connectors for Composing Software Components. In a component-based system, connectors are used to compose components. Connectors should have a semantics that makes them simple to construct and use. At the same time, their semantics should be rich enough to endow them with desirable properties such as genericity, compositionality and reusability. For connector construction, compositionality would be particularly useful, since it would facilitate systematic construction. In this paper we describe a hierarchical approach to connector definition and construction that allows connectors to be defined and constructed from sub-connectors. These composite connectors are indeed generic, compositional and reusable. They behave like design patterns, and provide powerful composition connectors.

Question: What is a connector?


Rationale-Augmented Convolutional Neural Networks for Text Classification. We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their constituent sentences. Specifically, we consider scenarios in which annotators explicitly mark sentences (or snippets) that support their overall document categorization, i.e., they provide rationales. Our model exploits such supervision via a hierarchical approach in which each document is represented by a linear combination of the vector representations of its component sentences. We propose a sentence-level convolutional model that estimates the probability that a given sentence is a rationale, and we then scale the contribution of each sentence to the aggregate document representation in proportion to these estimates. Experiments on five classification datasets that have document labels and associated rationales demonstrate that our approach consistently outperforms strong baselines. Moreover, our model naturally provides explanations for its predictions.

Question: What is the rationales for the document?