egoSlider: Visual Analysis of Egocentric Network Evolution. Ego-network, which represents relationships between a specific individual, i.e., the ego, and people connected to it, i.e., alters, is a critical target to study in social network analysis. Evolutionary patterns of ego-networks along time provide huge insights to many domains such as sociology, anthropology, and psychology. However, the analysis of dynamic ego-networks remains challenging due to its complicated time-varying graph structures, for example: alters come and leave, ties grow stronger and fade away, and alter communities merge and split. Most of the existing dynamic graph visualization techniques mainly focus on topological changes of the entire network, which is not adequate for egocentric analytical tasks. In this paper, we present egoSlider, a visual analysis system for exploring and comparing dynamic ego-networks. egoSlider provides a holistic picture of the data through multiple interactively coordinated views, revealing ego-network evolutionary patterns at three different layers: a macroscopic level for summarizing the entire ego-network data, a mesoscopic level for overviewing specific individuals' ego-network evolutions, and a microscopic level for displaying detailed temporal information of egos and their alters. We demonstrate the effectiveness of egoSlider with a usage scenario with the DBLP publication records. Also, a controlled user study indicates that in general egoSlider outperforms a baseline visualization of dynamic networks for completing egocentric analytical tasks.

Question: What is the difference between a macroscopic, mesoscopic, and microscopic level?


CamOdoCal: Automatic intrinsic and extrinsic calibration of a rig with multiple generic cameras and odometry. Multiple cameras are increasingly prevalent on robotic and human-driven vehicles. These cameras come in a variety of wide-angle, fish-eye, and catadioptric models. Furthermore, wheel odometry is generally available on the vehicles on which the cameras are mounted. For robustness, vision applications tend to use wheel odometry as a strong prior for camera pose estimation, and in these cases, an accurate extrinsic calibration is required in addition to an accurate intrinsic calibration. To date, there is no known work on automatic intrinsic calibration of generic cameras, and more importantly, automatic extrinsic calibration of a rig with multiple generic cameras and odometry. We propose an easy-to-use automated pipeline that handles both intrinsic and extrinsic calibration; we do not assume that there are overlapping fields of view. At the begining, we run an intrinsic calibration for each generic camera. The intrinsic calibration is automatic and requires a chessboard. Subsequently, we run an extrinsic calibration which finds all camera-odometry transforms. The extrinsic calibration is unsupervised, uses natural features, and only requires the vehicle to be driven around for a short time. The intrinsic parameters are optimized in a final bundle adjustment step in the extrinsic calibration. In addition, the pipeline produces a globally-consistent sparse map of landmarks which can be used for visual localization. The pipeline is publicly available as a standalone C++ package.

Question: How to calibrate a camera rig with multiple generic cameras and odometry?


Empirical Study on Deep Learning Models for QA. In this paper we explore deep learning models with memory component or attention mechanism for question answering task. We combine and compare three models, Neural Machine Translation [1], Neural Turing Machine [5], and Memory Networks [15] for a simulated QA data set [14]. This paper is the first one that uses Neural Machine Translation and Neural Turing Machines for solving QA tasks. Our results suggest that the combination of attention and memory have potential to solve certain QA problem.

Question: What is the first paper that uses neural machine translation and neural turing machines for solving question answering tasks?


Inferring Strategies for Sentence Ordering in Multidocument News Summarization. The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocument summarization where summary sentences may be drawn from different input articles. In this paper, we propose a methodology for studying the properties of ordering information in the news genre and describe experiments done on a corpus of multiple acceptable orderings we developed for the task. Based on these experiments, we implemented a strategy for ordering information that combines constraints from chronological order of events and topical relatedness. Evaluation of our augmented algorithm shows a significant improvement of the ordering over two baseline strategies.

Question: How to order sentences in a document?


Enduring effects for cognitive behavior therapy in the treatment of depression and anxiety.. Recent studies suggest that cognitive and behavioral interventions have enduring effects that reduce risk for subsequent symptom return following treatment termination. These enduring effects have been most clearly demonstrated with respect to depression and the anxiety disorders. It remains unclear whether these effects are a consequence of the amelioration of the causal processes that generate risk or the introduction of compensatory strategies that offset them and whether these effects reflect the mobilization of cognitive or other mechanisms. No such enduring effects have been observed for the psychoactive medications, which appear to be largely palliative in nature. Other psychosocial interventions remain largely untested, although claims that they produce lasting change have long been made. Whether such enduring effects extend to other disorders remains to be seen, but the capacity to reduce risk following treatment termination is one of the major benefits provided by the cognitive and behavioral interventions with respect to the treatment of depression and the anxiety disorders.

Question: What are the enduring effects of cognitive behavior therapy?


Distributed Packet Inspection By Using PIL-2 Language. Packet Inspection language PIL-2 incorporates directly support for distributed processing into the language.PIL-2 supports generic header filter, payload scanning, packet replication and traffic flow in network. By distributing PIL-2 based on the possibility created for heterogeneous network of processing node, which gives monitoring architecture facilities with high speed packet processing. The result shows that perform complex processing at terra bit speeds. This proposed frame work can be used to execute distinct task as load balancing, traffic monitoring, firewalls and IDS at high bandwidth links. Keywords—PIL-2,Load balancing, IDS, traffic monitoring.

Question: What is the difference between PIL-2 and PIL-3?


An empirical study on the use of i* by non-technical stakeholders: the case of strategic dependency diagrams. Early phases of information systems engineering include the understanding of the enterprise's context and the construction of models at different levels of decomposition, required to design the system architecture. These time-consuming activities are usually conducted by relatively large teams, composed of groups of non-technical stakeholders playing mostly an informative role (i.e. not involved in documentation and even less in modelling), led by few experienced technical consultants performing most of the documenting and modelling effort. This paper evaluates the ability of non-technical stakeholders to create strategic dependency diagrams written with the i* language in the design of the context model of a system architecture, and find out which difficulties they may encounter and what the quality of the models they build is. A case study involving non-technical stakeholders from 11 organizational areas in an Ecuadorian university held under the supervision and coordination of the two authors acting as consultants. The non-technical stakeholders identified the majority of the dependencies that should appear in the case study's context model, although they experienced some difficulties in declaring the type of dependency, representing such dependencies graphically and applying the description guidelines provided in the training. Managers were observed to make more mistakes than other more operational roles. From the observations of these results, a set of methodological advices were compiled for their use in future, similar endeavours. It is concluded that non-technical stakeholders can take an active role in the construction of the context model. This conclusion is relevant for both researchers and practitioners involved in technology transfer actions with use of i*.

Question: What is the role of non-technical stakeholders in the construction of the context model?


Aggregate Profile Clustering for Streaming Analytics. Many analytic applications require analyzing user interaction data. In particular, such data can be aggregated over a window to build user activity profiles. Clustering such aggregate profiles is useful for grouping together users with similar behaviors, so that common models could be built for them. In this paper, we present an approach for clustering profiles that are incrementally maintained over a stream of updates. Owing to the potentially large number of users and high rate of interactions, maintaining profile clusters can have high processing and memory resource requirements. To tackle this problem, we apply distributed stream processing. However, in the presence of distributed state, it is a major challenge to partition the profiles over nodes such that memory and computation balance is maintained, while keeping the clustering accuracy high. Furthermore, in order to adapt to potentially changing user interaction patterns, the partitioning of profiles to nodes should be continuously revised, yet one should minimize the migration of profiles so as not to disturb the online processing of updates. We develop a re-partitioning technique that achieves all these goals. To achieve this, we keep micro-cluster summaries at each node and periodically collect these summaries at a central node to perform re-partitioning. We use a greedy algorithm with novel affinity heuristics to revise the partitioning and update the routing tables without introducing a lengthy pause. We showcase the effectiveness of our approach using an application that clusters customers of a telecommunications company based on their aggregate calling profiles.

Question: How to cluster user profiles?


BLE device indoor localization based on RSS fingerprinting mapped by propagation modes. Nowadays, Bluetooth Low Energy (BLE) technology has a great attention in the field of wireless localization techniques, especially in the case of indoor scenarios. Study presented in this paper explores BLE localization performance in indoor scenarios based on a received signal strength (RSS). Firstly, we present a Ray-Launching based application to emulate BLE radio frequency (RF) signal propagation in the office environment. Secondly, an appropriate measurement workplace and its setup is proposed to measure RSS values. Obtained results are used to create an RSS-fingerprinting map. Furthermore, accuracy of position determination is calculated and evaluated. Results show advantage of BLE technology for indoor localization purposes (without calibration measurement). However, its performance highly depends on the number of considered BLE nodes and on applied evaluation method (e.g. number of considered sectors).

Question: How to measure RSS values in an office environment?


Performance analysis of low-power 1-bit CMOS full adder cells. A performance analysis of 1-bit full-adder cell is presented. The adder cell is anatomized into smaller modules. The modules are studied and evaluated extensively. Several designs of each of them are developed, prototyped, simulated and analyzed. Twenty different 1-bit full-adder cells are constructed (most of them are novel circuits) by connecting combinations of different designs of these modules. Each of these cells exhibits different power consumption, speed, area, and driving capability figures. Two realistic circuit structures that include adder cells are used for simulation. A library of full-adder cells is developed and presented to the circuit designers to pick the full-adder cell that satisfies their specific applications.

Question: What is a full adder cell?


Chatbot for IT Security Training: Using Motivational Interviewing to Improve Security Behaviour. We conduct a pre-study with 25 participants on Mechanical Turk to find out which security behavioural problems are most important for online users. These questions are based on motivational interviewing (MI), an evidence-based treatment methodology that enables to train people about different kinds of behavioural changes. Based on that the chatbot is developed using Artificial Intelligence Markup Language (AIML). The chatbot is trained to speak about three topics: passwords, privacy and secure browsing. These three topics were 'most-wanted' by the users of the pre-study. With the chatbot three training sessions with people are conducted.

Question: What is the chatbot for IT Security Training?


Quantifying the total wetted surface area of the world fleet: a first step in determining the potential extent of ships' biofouling. Ships' hulls can transport aquatic nuisance species, but there is little quantitative information about the magnitude of vessel biofouling on a global or regional scale. There does not exist a robust method to estimate the wetted surface area (WSA) of a particular fleet of ships, especially across the diversity of possible vessel types. An estimate of the total WSA of ship arrivals into a port or region is essential to determine the potential scope of biofouling and to inform management strategies to reduce the future invasions. Multiple statistical models were developed so commonly available ships' parameters could be used to estimate the WSA for a given set of fleet data. Using individual ship characteristics and publicly available data from  ~120,000 active commercial ships in the world fleet, the method results in a total global minimum WSA estimate of approximately 325 × 106 m2. The size of the global fleet employed here is greater than the commonly cited vessel number of approximately 80,000–90,000, as we include ships <100 gross tons. Over 190,000 vessels were initially identified, representing a WSA of 571 × 106 m2, but active status of only 120,000 vessels could be confirmed. Reliable data were unavailable on the operating status of many additional and especially smaller vessels. This approach, along with a contemporary and comprehensive estimate of global WSA, when combined with knowledge of the different operational profiles of ships that may reduce biofouling (port residence times, steaming speeds, extent of antifouling coatings, cleaning frequency, etc.), can inform current numerical models and risk assessments of bioinvasions.

Question: How many ships are there in the world fleet?


Hybrid distribution transformer: Concept development and field demonstration. Today's distribution system is expected to supply power to loads for which it was not designed. Moreover, high penetration of distributed generation units is redefining the requirements for the design, control and operation of the electric distribution system. A Hybrid Distribution Transformer is a potential cost-effective alternative solution to various distribution grid control devices. The Hybrid Distribution Transformer is realized by augmenting a regular transformer with a fractionally rated power electronic converter, which provides the transformer with additional control capabilities. The Hybrid Distribution Transformer concept can provide dynamic ac voltage regulation, reactive power compensation and, in future designs, form an interface with energy storage devices. Other potential functionalities that can be realized from the Hybrid Distribution Transformer include voltage phase angle control, harmonic compensation and voltage sag compensation. This paper presents the concept of a Hybrid Distribution Transformer and the status of our efforts towards a 500 kVA, 12.47 kV/480 V field demonstrator.

Question: What is the status of the hybrid distribution transformer?


A pseudo-class-AB telescopic-cascode operational amplifier. A class-AB architecture for single-stage operational amplifiers is presented. The structure employs a switched-capacitor level shifter to apply the signal to both sink and source output transistors to create the class-AB behavior. Using this structure, the current mirror circuit of traditional class-AB structures can be eliminated. Thus some power can be saved and the operation frequency can be increased. Simulation results of a fast-settling telescopic-cascode op-amp employing this pseudo-class-AB technique confirm the effectiveness of the approach to reduce the power consumption. This approach can be applied to the first stage of a two-stage op-amp as well, to increase the slew rate. The simulated 12-bit 100-Ms/s sample-and-hold amplifier employing the proposed pseudo-class-AB telescopic-cascode op-amp consumes only 16 mW from 3.3 V power supply.

Question: What is a pseudo-class-AB telescopic-cascode op-amp?


OAuth Demystified for Mobile Application Developers. OAuth has become a highly influential protocol due to its swift and wide adoption in the industry. The initial objective of the protocol was specific: it serves the authorization needs for websites. What motivates our work is the realization that the protocol has been significantly re-purposed and re-targeted over the years: (1) all major identity providers, e.g., Facebook, Google and Microsoft, have re-purposed OAuth for user authentication; (2) developers have re-targeted OAuth to the mobile platforms, in addition to the traditional web platform. Therefore, we believe that it is necessary and timely to conduct an in-depth study to demystify OAuth for mobile application developers. Our work consists of two pillars: (1) an in-house study of the OAuth protocol documentation that aims to identify what might be ambiguous or unspecified for mobile developers; (2) a field-study of over 600 popular mobile applications that highlights how well developers fulfill the authentication and authorization goals in practice. The result is really worrisome: among the 149 applications that use OAuth, 89 of them (59.7%) were incorrectly implemented and thus vulnerable. In the paper, we pinpoint the key portions in each OAuth protocol flow that are security critical, but are confusing or unspecified for mobile application developers. We then show several representative cases to concretely explain how real implementations fell into these pitfalls. Our findings have been communicated to vendors of the vulnerable applications. Most vendors positively confirmed the issues, and some have applied fixes. We summarize lessons learned from the study, hoping to provoke further thoughts about clear guidelines for OAuth usage in mobile applications.

Question: What is the difference between OAuth and OpenID?


DifNet: Semantic Segmentation by Diffusion Networks. Deep Neural Networks (DNNs) have recently shown state of the art performance on semantic segmentation tasks, however, they still suffer from problems of poor boundary localization and spatial fragmented predictions. The difficulties lie in the requirement of making dense predictions from a long path model all at once, since details are hard to keep when data goes through deeper layers. Instead, in this work, we decompose this difficult task into two relative simple sub-tasks: seed detection which is required to predict initial predictions without the need of wholeness and preciseness, and similarity estimation which measures the possibility of any two nodes belong to the same class without the need of knowing which class they are. We use one branch network for one sub-task each, and apply a cascade of random walks base on hierarchical semantics to approximate a complex diffusion process which propagates seed information to the whole image according to the estimated similarities. The proposed DifNet consistently produces improvements over the baseline models with the same depth and with the equivalent number of parameters, and also achieves promising performance on Pascal VOC and Pascal Context dataset. Our DifNet is trained end-to-end without complex loss functions.

Question: What is a difnet?


Involvement of gut microbiome in human health and disease: brief overview, knowledge gaps and research opportunities. The commensal, symbiotic, and pathogenic microbial community which resides inside our body and on our skin (the human microbiome) can perturb host energy metabolism and immunity, and thus significantly influence development of a variety of human diseases. Therefore, the field has attracted unprecedented attention in the last decade. Although a large amount of data has been generated, there are still many unanswered questions and no universal agreements on how microbiome affects human health have been agreed upon. Consequently, this review was written to provide an updated overview of the rapidly expanding field, with a focus on revealing knowledge gaps and research opportunities. Specifically, the review covered animal physiology, optimal microbiome standard, health intervention by manipulating microbiome, knowledge base building by text mining, microbiota community structure and its implications in human diseases and health monitoring by analyzing microbiome in the blood. The review should enhance interest in conducting novel microbiota investigations that will further improve health and therapy.

Question: What is the commensal, symbiotic, and pathogenic microbial community which resides inside our body and on our skin (the human microbiome)?


Data resource profile: the World Health Organization Study on global AGEing and adult health (SAGE).. Population ageing is rapidly becoming a global issue and will have a major impact on health policies and programmes. The World Health Organization's Study on global AGEing and adult health (SAGE) aims to address the gap in reliable data and scientific knowledge on ageing and health in low- and middle-income countries. SAGE is a longitudinal study with nationally representative samples of persons aged 50+ years in China, Ghana, India, Mexico, Russia and South Africa, with a smaller sample of adults aged 18-49 years in each country for comparisons. Instruments are compatible with other large high-income country longitudinal ageing studies. Wave 1 was conducted during 2007-2010 and included a total of 34 124 respondents aged 50+ and 8340 aged 18-49. In four countries, a subsample consisting of 8160 respondents participated in Wave 1 and the 2002/04 World Health Survey (referred to as SAGE Wave 0). Wave 2 data collection will start in 2012/13, following up all Wave 1 respondents. Wave 3 is planned for 2014/15. SAGE is committed to the public release of study instruments, protocols and meta- and micro-data: access is provided upon completion of a Users Agreement available through WHO's SAGE website (www.who.int/healthinfo/systems/sage) and WHO's archive using the National Data Archive application (http://apps.who.int/healthinfo/systems/surveydata).

Question: What is the study on global ageing and adult health?


Agile Modeling with the UML. This paper discusses a model-based approach to software development. It argues that an approach using models as central development artifact needs to be added to the portfolio of software engineering techniques, to further increase efficiency and flexibility of the development as well as quality and reusability of the results. Two major and strongly related techniques are identified and discussed: Test case modeling and an evolutionary approach to model transformation.

Question: What is a model-based approach to software development?


A design process for the development of innovative smart clothing that addresses end-user needs from technical, functional, aesthetic and cultural view points. The research and development of attractive smart clothing, with truly embedded technologies, demands the merging of science and technology with art and design. This paper looks at a comparatively new and unique design discipline that has been given little prior consideration. The concept of 'wearables' crosses the boundaries between many disciplines. A gap exists for a common 'language' that facilitates creativity and a systematic design process. Designers of smart clothing require guidance in their enquiry, as gaining an understanding of issues such as; usability, manufacture, fashion, consumer culture, recycling and end user needs can seem forbidding and difficult to prioritise. This paper presents a design tool in a format that can be understood by practitioners who come from a range of backgrounds. The representation of the 'critical path' is intended as a tool to guide the design research and development process in the application of smart technologies.

Question: What is a design tool?


TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency. In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence – both semantic and syntactic – but might face difficulty remembering long-range dependencies. Intuitively, these long-range dependencies are of semantic nature. In contrast, latent topic models are able to capture the global semantic structure of a document but do not account for word ordering. The proposed TopicRNN model integrates the merits of RNNs and latent topic models: it captures local (syntactic) dependencies using an RNN and global (semantic) dependencies using latent topics. Unlike previous work on contextual RNN language modeling, our model is learned endto-end. Empirical results on word prediction show that TopicRNN outperforms existing contextual RNN baselines. In addition, TopicRNN can be used as an unsupervised feature extractor for documents. We do this for sentiment analysis on the IMDB movie review dataset and report an error rate of 6.28%. This is comparable to the state-of-the-art 5.91% resulting from a semi-supervised approach. Finally, TopicRNN also yields sensible topics, making it a useful alternative to document models such as latent Dirichlet allocation.

Question: What is the difference between a recurrent neural network and a recurrent neural network language model?


Byte Rotation Encryption Algorithm through parallel processing and multi-core utilization. Securing digital data has become tedious task as the technology is increasing. Existing encryption algorithms such as AES,DES and Blowfish ensure information security but consume lot of time as the security level increases. In this paper, Byte Rotation Encryption Algorithm (BREA) has implemented using parallel processing and multi-core utilization. BREA divides data into fixed size blocks. Each block is processed parallely using random number key. So the set of blocks are executed in parallel by utilizing all the available CPU cores. Finally, from the experimental analysis, it is observed that the proposed BREA algorithm reduces execution time when the number of cores has increased.

Question: How to encrypt data using parallel processing and multi-core utilization?


Is it a tool suitable for learning? A critical review of the literature on Facebook as a technology-enhanced learning environment. Despite its continuing popularity as the social network site par excellence, the educational value of Facebook has not been fully determined, and results from the mainstream educational paradigms are contradictory, with some scholars emphasizing its pedagogical affordances (e.g., widening context of learning, mixing information and learning resources, hybridization of expertise) and others cautioning against its use for educational purposes. Moreover, systematic reviews about documented educational usage of Facebook as a learning environment are lacking. This article attempts to provide a critical overview of current studies focusing on the use of Facebook as a technology-enhanced learning environment, with the aim of exploring the extent to which its pedagogical potential is actually translated into practice. Only empirical studies published in peer-reviewed academic journals with a specific focus on Facebook as a learning environment have been considered for the review. The authors conducted a comprehensive literature search that identified 23 relevant articles that were subsequently analysed according to a simplified list of guidelines. These articles were further analysed and recoded through a set of emerging categories. The results show that pedagogical affordances of Facebook have only been partially implemented and that there are still many obstacles that may prevent a full adoption of Facebook as a learning environment such as implicit institutional, teacher and student pedagogies, and cultural issues. Finally, a broad observation on the implications of the study is developed with some suggestions for future research.

Question: What is the pedagogical affordances of Facebook?


Cognitive Reduced Prefrontal Connectivity in Psychopathy. Julian C. Motzkin,1 Joseph P. Newman,2 Kent A. Kiehl,3,4 and Michael Koenigs1 1Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin 53719, 2Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin 53706, 3The MIND Research Network, Albuquerque, New Mexico 87131, and 4Departments of Psychology and Neuroscience, University of New Mexico, Albuquerque, New Mexico 87131

Question: What is the difference between the prefrontal cortex and the frontal lobe?


PSIM-based modeling of automotive power systems: conventional, electric, and hybrid electric vehicles. Automotive manufacturers have been taking advantage of simulation tools for modeling and analyzing various types of vehicles, such as conventional, electric, and hybrid electric vehicles. These simulation tools are of great assistance to engineers and researchers to reduce product-development cycle time, improve the quality of the design, and simplify the analysis without costly and time-consuming experiments. In this paper, a modeling tool that has been developed to study automotive systems using the power electronics simulator (PSIM) software is presented. PSIM was originally made for simulating power electronic converters and motor drives. This user-friendly simulation package is able to simulate electric/electronic circuits; however, it has no capability for simulating the entire system of an automobile. This paper discusses the PSIM validity as an automotive simulation tool by creating module boxes for not only the electrical systems, but also the mechanical, energy-storage, and thermal systems of the vehicles. These modules include internal combustion engines, fuel converters, transmissions, torque couplers, and batteries. Once these modules are made and stored in the library, the user can make the car model either a conventional, an electric, or a hybrid vehicle at will, just by dragging and dropping onto a schematic blank page.

Question: What is a PSIM-based modeling tool?


Pharmacogenetics and adverse drug reactions. Polymorphisms in the genes that code for drug-metabolising enzymes, drug transporters, drug receptors, and ion channels can affect an individual's risk of having an adverse drug reaction, or can alter the efficacy of drug treatment in that individual. Mutant alleles at a single gene locus are the best studied individual risk factors for adverse drug reactions, and include many genes coding for drug-metabolising enzymes. These genetic polymorphisms of drug metabolism produce the phenotypes of "poor metabolisers" or "ultrarapid metabolisers" of numerous drugs. Together, such phenotypes make up a substantial proportion of the population. Pharmacogenomic techniques allow efficient analysis of these risk factors, and genotyping tests have the potential to optimise drug therapy in the future.

Question: What is pharmacogenetics?


Conservative treatment of fractures of the thoracolumbar spine. We reviewed 48 patients with thoracolumbar fractures treated conservatively between 1988 and 1999. The average follow-up was 77.5 (31–137) months and average patient age (23 women, 25 men) was 46 (18–76) years. Twenty-nine patients suffered a fall from a height and 13 patients were injured in traffic accidents. Thirty-two patients had compression-type fractures and 16 burst-type fractures. There were no neurological deficits. Twenty-nine patients were treated by orthosis, 13 by body cast and six by bed rest. In addition to pain and functional scoring, we measured a number of radiographic parameters at the time of admission and at latest follow-up and compared the values. In patients with compression fractures there were significant changes in scoliosis angle and wedging index (p<0.05). The mean pain score was 1.66 and mean functional score 1.03. In patients with burst fractures, vertebral index, wedging index and height loss increased after treatment (p<0.05). The mean pain score was 1.26 and functional score 0.93. Compression fractures with kyphosis angle <30° are supposed to be stable and can be treated conservatively. If the kyphosis angle is more than 30°, magnetic resonance imaging (MRI) should be performed, and if the posterior ligamentous complex is damaged, surgery should be considered. In burst fractures, MRI should always be performed and conservative treatment should only be considered if there is no neurological deficit and the ligaments are intact. Nous avons examiné 48 malades avec une fracture dorsolombaire traitée d'une manière conservatrice entre 1988 et 1999. Le suivi moyen était de 77,5 (31–137) mois et l'âge moyen des malades (23 femmes, 25 hommes) était de 46 (18–76) ans. Vingt-neuf malades avaient fait une chute de grande hauteur et 13 malades ont été blessés dans un accident de la circulation. Trente-deux malades avaient des fractures du type compression et 16 fractures du type ex

Question: What is the average age of patients with thoracolumbar fractures?


Emergence of neural encoding of auditory objects while listening to competing speakers.. A visual scene is perceived in terms of visual objects. Similar ideas have been proposed for the analogous case of auditory scene analysis, although their hypothesized neural underpinnings have not yet been established. Here, we address this question by recording from subjects selectively listening to one of two competing speakers, either of different or the same sex, using magnetoencephalography. Individual neural representations are seen for the speech of the two speakers, with each being selectively phase locked to the rhythm of the corresponding speech stream and from which can be exclusively reconstructed the temporal envelope of that speech stream. The neural representation of the attended speech dominates responses (with latency near 100 ms) in posterior auditory cortex. Furthermore, when the intensity of the attended and background speakers is separately varied over an 8-dB range, the neural representation of the attended speech adapts only to the intensity of that speaker but not to the intensity of the background speaker, suggesting an object-level intensity gain control. In summary, these results indicate that concurrent auditory objects, even if spectrotemporally overlapping and not resolvable at the auditory periphery, are neurally encoded individually in auditory cortex and emerge as fundamental representational units for top-down attentional modulation and bottom-up neural adaptation.

Question: How do we encode auditory objects?


Mobile Multimedia Recommendation in Smart Communities: A Survey. Due to the rapid growth of Internet broadband access and proliferation of modern mobile devices, various types of multimedia (e.g., text, images, audios, and videos) have become ubiquitously available anytime. Mobile device users usually store and use multimedia contents based on their personal interests and preferences. Mobile device challenges such as storage limitation have, however, introduced the problem of mobile multimedia overload to users. To tackle this problem, researchers have developed various techniques that recommend multimedia for mobile users. In this paper, we examine the importance of mobile multimedia recommendation systems from the perspective of three smart communities, namely mobile social learning, mobile event guide, and context-aware services. A cautious analysis of existing research reveals that the implementation of proactive, sensor-based and hybrid recommender systems can improve mobile multimedia recommendations. Nevertheless, there are still challenges and open issues such as the incorporation of context and social properties, which need to be tackled to generate accurate and trustworthy mobile multimedia recommendations.

Question: What is the importance of mobile multimedia recommendation systems from the perspective of three smart communities?


Performance evaluation of multi-target tracking using the OSPA metric. Performance evaluation of multi-target tracking algorithms is of great practical importance in the design and comparison of tracking systems. Recently a consistent metric for performance evaluation of multi-object filters (referred to as OSPA metric) has been proposed. In this paper we describe how the OSPA metric can be adapted to evaluate the performance of multi-target tracking algorithms. The main idea is to introduce the track label error into consideration in order to capture the data association performance, in addition to the existing cardinality and localisation errors. The paper demonstrates the proposed method by assessing and comparing the performance of two particle filters for multi-target tracking.

Question: How to evaluate the performance of multi-target tracking algorithms?


Unsupervised Learning of the Morphology of a Natural Language. This study reports the results of using minimum description length (MDL) analysis to model unsupervised learning of the morphological segmentation of European languages, using corpora ranging in size from 5,000 words to 500,000 words. We develop a set of heuristics that rapidly develop a probabilistic morphological grammar, and use MDL as our primary tool to determine whether the modifications proposed by the heuristics will be adopted or not. The resulting grammar matches well the analysis that would be developed by a human morphologist. In the final section, we discuss the relationship of this style of MDL grammatical analysis to the notion of evaluation metric in early generative grammar.

Question: What is the relationship between MDL and early generative grammar?


E-LAMP: integration of innovative ideas for multimedia event detection. Detecting multimedia events in web videos is an emerging hot research area in the fields of multimedia and computer vision. In this paper, we introduce the core methods and technologies of the framework we developed recently for our Event Labeling through Analytic Media Processing (E-LAMP) system to deal with different aspects of the overall problem of event detection. More specifically, we have developed efficient methods for feature extraction so that we are able to handle large collections of video data with thousands of hours of videos. Second, we represent the extracted raw features in a spatial bag-of-words model with more effective tilings such that the spatial layout information of different features and different events can be better captured, thus the overall detection performance can be improved. Third, different from widely used early and late fusion schemes, a novel algorithm is developed to learn a more robust and discriminative intermediate feature representation from multiple features so that better event models can be built upon it. Finally, to tackle the additional challenge of event detection with only very few positive exemplars, we have developed a novel algorithm which is able to effectively adapt the knowledge learnt from auxiliary sources to assist the event detection. Both our empirical results and the official evaluation results on TRECVID MED'11 and MED'12 demonstrate the excellent performance of the integration of these ideas.

Question: What is the e-lamp system?


Digital Nudging. Digital nudging is the use of user-interface design elements to guide people's behavior in digital choice environments. Digital choice environments are user interfaces – such as web-based forms and ERP screens – that require people to make judgments or decisions. Humans face choices every day, but the outcome of any choice is influenced not only by rational deliberations of the available options but also by the design of the choice environment in which information is presented, which can exert a subconscious influence on the outcome. In other words, ''what is chosen often depends upon how the choice is presented'' (Johnson et al. 2012, p. 488) such that the ''choice architecture alters people's behavior in a predictable way'' (Thaler and Sunstein 2008, p. 6). Even simple modifications of the choice environment in which options are presented can influence people's choices and ''nudge'' them into behaving in particular ways. In fact, there is no neutral way to present choices. For example, Johnson and Goldstein (2003) showed that simply changing default options (from opt-in to opt-out) in the context of organ donation nearly doubled the percentage of people who consent to being organ donors. Many choices are made in online environments. As the design of digital choice environments always (either deliberately or accidentally) influences people's choices, understanding the effects of digital nudges in these environments can help designers lead users to the most desirable choice. For example, the mobile payment app Square nudges people into giving tips by setting the default to ''tipping'' so that customers must actively select a ''no tipping'' option if they choose not to give a tip. Using this simple nudge has raised tip amounts, especially where little or no tipping has been common (Carr 2013). These examples show that simply changing the default option affects the outcome.

Question: What is the default option?


Diagnosis and compensation of amplitude imbalance, imperfect quadrant and offset in resolver signals. In this paper, a new method for detecting some errors in resolver signals is presented. Sin and cosine signals in resolver to digital converter's (RDC's) input might have errors, such as amplitude imbalance, imperfect quadrant or DC offset. Like other sensors some defects in mechanical structure and electrical or magnetic circuits cause to these errors in resolver output signals. The amplitude and phase of resolver output signals and consequently calculated angle are affected by these errors at any moment. Resolver output signals are more affected in their peak points than the other points. These peak points are easier and more accurate than the other points for analyzing, too. It is obvious that any error has unique effects on the signals. Therefore, by analyzing the effect on signals' peak points the error type and the value can be detected. To avoid wrong detection, error signal is defined, which detects the per unit signals deviation from the unit circle. Also, for error detection, there is no need to interrupt converter's operation. The proposed method is simulated in Matlab/Simulink environment, and the obtained results validate its effectiveness. Also, it is simulated on TMS320F2812 DSP using CCS 3.3, and the results are presented. Furthermore, by implementing the present RDC on motor controller DSP, it will be a low-cost approach.

Question: What is the difference between a resolver and a resolver signal?


Learning to generate chairs with convolutional neural networks. We train a generative convolutional neural network which is able to generate images of objects given object type, viewpoint, and color. We train the network in a supervised manner on a dataset of rendered 3D chair models. Our experiments show that the network does not merely learn all images by heart, but rather finds a meaningful representation of a 3D chair model allowing it to assess the similarity of different chairs, interpolate between given viewpoints to generate the missing ones, or invent new chair styles by interpolating between chairs from the training set. We show that the network can be used to find correspondences between different chairs from the dataset, outperforming existing approaches on this task.

Question: How to generate chairs with convolutional neural networks?


You Talking to Me? A Corpus and Algorithm for Conversation Disentanglement. When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately. We refer to this task as disentanglement. We present a corpus of Internet Relay Chat (IRC) dialogue in which the various conversations have been manually disentangled, and evaluate annotator reliability. This is, to our knowledge, the first such corpus for internet chat. We propose a graph-theoretic model for disentanglement, using discourse-based features which have not been previously applied to this task. The model's predicted disentanglements are highly correlated with manual annotations.

Question: How do you disentangle conversations?


ExtRA: Extracting Prominent Review Aspects from Customer Feedback. Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects. Conventionally, the prominent review aspects of a product type are determined manually. This costly approach cannot scale to large and cross-domain services such as Amazon.com, Taobao.com or Yelp.com where there are a large number of product types and new products emerge almost everyday. In this paper, we propose a novel framework, for extracting the most prominent aspects of a given product type from textual reviews. The proposed framework, ExtRA, extracts K most prominent aspect terms or phrases which do not overlap semantically automatically without supervision. Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types.

Question: What is the state-of-the-art performance on a dataset consisting of different product types?


No-Reference Image Quality Assessment in the Spatial Domain. We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of "naturalness" in the image due to the presence of distortions, thereby leading to a holistic measure of quality. The underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model. No transformation to another coordinate frame (DCT, wavelet, etc.) is required, distinguishing it from prior NR IQA approaches. Despite its simplicity, we are able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms. BRISQUE has very low computational complexity, making it well suited for real time applications. BRISQUE features may be used for distortion-identification as well. To illustrate a new practical application of BRISQUE, we describe how a nonblind image denoising algorithm can be augmented with BRISQUE in order to perform blind image denoising. Results show that BRISQUE augmentation leads to performance improvements over state-of-the-art methods. A software release of BRISQUE is available online: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip for public use and evaluation.

Question: What is the difference between blind and no-reference image quality assessment?


An empirical examination of the factor structure of compassion. Compassion has long been regarded as a core part of our humanity by contemplative traditions, and in recent years, it has received growing research interest. Following a recent review of existing conceptualisations, compassion has been defined as consisting of the following five elements: 1) recognising suffering, 2) understanding the universality of suffering in human experience, 3) feeling moved by the person suffering and emotionally connecting with their distress, 4) tolerating uncomfortable feelings aroused (e.g., fear, distress) so that we remain open to and accepting of the person suffering, and 5) acting or being motivated to act to alleviate suffering. As a prerequisite to developing a high quality compassion measure and furthering research in this field, the current study empirically investigated the factor structure of the five-element definition using a combination of existing and newly generated self-report items. This study consisted of three stages: a systematic consultation with experts to review items from existing self-report measures of compassion and generate additional items (Stage 1), exploratory factor analysis of items gathered from Stage 1 to identify the underlying structure of compassion (Stage 2), and confirmatory factor analysis to validate the identified factor structure (Stage 3). Findings showed preliminary empirical support for a five-factor structure of compassion consistent with the five-element definition. However, findings indicated that the 'tolerating' factor may be problematic and not a core aspect of compassion. This possibility requires further empirical testing. Limitations with items from included measures lead us to recommend against using these items collectively to assess compassion. Instead, we call for the development of a new self-report measure of compassion, using the five-element definition to guide item generation. We recommend including newly genera

Question: What is the five-element definition of compassion?


Real-time procedural generation of 'pseudo infinite' cities. We present an approach to procedural generation of `pseudo infinite' virtual cities in real-time. The cities contain geometrically varied buildings that are generated as needed. The building generation parameters are created by a pseudo random number generator, seeded with an integer derived from the building's position. The varied building geometries are extruded from a set of floor plans. The floor plans for each building are created by combining randomly generated polygons in an iterative process. A display list caching and frustum filling approach manages the generation of buildings and the use of system resources. This approach has been implemented on commodity PC hardware, resulting in interactive frame rates.

Question: How to generate pseudo infinite cities?


On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control. Recently, deep learning has appeared as a breakthrough machine learning technique for various areas in computer science as well as other disciplines. However, the application of deep learning for network traffic control in wireless/heterogeneous networks is a relatively new area. With the evolution of wireless networks, efficient network traffic control such as routing methodology in the wireless backbone network appears as a key challenge. This is because the conventional routing protocols do not learn from their previous experiences regarding network abnormalities such as congestion and so forth. Therefore, an intelligent network traffic control method is essential to avoid this problem. In this article, we address this issue and propose a new, real-time deep learning based intelligent network traffic control method, exploiting deep Convolutional Neural Networks (deep CNNs) with uniquely characterized inputs and outputs to represent the considered Wireless Mesh Network (WMN) backbone. Simulation results demonstrate that our proposal achieves significantly lower average delay and packet loss rate compared to those observed with the existing routing methods. We particularly focus on our proposed method's independence from existing routing protocols, which makes it a potential candidate to remove routing protocol(s) from future wired/ wireless networks.

Question: What is the difference between a wireless mesh network and a wireless network?


Adversarial Perturbations Against Deep Neural Networks for Malware Classification. Deep neural networks, like many other machine learning models, have recently been shown to lack robustness against adversarially crafted inputs. These inputs are derived from regular inputs by minor yet carefully selected perturbations that deceive machine learning models into desired misclassifications. Existing work in this emerging field was largely specific to the domain of image classification, since the highentropy of images can be conveniently manipulated without changing the images' overall visual appearance. Yet, it remains unclear how such attacks translate to more securitysensitive applications such as malware detection–which may pose significant challenges in sample generation and arguably grave consequences for failure. In this paper, we show how to construct highly-effective adversarial sample crafting attacks for neural networks used as malware classifiers. The application domain of malware classification introduces additional constraints in the adversarial sample crafting problem when compared to the computer vision domain: (i) continuous, differentiable input domains are replaced by discrete, often binary inputs; and (ii) the loose condition of leaving visual appearance unchanged is replaced by requiring equivalent functional behavior. We demonstrate the feasibility of these attacks on many different instances of malware classifiers that we trained using the DREBIN Android malware data set. We furthermore evaluate to which extent potential defensive mechanisms against adversarial crafting can be leveraged to the setting of malware classification. While feature reduction did not prove to have a positive impact, distillation and re-training on adversarially crafted samples show promising results.

Question: How to craft adversarial samples for malware classification?


A 5.8-Gb/s Adaptive Integrating Duobinary DFE Receiver for Multi-Drop Memory Interface. This paper describes a 5.8 Gb/s adaptive integrating duobinary decision-feedback equalizer (DFE) for use in next-generation multi-drop memory interface. The proposed receiver combines traditional interface techniques like the integrated signaling and the duobinary signaling, in which the duobinary signal is generated by current integration in the receiver. It can address issues such as input data dependence during integration, need for precursor equalization, high equalizer gain boosting, and sensitivity to high-frequency noise. The proposed receiver also alleviates DFE critical timing to provide gain in speed, and embed DFE taps in duobinary decoding to provide gain in power and area. The adaptation for adjusting the equalizer common-mode level, duobinary zero level, tap coefficient values, and timing recovery is incorporated. The proposed DFE receiver was fabricated in a 45 nm CMOS process, whose measurement results indicated that it worked at 5.8 Gb/s speed in a four-drop channel configuration with seven slave ICs, and the bathtub curve shows 36% open for  $10^{-10}$  bit error rate.

Question: What is the duobinary signaling?


Bullying among young adolescents: the strong, the weak, and the troubled.. OBJECTIVES Bullying and being bullied have been recognized as health problems for children because of their association with adjustment problems, including poor mental health and more extreme violent behavior. It is therefore important to understand how bullying and being bullied affect the well-being and adaptive functioning of youth. We sought to use multiple data sources to better understand the psychological and social problems exhibited by bullies, victims, and bully-victims.   DESIGN, SETTING, AND PARTICIPANTS Analysis of data from a community sample of 1985 mostly Latino and black 6th graders from 11 schools in predominantly low socioeconomic status urban communities (with a 79% response rate).   MAIN OUTCOME MEASURES Peer reports of who bullies and who is victimized, self-reports of psychological distress, and peer and teacher reports of a range of adjustment problems.   RESULTS Twenty-two percent of the sample was classified as involved in bullying as perpetrators (7%), victims (9%), or both (6%). Compared with other students, these groups displayed school problems and difficulties getting along with classmates. Despite increased conduct problems, bullies were psychologically strongest and enjoyed high social standing among their classmates. In contrast, victims were emotionally distressed and socially marginalized among their classmates. Bully-victims were the most troubled group, displaying the highest level of conduct, school, and peer relationship problems.   CONCLUSIONS To be able to intervene with bullying, it is important to recognize the unique problems of bullies, victims, and bully-victims. In addition to addressing these issues directly with their patients, pediatricians can recommend school-wide antibullying approaches that aim to change peer dynamics that support and maintain bullying.

Question: What is the difference between a bully and a victim?


Missing Modalities Imputation via Cascaded Residual Autoencoder. Affordable sensors lead to an increasing interest in acquiring and modeling data with multiple modalities. Learning from multiple modalities has shown to significantly improve performance in object recognition. However, in practice it is common that the sensing equipment experiences unforeseeable malfunction or configuration issues, leading to corrupted data with missing modalities. Most existing multi-modal learning algorithms could not handle missing modalities, and would discard either all modalities with missing values or all corrupted data. To leverage the valuable information in the corrupted data, we propose to impute the missing data by leveraging the relatedness among different modalities. Specifically, we propose a novel Cascaded Residual Autoencoder (CRA) to impute missing modalities. By stacking residual autoencoders, CRA grows iteratively to model the residual between the current prediction and original data. Extensive experiments demonstrate the superior performance of CRA on both the data imputation and the object recognition task on imputed data.

Question: How to impute missing modalities?


Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling. In this paper, we propose an optimization framework of offloading from a single mobile device (MD) to multiple edge devices. We aim to minimize both total tasks' execution latency and the MD's energy consumption by jointly optimizing the task allocation decision and the MD's central process unit (CPU) frequency. This paper considers two cases for the MD, i.e., fixed CPU frequency and elastic CPU frequency. Since these problems are NP-hard, we propose a linear relaxation-based approach and a semidefinite relaxation (SDR)-based approach for the fixed CPU frequency case, and an exhaustive search-based approach and an SDR-based approach for the elastic CPU frequency case. Our simulation results show that the SDR-based algorithms achieve near optimal performance. Performance improvement can be obtained with the proposed scheme in terms of energy consumption and tasks' execution latency when multiple edge devices and elastic CPU frequency are considered. Finally, we show that the MD's flexible CPU range can have an impact on the task allocation.

Question: How to offload in mobile edge computing?


Real-time Vision-based Hand Gesture Recognition Using Haar-like Features. This paper proposes a two level approach to solve the problem of real-time vision-based hand gesture classification. The lower level of the approach implements the posture recognition with Haar-like features and the AdaBoost learning algorithm. With this algorithm, real-time performance and high recognition accuracy can be obtained. The higher level implements the linguistic hand gesture recognition using a context-free grammar-based syntactic analysis. Given an input gesture, based on the extracted postures, the composite gestures can be parsed and recognized with a set of primitives and production rules.

Question: What is the difference between a context-free grammar and a context-sensitive grammar?


Methods for Designing Multiple Classifier Systems. In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this paper, the problem of design of multiple classifier system is discussed. Six design methods based on the so-called "overproduce and choose" paradigm are described and compared by experiments. Although these design methods exhibited some interesting features, they do not guarantee to design the optimal multiple classifier system for the classification task at hand. Accordingly, the main conclusion of this paper is that the problem of the optimal MCS design still remains open.

Question: What is the problem of the optimal MCS design?


Detection of Occluding Contours and Occlusion by Active Binocular Stereo. |We propose a reliable method to detect occluding contours and occlusion by active binocular stereo with an occluding contour model that describes the correspondence between the contour of curved objects and its projected image. Applying the image ow generated by moving one camera to the model, we can restrict possible matched points seen by the other camera. We detect occluding contours by tting these matched points to the model. This method can nd occluding contours and occlusion more reliably than conventional ones because stereo matching is performed by using the geometric constraint based on the occluding contour model instead of heuristic constraints. Experimental results of the proposed method applied to the actual scene are presented.

Question: How to detect occluding contours and occlusion?


Polarization-agile antennas. A polarization-agile antenna is a type of antenna the polarization state of which can be changed dynamically, i.e., it can have either linear (vertical or horizontal) or circular polarization (left hand or right hand), depending on the requirements of its specific application. This special characteristic offers great flexibility for antenna systems, because a single antenna could be used to satisfy the different requirements of several systems. This paper gives a comprehensive review of polarization-agile antennas. The basics of polarization in antennas and polarization-agile antennas are explained. Various examples of recent developments are then provided, and the antenna configurations and design principles are illustrated and discussed. Finally, the future development of polarization-agile antennas for wireless systems is discussed

Question: What is the difference between a polarization-agile antenna and a polarization-stable antenna?


Political Polarization on Twitter. In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between leftand right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis.

Question: How does political polarization on twitter affect the network of political communication?


Chip Level Techniques for EMI Reduction in LCD Panels. This paper presents chip level techniques to improve electro-magnetic interference (EMI) characteristics of LCD-TV panels. A timing controller (TCON) uses over-driving algorithms to improve the response time of liquid crystals (LC). Since this algorithm needs previous frame data, external memory such as double data rate synchronous DRAM (DDR SDRAM) is widely used as a frame buffer. A TTL interface between the TCON and memory is used for read and write operations, generating EMI noise. For reduction of this EMI, three methods are described. The first approach is to reduce the driving current of data I/O buffers. The second is to adopt spread spectrum clock generation (SSCG), and the third is to apply a proposed algorithm which minimizes data transitions. EMI measurement of a 32" LCD-TV panel shows that these approaches are very effective for reduction of EMI, achieving 20dB reduction at 175MHz.

Question: How to reduce EMI in LCD panels?


Epigenetics and Signaling Pathways in Glaucoma. Glaucoma is the most common cause of irreversible blindness worldwide. This neurodegenerative disease becomes more prevalent with aging, but predisposing genetic and environmental factors also contribute to increased risk. Emerging evidence now suggests that epigenetics may also be involved, which provides potential new therapeutic targets. These three factors work through several pathways, including TGF-β, MAP kinase, Rho kinase, BDNF, JNK, PI-3/Akt, PTEN, Bcl-2, Caspase, and Calcium-Calpain signaling. Together, these pathways result in the upregulation of proapoptotic gene expression, the downregulation of neuroprotective and prosurvival factors, and the generation of fibrosis at the trabecular meshwork, which may block aqueous humor drainage. Novel therapeutic agents targeting these pathway members have shown preliminary success in animal models and even human trials, demonstrating that they may eventually be used to preserve retinal neurons and vision.

Question: What is the most common cause of blindness worldwide?


Functional inactivation of the amygdala before but not after auditory fear conditioning prevents memory formation.. Two competing theories predict different effects on memory consolidation when the amygdala is inactivated after fear conditioning. One theory, based on studies using inhibitory avoidance training, proposes that the amygdala modulates the strength of fear learning, and post-training amygdala manipulations interfere with memory consolidation. The other, based on studies using Pavlovian fear conditioning, hypothesizes that fear learning occurs in the amygdala, and post-training manipulations after acquisition will not affect memory consolidation. We infused the GABAA agonist muscimol (4.4 nmol/side) or vehicle into lateral and basal amygdala (LBA) of rats either before or immediately after tone-foot shock Pavlovian fear conditioning. Pre-training infusions eliminated acquisition, whereas post-training infusions had no effect. These findings indicate that synaptic activity in LBA is necessary during learning, but that amygdala inactivation directly after training does not affect memory consolidation. Results suggest that essential aspects of plasticity underlying auditory fear conditioning take place within LBA during learning.

Question: How does the amygdala affect memory consolidation?


Katyusha: the first direct acceleration of stochastic gradient methods. Nesterov's momentum trick is famously known for accelerating gradient descent, and has been proven useful in building fast iterative algorithms. However, in the stochastic setting, counterexamples exist and prevent Nesterov's momentum from providing similar acceleration, even if the underlying problem is convex.  We introduce Katyusha, a direct, primal-only stochastic gradient method to fix this issue. It has a provably accelerated convergence rate in convex (off-line) stochastic optimization. The main ingredient is Katyusha momentum, a novel "negative momentum" on top of Nesterov's momentum that can be incorporated into a variance-reduction based algorithm and speed it up. Since variance reduction has been successfully applied to a growing list of practical problems, our paper suggests that in each of such cases, one could potentially give Katyusha a hug.

Question: What is the convergence rate of Katyusha?


Stealthy malware detection through vmm-based "out-of-the-box" semantic view reconstruction. An alarming trend in malware attacks is that they are armed with stealthy techniques to detect, evade, and subvert malware detection facilities of the victim. On the defensive side, a fundamental limitation of traditional host-based anti-malware systems is that they run inside the very hosts they are protecting ("in the box"), making them vulnerable to counter-detection and subversion by malware. To address this limitation, recent solutions based on virtual machine (VM) technologies advocate placing the malware detection facilities outside of the protected VM ("out of the box"). However, they gain tamper resistance at the cost of losing the native, semantic view of the host which is enjoyed by the "in the box" approach, thus leading to a technical challenge known as the semantic gap.  In this paper, we present the design, implementation, and evaluation of VMwatcher - an "out-of-the-box" approach that overcomes the semantic gap challenge. A new technique called guest view casting is developed to systematically reconstruct internal semantic views (e.g., files, processes, and kernel modules) of a VM from the outside in a non-intrusive manner. Specifically, the new technique casts semantic definitions of guest OS data structures and functions on virtual machine monitor (VMM)-level VM states, so that the semantic view can be reconstructed. With the semantic gap bridged, we identify two unique malware detection capabilities: (1) view comparison-based malware detection and its demonstration in rootkit detection and (2) "out-of-the-box" deployment of host-based anti-malware software with improved detection accuracy and tamper-resistance. We have implemented a proof-of-concept prototype on both Linux and Windows platforms and our experimental results with real-world malware, including elusive kernel-level rootkits, demonstrate its practicality and effectiveness.

Question: What is the difference between a "semantic view" and a "semantic definition"?


Human Interaction With Robot Swarms: A Survey. Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human-swarm interaction (HSI) and identifies the core concepts needed to design a human-swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human-swarm communication, state estimation and visualization, and human control of swarms. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works.

Question: What is the difference between a robot and a swarm?


Broadcasting Intermediate Blocks as a Defense Mechanism Against Selfish-Mine in Bitcoin. Although adopted by many cryptocurrencies, the Bitcoin mining protocol is not incentive-compatible, as the selfish mining strategy enables a miner to gain unfair mining rewards. Existing defenses either demand fundamental changes to block validity rules or have little effect on an attacker with more than one third of the total mining power. This paper proposes an effective defense mechanism against resourceful selfish miners. Our defense requires miners to publish intermediate blocks, the by-products of the mining process, then build on each other's work. By adding transparency to the mining process, block forks are resolved by comparing the amount of work of each branch. Moreover, this mechanism has the advantages of backward compatibility, low communication and computational costs, accelerating block propagation, and mitigating double-spending attacks on fast payments. To evaluate our design, we computed the most profitable mining strategy within our defense with analysis and simulation. Our simulation showed that within our defense, a selfish miner with almost half of the total mining power can only gain marginal unfair block rewards.

Question: How can we defend against selfish mining?


W-BAND MICROSTRIP-TO-WAVEGUIDE TRANSITION USING VIA FENCES. Abstract—The paper presents integrated probe for direct coupling to the WR-10 waveguide with the use of metal filled vias on both sides of the microstrip line. Design and optimization of this novel microstripto-waveguide transition has been performed using 3-D finite element method based software HFSS (High Frequency Structure Simulator). A back-to-back transition has been fabricated and measured between 75–110GHz. The measured return loss is higher than 10 dB and the insertion loss for a single microstrip-to-waveguide transition is about 1.15 dB.

Question: What is the return loss of a microstrip-to-waveguide transition?


USER ACCEPTANCE OF COMPUTER TECHNOLOGY : A COMPARISON OF TWO THEORETICAL MODELS *. Computer systems cannot improve organizational performance if they aren't used. Unfortunately, resistance to end-user systems by managers and professionals is a widespread problem. To better predict, explain, and increase user acceptance, we need to better understand why people accept or reject computers. This research addresses the ability to predict peoples' computer acceptance from a measure oftheir intentions, and the ability to explain their intentions in terms oftheir attitudes, subjective norms, perceived usefulness, perceived ease of use, and related variables. In a longitudinal study of 107 users, intentions to use a specific system, measured after a onehour introduction to the system, were correlated 0.35 with system use 14 weeks later. The intentionusage correlation was 0.63 at the end of this time period. Perceived usefulness strongly influenced peoples' intentions, explaining more than half of the variance in intentions at the end of 14 weeks. Perceived ease of use had a small but significant effect on intentions as well, although this effect subsided over time. Attitudes only partially mediated the effects of these beliefs on intentions. Subjective norms had no effect on intentions. These results suggest the possibility of simple but powerful models ofthe determinants of user acceptance, with practical value for evaluating systems and guiding managerial interventions aimed at reducing the problem of underutilized computer technology. (INFORMATION TECHNOLOGY; USER ACCEPTANCE; INTENTION MODELS)

Question: What is the difference between the two models?


Optimal stopping of Markov processes: Hilbert space theory, approximation algorithms, and an application to pricing high-dimensional financial derivatives. We develop a theory characterizing optimal stopping times for discrete-time ergodic Markov processes with discounted rewards. The theory differs from prior work by its view of per-stage and terminal reward functions as elements of a certain Hilbert space. In addition to a streamlined analysis establishing existence and uniqueness of a solution to Bellman's equation, this approach provides an elegant framework for the study of approximate solutions. In particular, we propose a stochastic approximation algorithm that tunes weights of a linear combination of basis functions in order to approximate a value function. We prove that this algorithm converges (almost surely) and that the limit of convergence has some desirable properties. We discuss how variations on this line of analysis can be used to develop similar results for other classes of optimal stopping problems, including those involving independent increment processes, finite horizons, and two-player zero-sum games. We illustrate the approximation method with a computational case study involving the pricing of a path-dependent financial derivative security that gives rise to an optimal stopping problem with a one-hundred-dimensional state space.

Question: What is the optimal stopping time for a Markov process?


Software-based resolver-to-digital converter using the PLL tracking algorithm. In this paper, an all-digital resolver-to-digital conversion (RDC) system using phase-locked loop (PLL) tracking algorithm is proposed. Excitation signal is obtained from PWM module of a TMS320F2812 digital signal processor (DSP). According to the excitation signal, the resolver outputs differential sine and cosine signals, which are sampled by a 16-bit peripheral AD7606 module. The rotor angle position is estimated with the PLL tracking algorithm based on DSP. The proposed RDC system is applied to a servo synchronous motor with a resolver coupled to the shaft, and the electrical error of the resolver is ±10'. Finally, responding simulations and experiments are carried out. The results demonstrate that the RDC system has the features with simple hardware structure and low cost, and high-precision resolver-to-digital conversion is achieved.

Question: What is the difference between a resolver and a resolver-to-digital converter?


SuggestBot: using intelligent task routing to help people find work in wikipedia. Member-maintained communities ask their users to perform tasks the community needs. From Slashdot, to IMDb, to Wikipedia, groups with diverse interests create community-maintained artifacts of lasting value (CALV) that support the group's main purpose and provide value to others. Said communities don't help members find work to do, or do so without regard to individual preferences, such as Slashdot assigning meta-moderation randomly. Yet social science theory suggests that reducing the cost and increasing the personal value of contribution would motivate members to participate more.We present SuggestBot, software that performs intelligent task routing (matching people with tasks) in Wikipedia. SuggestBot uses broadly applicable strategies of text analysis, collaborative filtering, and hyperlink following to recommend tasks. SuggestBot's intelligent task routing increases the number of edits by roughly four times compared to suggesting random articles. Our contributions are: 1) demonstrating the value of intelligent task routing in a real deployment; 2) showing how to do intelligent task routing; and 3) sharing our experience of deploying a tool in Wikipedia, which offered both challenges and opportunities for research.

Question: What is the value of intelligent task routing?


Value co-creation between firms and customers: The role of big data-based cooperative assets. To better understand how big data interconnects firms and customers in promoting value co-creation, we propose a theoretical framework of big data-based cooperative assets based on evidence of multiple case studies. We identify four types of big data resources and four types of associated digital platforms, and we explore how firms develop the cooperative assets by transforming big data resources via the theoretical lens of service-dominant logic. This study offers a new theoretical perspective on value co-creation and an alternative competitive strategy in the era of big data for firms. ã 2016 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Question: What is the role of big data in value co-creation?


Privacy and Security in Internet of Things and Wearable Devices. Enter the nascent era of Internet of Things (IoT) and wearable devices, where small embedded devices loaded with sensors collect information from its surroundings, process it, and relay it to remote locations for further analysis. Albeit looking harmless, these nascent technologies raise security and privacy concerns. We pose the question of the possibility and effects of compromising such devices. Concentrating on the design flow of IoT and wearable devices, we discuss some common design practices and their implications on security and privacy. Two representatives from each category, the Google Nest Thermostat and the Nike+ Fuelband, are selected as examples on how current industry practices of security as an afterthought or an add-on affect the resulting device and the potential consequences to the user's security and privacy. We then discuss design flow enhancements, through which security mechanisms can efficiently be added into a device, vastly differing from traditional practices.

Question: What is the difference between a wearable device and an IoT device?


Adaptive Decentralized Droop Controller to Preserve Power Sharing Stability of Paralleled Inverters in Distributed Generation Microgrids. This paper addresses the low-frequency relative stability problem in paralleled inverter-based distributed generation (DG) units in microgrids. In the sense of the small-signal dynamics of a microgrid, it can be shown that as the demanded power of each inverter changes, the low-frequency modes of the power sharing dynamics drift to new locations and the relative stability is remarkably affected, and eventually, instability can be yielded. To preserve the power sharing stability, an adaptive decentralized droop controller of paralleled inverter-based DG units is presented in this paper. The proposed power sharing strategy is based on the static droop characteristics combined with an adaptive transient droop function. Unlike conventional droop controllers, which yield 1-DOF tunable controller, the proposed droop controller yields 2-DOF tunable controller. Subsequently, the dynamic performance of the power sharing mechanism can be adjusted, without affecting the static droop gain, to damp the oscillatory modes of the power sharing controller. To account for the power modes immigration at different loading conditions, the transient droop gains are adaptively scheduled via small-signal analysis of the power sharing mechanism along the loading trajectory of each DG unit to yield the desired transient and steady-state response. The gain adaptation scheme utilizes the filtered active and reactive powers as indices; therefore, a stable and smooth power injection performance can be obtained at different loading conditions. The adaptive nature of the proposed controller ensures active damping of power oscillations at different operating conditions, and yields a stable and robust performance of the paralleled inverter system.

Question: How to preserve power sharing stability in paralleled inverter-based distributed generation microgrids?


Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. A broad range of neural and behavioral data suggests that the brain contains multiple systems for behavioral choice, including one associated with prefrontal cortex and another with dorsolateral striatum. However, such a surfeit of control raises an additional choice problem: how to arbitrate between the systems when they disagree. Here, we consider dual-action choice systems from a normative perspective, using the computational theory of reinforcement learning. We identify a key trade-off pitting computational simplicity against the flexible and statistically efficient use of experience. The trade-off is realized in a competition between the dorsolateral striatal and prefrontal systems. We suggest a Bayesian principle of arbitration between them according to uncertainty, so each controller is deployed when it should be most accurate. This provides a unifying account of a wealth of experimental evidence about the factors favoring dominance by either system.

Question: How do we arbitrate between two competing systems?


Satisfaction, trust and online purchase intention: A study of consumer perceptions. This paper tries to do the research of analyzing the relation of the customer trust, the customer satisfaction and purchase intention under the electronic commerce shopping environment. Drawing on the theory of customer trust, customer satisfaction and purchase intention, based on a survey of 102 respondents from the University in China, the United Kingdom, and the United States, the results showed that there is significant correlation between satisfaction and trust, satisfaction and purchase intention, trust and purchase intention, and more than 50% of respondents chose Taobao and Tmall, and the second is Amazon when they shopping onling. It could be expected that this kind of study is able to provide suggestion and guide for internet merchant.

Question: What is the correlation between satisfaction and trust?


Spatial reconstruction of single-cell gene expression data. Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.

Question: How do you map the spatial expression of genes in a zebrafish embryo?


Does Online Technology Make Us More or Less Sociable? A Preliminary Review and Call for Research.. How does online technology affect sociability? Emerging evidence-much of it inconclusive-suggests a nuanced relationship between use of online technology (the Internet, social media, and virtual reality) and sociability (emotion recognition, empathy, perspective taking, and emotional intelligence). Although online technology can facilitate purely positive behavior (e.g., charitable giving) or purely negative behavior (e.g., cyberbullying), it appears to affect sociability in three ways, depending on whether it allows a deeper understanding of people's thoughts and feelings: (a) It benefits sociability when it complements already-deep offline engagement with others, (b) it impairs sociability when it supplants deeper offline engagement for superficial online engagement, and (c) it enhances sociability when deep offline engagement is otherwise difficult to attain. We suggest potential implications and moderators of technology's effects on sociability and call for additional causal research.

Question: Does online technology make us more or less sociable?


At the end of the 14C time scale--the Middle to Upper Paleolithic record of western Eurasia.. The dynamics of change underlying the demographic processes that led to the replacement of Neandertals by Anatomically Modern Humans (AMH) and the emergence of what are recognized as Upper Paleolithic technologies and behavior can only be understood with reference to the underlying chronological framework. This paper examines the European chronometric (mainly radiocarbon-based) record for the period between ca. 40 and 30 ka 14C BP and proposes a relatively rapid transition within some 2,500 years. This can be summarized in the following falsifiable hypotheses: (1) final Middle Paleolithic (FMP) "transitional" industries (Uluzzian, Chatelperronian, leaf-point industries) were made by Neandertals and date predominantly to between ca. 41 and 38 ka 14C BP, but not younger than 35/34 ka 14C BP; (2) initial (IUP) and early (EUP) Upper Paleolithic "transitional" industries (Bachokirian, Bohunician, Protoaurignacian, Kostenki 14) will date to between ca. 39/38 and 35 ka 14C BP and document the appearance of AMH in Europe; (3) the earliest Aurignacian (I) appears throughout Europe quasi simultaneously at ca. 35 ka 14C BP. The earliest appearance of figurative art is documented only for a later phase ca. 33.0/32.5-29.2 ka 14C BP. Taken together, the Middle to Upper Paleolithic transition appears to be a cumulative process involving the acquisition of different elements of "behavioral modernity" through several "stages of innovation."

Question: What is the earliest date for the appearance of figurative art in Europe?


HoPE: Hot-Cacheline Prediction for Dynamic Early Decompression in Compressed LLCs. Data compression plays a pivotal role in improving system performance and reducing energy consumption, because it increases the logical effective capacity of a compressed memory system without physically increasing the memory size. However, data compression techniques incur some cost, such as non-negligible compression and decompression overhead. This overhead becomes more severe if compression is used in the cache. In this article, we aim to minimize the read-hit decompression penalty in compressed Last-Level Caches (LLCs) by speculatively decompressing frequently used cachelines. To this end, we propose a Hot-cacheline Prediction and Early decompression (HoPE) mechanism that consists of three synergistic techniques: Hot-cacheline Prediction (HP), Early Decompression (ED), and Hit-history-based Insertion (HBI). HP and HBI efficiently identify the hot compressed cachelines, while ED selectively decompresses hot cachelines, based on their size information. Unlike previous approaches, the HoPE framework considers the performance balance/tradeoff between the increased effective cache capacity and the decompression penalty. To evaluate the effectiveness of the proposed HoPE mechanism, we run extensive simulations on memory traces obtained from multi-threaded benchmarks running on a full-system simulation framework. We observe significant performance improvements over compressed cache schemes employing the conventional Least-Recently Used (LRU) replacement policy, the Dynamic Re-Reference Interval Prediction (DRRIP) scheme, and the Effective Capacity Maximizer (ECM) compressed cache management mechanism. Specifically, HoPE exhibits system performance improvements of approximately 11%, on average, over LRU, 8% over DRRIP, and 7% over ECM by reducing the read-hit decompression penalty by around 65%, over a wide range of applications.

Question: What is the hot cacheline prediction?


Tuning of PID controller using Ziegler-Nichols method for speed control of DC motor. In this paper, a weighted tuning methods of a PID speed controller for separately excited Direct current motor is presented, based on Empirical Ziegler-Nichols tuning formula and modified Ziegler-Nichol PID tuning formula. Both these methods are compared on the basis of output response, minimum settling time, and minimum overshoot for speed demand application of DC motor. Computer simulation shows that the performance of PID controller using Modified Ziegler-Nichols technique is better than that of traditional Ziegler-Nichols technique.

Question: How to tune a PID controller for speed control of a DC motor?


Automatic detection of incomplete requirements via symbolic analysis. The usefulness of a system specification depends in part on the completeness of the requirements. However, enumerating all necessary requirements is difficult, especially when requirements interact with an unpredictable environment. A specification built with an idealized environmental view is incomplete if it does not include requirements to handle non-idealized behavior. Often incomplete requirements are not detected until implementation, testing, or worse, after deployment. Even when performed during requirements analysis, detecting incomplete requirements is typically an error prone, tedious, and manual task. This paper introduces Ares, a design-time approach for detecting incomplete requirements decomposition using symbolic analysis of hierarchical requirements models. We illustrate our approach by applying Ares to a requirements model of an industry-based automotive adaptive cruise control system. Ares is able to automatically detect specific instances of incomplete requirements decompositions at design-time, many of which are subtle and would be difficult to detect, either manually or with testing.

Question: What is a requirements model?


MAGIC—Memristor-Aided Logic. Memristors are passive components with a varying resistance that depends on the previous voltage applied across the device. While memristors are naturally used as memory, memristors can also be used for other applications, including logic circuits. In this brief, a memristor-only logic family, i.e., memristor-aided logic (MAGIC), is presented. In each MAGIC logic gate, memristors serve as an input with previously stored data, and an additional memristor serves as an output. The topology of a MAGIC nor gate is similar to the structure of a common memristor-based crossbar memory array. A MAGIC nor gate can therefore be placed within memory, providing opportunities for novel non-von Neumann computer architectures. Other MAGIC gates also exist (e.g., and, or, not, and nand) and are described in this brief.

Question: What is a memristor?


Affective Man-Machine Interface: Unveiling Human Emotions through Biosignals. As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals. That men are machines (whatever else they may be) has long been suspected; but not till our generation have men fairly felt in concrete just what wonderful psycho-neuro-physical mechanisms they are. William James (1893; 1842 – 1910) A. Fred, J. Filipe, and H. Gamboa (Eds.): BIOSTEC 2009, CCIS 52, pp. 21–47, 2010. c © Springer-Verlag Berlin Heidelberg 2010 22 E.L. van den Broek et al.

Question: What is the difference between biosignals and biosignals?


Effective Value of Decision Tree with KDD 99 Intrusion Detection Datasets for Intrusion Detection System. A decision tree is a outstanding method for the data mining. In intrusion detection systems (IDSs), the data mining techniques are useful to detect the attack especially in anomaly detection. For the decision tree, we use the DARPA 98 Lincoln Laboratory Evaluation Data Set (DARPA Set) as the training data set and the testing data set. KDD 99 Intrusion Detection data set is also based on the DARPA Set. These three entities are widely used in IDSs. Hence, we describe the total process to generate the decision tree learned from the DARPA Sets. In this paper, we also evaluate the effective value of the decision tree as the data mining method for the IDSs, and the DARPA Set as the learning data set for the decision trees.

Question: What is the effective value of decision tree?