Integrating constraints and metric learning in semi-supervised clustering. Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has utilized supervised data in one of two approaches: 1) constraint-based methods that guide the clustering algorithm towards a better grouping of the data, and 2) distance-function learning methods that adapt the underlying similarity metric used by the clustering algorithm. This paper provides new methods for the two approaches as well as presents a new semi-supervised clustering algorithm that integrates both of these techniques in a uniform, principled framework. Experimental results demonstrate that the unified approach produces better clusters than both individual approaches as well as previously proposed semi-supervised clustering algorithms.

Question: What is the difference between constraint-based and distance-function learning methods?


Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN. Text classification to a hierarchical taxonomy of topics is a common and practical problem. Traditional approaches simply use bag-of-words and have achieved good results. However, when there are a lot of labels with different topical granularities, bagof-words representation may not be enough. Deep learning models have been proven to be effective to automatically learn different levels of representations for image data. It is interesting to study what is the best way to represent texts. In this paper, we propose a graph-CNN based deep learning model to first convert texts to graph-of-words, and then use graph convolution operations to convolve the word graph. Graph-ofwords representation of texts has the advantage of capturing non-consecutive and long-distance semantics. CNN models have the advantage of learning different level of semantics. To further leverage the hierarchy of labels, we regularize the deep architecture with the dependency among labels. Our results on both RCV1 and NYTimes datasets show that we can significantly improve large-scale hierarchical text classification over traditional hierarchical text classification and existing deep models.

Question: What is the best way to represent texts?


Topics in semantic representation.. Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. This article analyzes the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. This leads to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics. The topic model performs well in predicting word association and the effects of semantic association and ambiguity on a variety of language-processing and memory tasks. It also provides a foundation for developing more richly structured statistical models of language, as the generative process assumed in the topic model can easily be extended to incorporate other kinds of semantic and syntactic structure.

Question: What is the abstract computational problem underlying the extraction and use of gist?


Deformation Constraints in a Mass-Spring Model to Describe Rigid Cloth Behavior. This paper describes a physically-based model for animating cloth objects, derived from elastically deformable models, and improved in order to take into account the non-elastic properties of woven fabrics . A cloth object is first approximated to a deformable surface composed of a network of masses and springs, the movement of which is evaluated using the numerical integration of the fundamental law of dynamics. We show that when a concentration of high stresses occurs in a small region of the surface, the local deformation becomes unrealistic compared to real deformations of textiles . With such an elastic model , the only solution to decrease these deformations has been so far to increase the stiffness of the deformed springs, but we show that it dramatically increases the cost of the algorithm. We present therefore a new method to adapt our model to the particularly stiff properties of textiles, inspired from dynamic inverse procedures. R esume Cet article decrit un modele physique d'animation des tissus, variante des modeles elastiques deformables, et ameliore de fa<;on a prendre en compte les proprietes non elastiques des textiles . Nous modelisons tout d 'abord une piece de tissu par une surface deformable, constituee d 'un reseau de masses et de ressorts. Son mouvement est evalue grace a I'integration numerique de la loi fondamentale de la dynamique. Nous montrons que lorsqu'une forte concentration de contraintes apparait a certains endroits de la surface, la deformation locale y devient irrealiste comparee aux deformations rencontrees dans les tissus reels . A vec un tel modele elastique, la seule solution permettant d'attenuer cette deformation etait jusqu'a present d 'augmenter la raideur des ressorts deformes, mais nous montrons que ceci faisait croitre dramatiquement le cout de I'algorithme. Nous presentons donc ici une nouvelle methode permettant d 'adapter notre mode le a

Question: What is the cost of the algorithm?


Software traceability: trends and future directions. Software traceability is a sought-after, yet often elusive quality in software-intensive systems. Required in safety-critical systems by many certifying bodies, such as the USA Federal Aviation Authority, software traceability is an essential element of the software development process. In practice, traceability is often conducted in an ad-hoc, after-the-fact manner and, therefore, its benefits are not always fully realized. Over the past decade, researchers have focused on specific areas of the traceability problem, developing more sophisticated tooling, promoting strategic planning, applying information retrieval techniques capable of semi-automating the trace creation and maintenance process, developing new trace query languages and visualization techniques that use trace links, and applying traceability in specific domains such as Model Driven Development, product line systems, and agile project environments. In this paper, we build upon a prior body of work to highlight the state-of-the-art in software traceability, and to present compelling areas of research that need to be addressed.

Question: What is the state of the art in software traceability?


Measuring user acceptance of emerging information technologies: an assessment of possible method biases. The measurement scales for the perceived usefulness and perceived ease of we constructs introduced by Davis [12/ have become widely used for forecasting user acceptance of emerging information technologies. An experiment was conducted to examine whether grouping of items caused artifactual inflation of reliability and validity measures. We found support for our hypothesis that the reliability and validity stemmed notfrom item grouping butfrom the constructs of perceived usefulness and perceived ease of use being clearly defined, and the items used co measure each of these consn-ucts clearly capturing the essence of the conscrucI.

Question: How do you measure user acceptance of emerging information technologies?


Designing Sketches for Similarity Filtering. The amounts of currently produced data emphasize the importance of techniques for efficient data processing. Searching big data collections according to similarity of data well corresponds to human perception. This paper is focused on similarity search using the concept of sketches – a compact bit string representations of data objects compared by Hamming distance, which can be used for filtering big datasets. The object-to-sketch transformation is a form of the dimensionality reduction and thus there are two basic contradictory requirements: (1) The length of the sketches should be small for efficient manipulation, but (2) longer sketches retain more information about the data objects. First, we study various sketching methods for data modeled by metric space and we analyse their quality. Specifically, we study importance of several sketch properties for similarity search and we propose a high quality sketching technique. Further, we focus on the length of sketches by studying mutual influence of sketch properties such as correlation of their bits and the intrinsic dimensionality of a set of sketches. The outcome is an equation that allows us to estimate a suitable length of sketches for an arbitrary given dataset. Finally, we empirically verify proposed approach on two real-life datasets.

Question: What is the length of a sketch?


Vector filtering for color imaging. Vector processing operations use essential spectral and spatial information to remove noise and localize microarray spots. The proposed fully automated vector technique can be easily implemented in either hardware or software; and incorporated in any existing microarray image analysis and gene expression tool.

Question: What is a vector?


A Better Method to Analyze Blockchain Consistency. The celebrated Nakamoto consensus protocol [16] ushered in several new consensus applications including cryptocurrencies. A few recent works [7, 17] have analyzed important properties of blockchains, including most significantly, consistency, which is a guarantee that all honest parties output the same sequence of blocks throughout the execution of the protocol. To establish consistency, the prior analysis of Pass, Seeman and Shelat [17] required a careful counting of certain combinatorial events that was difficult to apply to variations of Nakamoto. The work of Garay, Kiayas, and Leonardas [7] provides another method of analyzing the blockchain under the simplifying assumption that the network was synchronous. The contribution of this paper is the development of a simple Markov-chain based method for analyzing consistency properties of blockchain protocols. The method includes a formal way of stating strong concentration bounds as well as easy ways to concretely compute the bounds. We use our new method to answer a number of basic questions about consistency of blockchains: Our new analysis provides a tighter guarantee on the consistency property of Nakamoto's protocol, including for parameter regimes which [17] could not consider; We analyze a family of delaying attacks first presented in [17], and extend them to other protocols; We analyze how long a participant should wait before considering a high-value transaction "confirmed"; We analyze the consistency of CliqueChain, a variation of the Chainweb [14] system; We provide the first rigorous consistency analysis of GHOST [20] and also analyze a folklore "balancing"-attack. In each case, we use our framework to experimentally analyze the consensus bounds for various network delay parameters and adversarial computing percentages. We hope our techniques enable authors of future blockchain proposals to provide a more rigorous analysis of their schemes.

Question: What is the difference between Nakamoto and Nakamoto-like consensus protocols?


Incremental Spectral Clustering With Application to Monitoring of Evolving Blog Communities. In recent years, spectral clustering method has gained attentions because of its superior performance compared to other traditional clustering algorithms such as K-means algorithm. The existing spectral clustering algorithms are all off-line algorithms, i.e., they can not incrementally update the clustering result given a small change of the data set. However, the capability of incrementally updating is essential to some applications such as real time monitoring of the evolving communities of websphere or blogsphere. Unlike traditional stream data, these applications require incremental algorithms to handle not only insertion/deletion of data points but also similarity changes between existing items. This paper extends the standard spectral clustering to such evolving data by introducing the incidence vector/matrix to represent two kinds of dynamics in the same framework and by incrementally updating the eigenvalue system. Our incremental algorithm, initialized by a standard spectral clustering, continuously and efficiently updates the eigenvalue system and generates instant cluster labels, as the data set is evolving. The algorithm is applied to a blog data set. Compared with recomputation of the solution by standard spectral clustering, it achieves similar accuracy but with much lower computational cost. Close inspection into the blog content shows that the incremental approach can discover not only the stable blog communities but also the evolution of the individual multi-topic blogs.

Question: Incremental Spectral Clustering With Application to Monitoring of Evolving Blog Communities


The Cascade-Correlation Learning Architecture. Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology, Cascade-Correlation begins with a minimal network, then automatically trains and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights are frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detectors. The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network. This research was sponsored in part by the National Science Foundation under Contract Number EET-8716324 and by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 4976 under Contract F33615-87-C-1499 and monitored by: Avionics Laboratory, Air Force Wright Aeronautical Laboratories, Aeronautical Systems Division (AFSC), Wright-Patterson AFB, OH 45433-6543. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the US Government. 1. Why is Back-Propagation Learning So Slow? The Cascade-Correlation learning algorithm was developed in an attempt to overcome certain problems and limitations of the popular back-propagation (or "backprop") learning algorithm [Rumelhart, 1986]. The most important of these limitations is the slow pace at which backprop learns from examples. Even on simple benchmark problems, a back-propagation network may require many thousands o

Question: What is the difference between back-propagation and cascade-correlation?


A segmental CRF approach to large vocabulary continuous speech recognition. This paper proposes a segmental conditional random field framework for large vocabulary continuous speech recognition. Fundamental to this approach is the use of acoustic detectors as the basic input, and the automatic construction of a versatile set of segment-level features. The detector streams operate at multiple time scales (frame, phone, multi-phone, syllable or word) and are combined at the word level in the CRF training and decoding processes. A key aspect of our approach is that features are defined at the word level, and are naturally geared to explain long span phenomena such as formant trajectories, duration, and syllable stress patterns. Generalization to unseen words is possible through the use of decomposable consistency features [1], [2], and our framework allows for the joint or separate discriminative training of the acoustic and language models. An initial evaluation of this framework with voice search data from the Bing Mobile (BM) application results in a 2% absolute improvement over an HMM baseline.

Question: What is a segmental CRF?


A Recurrent Latent Variable Model for Sequential Data. In this paper, we explore the inclusion of latent random variables into the dynamic hidden state of a recurrent neural network (RNN) by combining elements of the variational autoencoder. We argue that through the use of high-level latent random variables, our variational RNN (VRNN) is able to learn to model the kind of variability observed in highly-structured sequential data (such as speech). We empirically evaluate the proposed model against related sequential models on five sequential datasets, four of speech and one of handwriting. Our results show the importance of the role that latent random variables can play in the RNN dynamic hidden state.

Question: What is a recurrent neural network?


Attention-Fused Deep Matching Network for Natural Language Inference. Natural language inference aims to predict whether a premise sentence can infer another hypothesis sentence. Recent progress on this task only relies on a shallow interaction between sentence pairs, which is insufficient for modeling complex relations. In this paper, we present an attention-fused deep matching network (AF-DMN) for natural language inference. Unlike existing models, AF-DMN takes two sentences as input and iteratively learns the attention-aware representations for each side by multi-level interactions. Moreover, we add a selfattention mechanism to fully exploit local context information within each sentence. Experiment results show that AF-DMN achieves state-of-the-art performance and outperforms strong baselines on Stanford natural language inference (SNLI), multigenre natural language inference (MultiNLI), and Quora duplicate questions datasets.

Question: What is the state-of-the-art performance on Stanford natural language inference?


Multiuser MIMO-OFDM for Next-Generation Wireless Systems. This overview portrays the evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station's or radio port's coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment in multiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems.

Question: What is the difference between OFDM and MIMO?


Balancing Performance, Energy, and Quality in Pervasive Computing. We describe Spectra, a remote execution system for batterypowered clients used in pervasive computing. Spectra enables applications to combine the mobility of small devices with the greater processing power of static compute servers. Spectra is self-tuning: it monitors both application resource usage and the availability of resources in the environment, and dynamically determines how and where to execute application components. In making this determination, Spectra balances the competing goals of performance, energy conservation, and application quality. We have validated Spectra's approach on the Compaq Itsy v2.2 and IBM ThinkPad 560X using a speech recognizer, a document preparation system, and a natural language translator. Our results confirm that Spectra almost always selects the best execution plan, and that its few suboptimal choices are very close to optimal.

Question: What is the name of the battery powered client?


A Memory Soft Error Measurement on Production Systems. Memory state can be corrupted by the impact of particles causing single-event upsets (SEUs). Understanding and dealing with these soft (or transient) errors is important for system reliability. Several earlier studies have p rovided field test measurement results on memory soft error rate, but no results were available for recent production computer systems. We believe the measurement results on real production systems are uniquely valuable due to various environmental effects. This paper presents methodologies for memory soft error measurement on production systems where performance impact on existing running applications must be negligible and the system administrativ e control might or might not be available. We conducted measurements in three distinct system environments: a rack-mounted server farm for a popular Internet service (Ask.com search engine), a set of office desktop computers (Univ. of Rochester), and a geographically distributed network testbed (PlanetLab). Our preliminary measurement on over 300 machines for varying multi-month periods finds 2 suspected soft errors. In particular, our result on the Internet servers indicates that, with high probability, the soft error rate is at least two orders of magnitude lower than those reported previously. We provide discussions that attribute the low error rate to several factors in today's production system environments . As a contrast, our measurement unintentionally discovers permanent (or hard) memory faults on 9 out of 212 Ask.com machines, suggesting the relative commonness of

Question: What is the soft error rate of a memory?


Point-and-shoot memories: the influence of taking photos on memory for a museum tour.. Two studies examined whether photographing objects impacts what is remembered about them. Participants were led on a guided tour of an art museum and were directed to observe some objects and to photograph others. Results showed a photo-taking-impairment effect: If participants took a photo of each object as a whole, they remembered fewer objects and remembered fewer details about the objects and the objects' locations in the museum than if they instead only observed the objects and did not photograph them. However, when participants zoomed in to photograph a specific part of the object, their subsequent recognition and detail memory was not impaired, and, in fact, memory for features that were not zoomed in on was just as strong as memory for features that were zoomed in on. This finding highlights key differences between people's memory and the camera's "memory" and suggests that the additional attentional and cognitive processes engaged by this focused activity can eliminate the photo-taking-impairment effect.

Question: What is the photo-taking-impairment effect?


Symbol Interdependency in Symbolic and Embodied Cognition. Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to a specific statistical process or to activation of embodied representations should be attributed to language itself. Specifically, the performance of LSA can be attributed to the linguistic surface structure, more than special characteristics of the algorithm, and embodiment findings attributed to perceptual simulations can be explained by distributional linguistic information.

Question: What is the difference between symbolic and embodied cognition?


A Meta-Analysis of Methodologies for Research in Knowledge Management, Organizational Learning and Organizational Memory: Five Years at HICSS. The Task Force on Organizational Memory presented a report at the Hawaii International Conference for System Sciences in January 1998. The report included perspectives on knowledge-oriented research, conceptual models for organizational memory, and research methodologies for researchers considering work in organizational memory. This paper builds on the ideas originally presented in the 1998 report by examining research presented at HICSS in the general areas of knowledge management, organizational memory and organizational learning in the five years since the original task force report.

Question: What is the task force on organizational memory?


Locating the fovea center position in digital fundus images using thresholding and feature extraction techniques. A new methodology for detecting the fovea center position in digital retinal images is presented in this paper. A pixel is firstly searched for within the foveal region according to its known anatomical position relative to the optic disc and vascular tree. Then, this pixel is used to extract a fovea-containing subimage on which thresholding and feature extraction techniques are applied so as to find fovea center. The methodology was evaluated on 1200 fundus images from the publicly available MESSIDOR database, 660 of which present signs of diabetic retinopathy. In 93.92% of these images, the distance between the methodology-provided and actual fovea center position remained below 1/4 of one standard optic disc radius (i.e., 17, 26, and 27 pixels for MESSIDOR retinas of 910, 1380 and 1455 pixels in size, respectively). These results outperform all the reviewed methodologies available in literature. Its effectiveness and robustness with different illness conditions makes this proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.

Question: How to locate the fovea center position in digital fundus images using thresholding and feature extraction techniques.


Autonomous Helicopter Flight via Reinforcement Learning. Autonomous helicopter flight represents a challenging cont rol problem, with complex, noisy, dynamics. In this paper, we describe a s uccessful application of reinforcement learning to autonomous helic opter flight. We first fit a stochastic, nonlinear model of the helicopter dy namics. We then use the model to learn to hover in place, and to fly a number of maneuvers taken from an RC helicopter competition.

Question: How to fly a helicopter?


Validity in Quantitative Content Analysis. Over the past 15 years, educational technologists have been dabbling with a research technique known as quantitative content analysis (QCA). Although it is characterized as a systematic and objective procedure for describing communication, readers find insufficient evidence of either quality in published reports. In this paper, it is argued that QCA should be conceived of as a form of testing and measurement. If this argument is successful, it becomes possible to frame many of the problems associated with QCA studies under the well-articulated rubric of test validity. Two sets of procedures for developing the validity of a QCA coding protocol are provided, (a) one for developing a protocol that is theoretically valid and (b) one for establishing its validity empirically. The paper is concerned specifically with the use of QCA to study educational applications of computer-mediated communication. The primary role of networked computers in higher education has shifted from presenting structured, preprogrammed learning materials to facilitating communication. In turn, the role of educational technology researchers has expanded to include the role of communication researcher. In the late 1980s, studies began to appear that incorporated new perspectives, new methods, and new techniques. One of the most promising was quantitative content analysis (QCA). Berelson (1952) defined QCA as "a research technique for the systematic, objective, and quantitative description of the manifest content of communication" (p. 18). In this context, description is a process that includes segmenting communication content into units, assigning each unit to a category, and providing tallies for each category. Bullen (1998), for instance, studied participation and critical thinking in an online discussion by counting the number of times each student contributed to the discussion and by assigning each contribution to one of three categories of critical t

Question: What is the role of educational technology researchers?


Magnet design based on transient behavior of an IPMSM in event of malfunction. This paper deals with the approach of analyzing the magnet design based on transient behavior of an IPMSM during switching processes in the event of malfunction for automotive applications. Depending on the maximal current increase during the transient process the needed percentage of Dysprosium for the magnet design is conducted. Switching off strategy is introduced for both Voltage-Source- and Current-Source-Inverter for automotive application. Both inverters are compared concerning the transient current increase and respectively the Dy-content for the magnet design.

Question: What is the transient behavior of an IPMSM?


Basics of dermal filler rheology.. BACKGROUND Hyaluronic acid injectable fillers are the most widely used dermal fillers to treat facial volume deficits, providing long-term facial aesthetic enhancement outcomes for the signs of aging and/or facial contouring.   OBJECTIVES The purpose of this article was to explain how rheology, the study of the flow of matter, can be used to help physicians differentiate between dermal fillers targeted to certain areas of the face.   METHODS This article describes how rheological properties affect performance when filler is used in various parts of the face and exposed to mechanical stress (shear deformation and compression/stretching forces) associated with daily facial animation and other commonly occurring external forces.   RESULTS Improving facial volume deficits with filler is linked mainly to gel viscoelasticity and cohesivity. These 2 properties set the level of resistance to lateral and vertical deformations of the filler and influence filler tissue integration through control of gel spreading.   CONCLUSION Selection of dermal filler with the right rheological properties is a key factor in achieving a natural-looking long-lasting desired aesthetic outcome.

Question: What is the difference between dermal fillers?


A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data. The ability to determine patient acuity (or severity of illness) has immediate practical use for clinicians. We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical notes. The learned multi-task GP (MTGP) hyperparameters are then used to assess and forecast patient acuity. Experiments were conducted with two real clinical data sets acquired from ICU patients: firstly, estimating cerebrovascular pressure reactivity, an important indicator of secondary damage for traumatic brain injury patients, by learning the interactions between intracranial pressure and mean arterial blood pressure signals, and secondly, mortality prediction using clinical progress notes. In both cases, MTGPs provided improved results: an MTGP model provided better results than single-task GP models for signal interpolation and forecasting (0.91 vs 0.69 RMSE), and the use of MTGP hyperparameters obtained improved results when used as additional classification features (0.812 vs 0.788 AUC).

Question: How do you forecast patient acuity?


Dysbiosis of the gut microbiota in disease. There is growing evidence that dysbiosis of the gut microbiota is associated with the pathogenesis of both intestinal and extra-intestinal disorders. Intestinal disorders include inflammatory bowel disease, irritable bowel syndrome (IBS), and coeliac disease, while extra-intestinal disorders include allergy, asthma, metabolic syndrome, cardiovascular disease, and obesity.

Question: What is the gut microbiota?


Misinformation in Online Social Networks: Detect Them All with a Limited Budget. Online social networks have become an effective and important social platform for communication, opinions exchange, and information sharing. However, they also make it possible for rapid and wide misinformation diffusion, which may lead to pernicious influences on individuals or society. Hence, it is extremely important and necessary to detect the misinformation propagation by placing monitors.  In this article, we first define a general misinformation-detection problem for the case where the knowledge about misinformation sources is lacking, and show its equivalence to the influence-maximization problem in the reverse graph. Furthermore, considering node vulnerability, we aim to detect the misinformation reaching to a specific user. Therefore, we study a τ-Monitor Placement problem for cases where partial knowledge of misinformation sources is available and prove its #P complexity. We formulate a corresponding integer program, tackle exponential constraints, and propose a Minimum Monitor Set Construction (MMSC) algorithm, in which the cut-set2 has been exploited in the estimation of reachability of node pairs. Moreover, we generalize the problem from a single target to multiple central nodes and propose another algorithm based on a Monte Carlo sampling technique. Extensive experiments on real-world networks show the effectiveness of proposed algorithms with respect to minimizing the number of monitors.

Question: Detect misinformation in online social networks


An Open Source Tracking Testbed and Evaluation Web Site. We have implemented a GUI-based tracking testbed system in C and Intel OpenCV, running within the Microsoft Windows environment. The motivation for the testbed is to run and log tracking experiments in near real-time. The testbed allows a tracking experiment to be repeated from the same starting state using different tracking algorithms and parameter settings, thereby facilitating comparison of algorithms. We have also developed a tracking evaluation web site to encourage third-party self-evaluation of state-of-the-art tracking algorithms. The site hosts source code for the tracking testbed, a set of ground-truth datasets, and a method for on-line evaluation of uploaded tracking results.

Question: What is an open source tracking testbed?


Conditional Generation and Snapshot Learning in Neural Dialogue Systems. Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks. In this work we study various model architectures and different ways to represent and aggregate the source information in an endto-end neural dialogue system framework. A method called snapshot learning is also proposed to facilitate learning from supervised sequential signals by applying a companion cross-entropy objective function to the conditioning vector. The experimental and analytical results demonstrate firstly that competition occurs between the conditioning vector and the LM, and the differing architectures provide different trade-offs between the two. Secondly, the discriminative power and transparency of the conditioning vector is key to providing both model interpretability and better performance. Thirdly, snapshot learning leads to consistent performance improvements independent of which architecture is used.

Question: What is the difference between a conditional language model and a neural dialogue system?


A greedy approach to sparse canonical correlation analysis. We consider the problem of sparse canonical correlation analysis (CCA), i.e., the search for two linear combi nations, one for each multivariate, that yield maximum correlation using a specified number of variables. We propose an efficient numeri cal approximation based on a direct greedy approach which bound s the correlation at each stage. The method is specifically des igned to cope with large data sets and its computational complexit y depends only on the sparsity levels. We analyze the algorith m's performance through the tradeoff between correlation and parsimony. The results of numerical simulation suggest that a significant portion of the correlation may be captured using a relatively small number of variables. In addition, we exami ne the use of sparse CCA as a regularization method when the number of available samples is small compared to the dimensions of t he multivariates. I. I NTRODUCTION Canonical correlation analysis (CCA), introduced by Harol d Hotelling [1], is a standard technique in multivariate data n lysis for extracting common features from a pair of data sourc es [2], [3]. Each of these data sources generates a random vecto r that we call a multivariate. Unlike classical dimensionali ty reduction methods which address one multivariate, CCA take s into account the statistical relations between samples fro m two spaces of possibly different dimensions and structure. In particular, it searches for two linear combinations, one fo r each multivariate, in order to maximize their correlation. It is used in different disciplines as a stand-alone tool or as a preprocessing step for other statistical methods. Further more, CCA is a generalized framework which includes numerous classical methods in statistics, e.g., Principal Componen t Analysis (PCA), Partial Least Squares (PLS) and Multiple Linear Regression (MLR) [4]. CCA has recently regained attention with the advent of kernel CCA and its

Question: What is the difference between canonical correlation analysis and principal component analysis?


Marker-controlled watershed segmentation of nuclei in H&E stained breast cancer biopsy images. In this paper we present an unsupervised automatic method for segmentation of nuclei in H&E stained breast cancer biopsy images. Colour deconvolution and morphological operations are used to preprocess the images in order to remove irrelevant structures. Candidate nuclei locations, obtained with the fast radial symmetry transform, act as markers for a marker-controlled watershed segmentation. Watershed regions that are unlikely to represent nuclei are removed in the postprocessing stage. The proposed algorithm is evaluated on a number of images that are representative of the diversity in pathology in this type of tissue. The method shows good performance in terms of the number of segmented nuclei and segmentation accuracy.

Question: What is the marker-controlled watershed segmentation?


SIMPLIFIED SOLAR TRACKING PROTOTYPE. Solar energy is rapidly advancing as an important means of renewable energy resource. More energy is produced by tracking the solar panel to remain aligned to the sun at a right angle to the rays of light. This paper describes in detail the design and construction of a prototype for solar tracking system with two degrees of freedom, which detects the sunlight using photocells. The control circuit for the solar tracker is based on a PIC16F84A microcontroller (MCU). This is programmed to detect the sunlight through the photocells and then actuate the motor to position the solar panel where it can receive maximum sunlight. Keyword: PIC MCU, tracking, photocell, frame, motion system

Question: What is the difference between a PIC and a MCU?


3D-stackable crossbar resistive memory based on Field Assisted Superlinear Threshold (FAST) selector. We report the integration of 3D-stackable 1S1R passive crossbar RRAM arrays utilizing a Field Assisted Superlinear Threshold (FAST) selector. The sneak path issue in crossbar memory integration has been solved using the highest reported selectivity of 1010. Excellent selector performance is presented such as extremely sharp switching slope of <; 5mV/dec., selectivity of 1010, sub-50ns operations, > 100M endurance and processing temperature less than 300°C. Measurements on the 4Mb 1S1R crossbar array show that the sneak current is suppressed below 0.1nA, while maintaining 102 memory on/off ratio and > 106 selectivity during cycling, enabling high density memory applications.

Question: What is the sneak path issue in crossbar memory integration?


The End of an Architectural Era (It's Time for a Complete Rewrite). In previous papers [SC05, SBC+07], some of us predicted the end of "one size fits all" as a commercial relational DBMS paradigm. These papers presented reasons and experimental evidence that showed that the major RDBMS vendors can be outperformed by 1-2 orders of magnitude by specialized engines in the data warehouse, stream processing, text, and scientific database markets. Assuming that specialized engines dominate these markets over time, the current relational DBMS code lines will be left with the business data processing (OLTP) market and hybrid markets where more than one kind of capability is required. In this paper we show that current RDBMSs can be beaten by nearly two orders of magnitude in the OLTP market as well. The experimental evidence comes from comparing a new OLTP prototype, H-Store, which we have built at M.I.T., to a popular RDBMS on the standard transactional benchmark, TPC-C. We conclude that the current RDBMS code lines, while attempting to be a "one size fits all" solution, in fact, excel at nothing. Hence, they are 25 year old legacy code lines that should be retired in favor of a collection of "from scratch" specialized engines. The DBMS vendors (and the research community) should start with a clean sheet of paper and design systems for tomorrow's requirements, not continue to push code lines and architectures designed for yesterday's needs.

Question: What is the difference between OLTP and OLAP?


A survey of B-tree locking techniques. B-trees have been ubiquitous in database management systems for several decades, and they are used in other storage systems as well. Their basic structure and basic operations are well and widely understood including search, insertion, and deletion. Concurrency control of operations in B-trees, however, is perceived as a difficult subject with many subtleties and special cases. The purpose of this survey is to clarify, simplify, and structure the topic of concurrency control in B-trees by dividing it into two subtopics and exploring each of them in depth.

Question: What is a b-tree?


Memorability of natural scenes: The role of attention. The image memorability consists in the faculty of an image to be recalled after a period of time. Recently, the memorability of an image database was measured and some factors responsible for this memorability were highlighted. In this paper, we investigate the role of visual attention in image memorability around two axis. The first one is experimental and uses results of eye-tracking performed on a set of images of different memorability scores. The second investigation axis is predictive and we show that attention-related features can advantageously replace low-level features in image memorability prediction. From our work it appears that the role of visual attention is important and should be more taken into account along with other low-level features.

Question: What is the role of visual attention in image memorability?


Visual landmarks sharpen grid cell metric and confer context specificity to neurons of the medial entorhinal cortex. Neurons of the medial entorhinal cortex (MEC) provide spatial representations critical for navigation. In this network, the periodic firing fields of grid cells act as a metric element for position. The location of the grid firing fields depends on interactions between self-motion information, geometrical properties of the environment and nonmetric contextual cues. Here, we test whether visual information, including nonmetric contextual cues, also regulates the firing rate of MEC neurons. Removal of visual landmarks caused a profound impairment in grid cell periodicity. Moreover, the speed code of MEC neurons changed in darkness and the activity of border cells became less confined to environmental boundaries. Half of the MEC neurons changed their firing rate in darkness. Manipulations of nonmetric visual cues that left the boundaries of a 1D environment in place caused rate changes in grid cells. These findings reveal context specificity in the rate code of MEC neurons.

Question: How does the grid cell periodicity depend on visual landmarks?


Study of Hand Gesture Recognition Technologies and Applications for Human Vehicle Interaction. This paper describes the primary and secondary driving task together with Human Machine Interface (HMI) trends and issues which are driving automotive user interface designers to consider hand gesture recognition as a realistic alternative for user controls. A number of hand gesture recognition technologies and applications for Human Vehicle Interaction (HVI) are also discussed including a summary of current automotive hand gesture recognition research.

Question: What is the primary and secondary driving task?


TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. A single-valued neutrosophic set is a special case of neutrosophic set. It has been proposed as a generalization of crisp sets, fuzzy sets, and intuitionistic fuzzy sets in order to deal with incomplete information. In this paper, a new approach for multi-attribute group decision-making problems is proposed by extending the technique for order preference by similarity to ideal solution to single-valued neutrosophic environment. Ratings of alternative with respect to each attribute are considered as single-valued neutrosophic set that reflect the decision makers' opinion based on the provided information. Neutrosophic set characterized by three independent degrees namely truth-membership degree (T), indeterminacy-membership degree (I), and falsity-membership degree (F) is more capable to catch up incomplete information. Single-valued neutrosophic set-based weighted averaging operator is used to aggregate all the individual decision maker's opinion into one common opinion for rating the importance of criteria and alternatives. Finally, an illustrative example is provided in order to demonstrate its applicability and effectiveness of the proposed approach.

Question: What is TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment?


Exercise prescription for overhead athletes with shoulder pathology: a systematic review with best evidence synthesis.. OBJECTIVE To produce a best evidence synthesis of exercise prescription used when treating shoulder pathology in the overhead athlete.   DESIGN A systematic review of exercises used in overhead athletes including case studies and clinical commentaries.   DATA SOURCES MEDLINE, PubMed, SPORTDiscus and CINAHL from database inception through July 8, 2016.   METHODS We examined data from randomised controlled trials and prospective cohort (level I-IV evidence) studies that addressed exercise intervention in the rehabilitation of the overhead athlete with shoulder pathology. Case studies and clinical commentaries (level V evidence) were examined to account for expert opinion-based research. Data were combined using best evidence synthesis and graded (A-F) recommendations (Centre for Evidence-Based Medicine).   RESULTS There were 33 unique exercises in six level I-IV studies that met our inclusion criteria. Most exercises were single-plane, upper extremity exercises performed below 90o of elevation. There were 102 unique exercises in 33 level V studies that met our inclusion criteria. These exercises emphasised plyometrics, kinetic chain and sport-specific training.   CONCLUSIONS AND RELEVANCE Overall, evidence for exercise interventions in overhead athletes with shoulder pathology is dominated by expert opinion (grade D). There is great variability between exercise approaches suggested by experts and those investigated in research studies and the overall level of evidence is low. The strongest available evidence (level B) supports the use of single-plane, open chain upper extremity exercises performed below 90° of elevation and closed chain upper extremity exercises. Clinical expert pieces support a more advanced, global treatment approach consistent with the complex, multidimensional nature of sport.

Question: What exercises are used to treat shoulder pathology in overhead athletes?


Compact wideband directional antenna with three-dimensional structure for microwave-based head imaging systems. Microwave-based head imaging systems demand wideband antennas with compact size. To ensure high scattered signals from the intended target point, the antennas need to have directional radiation patterns. Designing an antenna by addressing all these issues is challenging as the head imaging system utilizes low microwave frequencies (around 1-3 GHz) to maintain high signal penetration in the lossy human head. This paper reports a coplanar waveguide-fed design that overcomes all these limitations. The antenna is constructed in a three-dimensional manner by combining a dipole antenna with a folded parasitic structure printed on two substrate blocks. The antenna mechanism is explained with the description of its design parameters. The antenna covers 101% fractional bandwidth with a stable gain, good radiation efficiency and well directional radiation characteristics. The overall volume of the antenna is 0.24 × 0.1 × 0.05 λm where λm is the wavelength at the lowest frequency of operation.

Question: What is the volume of the antenna?


Peer-to-Peer Lending – A ( Financial Stability ) Risk Perspective. The aim of this paper is to discuss P2P lending, a subcategory of crowdfunding, from a (financial stability) risk perspective. The discussion focuses on a number of dimensions such as the role of soft information, herding, platform default risk, liquidity risk, and the institutionalization of P2P markets. Overall, we conclude that P2P lending is more risky than traditional banking. However, it is important to recognize that a constant conclusion would be misleading. P2P platforms have evolved and changed their appearance markedly over time, which implies that although our final conclusion of increased riskiness through P2P markets remains valid over time, it is based on different arguments at different points in time. In addition, we discuss that acting on P2P online platforms satisfies most possible definitions of shadow banking and shows significant similarities with many observed aspects of shadow banking. We thus infer that P2P lending can be considered part of the shadow banking sector. JEL Classification: F34, G21, G23

Question: What is the difference between peer-to-peer lending and crowdfunding?


Single-Channel and Multi-Channel Sinusoidal Audio Coding Using Compressed Sensing. Compressed sensing (CS) samples signals at a much lower rate than the Nyquist rate if they are sparse in some basis. In this paper, the CS methodology is applied to sinusoidally modeled audio signals. As this model is sparse by definition in the frequency domain (being equal to the sum of a small number of sinusoids), we investigate whether CS can be used to encode audio signals at low bitrates. In contrast to encoding the sinusoidal parameters (amplitude, frequency, phase) as current state-of-the-art methods do, we propose encoding few randomly selected samples of the time-domain description of the sinusoidal component (per signal segment). The potential of applying compressed sensing both to single-channel and multi-channel audio coding is examined. The listening test results are encouraging, indicating that the proposed approach can achieve comparable performance to that of state-of-the-art methods. Given that CS can lead to novel coding systems where the sampling and compression operations are combined into one low-complexity step, the proposed methodology can be considered as an important step towards applying the CS framework to audio coding applications.

Question: What is the difference between single-channel and multi-channel audio coding?


Towards an intelligent framework for multimodal affective data analysis. An increasingly large amount of multimodal content is posted on social media websites such as YouTube and Facebook everyday. In order to cope with the growth of such so much multimodal data, there is an urgent need to develop an intelligent multi-modal analysis framework that can effectively extract information from multiple modalities. In this paper, we propose a novel multimodal information extraction agent, which infers and aggregates the semantic and affective information associated with user-generated multimodal data in contexts such as e-learning, e-health, automatic video content tagging and human-computer interaction. In particular, the developed intelligent agent adopts an ensemble feature extraction approach by exploiting the joint use of tri-modal (text, audio and video) features to enhance the multimodal information extraction process. In preliminary experiments using the eNTERFACE dataset, our proposed multi-modal system is shown to achieve an accuracy of 87.95%, outperforming the best state-of-the-art system by more than 10%, or in relative terms, a 56% reduction in error rate.

Question: What is the eNTERFACE dataset?


Micro-punch technique for treatment of Fordyce spots: a surgical approach for an unpleasant condition of the male genital.. Fordyce spots are ectopic sebaceous glands, ranging between 2 and 3 mm in diameter. These benign lesions are most frequently located in the oral mucosa and the genital skin. Especially in the male genital region they can cause itching, discomfort during sexual activities and are aesthetically unpleasant. So far, a variety of therapeutic procedures have been reported with varying success and recurrence rates. In the present retrospective study (n = 23 patients between 2003 and 2011), we present our surgical approach by means of the micro-punch technique. Using this effective method, we achieved very satisfactory functional and cosmetic results. There were no signs of recurrence during postoperative observations from 12 up to 84 months (median = 51.3 months).

Question: What is the micro-punch technique?


Balance control of two-wheeled self-balancing mobile robot based on TS fuzzy model. Two-wheeled self-balancing robot is a mechanically unstable, nonholonomic constraint robot. This paper has established a dynamics and kinematics for nonholonomic mobile robot. In order to control more effective for self-balancing robot's balance model, has designed fuzzy controller based on the T-S fuzzy model with the parallel distribution compensator (PDC) the structure. It realizes the robot balance and the movement control. Finally, validity and feasibility of the system modeling and the designed controller were confirmed through the actual test results and the simulation results contrast analysis.

Question: How to control the balance of a robot?


Study of the therapeutic effects of a hippotherapy simulator in children with cerebral palsy: a stratified single-blind randomized controlled trial.. OBJECTIVE To investigate whether hippotherapy (when applied by a simulator) improves postural control and balance in children with cerebral palsy.   DESIGN Stratified single-blind randomized controlled trial with an independent assessor. Stratification was made by gross motor function classification system levels, and allocation was concealed.   SUBJECTS Children between 4 and 18 years old with cerebral palsy.   INTERVENTIONS Participants were randomized to an intervention (simulator ON) or control (simulator OFF) group after getting informed consent. Treatment was provided once a week (15 minutes) for 10 weeks.   MAIN MEASURES Gross Motor Function Measure (dimension B for balance and the Total Score) and Sitting Assessment Scale were carried out at baseline (prior to randomization), end of intervention and 12 weeks after completing the intervention.   RESULTS Thirty-eight children participated. The groups were balanced at baseline. Sitting balance (measured by dimension B of the Gross Motor Function Measure) improved significantly in the treatment group (effect size = 0.36; 95% CI 0.01-0.71) and the effect size was greater in the severely disabled group (effect size = 0.80; 95% CI 0.13-1.47). The improvements in sitting balance were not maintained over the follow-up period. Changes in the total score of the Gross Motor Function Measure and the Sitting Assessment Scale were not significant.   CONCLUSION Hippotherapy with a simulator can improve sitting balance in cerebral palsy children who have higher levels of disability. However, this did not lead to a change in the overall function of these children (Gross Motor Function Classification System level V).

Question: What is the effect of hippotherapy on children with cerebral palsy?


Recent Development in Big Data Analytics for Business Operations and Risk Management. "Big data" is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., wireless sensor networks, Internet-based systems, etc.). Big data research, however, is still in its infancy. Its focus is rather unclear and related studies are not well amalgamated. This paper aims to present the challenges and opportunities of big data analytics in this unique application domain. Technological development and advances for industrial-based business systems, reliability and security of industrial systems, and their operational risk management are examined. Important areas for future research are also discussed and revealed.

Question: What is the focus of big data analytics in business operations and risk management?


Microservices approach for the internet of things. The microservice approach has created a hype in the domain of cloud and enterprise application business. Before, grown, monolithic, software has been pushed to the limits of maintainability and scalability. The microservice architecture approach utilizes the service oriented architecture together with best practices and recent developments in software virtualization to overcome those issues. One monolithic application is split up into a set of distributed services. Those are strongly decoupled to enable high maintainability and scalability. In this case an application is split up in a top down manner. In the internet of things, applications need to be put together from a set of small and independent services. Thus, creating value added services would require to freely combine services of different vendors to fully make use of the IoT's heterogeneity. Even though the direction is different, many of the requirements in microservices are similar to those of the internet of things. This paper investigates patterns and best practices that are used in the microservices approach and how they can be used in the internet of things. Since the companies using microservices have made considerations on how services have to be designed to work together properly, IoT applications might adopt several of these design decisions to improve the ability to create value added applications from a multitude of services.

Question: What is the microservices approach?


An Inside Look at Botnets. The continued growth and diversification of the Internet has been accompanied by an increasing prevalence of attacks and intrusions [40]. It can be argued, however, that a significant change in motivation for malicious activi ty has taken place over the past several years: from vandalism and recognition in th e hacker community, to attacks and intrusions for financial gain. This shift has bee n marked by a growing sophistication in the tools and methods used to conduct atta cks, thereby escalating the network security arms race. Our thesis is that the reactivemethods for network security that are predominant today are ultimately insufficient and that more proactivemethods are required. One such approach is to develop a foundational understanding of the mechanisms employed by malicious software (malware) which is often readi ly available in source form on the Internet. While it is well known that large IT secu rity companies maintain detailed databases of this information, these are not o penly available and we are not aware of any such open repository. In this paper we begin t he process of codifying the capabilities of malware by dissecting four widely-u sed Internet Relay Chat (IRC) botnet codebases. Each codebase is classified along se ven k y dimensions including botnet control mechanisms, host control mechani sms, propagation mechanisms, exploits, delivery mechanisms, obfuscation and de ception mechanisms. Our study reveals the complexity of botnet software, and we disc us es implications for defense strategies based on our analysis.

Question: What is the difference between a botnet and a bot?


WOMEN ' S VIOLENCE TO MEN IN INTIMATE RELATIONSHIPS Working on a Puzzle. Different notions among researchers about the nature of intimate partner violence have long been the subjects of popular and academic debate. Research findings are contradictory and point in two directions, with some revealing that women are as likely as men to perpetrate violence against an intimate partner (symmetry) and others showing that it is overwhelmingly men who perpetrate violence against women partners (asymmetry). The puzzle about who perpetrates intimate partner violence not only concerns researchers but also policy makers and community advocates who, in differing ways, have a stake in the answer to this question, since it shapes the focus of public concern, legislation, public policy and interventions for victims and offenders. The question of who are the most usual victims and perpetrators rests, to a large extent, on 'what counts' as violence. It is here that we begin to try to unravel the puzzle, by focusing on concept formation, definitions, forms of measurement, context, consequences and approaches to claim-making, in order better to understand how researchers have arrived at such apparently contradictory findings and claims. The question also turns on having more detailed knowledge about the nature, extent and consequences of women's violence, in order to consider the veracity of these contradictory findings. To date, there has been very little in-depth research about women's violence to male partners and it is difficult, if not impossible, to consider this debate without such knowledge. We present quantitative and qualitative findings from 190 interviews with 95 couples in which men and women reported separately upon their own violence and upon that of their partner. Men's and women's violence are compared. The findings suggest that intimate partner violence is primarily an asymmetrical problem of men's violence to women, and women's violence does not equate to men's in ter

Question: What is the difference between men and women's violence?


Time series forecasting using Artificial Neural Networks vs. evolving models. Time series forecasting plays an important role in many fields such as economics, finance, business intelligence, natural sciences, and the social sciences. This forecasting task can be achieved by using different techniques such as statistical methods or Artificial Neural Networks (ANN). In this paper, we present two different approaches to time series forecasting: evolving Takagi-Sugeno (eTS) fuzzy model and ANN. These two different methods will be compared taking into account the different characteristic of each approach.

Question: What is a time series forecasting?


Metaheuristics in combinatorial optimization: Overview and conceptual comparison. The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.

Question: What is a metaheuristic?


Compositionality and the angular gyrus: A multi-voxel similarity analysis of the semantic composition of nouns and verbs. The cognitive and neural systems that enable conceptual processing must support the ability to combine (and recombine) concepts to form an infinite number of ideas. Two candidate neural systems for conceptual combination-the left anterior temporal lobe (ATL) and the left angular gyrus (AG)-have been characterized as "semantic hubs" due to both functional and anatomical properties; however, these two regions likely support different aspects of composition. Here we consider two hypotheses for the role of AG in conceptual combination, both of which differ from a putative role for the ATL in "feature-based" combinatorics (i.e., meaning derived by combining concepts' features). Firstly, we examine whether AG is more sensitive to function-argument relations of the sort that arise when a predicate is combined with its arguments. Secondly, we examine the non-mutually exclusive possibility that AG represents information carried on a verb in particular, whether this be information about event composition or about thematic relations denoted uniquely by verbs. We identified voxels that respond differentially to two-word versus one-word stimuli, and we measured the similarity of the patterns in these voxels evoked by (1) pairs of two-word phrases that shared a noun that was an argument, thus sharing function-argument composition (e.g. eats meat and with meat), in comparison with two-word phrases that shared only a noun, not an argument (e.g., eats meat and tasty meat); and (2) stimulus pairs that shared only an event (operationalized here as sharing a verb; e.g. eats meat and eats quickly), in comparison to both of the above. We found that activity patterns in left AG tracked information relating to the presence of an event-denoting verb in a pair of two-word phrases. We also found that the neural similarity in AG voxel patterns between two phrases sharing a

Question: What is the difference between the left anterior temporal lobe and the left angular gyrus?


Political Issue Extraction Model: A Novel Hierarchical Topic Model That Uses Tweets By Political And Non-Political Authors. People often use social media to discuss opinions, including political ones. We refer to relevant topics in these discussions as political issues, and the alternate stands towards these topics as political positions. We present a Political Issue Extraction (PIE) model that is capable of discovering political issues and positions from an unlabeled dataset of tweets. A strength of this model is that it uses twitter timelines of political and non-political authors, and affiliation information of only political authors. The model estimates word-specific distributions (that denote political issues and positions) and hierarchical author/group-specific distributions (that show how these issues divide people). Our experiments using a dataset of 2.4 million tweets from the US show that this model effectively captures the desired properties (with respect to words and groups) of political discussions. We also evaluate the two components of the model by experimenting with: (a) Use to alternate strategies to classify words, and (b) Value addition due to incorporation of group membership information. Estimated distributions are then used to predict political affiliation with 68% accuracy.

Question: What is a political issue?


Argumentation Theory: A Very Short Introduction. Since the time of the ancient Greek philosophers and rhetoricians, argumentation theorists have searched for the requirements that make an argument correct, by some appropriate standard of proof, by examining the errors of reasoning we make when we try to use arguments. These errors have long been called fallacies, and the logic textbooks have for over 2000 years tried to help students to identify these fallacies, and to deal with them when they are encountered. The problem was that deductive logic did not seem to be much use for this purpose, and there seemed to be no other obvious formal structure that could usefully be applied to them. The radical approach taken by Hamblin (1970) was to refashion the concept of an argument to think of it not just as an arbitrarily designated set of propositions, but as a move one party makes in a dialog to offer premises that may be acceptable to another party who doubts the conclusion of the argument. Just after Hamblin's time a school of thought called informal logic grew up that wanted to take a new practical approach to teaching students skills of critical thinking by going beyond deductive logic to seek other methods for analyzing and evaluating arguments. Around the same time, an interdisciplinary group of scholars associated with the term 'argumentation', coming from fields like speech communication, joined with the informal logic group to help build up such practical methods and apply them to real examples of argumentation (Johnson and Blair, 1987). The methods that have been developed so far are still in a process of rapid evolution. More recently, improvements in them have been due to some computer scientists joining the group, and to collaborative research efforts between argumentation theorists and computer scientists. Another recent development has been the adaption of argumentation models and techniques to fields in artificial intelligence, like multi-agent systems an

Question: What is argumentation theory?


Sensorless speed control of a PM synchronous motor based on sliding mode observer and extended Kalman filter. In this paper a method for rotor speed and position detection in permanent magnet synchronous motors is presented, suitable for applications where low speed and standstill operations are not required. The approach is based on the estimation of the motor back-EMF through a sliding mode observer and a cascaded Kalman filtering. Due to its characteristics, the technique can be applied to motors having distorted and whatever shaped back-EMF waveform. The equation error for the rotor position estimation is derived and discussed. Test results are presented, which show the system performance including start-up capability, which is one of the most critical conditions in back-EMF based sensorless schemes.

Question: How to detect the rotor position of a PM synchronous motor?


The role of anterior insular cortex in social emotions. Functional neuroimaging investigations in the fields of social neuroscience and neuroeconomics indicate that the anterior insular cortex (AI) is consistently involved in empathy, compassion, and interpersonal phenomena such as fairness and cooperation. These findings suggest that AI plays an important role in social emotions, hereby defined as affective states that arise when we interact with other people and that depend on the social context. After we link the role of AI in social emotions to interoceptive awareness and the representation of current global emotional states, we will present a model suggesting that AI is not only involved in representing current states, but also in predicting emotional states relevant to the self and others. This model also proposes that AI enables us to learn about emotional states as well as about the uncertainty attached to events, and implies that AI plays a dominant role in decision making in complex and uncertain environments. Our review further highlights that dorsal and ventro-central, as well as anterior and posterior subdivisions of AI potentially subserve different functions and guide different aspects of behavioral regulation. We conclude with a section summarizing different routes to understanding other people's actions, feelings and thoughts, emphasizing the notion that the predominant role of AI involves understanding others' feeling and bodily states rather than their action intentions or abstract beliefs.

Question: What is the role of anterior insular cortex in social emotions?


Single carrier FDMA for uplink wireless transmission. Single carrier frequency division multiple access (SC FDMA), a modified form of orthogonal FDMA (OFDMA), is a promising technique for high data rate uplink communications in future cellular systems. SC-FDMA has similar throughput performance and essentially the same overall complexity as OFDMA. A principal advantage of SC-FDMA is the peak-to-average power ratio (PARR), which is lower than that of OFDMA. SC FDMA is currently a strong candidate for the uplink multiple access scheme in the long term evolution of cellular systems under consideration by the third generation partnership project (3GPP). In this paper, we give an overview of SC-FDMA. We also analyze the effects of subcarrier mapping on throughput and PARR. Among the possible subcarrier mapping approaches, we find that localized FDMA (LFDMA) with channel-dependent scheduling (CDS) results in higher throughput than interleaved FDMA (JFDMA). However, the PARR performance of IFDMA is better than that of LFDMA. As in other communications systems there are complex tradeoffs between design parameters and performance in an SC-FDMA system

Question: What is the difference between SC-FDMA and OFDMA?


Coarse-to-fine volumetric segmentation of teeth in Cone-Beam CT. We consider the problem of localizing and segmenting individual teeth inside 3D Cone-Beam Computed Tomography (CBCT) images. To handle large image sizes we approach this task with a coarse-to-fine framework, where the whole volume is first analyzed as a 33-class semantic segmentation (adults have up to 32 teeth) in coarse resolution, followed by binary semantic segmentation of the cropped region of interest in original resolution. To improve the performance of the challenging 33-class segmentation, we first train the Coarse step model on a large weakly labeled dataset, then fine-tune it on a smaller precisely labeled dataset. The Fine step model is trained with precise labels only. Experiments using our inhouse dataset show significant improvement for both weaklysupervised pretraining and for the addition of the Fine step. Empirically, this framework yields precise teeth masks with low localization errors sufficient for many real-world applications.

Question: How to segment teeth in cone beam CT?


Automotive Safety and Security Integration Challenges. The ever increasing complexity of automotive vehicular systems, their connection to external networks, to the internet of things as well as their greater internal networking opens doors to hacking and malicious attacks. Security an d privacy risks in modern automotive vehicular systems are well publicized by now. That violation of securit y could lead to safety violations – is a well-argued and accepted argument. The safety discipline has matured over decades , but the security discipline is much younger . There are arguments and rightfully so, that the security engineering process is similar to the functional safety engineering process (formalized by the norm ISO 26262 ) and that they could be laid side -by-side and could be performed together but, by a different set of experts. There are moves to define a security engineering process along the lines of a functional safety engineering process for automotive vehicular systems . But, are these efforts at formalizing safety-security sufficient to produce safe and secure systems? When one sets out on this path with the idea of building safe and s ecure systems , one realizes that there are quite a few challenges, contradictions , dis imilarities, concerns to be addressed before safe and secure systems started coming out of production lines. The effort of this paper is to bring some such challeng e areas to the notice of the community and to suggest a way forward.

Question: What are the challenges of automotive security?


Classifying latent user attributes in twitter. Social media outlets such as Twitter have become an important forum for peer interaction. Thus the ability to classify latent user attributes, including gender, age, regional origin, and political orientation solely from Twitter user language or similar highly informal content has important applications in advertising, personalization, and recommendation. This paper includes a novel investigation of stacked-SVM-based classification algorithms over a rich set of original features, applied to classifying these four user attributes. It also includes extensive analysis of features and approaches that are effective and not effective in classifying user attributes in Twitter-style informal written genres as distinct from the other primarily spoken genres previously studied in the user-property classification literature. Our models, singly and in ensemble, significantly outperform baseline models in all cases. A detailed analysis of model components and features provides an often entertaining insight into distinctive language-usage variation across gender, age, regional origin and political orientation in modern informal communication.

Question: What is the canadian military?


Hourly rounding to improve nursing responsiveness: a systematic review.. UNLABELLED The aims of this study were to synthesize the evidence concerning the effect of hourly rounding programs on patient satisfaction with nursing care and discuss implications for nurse administrators.   BACKGROUND Patient satisfaction is a key metric that influences both hospital ratings and reimbursement. Studies have suggested that purposeful nursing rounds can improve patient satisfaction, but the evidence to date has not been systematically examined.   METHODS A systematic review of published literature and GRADE analysis of evidence regarding nursing rounds were conducted.   RESULTS There is little consistency in how results of hourly rounds were measured, precluding quantitative analysis. There is moderate-strength evidence that hourly rounding programs improve patients' perception of nursing responsiveness. There is also moderate-strength evidence that these programs reduce patient falls and call light use.   CONCLUSIONS Nurse administrators should consider implementing an hourly rounding program while controlled trials discern the most cost-effective approach.

Question: What is the evidence that hourly rounding programs improve patient satisfaction?


Viewing Real-World Faces in 3D. We present a data-driven method for estimating the 3D shapes of faces viewed in single, unconstrained photos (aka "in-the-wild"). Our method was designed with an emphasis on robustness and efficiency - with the explicit goal of deployment in real-world applications which reconstruct and display faces in 3D. Our key observation is that for many practical applications, warping the shape of a reference face to match the appearance of a query, is enough to produce realistic impressions of the query's 3D shape. Doing so, however, requires matching visual features between the (possibly very different) query and reference images, while ensuring that a plausible face shape is produced. To this end, we describe an optimization process which seeks to maximize the similarity of appearances and depths, jointly, to those of a reference model. We describe our system for monocular face shape reconstruction and present both qualitative and quantitative experiments, comparing our method against alternative systems, and demonstrating its capabilities. Finally, as a testament to its suitability for real-world applications, we offer an open, on-line implementation of our system, providing unique means of instant 3D viewing of faces appearing in web photos.

Question: How to reconstruct a face from a single image?


A neostriatal habit learning system in humans.. Amnesic patients and nondemented patients with Parkinson's disease were given a probabilistic classification task in which they learned which of two outcomes would occur on each trial, given the particular combination of cues that appeared. Amnesic patients exhibited normal learning of the task but had severely impaired declarative memory for the training episode. In contrast, patients with Parkinson's disease failed to learn the probabilistic classification task, despite having intact memory for the training episode. This double dissociation shows that the limbic-diencephalic regions damaged in amnesia and the neostriatum damaged in Parkinson's disease support separate and parallel learning systems. In humans, the neostriatum (caudate nucleus and putamen) is essential for the gradual, incremental learning of associations that is characteristic of habit learning. The neostriatum is important not just for motor behavior and motor learning but also for acquiring nonmotor dispositions and tendencies that depend on new associations.

Question: What is the neostriatum?


River flow time series prediction with a range-dependent neural network. Artificial neural networks provide a promising alternative to hydrological time series modelling. However, there are still many fundamental problems requiring further analyses, such as structure identification, parameter estimation, generalization, performance improvement, etc. Based on a proposed clustering algorithm for the training pairs, a new neural network, namely the range-dependent neural network (RDNN) has been developed for better accuracy in hydrological time series prediction. The applicability and potentials of the RDNN in daily streamflow and annual reservoir inflow prediction are examined using data from two watersheds in China. Empirical comparisons of the predictive accuracy, in terms of the model efficiency R and absolute relative errors (ARE), between the RDNN, backpropagation (BP) networks and the threshold auto-regressive (TAR) model are made. The case studies demonstrated that the RDNN network performed significantly better than the BP network, especially for reproducing low-flow events.

Question: What is the range-dependent neural network?


Bad Subsequences of Well-Known Linear Congruential Pseudo-Random Number Generators. We present a spectral test analysis of full-period subsequences with small step sizes generated by well-known linear congruential pseudorandom number generators. Subsequences may occur in certain simulation problems or as a method to get parallel streams of pseudorandom numbers. Applying the spectral test, it is possible to find bad subsequences with small step sizes for almost all linear pseudorandom number generators currently in use.

Question: What is a linear congruential pseudorandom number generator?


Wrapper Induction for Information Extraction. Many Internet information resources present relational data|telephone directories, product catalogs, etc. Because these sites are formatted for people, mechanically extracting their content is di cult. Systems using such resources typically use hand-coded wrappers, procedures to extract data from information resources. We introduce wrapper induction, a method for automatically constructing wrappers, and identify hlrt, a wrapper class that is e ciently learnable, yet expressive enough to handle 48% of a recently surveyed sample of Internet resources. We use PAC analysis to bound the problem's sample complexity, and show that the system degrades gracefully with imperfect labeling knowledge.

Question: What is wrapper induction?


Does Life Satisfaction influence the intention (We-Intention) to use Facebook?. Several studies have investigated a variety of factors affecting use of social networking sites (SNSs), but the investigation of these factors is still under development. In this study, we aim to contribute to the literature by extending and testing an existent conceptual model in a new context, including Life Satisfaction as an independent variable to explain the intention (We-Intention) to use Facebook. The previous models has Subjective Norm, Group Norms, Social Identity, Purposive Value, Self-Discovery, Maintaining Interpersonal Interconnectivity, Social Enhancement, Entertainment Value and Social Presence as the predictor variables. An online survey with Brazilians (n = 1111) was conducted. Our structural equation modeling reveals that Life Satisfaction influence on We-Intention is mediated by Subjective Norm, Group Norm, Social Identity, Entertainment Value, and Maintaining Interpersonal Interconnectivity (R-squared value is 0.36). Our findings, while consistent with existing literature in terms of theories, reveal different arrangements among factors influencing Brazilian consumers' behavior. 2015 Elsevier Ltd. All rights reserved.

Question: Does life satisfaction influence the intention to use facebook?


The Abandoned Side of the Internet: Hijacking Internet Resources When Domain Names Expire. The vulnerability of the Internet has been demonstrated by prominent IP prefix hijacking events. Major outages such as the China Telecom incident in 2010 stimulate speculations about malicious intentions behind such anomalies. Surprisingly, almost all discussions in the current literature assume that hijacking incidents are enabled by the lack of security mechanisms in the inter-domain routing protocol BGP. In this paper, we discuss an attacker model that accounts for the hijacking of network ownership information stored in Regional Internet Registry (RIR) databases. We show that such threats emerge from abandoned Internet resources (e.g., IP address blocks, AS numbers). When DNS names expire, attackers gain the opportunity to take resource ownership by re-registering domain names that are referenced by corresponding RIR database objects. We argue that this kind of attack is more attractive than conventional hijacking, since the attacker can act in full anonymity on behalf of a victim. Despite corresponding incidents have been observed in the past, current detection techniques are not qualified to deal with these attacks. We show that they are feasible with very little effort, and analyze the risk potential of abandoned Internet resources for the European service region: our findings reveal that currently 73 /24 IP prefixes and 7 ASes are vulnerable to be attacked. We discuss countermeasures and outline research directions towards preventive solutions.

Question: What is the risk potential of abandoned Internet resources for the European service region?


Bias and ignorance in demographic perception. When it comes to knowledge of demographic facts, misinformation appears to be the norm. Americans massively overestimate the proportions of their fellow citizens who are immigrants, Muslim, LGBTQ, and Latino, but underestimate those who are White or Christian. Previous explanations of these estimation errors have invoked topic-specific mechanisms such as xenophobia or media bias. We reconsidered this pattern of errors in the light of more than 30 years of research on the psychological processes involved in proportion estimation and decision-making under uncertainty. In two publicly available datasets featuring demographic estimates from 14 countries, we found that proportion estimates of national demographics correspond closely to what is found in laboratory studies of quantitative estimates more generally. Biases in demographic estimation, therefore, are part of a very general pattern of human psychology—independent of the particular topic or demographic under consideration—that explains most of the error in estimates of the size of politically salient populations. By situating demographic estimates within a broader understanding of general quantity estimation, these results demand reevaluation of both topic-specific misinformation about demographic facts and topic-specific explanations of demographic ignorance, such as media bias and xenophobia.

Question: What is the size of the canadian military?


The Role of Deliberate Practice in the Acquisition of Expert Performance. The theoretical framework presented in this article explains expert performance as the end result of individuals' prolonged efforts to improve performance while negotiating motivational and external constraints. In most domains of expertise, individuals begin in their childhood a regimen of effortful activities (deliberate practice) designed to optimize improvement. Individual differences, even among elite performers, are closely related to assessed amounts of deliberate practice. Many characteristics once believed to reflect innate talent are actually the result of intense practice extended for a minimum of 10 years. Analysis of expert performance provides unique evidence on the potential and limits of extreme environmental adaptation and learning.

Question: What is the role of deliberate practice in the acquisition of expert performance?


Model-free control. Model-free control" and the corresponding "intelligent" PID controllers (iPIDs), which already had many successful concrete applications, are presented here for the first time in an unified manner, where the new advances are taken into account. The basics of model-free control is now employing some old functional analysis and some elementary differential algebra. The estimation techniques become quite straightforward via a recent online parameter identification approach. The importance of iPIs and especially of iPs is deduced from the presence of friction. The strange industrial ubiquity of classic PID's and the great difficulty for tuning them in complex situations is deduced, via an elementary sampling, from their connections with iPIDs. Several numerical simulations are presented which include some infinite-dimensional systems. They demonstrate not only the power of our intelligent controllers but also the great simplicity for tuning them.

Question: What is model-free control?


A Survey of Utility-Oriented Pattern Mining. The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of patterns, many techniques/constraints have been proposed, such as support, confidence, sequence order, and utility parameters (e.g., weight, price, profit, quantity, etc.). In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM). UPM is a vital task, with numerous high-impact applications, including cross-marketing, e-commerce, finance, medical, and biomedical applications. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of UPM. First, we introduce an in-depth understanding of UPM, including concepts, examples, and comparisons with related concepts. A taxonomy of the most common and state-of-the-art approaches for mining different kinds of high-utility patterns is presented, including Apriori-based, tree-based, projection-based, vertical-/horizontal-data-format-based, and other hybrid approaches. A comprehensive review of advanced topics of existing high-utility pattern mining techniques is offered, with a discussion of their pros and cons. Finally, we present several well-known open-source software packages for UPM. We conclude our survey with a discussion on open and practical challenges in this field.

Question: What is a utility-oriented pattern mining?


Latent Fingerprint Enhancement via Multi-Scale Patch Based Sparse Representation. Latent fingerprint identification plays an important role for identifying and convicting criminals in law enforcement agencies. Latent fingerprint images are usually of poor quality with unclear ridge structure and various overlapping patterns. Although significant advances have been achieved on developing automated fingerprint identification system, it is still challenging to achieve reliable feature extraction and identification for latent fingerprints due to the poor image quality. Prior to feature extraction, fingerprint enhancement is necessary to suppress various noises, and improve the clarity of ridge structures in latent fingerprints. Motivated by the recent success of sparse representation in image denoising, this paper proposes a latent fingerprint enhancement algorithm by combining the total variation model and multiscale patch-based sparse representation. First, the total variation model is applied to decompose the latent fingerprint into cartoon and texture components. The cartoon component with most of the nonfingerprint patterns is removed as the structured noise, whereas the texture component consisting of the weak latent fingerprint is enhanced in the next stage. Second, we propose a multiscale patch-based sparse representation method for the enhancement of the texture component. Dictionaries are constructed with a set of Gabor elementary functions to capture the characteristics of fingerprint ridge structure, and multiscale patch-based sparse representation is iteratively applied to reconstruct high-quality fingerprint image. The proposed algorithm cannot only remove the overlapping structured noises, but also restore and enhance the corrupted ridge structures. In addition, we present an automatic method to segment the foreground of latent image with the sparse coefficients and orientation coherence. Experimental results and comparisons on NIST SD27 latent fingerprin

Question: How to enhance the fingerprint image?


Design of a new broadband EMC double ridged guide horn antenna. A new design for broadband EMC double ridged guide horn (DRGH) antennas is presented. A conventional 1-18 GHz double ridged guide horn has been investigated rigorously. Then some modifications have been performed in the structure of the antenna. Elimination of radiation pattern deficiencies especially at higher frequencies accompanying with better EM characteristics of the antenna have been the main purposes for these modifications. The main modifications are imposed on the profile of ridges, H-plane flares and E-plane flares. The resulting antenna not only has considerably better performance but also has smaller physical dimensions and less weight in comparison with the conventional one.

Question: What is the design of a broadband EMC double ridged guide horn antenna?


Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss. Monocular depth estimation benefits greatly from learning based techniques. By studying the training data, we observe that the per-pixel depth values in existing datasets typically exhibit a long-tailed distribution. However, most previous approaches treat all the regions in the training data equally regardless of the imbalanced depth distribution, which restricts the model performance particularly on distant depth regions. In this paper, we investigate the long tail property and delve deeper into the distant depth regions (i.e. the tail part) to propose an attentiondriven loss for the network supervision. In addition, to better leverage the semantic information for monocular depth estimation, we propose a synergy network to automatically learn the information sharing strategies between the two tasks. With the proposed attention-driven loss and synergy network, the depth estimation and semantic labeling tasks can be mutually improved. Experiments on the challenging indoor dataset show that the proposed approach achieves state-of-the-art performance on both monocular depth estimation and semantic labeling tasks.

Question: What is the long tail property?


Nonvolatile memory design based on ferroelectric FETs. Ferroelectric FETs (FEFETs) offer intriguing possibilities for the design of low power nonvolatile memories by virtue of their three-terminal structure coupled with the ability of the ferroelectric (FE) material to retain its polarization in the absence of an electric field. Utilizing the distinct features of FEFETs, we propose a 2-transistor (2T) FEFET-based nonvolatile memory with separate read and write paths. With proper co-design at the device, cell and array levels, the proposed design achieves non-destructive read and lower write power at iso-write speed compared to standard FERAM. In addition, the FEFET-based memory exhibits high distinguishability with six orders of magnitude difference in the read currents corresponding to the two states. Comparative analysis based on experimentally calibrated models shows significant improvement of access energy-delay. For example, at a fixed write time of 550ps, the write voltage and energy are 58.5% and 67.7% lower than FERAM, respectively. These benefits are achieved with 2.4 times the area overhead. Further exploration of the proposed FEFET memory in energy harvesting nonvolatile processors shows an average improvement of 27% in forward progress over FERAM.

Question: What is the difference between ferroelectric and nonvolatile memory?