Polysemy in Sentence Comprehension: Effects of Meaning Dominance.. Words like church are polysemous, having two related senses (a building and an organization). Three experiments investigated how polysemous senses are represented and processed during sentence comprehension. On one view, readers retrieve an underspecified, core meaning, which is later specified more fully with contextual information. On another view, readers retrieve one or more specific senses. In a reading task, context that was neutral or biased towards a particular sense preceded a polysemous word. Disambiguating material consistent with only one sense followed, in a second sentence (Experiment 1) or the same sentence (Experiments 2 & 3). Reading the disambiguating material was faster when it was consistent with that context, and dominant senses were committed to more strongly than subordinate senses. Critically, following neutral context, the continuation was read more quickly when it selected the dominant sense, and the degree of sense dominance partially explained the reading time advantage. Similarity of the senses also affected reading times. Across experiments, we found that sense selection may not be completed immediately following a polysemous word but is completed at a sentence boundary. Overall, the results suggest that readers select an individual sense when reading a polysemous word, rather than a core meaning.

Question: How do readers select a sense when reading a polysemous word?


Xen and the art of virtualization. Numerous systems have been designed which use virtualization to subdivide the ample resources of a modern computer. Some require specialized hardware, or cannot support commodity operating systems. Some target 100% binary compatibility at the expense of performance. Others sacrifice security or functionality for speed. Few offer resource isolation or performance guarantees; most provide only best-effort provisioning, risking denial of service.This paper presents Xen, an x86 virtual machine monitor which allows multiple commodity operating systems to share conventional hardware in a safe and resource managed fashion, but without sacrificing either performance or functionality. This is achieved by providing an idealized virtual machine abstraction to which operating systems such as Linux, BSD and Windows XP, can be ported with minimal effort.Our design is targeted at hosting up to 100 virtual machine instances simultaneously on a modern server. The virtualization approach taken by Xen is extremely efficient: we allow operating systems such as Linux and Windows XP to be hosted simultaneously for a negligible performance overhead --- at most a few percent compared with the unvirtualized case. We considerably outperform competing commercial and freely available solutions in a range of microbenchmarks and system-wide tests.

Question: What is the idealized virtual machine abstraction?


False Data Injection Attacks in Control Systems. This paper analyzes the effects of false data injection attacks on Control System. We assume that the system, equipped with a Kalman filter and LQG controller, is used to monitor and control a discrete linear time invariant Gaussian system. We further assume that the system is equipped with a failure detector. An attacker wishes to destabilize the system by compromising a subset of sensors and sending corrupted readings to the state estimator. In order to inject fake sensor measurements without being detected the attacker needs to carefully design its inputs to fool the failure detector, since abnormal sensor measurements usually trigger an alarm from the failure detector. We will provide a necessary and sufficient condition under which the attacker could destabilize the system while successfully bypassing the failure detector. A design method for the defender to improve the resilience of the CPS against such kind of false data injection attacks is also provided.

Question: How to design a system to be resilient against false data injection attacks?


Smith-Petersen osteotomy in thoracolumbar deformity surgery.. OBJECTIVE To describe the technique and indications of a Smith-Petersen osteotomy in spinal deformity surgery.   METHODS Pertinent literature was reviewed to describe the indications and reported complications of this corrective technique.   RESULTS The operative nuances of the technique are described.   CONCLUSION A Smith-Petersen osteotomy is a safe and effective surgical technique to obtain correction of spinal deformity in both the sagittal and coronal planes.

Question: What is the Smith-Petersen osteotomy?


Toward tense as a clinical marker of specific language impairment in English-speaking children.. A critical clinical issue is the identification of a clinical marker, a linguistic form or principle that can be shown to be characteristic of children with Specific Language Impairment (SLI). In this paper we evaluate, as candidate clinical markers, a set of morphemes that mark Tense. In English, this includes -s third person singular, -ed regular past, BE, and DO. According to the Extended Optional Infinitive Account (EOI) of Rice, Wexler, and Cleave (1995), this set of morphemes is likely to appear optionally in the grammars of children with SLI at a rate lower than the optionality evident in younger controls. Three groups of preschool children participated: 37 children with SLI, and two control groups, one of 40 MLU-equivalent children and another of 45 age-equivalent children. Three kinds of evidence support the conclusion that a set of morphemes that marks Tense can be considered a clinical marker: (a) low levels of accuracy for the target morphemes for the SLI group relative to either of the two control groups; (b) affectedness for the set of morphemes defined by the linguistic function of Tense, but not for morphemes unrelated to Tense; and (c) a bimodal distribution for Tense-marking morphemes relative to age peers, in which the typical children are at essentially adult levels of the grammar, whereas children in the SLI group were at low (i.e., non-adultlike) levels of performance. The clinical symptoms are evident in omissions of surface forms. Errors of subject-verb agreement and syntactic misuses are rare, showing that, as predicted, children in an EOI stage who are likely to mark Tense optionally at the same time know a great deal about the grammatical properties of finiteness and agreement in the adult grammar. The findings are discussed in terms of alternative accounts of the grammatical limitations of children with SLI and implications for clinical identi

Question: What is the difference between a clinical marker and a clinical symptom?


Fault Classification using Pseudomodal Energies and Neuro-fuzzy modelling. This paper presents a fault classification method which makes use of a Takagi-Sugeno neuro-fuzzy model and Pseudomodal energies calculated from the vibration signals of cylindrical shells. The calculation of Pseudomodal Energies, for the purposes of condition monitoring, has previously been found to be an accurate method of extracting features from vibration signals. This calculation is therefore used to extract features from vibration signals obtained from a diverse population of cylindrical shells. Some of the cylinders in the population have faults in different substructures. The pseudomodal energies calculated from the vibration signals are then used as inputs to a neuro-fuzzy model. A leave-one-out cross-validation process is used to test the performance of the model. It is found that the neuro-fuzzy model is able to classify faults with an accuracy of 91.62%, which is higher than the previously used multilayer perceptron.

Question: What is a neuro-fuzzy model?


SnipSuggest: Context-Aware Autocompletion for SQL. In this paper, we present SnipSuggest, a system that provides onthe-go, context-aware assistance in the SQL composition process. SnipSuggest aims to help the increasing population of non-expert database users, who need to perform complex analysis on their large-scale datasets, but have difficulty writing SQL queries. As a user types a query, SnipSuggest recommends possible additions to various clauses in the query using relevant snippets collected from a log of past queries. SnipSuggest's current capabilities include suggesting tables, views, and table-valued functions in the FROM clause, columns in the SELECT clause, predicates in the WHERE clause, columns in the GROUP BY clause, aggregates, and some support for sub-queries. SnipSuggest adjusts its recommendations according to the context: as the user writes more of the query, it is able to provide more accurate suggestions. We evaluate SnipSuggest over two query logs: one from an undergraduate database class and another from the Sloan Digital Sky Survey database. We show that SnipSuggest is able to recommend useful snippets with up to 93.7% average precision, at interactive speed. We also show that SnipSuggest outperforms naı̈ve approaches, such as recommending popular snippets.

Question: What is the context of the query?


Context-Aware Filtering and Visualization of Web Service Clusters. Web service filtering is an efficient approach to address some big challenges in service computing, such as discovery, clustering and recommendation. The key operation of the filtering process is measuring the similarity of services. Several methods are used in current similarity calculation approaches such as string-based, corpus-based, knowledge-based and hybrid methods. These approaches do not consider domain-specific contexts in measuring similarity because they have failed to capture the semantic similarity of Web services in a given domain and this has affected their filtering performance. In this paper, we propose a context-aware similarity method that uses a support vector machine and a domain dataset from a context-specific search engine query. Our filtering approach uses a spherical associated keyword space algorithm that projects filtering results from a three-dimensional sphere to a two-dimensional (2D) spherical surface for 2D visualization. Experimental results show that our filtering approach works efficiently.

Question: How to measure the similarity of web services?


NoSQL Databases and Data Modeling Techniques for a Document-oriented NoSQL Database. NoSQL databases are an important component of Big Data for storing and retrieving large volumes of data. Traditional Relational Database Management Systems (RDBMS) use the ACID theorem for data consistency, whereas NoSQL Databases use a non-transactional approach called BASE. RDBMS scale vertically and NoSQL Databases can scale both horizontally (sharding) and vertically. Four types of NoSQL databases are Document-oriented, Key-Value Pairs, Column-oriented and Graph. Data modeling for Document-oriented databases is similar to data modeling for traditional RDBMS during the conceptual and logical modeling phases. However, for a physical data model, entities can be combined (denormalized) by using embedding. What was once called a foreign key in a traditional RDBMS is now called a reference in a Documentoriented NoSQL database.

Question: What is the difference between a relational database and a document-oriented database?


Combining multispectral aerial imagery and digital surface models to extract urban buildings. This paper presents an automated classification of buildings in Coleraine, Northern Ireland. The classification was generated using very high spatial resolution data (10 cm) from a Digital Mapping Camera (DMC) for March 2009. The visible to near infrared (VNIR) bands of the DMC enabled a supervised classification to be performed to extract buildings from vegetation. A Digital Surface Model (DSM) was also created from the image to differentiate between buildings and other land classes with similar spectral profiles, such as roads. The supervised classification had the lowest classification accuracy (50%) while the DSM had an accuracy of 81%. The combination of the DSM and the supervised classification achieved an overall classification accuracy of 95%. Two spatial metrics (percentage of the landscape and number of patches) were also used to test the level of agreement between the classification and digitised building data. The results suggest that fine resolution multispectral aerial imagery can automatically detect buildings to a very high level of accuracy. Current space borne sensors, such as IKONOS and QuickBird, lag behind airborne sensors with VNIR bands provided at a much coarser spatial resolution (4m and 2.4m respectively). Techniques must be developed from current airborne sensors that can be applied to new space borne sensors in the future. The ability to generate DSMs from high resolution aerial imagery will afford new insights into the three-dimensional aspects of urban areas which will in turn inform future urban planning. (Received 19th August 2010; Revised 26th January 2011; Accepted 9th February 2011) ISSN 1744-5647 doi:10.4113/jom.2011.1152 51 Journal of Maps, 2011, 51-59 McNally, A.J.D. & McKenzie, S.J.P.

Question: What is the accuracy of the classification?


Deep Saliency with Encoded Low Level Distance Map and High Level Features. Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene. These advances have demonstrated superior results over previous works that utilize hand-crafted low level features for saliency detection. In this paper, we demonstrate that hand-crafted features can provide complementary information to enhance performance of saliency detection that utilizes only high level features. Our method utilizes both high level and low level features for saliency detection under a unified deep learning framework. The high level features are extracted using the VGG-net, and the low level features are compared with other parts of an image to form a low level distance map. The low level distance map is then encoded using a convolutional neural network(CNN) with multiple 1 1 convolutional and ReLU layers. We concatenate the encoded low level distance map and the high level features, and connect them to a fully connected neural network classifier to evaluate the saliency of a query region. Our experiments show that our method can further improve the performance of state-of-the-art deep learning-based saliency detection methods.

Question: How can we improve the performance of saliency detection?


A Feature Learning Based Approach for Automated Fruit Yield Estimation. This paper demonstrates a generalised multi-scale feature learning approach to multi-class segmentation, applied to the estimation of fruit yield on treecrops. The learning approach makes the algorithm flexible and adaptable to different classification problems, and hence applicable to a wide variety of tree-crop applications. Extensive experiments were performed on a dataset consisting of 8000 colour images collected in an apple orchard. This paper shows that the algorithm was able to segment apples with different sizes and colours in an outdoor environment with natural lighting conditions, with a single model obtained from images captured using a monocular colour camera. The segmentation results are applied to the problem of fruit counting and the results are compared against manual counting. The results show a squared correlation coefficient of R2 = 0.81.

Question: What is the size of the apple?


TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-Rays. The chest X-ray (CXR) is by far the most commonly performed radiological examination for screening and diagnosis of many cardiac and pulmonary diseases. There is an immense world-wide shortage of physicians capable of providing rapid and accurate interpretation of this study. A radiologist-driven analysis of over two million CXR reports generated an ontology including the 40 most prevalent pathologies on CXR. By manually tagging a relatively small set of sentences, we were able to construct a training set of 959k studies. A deep learning model was trained to predict the findings given the patient frontal and lateral scans. For 12 of the findings we compare the model performance against a team of radiologists and show that in most cases the radiologists agree on average more with the algorithm than with each other.

Question: What is the most common disease on chest x-ray?


A Hybrid Course Recommendation System by Integrating Collaborative Filtering and Artificial Immune Systems. Abstract: This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP) correlation in affinity calculations for clustering and prediction. This research uses student information and professor information datasets of Yuan Ze University from the years 2005–2009 for the purpose of testing and training. The mean average error and confusion matrix analysis form the testing parameters. A minimum professor rating was tested to check the results, and observed that the recommendation systems herein provide highly accurate results for students with higher mean grades.

Question: What is the minimum professor rating?


A Review of Human Activity Recognition Methods. Recognizing human activities from video sequences or still images is a challenging task due to problems, such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance. Many applications, including video surveillance systems, human-computer interaction, and robotics for human behavior characterization, require a multiple activity recognition system. In this work, we provide a detailed review of recent and state-of-the-art research advances in the field of human activity classification. We propose a categorization of human activity methodologies and discuss their advantages and limitations. In particular, we divide human activity classification methods into two large categories according to whether they use data from different modalities or not. Then, each of these categories is further analyzed into sub-categories, which reflect how they model human activities and what type of activities they are interested in. Moreover, we provide a comprehensive analysis of the existing, publicly available human activity classification datasets and examine the requirements for an ideal human activity recognition dataset. Finally, we report the characteristics of future research directions and present some open issues on human activity recognition.

Question: What is the difference between human activity recognition and human activity classification?


Convolutional Self Organizing Map. Recently, deep learning became very popular, and was applied to many fields. The convolutional neural networks are often used for representing the layers for deep learning. In this paper, we propose Convolutional Self Organizing Map, which can be applicable to deep learning. Conventional Self Organizing Map uses single layered architecture, and can visualizes and classifies the input data on 2 dimensional map. SOMs which uses multiple layers are already proposed. In this paper, we propose Self Organizing Map algorithms which include convolutional layers. 2 types of convolution methods, which are based on conventional method and inspired from Self Organizing Map algorithm are proposed, and the performance of both method is examined in the experiments of clustering image data.

Question: What is convolutional neural network?


Neuropsychologic Theory and Findings in Attention-Deficit/Hyperactivity Disorder: The State of the Field and Salient Challenges for the Coming Decade. The past decade has witnessed the establishment of several now well-replicated findings in the neuropsychology of attention-deficit/hyperactivity disorder (ADHD), which have been confirmed by meta-analyses. Progress has been notable from the importing of cognitive science and neuroscience paradigms. Yet these findings point to many neural networks being involved in the syndrome and to modest effect sizes suggesting that any one neuropsychologic deficit will not be able to explain the disorder. In this article, leading theories and key findings are briefly reviewed in four key domains: attention, executive functions, state regulation and motivation, and temporal information processing. Key issues facing the field of neuropsychologic research and theory in ADHD include 1) the need for more integrative developmental accounts that address both multiple neural systems and the socialization processes that assure their development; 2) consideration of multiple models/measures in the same study so as to examine relative contributions, within-group heterogeneity, and differential deficit; and 3) better integration of cognitive process models with affective and temperament theories so that early precursors to ADHD can be better understood. Overall, the field has witnessed notable progress as it converges on an understanding of ADHD in relation to disruption of a multicomponent self-regulatory system. The next era must articulate multipathway, multilevel developmental accounts of ADHD that incorporate neuropsychologic effects.

Question: What is the state of the field of neuropsychology in ADHD?


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

Question: What is the best gait recognition algorithm?


Inverted files for text search engines. The technology underlying text search engines has advanced dramatically in the past decade. The development of a family of new index representations has led to a wide range of innovations in index storage, index construction, and query evaluation. While some of these developments have been consolidated in textbooks, many specific techniques are not widely known or the textbook descriptions are out of date. In this tutorial, we introduce the key techniques in the area, describing both a core implementation and how the core can be enhanced through a range of extensions. We conclude with a comprehensive bibliography of text indexing literature.

Question: What is the core of inverted files?


The Psychometric Properties of the Internet Addiction Test. There is growing concern about excessive Internet use and whether this can amount to an addiction. In researching this topic, a valid and reliable assessment instrument is essential. In her survey of Internet addiction, Young designed the Internet Addiction Test (IAT), which provides a basis for developments. The IAT has high face validity, but it has not been subjected to systematic psychometric testing. This study sought to replicate and expand Young's survey, and to examine the IAT more systematically. A questionnaire that existed as a Web page was devised, consisting of the IAT and 15 other questions regarding the respondents' demographic information and Internet usage. Participants were recruited through the Internet, yielding 86 valid responses (29 males and 57 females). Factor analysis of the IAT revealed six factors--salience, excessive use, neglecting work, anticipation, lack of control, and neglecting social life. These factors showed good internal consistency and concurrent validity, with salience being the most reliable. Younger and more recent users reported more problems, mainly concerning the neglect of work and social life. We expected interactive Internet functions to be more addictive; however, this was not found to be so. Overall, the IAT is a valid and reliable instrument that may be used in further research on Internet addiction.

Question: What is the IAT?


An Extensive Empirical Evaluation of Character-Based Morphological Tagging for 14 Languages. This paper investigates neural characterbased morphological tagging for languages with complex morphology and large tag sets. Character-based approaches are attractive as they can handle rarelyand unseen words gracefully. We evaluate on 14 languages and observe consistent gains over a state-of-the-art morphological tagger across all languages except for English and French, where we match the state-of-the-art. We compare two architectures for computing characterbased word vectors using recurrent (RNN) and convolutional (CNN) nets. We show that the CNN based approach performs slightly worse and less consistently than the RNN based approach. Small but systematic gains are observed when combining the two architectures by ensembling.

Question: What is the difference between a character-based morphological tagger and a morphological tagger?


Business Case and Technology Analysis for 5G Low Latency Applications. A large number of new consumer and industrial applications are likely to change the classic operator's business models and provide a wide range of new markets to enter. This paper analyzes the most relevant 5G use cases that require ultra-low latency, from both technical and business perspectives. Low latency services pose challenging requirements to the network, and to fulfill them, operators need to invest in costly changes in their network. In this sense, it is not clear whether such investments are going to be amortized with these new business models. In light of this, specific applications and requirements are described and the potential market benefits for operators are analyzed. Conclusions show that the operators have clear opportunities to add value and position themselves strongly with the increasing number of services to be provided by 5G.

Question: What is the potential market for 5G?


A meta-analysis of continuing medical education effectiveness.. INTRODUCTION We undertook a meta-analysis of the Continuing Medical Education (CME) outcome literature to examine the effect of moderator variables on physician knowledge, performance, and patient outcomes.   METHODS A literature search of MEDLINE and ERIC was conducted for randomized controlled trials and experimental design studies of CME outcomes in which physicians were a major group. CME moderator variables included the types of intervention, the types and number of participants, time, and the number of intervention sessions held over time.   RESULTS Thirty-one studies met the eligibility criteria, generating 61 interventions. The overall sample-size weighted effect size for all 61 interventions was r = 0.28 (0.18). The analysis of CME moderator variables showed that active and mixed methods had medium effect sizes (r = 0.33 [0.33], r = 0.33 [0.26], respectively), and passive methods had a small effect size (r = 0.20 [0.16], confidence interval 0.15, 0.26). There was a positive correlation between the effect size and the length of the interventions (r = 0.33) and between multiple interventions over time (r = 0.36). There was a negative correlation between the effect size and programs that involved multiple disciplines (r = -0.18) and the number of participants (r = -0.13). The correlation between the effect size and the length of time for outcome assessment was negative (r = -0.31).   DISCUSSION The meta-analysis suggests that the effect size of CME on physician knowledge is a medium one; however, the effect size is small for physician performance and patient outcome. The examination of moderator variables shows there is a larger effect size when the interventions are interactive, use multiple methods, and are designed for a small group of physicians from a single discipline.

Question: What is the effect of continuing medical education on physician knowledge?


Group formation in large social networks: membership, growth, and evolution. The processes by which communities come together, attract new members, and develop over time is a central research issue in the social sciences - political movements, professional organizations, and religious denominations all provide fundamental examples of such communities. In the digital domain, on-line groups are becoming increasingly prominent due to the growth of community and social networking sites such as MySpace and LiveJournal. However, the challenge of collecting and analyzing large-scale time-resolved data on social groups and communities has left most basic questions about the evolution of such groups largely unresolved: what are the structural features that influence whether individuals will join communities, which communities will grow rapidly, and how do the overlaps among pairs of communities change over time.Here we address these questions using two large sources of data: friendship links and community membership on LiveJournal, and co-authorship and conference publications in DBLP. Both of these datasets provide explicit user-defined communities, where conferences serve as proxies for communities in DBLP. We study how the evolution of these communities relates to properties such as the structure of the underlying social networks. We find that the propensity of individuals to join communities, and of communities to grow rapidly, depends in subtle ways on the underlying network structure. For example, the tendency of an individual to join a community is influenced not just by the number of friends he or she has within the community, but also crucially by how those friends are connected to one another. We use decision-tree techniques to identify the most significant structural determinants of these properties. We also develop a novel methodology for measuring movement of individuals between communities, and show how such movements are closely aligned with changes in the top

Question: How do the overlaps among pairs of communities change over time?


Extreme learning machine: Theory and applications. It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks. r 2006 Elsevier B.V. All rights reserved.

Question: What is the difference between feedforward neural networks and extreme learning machine?


3-D Reciprocal Collision Avoidance on Physical Quadrotor Helicopters with On-Board Sensing for Relative Positioning. In this paper, we present an implementation of 3D reciprocal collision avoidance on real quadrotor helicopters where each quadrotor senses the relative position and veloc ity of other quadrotors using an on-board camera. We show that using our approach, quadrotors are able to successfully avo id pairwise collisions GPS and motion-capture denied environ ments, without communication between the quadrotors, and even when human operators deliberately attempt to induce collision. To our knowledge, this is the first time that reciprocal collision avoidance has been successfully implemented on r eal robots where each agent independently observes the others using on-board sensors. We theoretically analyze the respo nse of the collision-avoidance algorithm to the violated assumptions by the use of real robots. We quantitatively analyze our experimental results. A particularly striking observation is that at times the quadrotors exhibit "reciprocal dance" behavior, which is also observed when humans move past each other in constrained environments. This seems to be the result of sensing uncertainty, which causes both robots involved to h ave a different belief about the relative positions and velocit ies and, as a result, choose the same side on which to pass.

Question: How large is the canadian military?


Perceived qualities of smart wearables: determinants of user acceptance. Wearable computers are one of the new technologies that are expected to be a part of users' lives extensively in near future. While some of the users have positive attitudes towards these new products, some users may reject to use them due to different reasons. User experience is subjective, and effected by various parameters. Among these the first impression, namely the perceived qualities has an important impact on product acceptance. This paper aims to explore the perceived qualities of wearables and define the relations between them. An empirical study is conducted, to find out the hierarchy and meaningful relationships between the perceived qualities of smart wearables. The study is based on personal construct theory and data is presented by Cross-Impact Analysis. The patterns behind affection and affected qualities are explored to understand the design requirements for the best integration of wearables into daily lives.

Question: What are the perceived qualities of smart wearables?


Design and implementation of home automation system. M2M wireless communication of various machines and devices in mobile networks is a fast growing business and application area in industry, maintenance business, customer service, security, and banking areas. This paper presents design and implementation of remote control system by means of GSM cellular communication network. The design integrates the device to be controlled, the microcontroller, and the GSM module so that it can be used for a wide range of applications. Detailed description and implementation of each design element are presented. To verify the principle operation of the M2M design, two home applications are experimentally tested using PC-based environment.

Question: What is the difference between M2M and M2M?


Dynamic program slicing methods. A dynamic program slice is that part of a program that ''affects'' the computation of a variable of interest during program execution on a specific program input. Dynamic program slicing refers to a collection of program slicing methods that are based on program execution and may significantly reduce the size of a program slice because run-time information, collected during program execution, is used to compute program slices. Dynamic program slicing was originally proposed only for program debugging, but its application has been extended to program comprehension, software testing, and software maintenance. Different types of dynamic program slices, together with algorithms to compute them, have been proposed in the literature. In this paper we present a classification of existing dynamic slicing methods and discuss the algorithms to compute dynamic slices. In the second part of the paper, we compare the existing methods of dynamic slice computation.  1998 Elsevier Science B.V. All rights reserved.

Question: What is a dynamic program slice?


Hypervisor Memory Forensics. Memory forensics is the branch of computer forensics that aims at extracting artifacts from memory snapshots taken from a running system. Even though it is a relatively recent field, it is rapidly growing and it is attracting considerable attention from both industrial and academic researchers. In this paper, we present a set of techniques to extend the field of memory forensics toward the analysis of hypervisors and virtual machines. With the increasing adoption of virtualization techniques (both as part of the cloud and in normal desktop environments), we believe that memory forensics will soon play a very important role in many investigations that involve virtual environments. Our approach, implemented in an open source tool as an extension of the Volatility framework, is designed to detect both the existence and the characteristics of any hypervisor that uses the Intel VT-x technology. It also supports the analysis of nested virtualization and it is able to infer the hierarchy of multiple hypervisors and virtual machines. Finally, by exploiting the techniques presented in this paper, our tool can reconstruct the address space of a virtual machine in order to transparently support any existing Volatility plugin allowing analysts to reuse their code for the analysis of virtual environments.

Question: What is the difference between a hypervisor and a virtual machine?


Family issues in child anxiety: attachment, family functioning, parental rearing and beliefs.. Family studies have found a large overlap between anxiety disorders in family members. In addition to genetic heritability, a range of family factors may also be involved in the intergenerational transmission of anxiety. Evidence for a relationship between family factors and childhood as well as parental anxiety is reviewed. Four groups of family variables are considered: (I) attachment; (II), aspects of family functioning, such as marital conflict, co-parenting, functioning of the family as a whole, and sibling relationships; (III) parental rearing strategies; and (IV) beliefs that parents hold about their child. The reviewed literature provides evidence for an association between each of these family factors and child anxiety. However, there is little evidence as yet that identified family factors are specific to child anxiety, rather than to child psychopathology in general. Moreover, evidence for a relationship between child anxiety and family factors is predominantly cross-sectional. Therefore, whether the identified family factors cause childhood anxiety still needs to be investigated. Further research that investigates mechanisms mediating the relationship between family factors and child anxiety is also called for. Finally, parental beliefs are identified as important predictors of parental behaviour that have largely not been investigated in relation to child anxiety disorders.

Question: What is the relationship between family factors and child anxiety?


Building Skeleton Models via 3-D Medial Surface/Axis Thinning Algorithms.

Question: How to build a skeleton model?


Vehicle Applications of Controller Area Network. The Controller Area Network (CAN) is a serial bus communications protocol developed by Bosch in the early 1980s. It defines a standard for efficient and reliable communication between sensor, actuator, controller, and other nodes in real-time applications. CAN is the de facto standard in a large variety of networked embedded control systems. The early CAN development was mainly supported by the vehicle industry: CAN is found in a variety of passenger cars, trucks, boats, spacecraft, and other types of vehicles. The protocol is also widely used today in industrial automation and other areas of networked embedded control, with applications in diverse products such as production machinery, medical equipment, building automation, weaving machines, and wheelchairs. In the automotive industry, embedded control has grown from stand-alone systems to highly integrated and networked control systems [11, 7]. By networking electro-mechanical subsystems, it becomes possible to modularize functionalities and hardware, which facilitates reuse and adds capabilities. Fig. 1 shows an example of an electronic control unit (ECU) mounted on a diesel engine of a Scania truck. The ECU handles the control of engine, turbo, fan, etc. but also the CAN communication. Combining networks and mechatronic modules makes it possible to reduce both the cabling and the number

Question: What is the CAN protocol?


Filler Rhinoplasty Evaluated by Anthropometric Analysis.. BACKGROUND There are no reports of objectively evaluating the efficacy of filler rhinoplasty by anthropometric techniques.   OBJECTIVE To objectively demonstrate the effectiveness of filler rhinoplasty by anthropometric analysis.   MATERIALS AND METHODS A total of 242 patients who revisited the clinic within 2 months of undergoing hyaluronic acid filler rhinoplasty were analyzed based on the injection site, injected volume, and the change in anthropometry.   RESULTS Among the 242 patients, 112 (46.3%) were in the nasal dorsum augmentation group, 8 (3.3%) were in the tip rotation group, and 122 (50.4%) were in the whole nose augmentation group. Average injection volume was 1 ± 0.4 mL for nasal dorsum and 0.9 ± 0.3 mL for tip rotation, whereas 1.6 ± 0.5 mL was used for whole nose augmentation. On follow-up, the radix height, nasofrontal angle, and nasolabial angle (NLA) had increased by 78.3%, 5.7 ± 4.1°, and 9.4 ± 4.5°, respectively, whereas the modified nasofacial angle had decreased by 1.9 ± 2.9°. Three cases (1.2%) of vascular complications were encountered.   CONCLUSION Filler rhinoplasty is a simple and effective treatment modality producing outcomes comparable with surgical augmentation rhinoplasty. Among various anthropometric measurements, the nasal radix height was the most useful for evaluating dorsum augmentation, whereas the NLA was the best for nasal tip rotation.

Question: What is the best way to evaluate the efficacy of filler rhinoplasty?


Affective style, psychopathology, and resilience: brain mechanisms and plasticity.. The brain circuitry underlying emotion includes several territories of the prefrontal cortex (PFC), the amygdala, hippocampus, anterior cingulate, and related structures. In general, the PFC represents emotion in the absence of immediately present incentives and thus plays a crucial role in the anticipation of the future affective consequences of action, as well as in the persistence of emotion following the offset of an elicitor. The functions of the other structures in this circuit are also considered. Individual differences in this circuitry are reviewed, with an emphasis on asymmetries within the PFC and activation of the amygdala as 2 key components of affective style. These individual differences are related to both behavioral and biological variables associated with affective style and emotion regulation. Plasticity in this circuitry and its implications for transforming emotion and cultivating positive affect and resilience are considered.

Question: What is the amygdala?


Foreground object detection from videos containing complex background. This paper proposes a novel method for detection and segmentation of foreground objects from a video which contains both stationary and moving background objects and undergoes both gradual and sudden "once-off" changes. A Bayes decision rule for classification of background and foreground from selected feature vectors is formulated. Under this rule, different types of background objects will be classified from foreground objects by choosing a proper feature vector. The stationary background object is described by the color feature, and the moving background object is represented by the color co-occurrence feature. Foreground objects are extracted by fusing the classification results from both stationary and moving pixels. Learning strategies for the gradual and sudden "once-off" background changes are proposed to adapt to various changes in background through the video. The convergence of the learning process is proved and a formula to select a proper learning rate is also derived. Experiments have shown promising results in extracting foreground objects from many complex backgrounds including wavering tree branches, flickering screens and water surfaces, moving escalators, opening and closing doors, switching lights and shadows of moving objects.

Question: How to detect foreground objects from videos containing complex background?


Bystander Responses to a Violent Incident in an Immersive Virtual Environment. Under what conditions will a bystander intervene to try to stop a violent attack by one person on another? It is generally believed that the greater the size of the crowd of bystanders, the less the chance that any of them will intervene. A complementary model is that social identity is critical as an explanatory variable. For example, when the bystander shares common social identity with the victim the probability of intervention is enhanced, other things being equal. However, it is generally not possible to study such hypotheses experimentally for practical and ethical reasons. Here we show that an experiment that depicts a violent incident at life-size in immersive virtual reality lends support to the social identity explanation. 40 male supporters of Arsenal Football Club in England were recruited for a two-factor between-groups experiment: the victim was either an Arsenal supporter or not (in-group/out-group), and looked towards the participant for help or not during the confrontation. The response variables were the numbers of verbal and physical interventions by the participant during the violent argument. The number of physical interventions had a significantly greater mean in the in-group condition compared to the out-group. The more that participants perceived that the Victim was looking to them for help the greater the number of interventions in the in-group but not in the out-group. These results are supported by standard statistical analysis of variance, with more detailed findings obtained by a symbolic regression procedure based on genetic programming. Verbal interventions made during their experience, and analysis of post-experiment interview data suggest that in-group members were more prone to confrontational intervention compared to the out-group who were more prone to make statements to try to diffuse the situation.

Question: How do bystanders respond to a violent incident in an immersive virtual environment?


COMPARISON OF LOSSLESS DATA COMPRESSION ALGORITHMS FOR TEXT DATA. Data compression is a common requirement for most of the computerized applications. There are number of data compression algorithms, which are dedicated to compress different data formats. Even for a single data type there are number of different compression algorithms, which use different approaches. This paper examines lossless data compression algorithms and compares their performance. A set of selected algorithms are examined and implemented to evaluate the performance in compressing text data. An experimental comparison of a number of different lossless data compression algorithms is presented in this paper. The article is concluded by stating which algorithm performs well for text data.

Question: Which algorithm is best for text data?


Improving Text Proposals for Scene Images with Fully Convolutional Networks. Text Proposals have emerged as a class-dependent version of object proposals – efficient approaches to reduce the search space of possible text object locations in an image. Combined with strong word classifiers, text proposals currently yield top state of the art results in end-to-end scene text recognition. In this paper we propose an improvement over the original Text Proposals algorithm of [1], combining it with Fully Convolutional Networks to improve the ranking of proposals. Results on the ICDAR RRC and the COCO-text datasets show superior performance over current state-of-the-art.

Question: How to improve text proposals for scene images?


Relation Extraction : A Survey. With the advent of the Internet, large amount of digital text is generated everyday in the form of news articles, research publications, blogs, question answering forums and social media. It is important to develop techniques for extracting information automatically from these documents, as lot of important information is hidden within them. This extracted information can be used to improve access and management of knowledge hidden in large text corpora. Several applications such as Question Answering, Information Retrieval would benefit from this information. Entities like persons and organizations, form the most basic unit of the information. Occurrences of entities in a sentence are often linked through well-defined relations; e.g., occurrences of person and organization in a sentence may be linked through relations such as employed at. The task of Relation Extraction (RE) is to identify such relations automatically. In this paper, we survey several important supervised, semi-supervised and unsupervised RE techniques. We also cover the paradigms of Open Information Extraction (OIE) and Distant Supervision. Finally, we describe some of the recent trends in the RE techniques and possible future research directions. This survey would be useful for three kinds of readers i) Newcomers in the field who want to quickly learn about RE; ii) Researchers who want to know how the various RE techniques evolved over time and what are possible future research directions and iii) Practitioners who just need to know which RE technique works best in various settings.

Question: What is the relation extraction?


The big five personality dimensions and entrepreneurial status: a meta-analytical review.. In this study, the authors used meta-analytical techniques to examine the relationship between personality and entrepreneurial status. Personality variables used in previous studies were categorized according to the five-factor model of personality. Results indicate significant differences between entrepreneurs and managers on 4 personality dimensions such that entrepreneurs scored higher on Conscientiousness and Openness to Experience and lower on Neuroticism and Agreeableness. No difference was found for Extraversion. Effect sizes for each personality dimension were small, although the multivariate relationship for the full set of personality variables was moderate (R = .37). Considerable heterogeneity existed for all of the personality variables except Agreeableness, suggesting that future research should explore possible moderators of the personality-entrepreneurial status relationship.

Question: What is the relationship between personality and entrepreneurial status?


Ontology-Aware Token Embeddings for Prepositional Phrase Attachment. Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed semantic concepts (or synsets) as defined in WordNet and represent a word token in a particular context by estimating a distribution over relevant semantic concepts. We use the new, context-sensitive embeddings in a model for predicting prepositional phrase (PP) attachments and jointly learn the concept embeddings and model parameters. We show that using context-sensitive embeddings improves the accuracy of the PP attachment model by 5.4% absolute points, which amounts to a 34.4% relative reduction in errors.

Question: What is the context-sensitive embedding?


Neuronlike adaptive elements that can solve difficult learning control problems. It is shown how a system consisting of two neuronlike adaptive elements can solve a difficult learning control problem. The task is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. It is argued that the learning problems faced by adaptive elements that are components of adaptive networks are at least as difficult as this version of the pole-balancing problem. The learning system consists of a single associative search element (ASE) and a single adaptive critic element (ACE). In the course of learning to balance the pole, the ASE constructs associations between input and output by searching under the influence of reinforcement feedback, and the ACE constructs a more informative evaluation function than reinforcement feedback alone can provide. The differences between this approach and other attempts to solve problems using neurolike elements are discussed, as is the relation of this work to classical and instrumental conditioning in animal learning studies and its possible implications for research in the neurosciences.

Question: How does a single associative search element solve a difficult learning control problem?


Analyzing Enterprise Architecture in National Governments: The Cases of Denmark and the Netherlands. National enterprise architectures (NEA) promise to fill the gap between policy and implementation. NEAs are embedded within an institutional environment consisting of active players capable of responding strategically and innovatively to architectural initiatives, which might complicate NEA adoption. In this paper we analyze the efforts of two European national governments in developing enterprise architecture. Grounded in institutional theory and practice we develop an analytical framework and use this framework to analyze the efforts of two countries, Denmark and the Netherlands. Our framework and analysis draws the attention to the need to take a broader perspective on enterprise architecture, especially governance aspects determine the adoption and diffusion of NEA

Question: What is the difference between a national enterprise architecture and a national enterprise architecture?


Precision CMOS current reference with process and temperature compensation. This paper presents a new first-order temperature compensated CMOS current reference. To achieve a compact architecture able to operate under low voltage with low power consumption, it is based on a self-biasing beta multiplier current generator. Compensation against temperature is achieved by using instead of an ordinary resistor two triode transistors in parallel, acting as a negative and a positive temperature coefficient resistor, that generate a proportional to absolute temperature and a complementary to absolute temperature current which can be directly added to attain a temperature compensated current. Programmability is included to adjust the temperature coefficient and the reference current magnitude over process variations. Results for a 0.18 μm CMOS implementation show that the proposed 500 nA reference operate with supplies down to 1.2 V accomplishing over a (-40 to +120°C) range temperature drifts below 120 ppm/°C.

Question: What is a current reference?


100-Mb/s NRZ Visible Light Communications Using a Postequalized White LED. This letter describes a high-speed visible light communications link that uses a white-light light-emitting diode (LED). Such devices have bandwidths of few megahertz, severely limiting the data rates of any communication system. Here, we demonstrate that by detecting only the blue component of the LED, and using a simple first-order analogue equalizer, a data rate of 100 Mb/s can be achieved using on-off keying nonreturn-to-zero modulation.

Question: What is the bandwidth of a visible light communication link?


Automatic fault tree generation from SysML system models. In this paper, a methodology is proposed to integrate safety analysis within a systems engineering approach. This methodology is based on SysML models and aims at generating (semi-) automatically safety analysis artifacts, mainly FMEA and FTA, from system models. Preliminary functional and component FMEA are automatically generated from the functional and structural models respectively, then completed by safety experts. By representing SysML structural diagram as a directed multi-graph, through a graph traversal algorithm and some identified patterns, generic fault trees are automatically derived with corresponding logic gates and events. The proposed methodology provides the safety expert with assistance during safety analysis. It helps reducing time and error proneness of the safety analysis process. It also helps ensuring consistency since the safety analysis artifacts are automatically generated from the latest system model version. The methodology is applied to a real case study, the electromechanical actuator EMA.

Question: How to generate fault trees from SysML models?


Why social preferences matter-the impact of non-selfish motives on competition , cooperation and incentives. A substantial number of people exhibit social preferences, which means they are not solely motivated by material self-interest but also care positively or negatively for the material payoffs of relevant reference agents. We show empirically that economists fail to understand fundamental economic questions when they disregard social preferences, in particular, that without taking social preferences into account, it is not possible to understand adequately (i) the effects of competition on market outcomes, (ii) laws governing cooperation and collective action, (iii) effects and the determinants of material incentives, (iv) which contracts and property rights arrangements are optimal, and (v) important forces shaping social norms and market failures. a) Ernst Fehr, Institute for Empirical Research in Economics, University of Zurich, Bluemlisalpstrasse 10, CH-8006 Zurich, Switzerland, email: efehr@iew.unizh.ch. b) Urs Fischbacher, Institute for Empirical Research in Economics, University of Zurich, Bluemlisalpstrasse 10, CH-8006 Zurich, Switzerland, email: fiba@iew.unizh.ch.

Question: Why social preferences matter


Synthetic Occlusion Augmentation with Volumetric Heatmaps for the 2018 ECCV PoseTrack Challenge on 3D Human Pose Estimation. In this paper we present our winning entry at the 2018 ECCV PoseTrack Challenge on 3D human pose estimation. Using a fully-convolutional backbone architecture, we obtain volumetric heatmaps per body joint, which we convert to coordinates using soft-argmax. Absolute person center depth is estimated by a 1D heatmap prediction head. The coordinates are back-projected to 3D camera space, where we minimize the L1 loss. Key to our good results is the training data augmentation with randomly placed occluders from the Pascal VOC dataset. In addition to reaching first place in the Challenge, our method also surpasses the state-of-the-art on the full Human3.6M benchmark when considering methods that use no extra pose datasets in training. Code for applying synthetic occlusions is availabe at https://github.com/ isarandi/synthetic-occlusion.

Question: How to apply synthetic occlusions?


Road scene analysis for determination of road traffic density. Road traffic density has always been a concern in large cities around the world, and many approaches were developed to assist in solving congestions related to slow traffic flow. This work proposes a congestion rate estimation approach that relies on real-time video scenes of road traffic, and was implemented and evaluated on eight different hotspots covering 33 different urban roads. The approach relies on road scene morphology for estimation of vehicles average speed along with measuring the overall video scenes randomness acting as a frame texture analysis indicator. Experimental results shows the feasibility of the proposed approach in reliably estimating traffic density and in providing an early warning to drivers on road conditions, thereby mitigating the negative effect of slow traffic flow on their daily lives.

Question: How to estimate traffic density?


Comparing Twitter and Traditional Media Using Topic Models. Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to compare these Twitter topics with topics from New York Times, taking into consideration topic categories and types. We also study the relation between the proportions of opinionated tweets and retweets and topic categories and types. Our comparisons show interesting and useful findings for downstream IR or DM applications.

Question: How can we compare Twitter and traditional media using topic models?


BFS and Coloring-Based Parallel Algorithms for Strongly Connected Components and Related Problems. Finding the strongly connected components (SCCs) of a directed graph is a fundamental graph-theoretic problem. Tarjan's algorithm is an efficient serial algorithm to find SCCs, but relies on the hard-to-parallelize depth-first search (DFS). We observe that implementations of several parallel SCC detection algorithms show poor parallel performance on modern multicore platforms and large-scale networks. This paper introduces the Multistep method, a new approach that avoids work inefficiencies seen in prior SCC approaches. It does not rely on DFS, but instead uses a combination of breadth-first search (BFS) and a parallel graph coloring routine. We show that the Multistep method scales well on several real-world graphs, with performance fairly independent of topological properties such as the size of the largest SCC and the total number of SCCs. On a 16-core Intel Xeon platform, our algorithm achieves a 20X speedup over the serial approach on a 2 billion edge graph, fully decomposing it in under two seconds. For our collection of test networks, we observe that the Multistep method is 1.92X faster (mean speedup) than the state-of-the-art Hong et al. SCC method. In addition, we modify the Multistep method to find connected and weakly connected components, as well as introduce a novel algorithm for determining articulation vertices of biconnected components. These approaches all utilize the same underlying BFS and coloring routines.

Question: What is the fastest algorithm for finding strongly connected components?


Joint SFM and detection cues for monocular 3D localization in road scenes. We present a system for fast and highly accurate 3D localization of objects like cars in autonomous driving applications, using a single camera. Our localization framework jointly uses information from complementary modalities such as structure from motion (SFM) and object detection to achieve high localization accuracy in both near and far fields. This is in contrast to prior works that rely purely on detector outputs, or motion segmentation based on sparse feature tracks. Rather than completely commit to tracklets generated by a 2D tracker, we make novel use of raw detection scores to allow our 3D bounding boxes to adapt to better quality 3D cues. To extract SFM cues, we demonstrate the advantages of dense tracking over sparse mechanisms in autonomous driving scenarios. In contrast to complex scene understanding, our formulation for 3D localization is efficient and can be regarded as an extension of sparse bundle adjustment to incorporate object detection cues. Experiments on the KITTI dataset show the efficacy of our cues, as well as the accuracy and robustness of our 3D object localization relative to ground truth and prior works.

Question: What is the difference between a monocular and a stereo camera?


Exploring the relationship between knowledge management practices and innovation performance. The process of innovation depends heavily on knowledge, and the management of knowledge and human capital should be an essential element of running any type of business. Recent research indicates that organisations are not consistent in their approach to knowledge management (KM), with KM approaches being driven predominantly within an information technology (IT) or humanist framework, with little if any overlap. This paper explores the relationship between KM approaches and innovation performance through a preliminary study focusing on the manufacturing industry. The most significant implication that has emerged from the study is that managers in manufacturing firms should place more emphasis on human resource management (HRM) practices when developing innovation strategies for product and process innovations. The study shows that KM contributes to innovation performance when a simultaneous approach of "soft HRM practices" and "hard IT practices" are implemented.

Question: What is the relationship between knowledge management and innovation?


MELOGRAPH: Multi-Engine WorkfLOw Graph Processing. This paper introduces MELOGRAPH, a new system that exposes in the front-end a domain specific language(DSL) for graph processing tasks and in the back-end identifies, ranks and generates source code for the top-N ranked engines. This approach lets the specialized MELOGRAPH be part of a more general multi-engine workflow optimizer. The candidate execution engines are chosen from the contemporaneous Big Data ecosystem: graph databases (e.g. Neo4j, TitanDB, OrientDB, Sparksee/DEX) and robust graph processing frameworks with Java API or packaged libraries of algorithms (e.g. Giraph, Okapi, Flink Gelly, Hama, Gremlin). As MELOGRAPH is work in progress, our current paper stresses upon the state of the art in this field, provides a general architecture and some early implementation insights.

Question: What is a graph database?


Researching Mental Health Disorders in the Era of Social Media: Systematic Review. BACKGROUND Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose.   OBJECTIVE The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research.   METHODS We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals.   RESULTS The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis.   CONCLUSIONS Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques.

Question: What is the most common mental health disorder?


Accounting Restatements and External Financing Choices. There is little research on how accounting information quality affects a firm's external financing choices. In this paper, we use the occurrence of accounting restatements as a proxy for the reduced credibility of accounting information and investigate how restatements affect a firm's external financing choices. We find that for firms that obtain external financing after restatements, they rely more on debt financing, especially private debt financing, and less on equity financing. The increase in debt financing is more pronounced for firms with more severe information problems and less pronounced for firms with prompt CEO/CFO turnover and auditor dismissal. Our evidence indicates that accounting information quality affects capital providers' resource allocation and that debt holders help alleviate information problems after accounting restatements.

Question: How does accounting information quality affect a firm's external financing choices?


Modeling Review Argumentation for Robust Sentiment Analysis. Most text classification approaches model text at the lexical and syntactic level only, lacking domain robustness and explainability. In tasks like sentiment analysis, such approaches can result in limited effectiveness if the texts to be classified consist of a series of arguments. In this paper, we claim that even a shallow model of the argumentation of a text allows for an effective and more robust classification, while providing intuitive explanations of the classification results. Here, we apply this idea to the supervised prediction of sentiment scores for reviews. We combine existing approaches from sentiment analysis with novel features that compare the overall argumentation structure of the given review text to a learned set of common sentiment flow patterns. Our evaluation in two domains demonstrates the benefit of modeling argumentation for text classification in terms of effectiveness and robustness.

Question: What is the difference between a shallow and a deep model?


Precise Flow-Insensitive May-Alias Analysis is NP-Hard. Determining aliases is one of the foundamental static analysis problems, in part because the precision with which this problem is solved can affect the precision of other analyses such as live variables, available expressions, and constant propagation. Previous work has investigated the complexity of flow-sensitive alias analysis. In this article we show that precise flow-insensitive may-alias analysis is NP-hard given arbitrary levels of pointers and arbitrary pointer dereferencing.

Question: What is the complexity of precise flow-insensitive may-alias analysis?


A Comprehensive Survey of Wireless Body Area Networks. Recent advances in microelectronics and integrated circuits, system-on-chip design, wireless communication and intelligent low-power sensors have allowed the realization of a Wireless Body Area Network (WBAN). A WBAN is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. In addition, it supports a number of innovative and interesting applications such as ubiquitous healthcare, entertainment, interactive gaming, and military applications. In this paper, the fundamental mechanisms of WBAN including architecture and topology, wireless implant communication, low-power Medium Access Control (MAC) and routing protocols are reviewed. A comprehensive study of the proposed technologies for WBAN at Physical (PHY), MAC, and Network layers is presented and many useful solutions are discussed for each layer. Finally, numerous WBAN applications are highlighted.

Question: What is a wireless body area network?


Psychopaths know right from wrong but don't care.. Adult psychopaths have deficits in emotional processing and inhibitory control, engage in morally inappropriate behavior, and generally fail to distinguish moral from conventional violations. These observations, together with a dominant tradition in the discipline which sees emotional processes as causally necessary for moral judgment, have led to the conclusion that psychopaths lack an understanding of moral rights and wrongs. We test an alternative explanation: psychopaths have normal understanding of right and wrong, but abnormal regulation of morally appropriate behavior. We presented psychopaths with moral dilemmas, contrasting their judgments with age- and sex-matched (i) healthy subjects and (ii) non-psychopathic, delinquents. Subjects in each group judged cases of personal harms (i.e. requiring physical contact) as less permissible than impersonal harms, even though both types of harms led to utilitarian gains. Importantly, however, psychopaths' pattern of judgments on different dilemmas was the same as those of the other subjects. These results force a rejection of the strong hypothesis that emotional processes are causally necessary for judgments of moral dilemmas, suggesting instead that psychopaths understand the distinction between right and wrong, but do not care about such knowledge, or the consequences that ensue from their morally inappropriate behavior.

Question: What is the difference between a psychopath and a non-psychopath?


DONet: A Data-Driven Overlay Network For Efficient Live Media Streaming. This paper presents DONet, a Data-driven Overlay Network for live media streaming. The core operations in DONet are very simple: every node periodically exchanges data availability information with a set of partners, and retrieves unavailable data from one or more partners, or supplies available data to partners. We emphasize three salient features of this data-driven design: 1) easy to implement , as it does not have to construct and maintain a complex global structure; 2) efficient, as data forwarding is dynamically determined according to data availability while not restricted by specific directions; and 3) robust and resilient , as the partnerships enable adaptive and quick switching among multi-suppliers. We show through analysis that DONet is scalable with bounded delay. We also address a set of practical challenges for realizing DONet, and propose an efficient memberand partnership management algorithm, together with an intelligent scheduling algorithm that achieves real-time and continuous distribution of streaming contents. We have extensively evaluated the performance of DONet over the PlanetLab. Our experiments, involving almost all the active PlanetLab nodes, demonstrate that DONet achieves quite good streaming quality even under formidable network conditions. Moreover, its control overhead and transmission delay are both kept at low levels. An Internet-based DONet implementation, calledCoolStreaming v.0.9, was released on May 30, 2004, which has attracted over 30000 distinct users with more than 4000 simultaneously being online at some peak times. We discuss the key issues toward designingCoolStreaming in this paper, and present several interesting observations from these large-scale tests; in particular, the larger the overlay size, the better the streaming quality it can deliver.

Question: What is the largest live streaming network?


A data-driven approach to predict the success of bank telemarketing. We propose a data mining (DM) approach to predict the success of telemarketing calls for selling bank long-term deposits. A Portuguese retail bank was addressed, with data collected from 2008 to 2013, thus including the effects of the recent financial crisis. We analyzed a large set of 150 features related with bank client, product and social-economic attributes. A semi-automatic feature selection was explored in the modeling phase, performed with the data prior to July 2012 and that allowed to select a reduced set of 22 features. We also compared four DM models: logistic regression, decision trees (DT), neural network (NN) and support vector machine. Using two metrics, area of the receiver operating characteristic curve (AUC) and area of the LIFT cumulative curve (ALIFT), the four models were tested on an evaluation phase, using the most recent data (after July 2012) and a rolling windows scheme. The NN presented the best results (AUC=0.8 and ALIFT=0.7), allowing to reach 79% of the subscribers by selecting the half better classified clients. Also, two knowledge extraction methods, a sensitivity analysis and a DT, were applied to the NN model and revealed several key attributes (e.g., Euribor rate, direction of the call and bank agent experience). Such knowledge extraction confirmed the obtained model as credible and valuable for telemarketing campaign managers. Preprint submitted to Elsevier 19 February 2014

Question: What is a data mining approach to predict the success of telemarketing calls for selling bank long-term deposits?


Fusion of time-of-flight depth and stereo for high accuracy depth maps. Time-of-flight range sensors have error characteristics which are complementary to passive stereo. They provide real time depth estimates in conditions where passive stereo does not work well, such as on white walls. In contrast, these sensors are noisy and often perform poorly on the textured scenes for which stereo excels. We introduce a method for combining the results from both methods that performs better than either alone. A depth probability distribution function from each method is calculated and then merged. In addition, stereo methods have long used global methods such as belief propagation and graph cuts to improve results, and we apply these methods to this sensor. Since time-of-flight devices have primarily been used as individual sensors, they are typically poorly calibrated. We introduce a method that substantially improves upon the manufacturerpsilas calibration. We show that these techniques lead to improved accuracy and robustness.

Question: What is the difference between a time-of-flight sensor and a passive stereo sensor?


Improved extraction of vegetable oils under high-intensity ultrasound and/or microwaves.. Ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE) techniques have been employed as complementary techniques to extract oils from vegetable sources, viz, soybean germ and a cultivated marine microalga rich in docosahexaenoic acid (DHA). Ultrasound (US) devices developed by ourselves, working at several frequencies (19, 25, 40 and 300 kHz), were used for US-based protocols, while a multimode microwave (MW) oven (operating with both open and closed vessels) was used for MAE. Combined treatments were also studied, such as simultaneous double sonication (at 19 and 25 kHz) and simultaneous US/MW irradiation, achieved by inserting a non-metallic horn in a MW oven. Extraction times and yields were compared with those resulting from conventional procedures. With soybean germ the best yield was obtained with a 'cavitating tube' prototype (19 kHz, 80 W), featuring a thin titanium cylinder instead of a conventional horn. Double sonication, carried out by inserting an immersion horn (25 kHz) in the same tube, improved the yield only slightly but halved the extraction time. Almost comparable yields were achieved by closed-vessel MAE and simultaneous US/MW irradiation. Compared with conventional methods, extraction times were reduced by up to 10-fold and yields increased by 50-500%. In the case of marine microalgae, UAE worked best, as the disruption by US of the tough algal cell wall considerably improved the extraction yield from 4.8% in soxhlet to 25.9%. Our results indicate that US and MW, either alone or combined, can greatly improve the extraction of bioactive substances, achieving higher efficiency and shorter reaction times at low or moderate costs, with minimal added toxicity.

Question: What is the best extraction technique for vegetable oils?


Retinal blood vessel segmentation using matched filter and Laplacian of Gaussian. Automated blood vessel segmentation of retinal images offers huge potential benefits for medical diagnosis of different ocular diseases. In this paper, 2D Matched Filters (MF) are applied to fundus retinal images to detect vessels which are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE) method. Due to the Gaussian nature of blood vessel profile, the MF with Gaussian kernel often misclassifies non-vascular structures (e.g., step, ramp or other transients) as vessels. To avoid such false detection, this paper introduces Laplacian of Gaussian (LoG) filters in the vessel segmentation process. The inherent zero-crossing property of LoG filter is used in the algorithm, along with the MF, in order to extract vessels reliably from the retinal images. The proposed method is validated against three publicly available databases, STARE, DRIVE and HRF. Simulation results show that the proposed method is able to segment vessels accurately from the three database images with an average accuracy that is competitive to the existing methodologies.

Question: How to segment blood vessels from retinal images?


Reproducibility of quantitative tractography methods applied to cerebral white matter. Tractography based on diffusion tensor imaging (DTI) allows visualization of white matter tracts. In this study, protocols to reconstruct eleven major white matter tracts are described. The protocols were refined by several iterations of intra- and inter-rater measurements and identification of sources of variability. Reproducibility of the established protocols was then tested by raters who did not have previous experience in tractography. The protocols were applied to a DTI database of adult normal subjects to study size, fractional anisotropy (FA), and T2 of individual white matter tracts. Distinctive features in FA and T2 were found for the corticospinal tract and callosal fibers. Hemispheric asymmetry was observed for the size of white matter tracts projecting to the temporal lobe. This protocol provides guidelines for reproducible DTI-based tract-specific quantification.

Question: How do you quantify the size of a white matter tract?


Why are some problems hard? Evidence from Tower of Hanoi. This paper analyzes the causes for large differences in difficulty of various isomorphic versions of the Tower of Hanoi problem. Some forms of the problem take 16 times as long to solve, on average, as other versions. Since isomorphism rules out size of task domain as a determinant of relative difficulty, these experiments seek and find causes for the differences in features of the problem representation. Analysis of verbal protocols and the temporal patterning of moves allows the problem-solving behavior to be divided into exploratory and final-path phases. Entry into the final-path phase depends on acquisition of the ability to plan pairs of moves, an achievement made difficult by the working memory load it entails. This memory load can be reduced by automating the rules governing moves, either through problem exploration or training. Once automation has occurred, the solution is obtained very rapidly. Memory load is also proposed as the locus of other differences in difficulty found between various problem rep-

Question: What is the tower of hanoi?


Design, modeling and control of an omni-directional aerial vehicle. In this paper we present the design and control of a novel six degrees-of-freedom aerial vehicle. Based on a static force and torque analysis for generic actuator configurations, we derive an eight-rotor configuration that maximizes the vehicle's agility in any direction. The proposed vehicle design possesses full force and torque authority in all three dimensions. A control strategy that allows for exploiting the vehicle's decoupled translational and rotational dynamics is introduced. A prototype of the proposed vehicle design is built using reversible motor-propeller actuators and capable of flying at any orientation. Preliminary experimental results demonstrate the feasibility of the novel design and the capabilities of the vehicle.

Question: How to design an omni-directional aerial vehicle?


A Nonisolated Three-Port DC–DC Converter and Three-Domain Control Method for PV-Battery Power Systems. In order to interface one photovoltaic (PV) port, one bidirectional battery port, and one load port of a PV-battery dc power system, a novel nonisolated three-port dc/dc converter named boost bidirectional buck converter (B3C) and its control method based on three-domain control are proposed in this paper. The power flow and operating principles of the proposed B3C are analyzed in detail, and then, the dc voltage relation between three ports is deduced. The proposed converter features high integration and single-stage power conversion from both PV and battery ports to the load port, thus leading to high efficiency. The current of all three ports is continuous; hence, the electromagnetic noise can be reduced. Furthermore, the control and modulation method for B3C has been proposed for realizing maximum power point tracking (MPPT), battery management, and bus voltage regulation simultaneously. The operation can be transited between conductance mode and MPPT mode automatically according to the load power. Finally, experimental verifications are given to illustrate the feasibility and effectiveness of the proposed topology and control method.

Question: What is the difference between a nonisolated three-port dc/dc converter and a nonisolated three-port dc/dc converter?


Risk factors for shoulder and elbow injuries in adolescent baseball pitchers.. BACKGROUND There is little evidence supporting current safety recommendations for adolescent pitchers.   HYPOTHESIS Pitching practices of adolescent pitchers without history of arm injury will be significantly different from those of adolescent pitchers who required shoulder or elbow surgery.   STUDY DESIGN Case control study; Level of evidence, 3.   METHODS Ninety-five adolescent pitchers who had shoulder or elbow surgery and 45 adolescent pitchers who never had a significant pitching-related injury completed a survey. Responses were compared between the 2 groups using t tests and chi(2) analyses. Multivariable logistic regression models were developed to identify the risk factors.   RESULTS The injured group pitched significantly more months per year, games per year, innings per game, pitches per game, pitches per year, and warm-up pitches before a game. These pitchers were more frequently starting pitchers, pitched in more showcases, pitched with higher velocity, and pitched more often with arm pain and fatigue. They also used anti-inflammatory drugs and ice more frequently to prevent an injury. Although the groups were age matched, the injured group was taller and heavier. There were no significant differences regarding private pitching instruction, coach's chief concern, pitcher's self-rating, exercise programs, stretching practices, relieving frequency, pitch type frequency, or age at which pitch types were first thrown.   CONCLUSION Pitching practices were significantly different between the groups. The factors with the strongest associations with injury were overuse and fatigue. High pitch velocity and participation in showcases were also associated with increased risk for injury.   CLINICAL RELEVANCE New recommendations were made based on these results. Adherence to the recommendations may reduce the incidence of significant injury to adolescent pitchers.

Question: What are the risk factors for shoulder and elbow injuries in adolescent baseball pitchers?


Optimizing Space Amplification in RocksDB. RocksDB is an embedded, high-performance, persistent keyvalue storage engine developed at Facebook. Much of our current focus in developing and configuring RocksDB is to give priority to resource efficiency instead of giving priority to the more standard performance metrics, such as response time latency and throughput, as long as the latter remain acceptable. In particular, we optimize space efficiency while ensuring read and write latencies meet service-level requirements for the intended workloads. This choice is motivated by the fact that storage space is most often the primary bottleneck when using Flash SSDs under typical production workloads at Facebook. RocksDB uses log-structured merge trees to obtain significant space efficiency and better write throughput while achieving acceptable read performance. This paper describes methods we used to reduce storage usage in RocksDB. We discuss how we are able to trade off storage efficiency and CPU overhead, as well as read and write amplification. Based on experimental evaluations of MySQL with RocksDB as the embedded storage engine (using TPC-C and LinkBench benchmarks) and based on measurements taken from production databases, we show that RocksDB uses less than half the storage that InnoDB uses, yet performs well and in many cases even better than the B-tree-based InnoDB storage engine. To the best of our knowledge, this is the first time a Log-structured merge treebased storage engine has shown competitive performance when running OLTP workloads at large scale.

Question: How much space does rocksdb use?


The social and ethical issues of post-genomic human biobanks. Biobanking — the organized collection of biological samples and associated data — ranges in scope from small collections of samples in academic or hospital settings to large-scale national repositories. Biobanks raise many ethical concerns, to which authorities are responding by introducing specific regulations. Genomics research, which thrives on the sharing of samples and information, is affected by two prominent ethical questions: do ethical principles prevent or promote the sharing of stored biological resources? How does the advent of large-scale biobanking alter the way in which ethical issues are addressed?

Question: What are the ethical issues of biobanking?


NFLB dropout: Improve generalization ability by dropping out the best -A biologically inspired adaptive dropout method for unsupervised learning. Generalization ability is widely acknowledged as one of the most important criteria to evaluate the quality of unsupervised models. The objective of our research is to find a better dropout method to improve the generalization ability of convolutional deep belief network (CDBN), an unsupervised learning model for vision tasks. In this paper, the phenomenon of low feature diversity during the training process is investigated. The attention mechanism of human visual system is more focused on rare events and depresses well-known facts. Inspired by this mechanism, No Feature Left Behind Dropout (NFLB Dropout), an adaptive dropout method is firstly proposed to automatically adjust the dropout rate feature-wisely. In the proposed method, the algorithm drops well-trained features and keeps poorly-trained ones with a high probability during training iterations. In addition, we apply two approximations of the quality of features, which are inspired by theory of saliency and optimization. Compared with the model trained by standard dropout, experiment results show that our NFLB Dropout method improves not only the accuracy but the convergence speed as well.

Question: How to improve the generalization ability of convolutional deep belief network?


The Open University ' s repository of research publications and other research outputs Learning analytics : drivers , developments and challenges. Learning analytics is a significant area of technology-enhanced learning that has emerged during the last decade. This review of the field begins with an examination of the technological, educational and political factors that have driven the development of analytics in educational settings. It goes on to chart the emergence of learning analytics, including their origins in the 20th century, the development of data-driven analytics, the rise of learningfocused perspectives and the influence of national economic concerns. It next focuses on the relationships between learning analytics, educational data mining and academic analytics. Finally, it examines developing areas of learning analytics research, and identifies a series of future challenges.

Question: What is the open university?


Bulk current injection test modeling using an equivalent circuit for 1.8V mobile ICs. This paper shows a novel simulation method for bulk current injection (BCI) tests of I/O buffer circuits of mobile system memory. The simulation model consists of BCI probe, directional coupler, PCB, PKG, and IC. The proposed method is based on a behavioural I/O buffer model using a pulse generator as an input. A detailed simulation flow is introduced and validated through simulations performed on several injection probe loading conditions using a power decoupling capacitor and an on-chip decoupling capacitor.

Question: How to simulate a bulk current injection test?


Semi-autonomous flying robot for physical interaction with environment. This contribution presents the first results of the development of an unmanned aerial vehicle (UAV) which is capable of applying force to a wall while maintaining flight stability. This is a novel idea since UAVs are used so far only for tasks without physical contact to the surrounding objects. The basis for the work presented is a quadrotor system which is stabilized with an inertial measurement unit. As a new approach an additional actuator was added to generate forces in physical contact while the UAV stays horizontal. A control architecture based on ultrasonic distance sensors and a CMOS-camera is proposed. The performance of the system was proved by several flight tests. Potential applications of the system can be physical tasks at high places like cleaning windows or walls as well as rescue or maintenance tasks.

Question: How can a robot be used to clean windows?


Weighted Low-Rank Approximations. Weighted-norms can arise in several situations. A zero/one weighted-norm, for example, arises when some of the entries in the matrix are not observed. External estimates of the noise variance associated with each measurement may be available (e.g. gene expression analysis) and using weights inversely proportional to the noise variance can lead to better reconstruction of the underlying structure. In other applications, entries in the target matrix represent aggregates of many samples. When using unweighted low-rank approximations (e.g. for separating style and content [4]), we assume a uniform number of samples for each entry. By incorporating weights, we can account for varying numbers of samples in such situations. Low-rank approximations are also used in the design of two-dimensional digital filters, in which case weights might arise from constraints of varying importance [3].

Question: How to use weights in low-rank approximations?


Cyber-Physical Systems: A New Frontier. The report of the President's Council of Advisors on Science and Technology (PCAST) has placed CPS on the top of the priority list for federal research investment [6]. This article first reviews some of the challenges and promises of CPS, followed by an articulation of some specific challenges and promises that are more closely related to the sensor networks, ubiquitous and trustworthy computing conference.

Question: What is cyber-physical systems?


PUMA: Planning Under Uncertainty with Macro-Actions. Planning in large, partially observable domains is challenging, especially when a long-horizon lookahead is necessary to obtain a good policy. Traditional POMDP planners that plan a different potential action for each future observation can be prohibitively expensive when planning many steps ahead. An efficient solution for planning far into the future in fully observable domains is to use temporallyextended sequences of actions, or "macro-actions." In this paper, we present a POMDP algorithm for planning under uncertainty with macro-actions (PUMA) that automatically constructs and evaluates open-loop macro-actions within forward-search planning, where the planner branches on observations only at the end of each macro-action. Additionally, we show how to incrementally refine the plan over time, resulting in an anytime algorithm that provably converges to an ǫ-optimal policy. In experiments on several large POMDP problems which require a long horizon lookahead, PUMA outperforms existing state-of-the art solvers. Most partially observable Markov decision process (POMDP) planners select actions conditioned on the prior observation at each timestep: we refer to such planners as fully-conditional. When good performance relies on considering different possible observations far into the future, both online and offline fully-conditional planners typically struggle. An extreme alternative is unconditional (or "openloop") planning where a sequence of actions is fixed and does not depend on the observations that will be received during execution. While open-loop planning can be extremely fast and perform surprisingly well in certain domains, acting well in most real-world domains requires plans where at least some action choices are conditional on the obtained observations. This paper focuses on the significant subset of POMDP domains, including scientific exploration, target surveillance, and chronic care management, where

Question: What is the difference between a fully-conditional and an open-loop planner?