Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture. In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling. We use a multiscale convolutional network that is able to adapt easily to each task using only small modifications, regressing from the input image to the output map directly. Our method progressively refines predictions using a sequence of scales, and captures many image details without any superpixels or low-level segmentation. We achieve state-of-the-art performance on benchmarks for all three tasks.

Question: How to predict depth, surface normals and semantic labels with a common multi-scale convolutional architecture?


LaBB-CAT: an Annotation Store. "ONZE Miner", an open-source tool for storing and automatically annotating Transcriber transcripts, has been redeveloped to use "annotation graphs" as its data model. The annotation graph framework provides the new software, "LaBB-CAT", greater flexibility for automatic and manual annotation of corpus data at various independent levels of granularity, and allows more sophisticated annotation structures, opening up new possibilities for corpus mining and conversion between tool formats.

Question: What is the open-source tool for storing and automatically annotating Transcriber transcripts?


A Review of the Design Issues and Techniques for Radial-Flux Brushless Surface and Internal Rare-Earth Permanent-Magnet Motors. This paper reviews many design issues and analysis techniques for the brushless permanent-magnet machine. It reviews the basic requirements for the use of both ac and dc machines and issues concerning the selection of pole number, winding layout, rotor topology, drive strategy, field weakening, and cooling. These are key issues in the design of a motor. Leading-edge design techniques are illustrated. This paper is aimed as a tutor for motor designers who may be unfamiliar with this particular type of machine.

Question: What is a radial-flux brushless permanent-magnet machine?


Adoption of Mobile Internet Services: An Exploratory Study of Mobile Commerce Early Adopters. Taylor & Francis makes every effort to ensure the accuracy of all the information (the "Content") contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

Question: What is the difference between a publisher and an agent?


AutoModeling: Integrated Approach for Automated Model Generation by Ensemble Selection of Feature Subset and Classifier. Feature subset selection and identification of appropriate classification method plays an important role to optimize the predictive performance of supervised machine learning system. Current literature makes isolated attempts to optimize the feature selection and classifier identification. However, feature set has an intrinsic relationship with classification technique and together they form a 'model' for classification task. In this paper, we propose AutoModeling that finds optimal learning model and jointly optimize the feature and hypothesis space to maximize performance measure objective function. It is an automated framework of selecting the ensemble model {selected feature subset, selected classifier} from a given superset of features and classifiers learned from given training dataset in a computational efficient manner. We introduce novel relax-greedy search with our proposed patience function as a wrapper feature selection that maximizes the predictive performance and eliminates the classical nesting effect. We perform extensive experimentations on different types of publicly available datasets and AutoModeling demonstrates superior performance over relevant state-of-the-art methods, expert-driven manual methods and deep neural networks.

Question: What is the difference between feature selection and feature subset selection?


Design on the low-leakage diode string for using in the power-rail ESD clamp circuits in a 0.35-/spl mu/m silicide CMOS process. A new design of the diode string with very low leakage current is proposed for use in the ESD clamp circuits across the power rails. By adding an NMOS-controlled lateral SCR (NCLSCR) device into the stacked diode string, the leakage current of this new diode string with six stacked diodes at 5 V (3.3 V) forward bias can be reduced to only 2.1 (1.07) nA at a temperature of 125/spl deg/C in a 0.35 /spl mu/m silicide CMOS process, whereas the previous designs have a leakage current in the order of mA. The total blocking voltage of this new design with NCLSCR can be linearly adjusted by changing the number of the stacked diodes in the diode string without causing latch-up danger across the power rails. From the experimental results, the human-body-model ESD level of the ESD clamp circuit with the proposed low-leakage diode string is greater than 8 kV in a 0.35 /spl mu/m silicide CMOS process by using neither ESD implantation nor the silicide-blocking process modifications.

Question: How to design a low-leakage diode string for ESD clamp circuits?


SAP HANA database: data management for modern business applications. The SAP HANA database is positioned as the core of the SAP HANA Appliance to support complex business analytical processes in combination with transactionally consistent operational workloads. Within this paper, we outline the basic characteristics of the SAP HANA database, emphasizing the distinctive features that differentiate the SAP HANA database from other classical relational database management systems. On the technical side, the SAP HANA database consists of multiple data processing engines with a distributed query processing environment to provide the full spectrum of data processing -- from classical relational data supporting both row- and column-oriented physical representations in a hybrid engine, to graph and text processing for semi- and unstructured data management within the same system.  From a more application-oriented perspective, we outline the specific support provided by the SAP HANA database of multiple domain-specific languages with a built-in set of natively implemented business functions. SQL -- as the lingua franca for relational database systems -- can no longer be considered to meet all requirements of modern applications, which demand the tight interaction with the data management layer. Therefore, the SAP HANA database permits the exchange of application semantics with the underlying data management platform that can be exploited to increase query expressiveness and to reduce the number of individual application-to-database round trips.

Question: What is the difference between a relational database and a graph database?


The Bitcoin Backbone Protocol: Analysis and Applications. Bitcoin is the first and most popular decentralized cryptocurrency to date. In this work, we extract and analyze the core of the Bitcoin protocol, which we term the Bitcoin backbone, and prove two of its fundamental properties which we call common prefix and chain quality in the static setting where the number of players remains fixed. Our proofs hinge on appropriate and novel assumptions on the "hashing power" of the adversary relative to network synchronicity; we show our results to be tight under high synchronization. Next, we propose and analyze applications that can be built "on top" of the backbone protocol, specifically focusing on Byzantine agreement (BA) and on the notion of a public transaction ledger. Regarding BA, we observe that Nakamoto's suggestion falls short of solving it, and present a simple alternative which works assuming that the adversary's hashing power is bounded by 1/3. The public transaction ledger captures the essence of Bitcoin's operation as a cryptocurrency, in the sense that it guarantees the liveness and persistence of committed transactions. Based on this notion we describe and analyze the Bitcoin system as well as a more elaborate BA protocol, proving them secure assuming high network synchronicity and that the adversary's hashing power is strictly less than 1/2, while the adversarial bound needed for security decreases as the network desynchronizes. Finally, we show that our analysis of the Bitcoin backbone protocol for synchronous networks extends with relative ease to the recently considered "partially synchronous" model, where there is an upper bound in the delay of messages that is unknown to the honest parties.

Question: What is the backbone protocol?


Computer proficiency questionnaire: assessing low and high computer proficient seniors.. PURPOSE OF THE STUDY Computers and the Internet have the potential to enrich the lives of seniors and aid in the performance of important tasks required for independent living. A prerequisite for reaping these benefits is having the skills needed to use these systems, which is highly dependent on proper training. One prerequisite for efficient and effective training is being able to gauge current levels of proficiency. We developed a new measure (the Computer Proficiency Questionnaire, or CPQ) to measure computer proficiency in the domains of computer basics, printing, communication, Internet, calendaring software, and multimedia use. Our aim was to develop a measure appropriate for individuals with a wide range of proficiencies from noncomputer users to extremely skilled users.   DESIGN AND METHODS To assess the reliability and validity of the CPQ, a diverse sample of older adults, including 276 older adults with no or minimal computer experience, was recruited and asked to complete the CPQ.   RESULTS The CPQ demonstrated excellent reliability (Cronbach's α = .98), with subscale reliabilities ranging from .86 to .97. Age, computer use, and general technology use all predicted CPQ scores. Factor analysis revealed three main factors of proficiency related to Internet and e-mail use; communication and calendaring; and computer basics. Based on our findings, we also developed a short-form CPQ (CPQ-12) with similar properties but 21 fewer questions.   IMPLICATIONS The CPQ and CPQ-12 are useful tools to gauge computer proficiency for training and research purposes, even among low computer proficient older adults.

Question: What is the difference between a computer and a computer?


Dual-Band Microstrip Bandpass Filter Using Stepped-Impedance Resonators With New Coupling Schemes. A microstrip bandpass filter using stepped-impedance resonators is designed in low-temperature co-fired ceramic technology for dual-band applications at 2.4 and 5.2 GHz. New coupling schemes are proposed to replace the normal counterparts. It is found that the new coupling scheme for the interstages can enhance the layout compactness of the bandpass filter; while the new coupling scheme at the input and output can improve the performance of the bandpass filter. To validate the design and analysis, a prototype of the bandpass filter was fabricated and measured. It is shown that the measured and simulated performances are in good agreement. The prototype of the bandpass filter achieved insertion loss of 1.25 and 1.87 dB, S11 of -29 and -40 dB, and bandwidth of 21% and 12.7% at 2.4 and 5.2 GHz, respectively. The bandpass filter is further studied for a single-package solution of dual-band radio transceivers. The bandpass filter is, therefore, integrated into a ceramic ball grid array package. The integration is analyzed with an emphasis on the connection of the bandpass filter to the antenna and to the transceiver die

Question: What is the dual-band radio transceiver?


Spatial Correlation and Mobility-Aware Traffic Modeling for Wireless Sensor Networks. Recently, there has been a great deal of research on using mobility in wireless sensor networks (WSNs) to facilitate surveillance and reconnaissance in a wide deployment area. Besides providing an extended sensing coverage, node mobility along with spatial correlation introduces new network dynamics, which could lead to the traffic patterns fundamentally different from the traditional (Markovian) models. In this paper, a novel traffic modeling scheme for capturing these dynamics is proposed that takes into account the statistical patterns of node mobility and spatial correlation. The contributions made in this paper are twofold. First, it is shown that the joint effects of mobility and spatial correlation can lead to bursty traffic. More specifically, a high mobility variance and small spatial correlation can give rise to pseudo-long-range-dependent (LRD) traffic (high bursty traffic), whose autocorrelation function decays slowly and hyperbolically up to a certain cutoff time lag. Second, due to the ad hoc nature of WSNs, certain relay nodes may have several routes passing through them, necessitating local traffic aggregations. At these relay nodes, our model predicts that the aggregated traffic also exhibits the bursty behavior characterized by a scaled power-law decayed autocovariance function. According to these findings, a novel traffic shaping protocol using movement coordination is proposed to facilitate effective and efficient resource provisioning strategy. Finally, simulation results reveal a close agreement between the traffic pattern predicted by our theoretical model and the simulated transmissions from multiple independent sources, under specific bounds of the observation intervals.

Question: How to model traffic in wireless sensor networks?


ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. We present a novel multi-robot simulator named ARGoS. ARGoS is designed to simulate complex experiments involving large swarms of robots of different types. ARGoS is the first multi-robot simulator that is at the same time both efficient (fast performance with many robots) and flexible (highly customizable for specific experiments). Novel design choices in ARGoS have enabled this breakthrough. First, in ARGoS, it is possible to partition the simulated space into multiple sub-spaces, managed by different physics engines running in parallel. Second, ARGoS' architecture is multi-threaded, thus designed to optimize the usage of modern multi-core CPUs. Finally, the architecture of ARGoS is highly modular, enabling easy addition of custom features and appropriate allocation of computational resources. We assess the efficiency of ARGoS and showcase its flexibility with targeted experiments. Experimental results demonstrate that simulation run-time increases linearly with the number of robots. A 2D-dynamics simulation of 10,000 e-puck robots can be performed in 60 % of the time taken by the corresponding real-world experiment. We show how ARGoS can be extended to suit the needs of an experiment in which custom functionality is necessary to achieve sufficient simulation accuracy. ARGoS is open source software licensed under GPL3 and is downloadable free of charge.

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


Game-Enhanced Second Language Vocabulary Acquisition Strategies: A Systematic Review. This paper is a synthesis of 17 qualitative, quantitative, and mixed-method studies published over the last decade. The synthesis suggests that game-enhanced learning provides a set of effective strategies such as language repetitions, contextual clues, interaction with native speakers and peers, and imagery to practice and use second language vocabulary in the authentic context. Some of the strategies such as word lists, dictionaries, and vocabulary exercises are more beneficial when combined with native speakers or peers' interactions. Due to high interactivity of games, note taking and media strategies provide less support for vocabulary learning. The lack of high quality studies and empirical data makes it difficult to draw conclusions about which video games strategies provide the most benefit. The synthesis of research identifies that generally game-enhanced practices are helpful for second language vocabulary enhancement.

Question: What is a systematic review?


Monte Carlo localization in hand-drawn maps. Robot localization is one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we can successfully identify the room in which the robot is located in 80% of the tests.

Question: How to localize a robot in a hand-drawn map?


Country-wide rainfall maps from cellular communication networks.. Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal's attenuation between transmitter and receiver. Here, we show how one such a network can be used to retrieve the space-time dynamics of rainfall for an entire country (The Netherlands, ∼35,500 km(2)), based on an unprecedented number of links (∼2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrates the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent.

Question: How many links are used in the canadian cellular communication network?


Ultra-wideband antipodal vivaldi antenna array with Wilkinson power divider feeding network. In this paper, a two element antipodal Vivaldi antenna array in combination with an ultra-wideband (UWB) Wilkinson power divider feeding network is presented. The antennas are placed in a stacked configuration to provide more gain and to limit cross talk between transmit and receive antennas in radar applications. Each part can be realized with standard planar technology. For a single antenna element, a comparison in performance is done between standard FR4 and a Rogers® RO3003 substrate. The antenna spacing is obtained using the parametric optimizer of CST MWS®. The performance of the power divider and the antenna array is analyzed by means of simulations and measurements.

Question: What is the performance of the antenna array?


Semi-supervised hashing for scalable image retrieval. Large scale image search has recently attracted considerable attention due to easy availability of huge amounts of data. Several hashing methods have been proposed to allow approximate but highly efficient search. Unsupervised hashing methods show good performance with metric distances but, in image search, semantic similarity is usually given in terms of labeled pairs of images. There exist supervised hashing methods that can handle such semantic similarity but they are prone to overfitting when labeled data is small or noisy. Moreover, these methods are usually very slow to train. In this work, we propose a semi-supervised hashing method that is formulated as minimizing empirical error on the labeled data while maximizing variance and independence of hash bits over the labeled and unlabeled data. The proposed method can handle both metric as well as semantic similarity. The experimental results on two large datasets (up to one million samples) demonstrate its superior performance over state-of-the-art supervised and unsupervised methods.

Question: What is the largest dataset of images?


Volumetric spatial transformer network for object recognition. Understanding 3D environments is a vital element of modern computer vision research due to paramount relevance in many vision systems, spanning a wide field of application scenarios from self-driving cars to autonomous robots [Qi et al. 2016]. At the present time, object recognition mainly employs two methods: volumetric CNNs [Wu Z 2015] and multi-view CNNs [Xu et al. 2015] [Xu et al. 2016]. In this paper, we propose a volumetric spatial transformer network for object recognition. It fills the gap between 3D CNN and 2D CNN for the first time, and provides an end-to-end training fashion. Given a 3D shape, the network can automatically select the best view that maximizes the accuracy of object recognition.

Question: What is the difference between volumetric CNN and multi-view CNN?


Delaunay Meshing of Isosurfaces. We present an isosurface meshing algorithm, DelIso, based on the Delaunay refinement paradigm. This paradigm has been successfully applied to mesh a variety of domains with guarantees for topology, geometry, mesh gradedness, and triangle shape. A restricted Delaunay tri- angulation, dual of the intersection between the surface and the three dimensional Voronoi diagram, is often the main ingredient in Delaunay refinement. Computing and storing three dimensional Voronoi/Delaunay diagrams become bottlenecks for Delaunay refinement techniques since isosurface computations generally have large input datasets and output meshes. A highlight of our algorithm is that we find a simple way to recover the restricted Delaunay triangulation of the surface without computing the full 3D structure. We employ techniques for efficient ray tracing of isosurfaces to generate surface sample points, and demonstrate the effectiveness of our implementation using a variety of volume datasets.

Question: How to compute the restricted Delaunay triangulation of an isosurface?


Mobile app conceptual browser: Online marketplaces information extraction. Online marketplaces are e-commerce websites where thousands of products are provided by multiple third parties. There are dozens of these differently structured marketplaces that need to be visited by the end users to reach their targets. This searching process consumes a lot of time and effort; moreover it negatively affects the user experience. In this paper, extensive analysis and evaluation of the existing e-marketplaces are performed to improve the end-users experience through a Mobile App. The main goal of this study is to find a solution that is capable of integrating multiple heterogeneous hidden data sources and unify the received responses into one single, structured and homogeneous source. Furthermore, the user can easily choose the desired product or reformulate the query through the interface. The proposed Android Mobile App is based on the multi-level conceptual analysis and modeling discipline, in which, data are analyzed in a way that helps in discovering the main concepts of any unknown domain captured from the hidden web. These concepts discovered through information extraction are then structured into a tree-based interface for easy navigation and query reformulation. The application has been evaluated through substantial experiments and compared to other existing mobile applications. The results showed that analyzing the query results and re-structuring the output before displaying to the end-user in a conceptual multilevel mechanism are reasonably effective in terms of number of clicks, time taken and number of navigation screens. Based on the proposed intelligent application, the interface is minimized to only two navigation screens, and the time needed to browse products from multiple marketplaces is kept reasonable in order to reach the target product.

Question: What is the main goal of this study?


Colors and emotions: preferences and combinations.. Within three age groups (7-year-old children, 11-year-old children, and adults), preferences for colors and emotions were established by means of two distinct paired-comparison tasks. In a subsequent task, participants were asked to link colors to emotions by selecting an appropriate color. It was hypothesized that the number of times that each color was tied to a specific emotion would be predictable from the separate preferences for colors and emotions. Within age groups, participants had consistent preferences for colors and emotions, but preferences differed from one age group to another. Especially in the youngest group, the pattern of combinations between colors and emotions appeared to be meaningfully related to the preference order for colors and emotions.

Question: What is the difference between the preferences for colors and emotions?


Efficiently inferring community structure in bipartite networks. Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of information through one-mode projections, and lack of interpretability. Here we solve the community detection problem for bipartite networks by formulating a bipartite stochastic block model, which explicitly includes vertex type information and may be trivially extended to k-partite networks. This bipartite stochastic block model yields a projection-free and statistically principled method for community detection that makes clear assumptions and parameter choices and yields interpretable results. We demonstrate this model's ability to efficiently and accurately find community structure in synthetic bipartite networks with known structure and in real-world bipartite networks with unknown structure, and we characterize its performance in practical contexts.

Question: How to infer community structure in bipartite networks?


Leveraging Peer Centrality in the Designof Socially-Informed Peer-to-Peer Systems. Social applications mine user social graphs to improve performance in search, provide recommendations, allow resource sharing and increase data privacy. When such applications are implemented on a peer-to-peer (P2P) architecture, the social graph is distributed on the P2P system: the traversal of the social graph translates into a socially-informed routing in the peer-to-peer layer. In this work we introduce the model of a projection graph that is the result of decentralizing a social graph onto a peer-to-peer network. We focus on three social network metrics: degree, node betweenness and edge betweenness centrality and analytically formulate the relation between metrics in the social graph and in the projection graph. We demonstrate with an application scenario on real social networks the usability of the projection graph properties in designing social search applications and unstructured P2P overlays that exhibit improved performance and reduced overhead over baseline scenarios.

Question: How to design a social search application?


Lower Bounds for Finding Stationary Points of Non-Convex , Smooth High-Dimensional Functions ∗. We establish lower bounds on the complexity of finding -stationary points of smooth, non-convex, high-dimensional functions. For functions with Lipschitz continuous pth derivative, we show that all algorithms—even randomized algorithms observing arbitrarily high-order derivatives—have worst-case iteration count Ω( −(p+1)/p). Our results imply that the O( −2) convergence rate of gradient descent is unimprovable without additional assumptions (e.g. Lipschitz Hessian), and that cubic regularization of Newton's method and pth order regularization in general are similarly optimal. Additionally, we prove that deterministic first-order methods, even applied to arbitrarily smooth functions, cannot achieve convergence rates better than O( −8/5), which is within −1/15 of the recently established Õ( −5/3) rate for accelerated gradient descent.

Question: What is the complexity of finding stationary points of non-convex functions?


A critical evaluation of the complex PTSD literature: implications for DSM-5.. Complex posttraumatic stress disorder (CPTSD) has been proposed as a diagnosis for capturing the diverse clusters of symptoms observed in survivors of prolonged trauma that are outside the current definition of PTSD. Introducing a new diagnosis requires a high standard of evidence, including a clear definition of the disorder, reliable and valid assessment measures, support for convergent and discriminant validity, and incremental validity with respect to implications for treatment planning and outcome. In this article, the extant literature on CPTSD is reviewed within the framework of construct validity to evaluate the proposed diagnosis on these criteria. Although the efforts in support of CPTSD have brought much needed attention to limitations in the trauma literature, we conclude that available evidence does not support a new diagnostic category at this time. Some directions for future research are suggested.

Question: What is the difference between complex PTSD and PTSD?


Predicting the fix time of bugs. Two important questions concerning the coordination of development effort are which bugs to fix first and how long it takes to fix them. In this paper we investigate empirically the relationships between bug report attributes and the time to fix. The objective is to compute prediction models that can be used to recommend whether a new bug should and will be fixed fast or will take more time for resolution. We examine in detail if attributes of a bug report can be used to build such a recommender system. We use decision tree analysis to compute and 10-fold cross validation to test prediction models. We explore prediction models in a series of empirical studies with bug report data of six systems of the three open source projects Eclipse, Mozilla, and Gnome. Results show that our models perform significantly better than random classification. For example, fast fixed Eclipse Platform bugs were classified correctly with a precision of 0.654 and a recall of 0.692. We also show that the inclusion of postsubmission bug report data of up to one month can further improve prediction models.

Question: How to predict the fix time of bugs?


Engineering exosomes as refined biological nanoplatforms for drug delivery. Exosomes, a subgroup of extracellular vesicles (EVs), have been recognized as important mediators of long distance intercellular communication and are involved in a diverse range of biological processes. Because of their ideal native structure and characteristics, exosomes are promising nanocarriers for clinical use. Exosomes are engineered at the cellular level under natural conditions, but successful exosome modification requires further exploration. The focus of this paper is to summarize passive and active loading approaches, as well as specific exosome modifications and examples of the delivery of therapeutic and imaging molecules. Examples of exosomes derived from a variety of biological origins are also provided. The biocompatible characteristics of exosomes, with suitable modifications, can increase the stability and efficacy of imaging probes and therapeutics while enhancing cellular uptake. Challenges in clinical translation of exosome-based platforms from different cell sources and the advantages of each are also reviewed and discussed.

Question: What is the difference between exosomes and extracellular vesicles?


Hierarchical Learning in Stochastic Domains: Preliminary Results. This paper presents the HDG learning algorithm, which uses a hierarchical decomposition of the state space to make learning to achieve goals more efficient with a small penalty in path quality. Special care must be taken when performing hierarchical planning and learning in stochastic domains, because macro-operators cannot be executed ballistically. The HDG algorithm, which is a descendent of Watkins' Q-learning algorithm, is described here and preliminary empirical results are presented.

Question: What is the HDG learning algorithm?


A unified protocol stack solution for LTE and WLAN in future mobile converged networks. The interworking of the LTE system and WLAN technologies has drawn much attention lately, due to the growing demands for various multimedia services and large data traffic in hotspot areas. Existing research studies have mostly investigated the coupling architectures for these two wireless communication standards at the network layer. However, in the current architectures, many important coordination functions and joint optimizations cannot be accomplished efficiently. To tackle this problem, a new CBS solution is proposed, which integrates different RATs at layer 2 in the true sense of convergence. We design a unified protocol stack that includes all the original functions of both LTE and WLAN systems. Then we propose a convergence architecture, the RMC sublayer, for joint management of these two RATs. The proposed CBS solution can support seamless offloading through soft handover, guaranteed QoS, forwarding management by a single IP address, and customized bandwidth aggregation service. Finally, our simulation and initial experiment results demonstrate the feasibility and efficiency of the CBS solution in future mobile converged networks.

Question: What is the difference between LTE and WLAN?


Conceptual Impact-Based Recommender System for CiteSeerx. CiteSeer is a digital library for scientific publications written by Computer Science researchers. Users are able to retrieve relevant documents from the database by searching by author name and/or keyword queries. Users may also receive recommendations of papers they might want to read provided by an existing conceptual recommender system. This system recommends documents based on an automaticallyconstructed user profile. Unlike traditional content-based recommender systems, the documents and the user profile are represented as concepts vectors rather than keyword vectors and papers are recommended based on conceptual matches rather than keyword matches between the profile and the documents. Although the current system provides recommendations that are on-topic, they are not necessarily high quality papers. In this work, we introduce the Conceptual Impact-Based Recommender (CIBR), a hybrid recommender system that extends the existing conceptual recommender system in CiteSeer by including an explicit quality factor as part of the recommendation criteria. To measure quality, our system considers the impact factor of each paper's authors as measured by the authors' h-index. Experiments to evaluate the effectiveness of our hybrid system show that the CIBR system recommends more relevant papers as compared to the conceptual recommender system.

Question: What is the impact factor of a paper?


A Comparison of Document Clustering Techniques. This paper presents the results of an experimental study of some common document clustering techniques. In particular, we compare the two main approaches to document clustering, agglomerative hierarchical clustering and K-means. (For K-means we used a "standard" K-means algorithm and a variant of K-means, "bisecting" K-means.) Hierarchical clustering is often portrayed as the better quality clustering approach, but is limited because of its quadratic time complexity. In contrast, K-means and its variants have a time complexity which is linear in the number of documents, but are thought to produce inferior clusters. Sometimes K-means and agglomerative hierarchical approaches are combined so as to "get the best of both worlds." However, our results indicate that the bisecting K-means technique is better than the standard K-means approach and as good or better than the hierarchical approaches that we tested for a variety of cluster evaluation metrics. We propose an explanation for these results that is based on an analysis of the specifics of the clustering algorithms and the nature of document data. 1 Background and Motivation Document clustering has been investigated for use in a number of different areas of text mining and information retrieval. Initially, document clustering was investigated for improving the precision or recall in information retrieval systems [Rij79, Kow97] and as an efficient way of finding the nearest neighbors of a document [BL85]. More recently, clustering has been proposed for use in browsing a collection of documents [CKPT92] or in organizing the results returned by a search engine in response to a user's query [ZEMK97]. Document clustering has also been used to automatically generate hierarchical clusters of documents [KS97]. (The automatic generation of a taxonomy of Web documents like that provided by Yahoo!

Question: What is the difference between a taxonomy and a hierarchy?


Minimum snap trajectory generation and control for quadrotors. We address the controller design and the trajectory generation for a quadrotor maneuvering in three dimensions in a tightly constrained setting typical of indoor environments. In such settings, it is necessary to allow for significant excursions of the attitude from the hover state and small angle approximations cannot be justified for the roll and pitch. We develop an algorithm that enables the real-time generation of optimal trajectories through a sequence of 3-D positions and yaw angles, while ensuring safe passage through specified corridors and satisfying constraints on velocities, accelerations and inputs. A nonlinear controller ensures the faithful tracking of these trajectories. Experimental results illustrate the application of the method to fast motion (5–10 body lengths/second) in three-dimensional slalom courses.

Question: How to generate trajectories for a quadrotor?


Dyscalculia : Characteristics , Causes , and Treatments. Developmental Dyscalculia (DD) is a learning disorder affecting the ability to acquire school-level arithmetic skills, affecting approximately 3-6% of individuals. Progress in understanding the root causes of DD and how best to treat it have been impeded by lack of widespread research and variation in characterizations of the disorder across studies. However, recent years have witnessed significant growth in the field, and a growing body of behavioral and neuroimaging evidence now points to an underlying deficit in the representation and processing of numerical magnitude information as a potential core deficit in DD. An additional product of the recent progress in understanding DD is the resurgence of a distinction between 'primary' and 'secondary' developmental dyscalculia. The first appears related to impaired development of brain mechanisms for processing numerical magnitude information, while the latter refers to mathematical deficits stemming from external factors such as poor teaching, low socio-economic status, and behavioral attention problems or domain-general cognitive deficits. Increased awareness of this distinction going forward, in combination with longitudinal empirical research, offers great potential for deepening our understanding of the disorder and developing effective educational interventions.

Question: What is dyscalculia?


Recoding, storage, rehearsal and grouping in verbal short-term memory: an fMRI study. Functional magnetic resonance imaging (fMRI) of healthy volunteers is used to localise the processes involved in verbal short-term memory (VSTM) for sequences of visual stimuli. Specifically, the brain areas underlying (i) recoding, (ii) storage, (iii) rehearsal and (iv) temporal grouping are investigated. Successive subtraction of images obtained from five tasks revealed a network of left-lateralised areas, including posterior temporal regions, supramarginal gyri, Broca's area and dorsolateral premotor cortex. The results are discussed in relation to neuropsychological distinctions between recoding and rehearsal, previous neuroimaging studies of storage and rehearsal, and, in particular, a recent connectionist model of VSTM that makes explicit assumptions about the temporal organisation of rehearsal. The functional modules of this model are tentatively mapped onto the brain in light of the imaging results. Our findings are consistent with the representation of verbal item information in left posterior temporal areas and short-term storage of phonological information in left supramarginal gyrus. They also suggest that left dorsolateral premotor cortex is involved in the maintenance of temporal order, possibly as the location of a timing signal used in the rhythmic organisation of rehearsal, whereas Broca's area supports the articulatory processes required for phonological recoding of visual stimuli.

Question: What is the difference between recoding and rehearsal?


Real-Time EMG Driven Lower Limb Actuated Orthosis for Assistance As Needed Movement Strategy. This paper presents a new approach to control a wearable knee joint exoskeleton driven through the wearer' s intention. A realistic bio-inspired musculoskeletal knee joint model is used to control the exoskeleton. This model takes in to account changes in muscle length and joint moment arms as well as the dynamics of muscle activation and muscle contrac tion during lower limb movements. Identification of the model parameters is done through an unconstrained optimization problem formulation. A control law strategy based on the principle of assistance as needed is proposed. This approach guarantees asymptotic stability of the knee joint orthosis and adaptat ion to human-orthosis interaction. Moreover, the proposed contr ol law is robust with respect to external disturbances. Experimental validations are conducted online on a healthy subject during flexion and extenion of their knee joint. The proposed control strategy has shown satisfactory performances in terms of tr acking trajectory and adaptation to human tasks completion.

Question: How does the control law work?


Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions. This document provides additional details about the experiments described in (Heilman and Smith, 2010). Note that while this document provides information about the datasets and experimental methods, it does not provide further results. If you have any further questions, please feel free to contact the first author. The preprocessed datasets (i.e., tagged and parsed) will be made available for research purposes upon request.

Question: What is the preprocessed datasets?


Tadalafil therapy for pulmonary arterial hypertension.. BACKGROUND Treatment options for pulmonary arterial hypertension target the prostacyclin, endothelin, or nitric oxide pathways. Tadalafil, a phosphodiesterase type-5 inhibitor, increases cGMP, the final mediator in the nitric oxide pathway.   METHODS AND RESULTS In this 16-week, double-blind, placebo-controlled study, 405 patients with pulmonary arterial hypertension (idiopathic or associated), either treatment-naive or on background therapy with the endothelin receptor antagonist bosentan, were randomized to placebo or tadalafil 2.5, 10, 20, or 40 mg orally once daily. The primary end point was the change from baseline to week 16 in the distance walked in 6 minutes. Changes in World Health Organization functional class, clinical worsening, and health-related quality of life were also assessed. Patients completing the 16-week study could enter a long-term extension study. Tadalafil increased the distance walked in 6 minutes in a dose-dependent manner; only the 40-mg dose met the prespecified level of statistical significance (P<0.01). Overall, the mean placebo-corrected treatment effect was 33 m (95% confidence interval, 15 to 50 m). In the bosentan-naive group, the treatment effect was 44 m (95% confidence interval, 20 to 69 m) compared with 23 m (95% confidence interval, -2 to 48 m) in patients on background bosentan therapy. Tadalafil 40 mg improved the time to clinical worsening (P=0.041), incidence of clinical worsening (68% relative risk reduction; P=0.038), and health-related quality of life. The changes in World Health Organization functional class were not statistically significant. The most common treatment-related adverse events reported with tadalafil were headache, myalgia, and flushing.   CONCLUSIONS In patients with pulmonary arterial hypertension, tadalafil 40 mg was well tolerated and improved exercise capacity and quality of life measures and reduced clinical worsening.

Question: What is the difference between a phosphodiesterase type-5 inhibitor and a phosphodiesterase type-5 inhibitor?


CampusSense — A smart vehicle parking monitoring and management system using ANPR cameras and android phones. Vehicle parking monitoring and management has become a big challenge for educational institutions with increasing enrollments, high percentage of vehicle ownership and decreasing parking supply which in result triggering blockage of vehicle, congestion, wastage of time and money. In university campuses particularly in Kingdom of Saudi Arabia, vehicle parking monitoring and management problem is getting worse and more frustrating due to the fact that majority of students, faculty and staff members own cars and drive through them to the university campuses. These common problems include finding out people (evidence) who are responsible for the damages (hitting, scraping, scratching and dents) to other cars and the blockage of car due to wrong car parking which takes much time to locate the owner of the car. Moreover, locating or forgetting their car park location another difficulty that is often faced by the students, faculty and staff members. The existing cameras located at the parking lots are only for video surveillance and cannot help in such situations as there is a lack of proper vehicle parking monitoring and management system. To cope with above mentioned problems and to ensure a better parking experience by accommodating increasing number of vehicles in a proper convenient manner, we propose a smart vehicle parking monitoring and management system called CampusSense. In CampusSense, Automatic Number Plate Recognition (ANPR) cameras and android based mobile application is developed to efficiently monitor, manage and protect the parking facilities at university campuses. Parking problems around the university campus faced by the students, faculty and staff members are analyzed by conducting a survey.

Question: What is the purpose of the project?


Nonspeech oral motor treatment issues related to children with developmental speech sound disorders.. PURPOSE This article examines nonspeech oral motor treatments (NSOMTs) in the population of clients with developmental speech sound disorders. NSOMTs are a collection of nonspeech methods and procedures that claim to influence tongue, lip, and jaw resting postures; increase strength; improve muscle tone; facilitate range of motion; and develop muscle control. In the case of developmental speech sound disorders, NSOMTs are employed before or simultaneous with actual speech production treatment.   METHOD First, NSOMTs are defined for the reader, and there is a discussion of NSOMTs under the categories of active muscle exercise, passive muscle exercise, and sensory stimulation. Second, different theories underlying NSOMTs along with the implications of the theories are discussed. Finally, a review of pertinent investigations is presented.   RESULTS The application of NSOMTs is questionable due to a number of reservations that include (a) the implied cause of developmental speech sound disorders, (b) neurophysiologic differences between the limbs and oral musculature, (c) the development of new theories of movement and movement control, and (d) the paucity of research literature concerning NSOMTs.   CLINICAL IMPLICATION There is no substantive evidence to support NSOMTs as interventions for children with developmental speech sound disorders.

Question: What is the difference between a speech therapist and a speech pathologist?


Why is image quality assessment so difficult?. Image quality assessment plays an important role in various image processing applications. A great deal of effort has been made in recent years to develop objective image quality metrics that correlate with perceived quality measurement. Unfortunately, only limited success has been achieved. In this paper, we provide some insights on why image quality assessment is so difficult by pointing out the weaknesses of the error sensitivity based framework, which has been used by most image quality assessment approaches in the literature. Furthermore, we propose a new philosophy in designing image quality metrics: The main function of the human eyes is to extract structural information from the viewing field, and the human visual system is highly adapted for this purpose. Therefore, a measurement of structural distortion should be a good approximation of perceived image distortion. Based on the new philosophy, we implemented a simple but effective image quality indexing algorithm, which is very promising as shown by our current results.

Question: Why is image quality assessment so difficult?


A 20 Gb/s 0.4 pJ/b Energy-Efficient Transmitter Driver Utilizing Constant- ${\rm G}_{\rm m}$  Bias. This paper describes a transmitter driver based on a CMOS inverter with a resistive feedback. By employing the proposed driver topology, the pre-driver can be greatly simplified, resulting in a remarkable reduction of the overall driver power consumption. It also offers another advantage that the implementation of equalization is straightforward, compared with a conventional voltage-mode driver. Furthermore, the output impedance remains relatively constant while the data is being transmitted, resulting in good signal integrity. For evaluation of the driver performance, a fully functional 20 Gb/s transmitter is implemented, including a PRBS generator, a serializer, and a half-rate clock generator. In order to enhance the overall speed of the digital circuits for 20 Gb/s data transmission, the resistive feedback is applied to the time-critical inverters, which enables shorter rise/fall times. The prototype chip is fabricated in a 65 nm CMOS technology. The implemented driver circuit operates up to the data rate of 20 Gb/s, exhibiting an energy efficiency of 0.4 pJ/b for the output swing of 250 mVpp,diff.

Question: What is the energy efficiency of the transmitter driver?


Automatic quantification of crack patterns by image processing. Image processing technologies are proposed to quantify crack patterns. On the basis of the technologies, a software "Crack Image Analysis System" (CIAS) has been developed. An image of soil crack network is used as an example to illustrate the image processing technologies and the operations of the CIAS. The quantification of the crack image involves the following three steps: image segmentation, crack identification and measurement. First, the image is converted to a binary image using a cluster analysis method; noise in the binary image is removed; and crack spaces are fused. Then, the medial axis of the crack network is extracted from the binary image, with which nodes and crack segments can be identified. Finally, various geometric parameters of the crack network can be calculated automatically, such as node number, crack number, clod area, clod perimeter, crack area, width, length, and direction. The thresholds used in the operations are specified by cluster analysis and other innovative methods. As a result, the objects (nodes, cracks and clods) in the crack network can be quantified automatically. The software may be used to study the generation and development of soil crack patterns and rock fractures. & 2013 Elsevier Ltd. All rights reserved.

Question: How to quantify crack patterns?


Deep Convolutional Neural Networks [Lecture Notes]. Neural networks are a subset of the field of artificial intelligence (AI). The predominant types of neural networks used for multidimensional signal processing are deep convolutional neural networks (CNNs). The term deep refers generically to networks having from a "few" to several dozen or more convolution layers, and deep learning refers to methodologies for training these systems to automatically learn their functional parameters using data representative of a specific problem domain of interest. CNNs are currently being used in a broad spectrum of application areas, all of which share the common objective of being able to automatically learn features from (typically massive) data bases and to generalize their responses to circumstances not encountered during the learning phase. Ultimately, the learned features can be used for tasks such as classifying the types of signals the CNN is expected to process. The purpose of this "Lecture Notes" article is twofold: 1) to introduce the fundamental architecture of CNNs and 2) to illustrate, via a computational example, how CNNs are trained and used in practice to solve a specific class of problems.

Question: What is deep learning?


Authentication Using Mobile Phone as a Security Token. Today security concerns are on the rise in all areas industries such as banks, governmental applications, healthcare industry, militaryorganization, educational institutions etc, with one common weak link being "passwords". Several proper strategies for using passwords have been proposed. Some of which are very difficult to use and others might not meet the company's security concerns. The usage of passwords for authentication is no longer sufficient and stronger authentication schemes are necessary. Two factor authentication uses elements or devices such as tokens and ATM cards. We have proposed to resolve the password problem, which could be inconvenient for the user and costly for the service providers to combat otherwise. To avoid the usage of additional device, the mobile phone is adopted as security token. In this paper several different authentication solutions using the mobile as authentication token are discussed, where these solutions vary in complexity, strength, security and user friendliness. One of the authentication schemes (OTP solution) is implemented to verify their usability. Hence, a classification and evaluation of the different solutions is provided according to defined criteria. Keywords‐ Two-factor authentication, Security token, UPIF, IMSI, IMEI, one time password (OTP).

Question: What is the difference between authentication and authorization?


Community Evaluation and Exchange of Word Vectors at wordvectors.org. Vector space word representations are useful for many natural language processing applications. The diversity of techniques for computing vector representations and the large number of evaluation benchmarks makes reliable comparison a tedious task both for researchers developing new vector space models and for those wishing to use them. We present a website and suite of offline tools that that facilitate evaluation of word vectors on standard lexical semantics benchmarks and permit exchange and archival by users who wish to find good vectors for their applications. The system is accessible at: www.wordvectors.org.

Question: What is a vector space word representation?


A Bayesian Approach to Filtering Junk E-Mail. In addressing the growing problem of junk E-mail on the Internet, we examine methods for the automated construction of filters to eliminate such unwanted messages from a user's mail stream. By casting this problem in a decision theoretic framework, we are able to make use of probabilistic learning methods in conjunction with a notion of differential misclassification cost to produce filters Which are especially appropriate for the nuances of this task. While this may appear, at first, to be a straight-forward text classification problem, we show that by considering domain-specific features of this problem in addition to the raw text of E-mail messages, we can produce much more accurate filters. Finally, we show the efficacy of such filters in a real world usage scenario, arguing that this technology is mature enough for deployment.

Question: What is a bayesian approach to filtering junk e-mail?


A Wideband Dual Circularly Polarized Full-Corporate Waveguide Array Antenna Fed by Triple-Resonant Cavities. A dual circularly polarized (CP) waveguide array antenna is presented for Ka-band wireless communication. A novel triple-resonant cavity is proposed to implement mode conversion and impedance matching in the <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula>-element subarray. A ridge waveguide polarizer is integrated to form the left-hand circular polarization (LHCP) and the right-hand circular polarization (RHCP). A flyover-shaped full-corporate feeding network is designed to accommodate the dual polarizations and keep the wideband characteristic for a large-scale array. Subarray spacing is used to optimize the port-to-port isolation and decrease the sidelobe level at some definite angles. Computer numerically controlled machining technology is applied to fabricating a <inline-formula> <tex-math notation="LaTeX">$16 \times 16$ </tex-math></inline-formula>-element prototype. Measured results demonstrate a bandwidth of 16% from 27.6 to 32.4 GHz for the dual polarizations, in terms of good reflection coefficient and axial ratio. A maximum gain of 32.8 dBic is achieved at 28.8 GHz. A total efficiency over 60% is obtained throughout the above band for both LHCP and RHCP ports.

Question: What is the maximum gain of the antenna?


Multicriteria classification and sorting methods: A literature review. The assignment of alternatives (observations/objects) into predefined homogenous groups is a problem of major practical and research interest. This type of problem is referred to as classification or sorting, depending on whether the groups are nominal or ordinal. Methodologies for addressing classification and sorting problems have been developed from a variety of research disciplines, including statistics/econometrics, artificial intelligent and operations research. The objective of this paper is to review the research conducted on the framework of the multicriteria decision aiding (MCDA). The review covers different forms of MCDA classification/sorting models, different aspects of the model development process, as well as real-world applications of MCDA classification/sorting techniques and their software implementations. 2002 Elsevier Science B.V. All rights reserved.

Question: What is the difference between classification and sorting?


What do students want?: towards an instrument for students' evaluation of quality of learning analytics services. Quality assurance in any organization is important for ensuring that service users are satisfied with the service offered. For higher education institutes, the use of service quality measures allows for ideological gaps to be both identified and resolved. The learning analytic community, however, has rarely addressed the concept of service quality. A potential outcome of this is the provision of a learning analytics service that only meets the expectations of certain stakeholders (e.g., managers), whilst overlooking those who are most important (e.g., students). In order to resolve this issue, we outline a framework and our current progress towards developing a scale to assess student expectations and perceptions of learning analytics as a service.

Question: What do students want?


Approximate Inference with Amortised MCMC. We propose a novel approximate inference algorithm that approximates a target distribution by amortising the dynamics of a user-selected MCMC sampler. The idea is to initialise MCMC using samples from an approximation network, apply the MCMC operator to improve these samples, and finally use the samples to update the approximation network thereby improving its quality. This provides a new generic framework for approximate inference, allowing us to deploy highly complex, or implicitly defined approximation families with intractable densities, including approximations produced by warping a source of randomness through a deep neural network. Experiments consider image modelling with deep generative models as a challenging test for the method. Deep models trained using amortised MCMC are shown to generate realistic looking samples as well as producing diverse imputations for images with regions of missing pixels.

Question: What is approximate inference?


A Time Series Classification Method for Behaviour-Based Dropout Prediction. Students' dropout rate is a key metric in online and open distance learning courses. We propose a time-series classification method to construct data based on students' behaviour and activities on a number of online distance learning modules. Further, we propose a dropout prediction model based on the time series forest (TSF) classification algorithm. The proposed predictive model is based on interaction data and is independent of learning objectives and subject domains. The model enables prediction of dropout rates without the requirement for pedagogical experts. Results show that the prediction accuracy on two selected datasets increases as the portion of data used in the model grows. However, a reasonable prediction accuracy of 0.84 is possible with only 5% of the dataset processed. As a result, early prediction can help instructors design interventions to encourage course completion before a student falls too far behind.

Question: What is a time series classification method?


Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. Context-predicting models (more commonly known as embeddings or neural language models) are the new kids on the distributional semantics block. Despite the buzz surrounding these models, the literature is still lacking a systematic comparison of the predictive models with classic, count-vector-based distributional semantic approaches. In this paper, we perform such an extensive evaluation, on a wide range of lexical semantics tasks and across many parameter settings. The results, to our own surprise, show that the buzz is fully justified, as the context-predicting models obtain a thorough and resounding victory against their count-based counterparts.

Question: What is the difference between context-counting and context-predicting models?


Design pattern implementation in Java and aspectJ. AspectJ implementations of the GoF design patterns show modularity improvements in 17 of 23 cases. These improvements are manifested in terms of better code locality, reusability, composability, and (un)pluggability.The degree of improvement in implementation modularity varies, with the greatest improvement coming when the pattern solution structure involves crosscutting of some form, including one object playing multiple roles, many objects playing one role, or an object playing roles in multiple pattern instances.

Question: What is the difference between the design patterns?


Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces. One of the major limitations of brain-computer interfaces (BCI) is their long calibration time, which limits their use in practice, both by patients and healthy users alike. Such long calibration times are due to the large between-user variability and thus to the need to collect numerous training electroencephalography (EEG) trials for the machine learning algorithms used in BCI design. In this paper, we first survey existing approaches to reduce or suppress calibration time, these approaches being notably based on regularization, user-to-user transfer, semi-supervised learning and a priori physiological information. We then propose new tools to reduce BCI calibration time. In particular, we propose to generate artificial EEG trials from the few EEG trials initially available, in order to augment the training set size. These artificial EEG trials are obtained by relevant combinations and distortions of the original trials available. We propose three different methods to do so. We also propose a new, fast and simple approach to perform user-to-user transfer for BCI. Finally, we study and compare offline different approaches, both old and new ones, on the data of 50 users from three different BCI data sets. This enables us to identify guidelines about how to reduce or suppress calibration time for BCI.

Question: How to reduce the calibration time of a brain-computer interface?


Inter-Session Modeling for Session-Based Recommendation. In recent years, research has been done on applying Recurrent Neural Networks (RNNs) as recommender systems. Results have been promising, especially in the session-based setting where RNNs have been shown to outperform state-of-the-art models. In many of these experiments, the RNN could potentially improve the recommendations by utilizing information about the user's past sessions, in addition to its own interactions in the current session. A problem for session-based recommendation, is how to produce accurate recommendations at the start of a session, before the system has learned much about the user's current interests. We propose a novel approach that extends an RNN recommender to be able to process the user's recent sessions, in order to improve recommendations. This is done by using a second RNN to learn from recent sessions, and predict the user's interest in the current session. By feeding this information to the original RNN, it is able to improve its recommendations. Our experiments on two different datasets show that the proposed approach can significantly improve recommendations throughout the sessions, compared to a single RNN working only on the current session. The proposed model especially improves recommendations at the start of sessions, and is therefore able to deal with the cold start problem within sessions.

Question: How to improve recommendations at the start of sessions?


The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations. Swarm is a multi agent software platform for the simulation of complex adaptive systems In the Swarm system the basic unit of simulation is the swarm a collec tion of agents executing a schedule of actions Swarm supports hierarchical model ing approaches whereby agents can be composed of swarms of other agents in nested structures Swarm provides object oriented libraries of reusable components for build ing models and analyzing displaying and controlling experiments on those models Swarm is currently available as a beta version in full free source code form It requires the GNU C Compiler Unix and X Windows More information about Swarm can be obtained from our web pages http www santafe edu projects swarm Santa Fe Institute Hyde Park Rd Santa Fe NM nelson santafe edu Deere Company John Deere Road Moline IL roger ci deere com Santa Fe Institute Hyde Park Rd Santa Fe NM cgl santafe edu Santa Fe Institute Hyde Park Rd Santa Fe NM manor santafe edu Swarm has been developed at the Santa Fe Institute with the support of Grant No N from the O ce of Naval Research and Grant No N G from the Naval Research Laboratory both acting in cooperation with the Defense Advanced Research Projects Agency Swarm bene ted from earlier support from The Carol O Donnell Foundation Mr and Mrs Michael Grantham and the National Science Foundation SFI also gratefully acknowledges invaluable support from Deere Company for this

Question: What is the name of the software?


On the selection of tags for tag clouds. We examine the creation of a tag cloud for exploring and understanding a set of objects (e.g., web pages, documents). In the first part of our work, we present a formal system model for reasoning about tag clouds. We then present metrics that capture the structural properties of a tag cloud, and we briefly present a set of tag selection algorithms that are used in current sites (e.g., del.icio.us, Flickr, Technorati) or that have been described in recent work. In order to evaluate the results of these algorithms, we devise a novel synthetic user model. This user model is specifically tailored for tag cloud evaluation and assumes an "ideal" user. We evaluate the algorithms under this user model, as well as the model itself, using two datasets: CourseRank (a Stanford social tool containing information about courses) and del.icio.us (a social bookmarking site). The results yield insights as to when and why certain selection schemes work best.

Question: What is a tag cloud?


Enhanced ARP: preventing ARP poisoning-based man-in-the-middle attacks. In this letter, an enhanced version of Address Resolution Protocol (ARP) is proposed to prevent ARP poisoning-based Man-in-the-Middle (MITM) attacks. The proposed mechanism is based on the following concept. When a node knows the correct Media Access Control (MAC) address for a given IP address, if it retains the IP/MAC address mapping while that machine is alive, then MITM attack is impossible for that IP address. In order to prevent MITM attacks even for a new IP address, a voting-based resolution mechanism is proposed. The proposed scheme is backward compatible with existing ARP and incrementally deployable.

Question: How to prevent ARP poisoning-based MITM attacks?


Multi-robot SLAM with Unknown Initial Correspondence: The Robot Rendezvous Case. This paper presents a new approach to the multi-robot map-alignment problem that enables teams of robots to build joint maps without initial knowledge of their relative poses. The key contribution of this work is an optimal algorithm for merging (not necessarily overlapping) maps that are created by different robots independently. Relative pose measurements between pairs of robots are processed to compute the coordinate transformation between any two maps. Noise in the robot-to-robot observations, propagated through the map-alignment process, increases the error in the position estimates of the transformed landmarks, and reduces the overall accuracy of the merged map. When there is overlap between the two maps, landmarks that appear twice provide additional information, in the form of constraints, which increases the alignment accuracy. Landmark duplicates are identified through a fast nearest-neighbor matching algorithm. In order to reduce the computational complexity of this search process, a kd-tree is used to represent the landmarks in the original map. The criterion employed for matching any two landmarks is the Mahalanobis distance. As a means of validation, we present experimental results obtained from two robots mapping an area of 4,800 m2

Question: How to merge maps without initial correspondence?


Multiple Imputation for Missing Data: Concepts and New Development. Multiple imputation provides a useful strategy for dealing with data sets with missing values. Instead of filling in a single value for each missing value, Rubin's (1987) multiple imputation procedure replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. These multiply imputed data sets are then analyzed by using standard procedures for complete data and combining the results from these analyses. No matter which complete-data analysis is used, the process of combining results from different imputed data sets is essentially the same. This results in valid statistical inferences that properly reflect the uncertainty due to missing values. This paper reviews methods for analyzing missing data, including basic concepts and applications of multiple imputation techniques. The paper also presents new SAS R procedures for creating multiple imputations for incomplete multivariate data and for analyzing results from multiply imputed data sets. These procedures are still under development and will be available in experimental form in Release 8.1 of the SAS System. Introduction Most SAS statistical procedures exclude observations with any missing variable values from the analysis. These observations are called incomplete cases. While using only complete cases has its simplicity, you lose information in the incomplete cases. This approach also ignores the possible systematic difference between the complete cases and incomplete cases, and the resulting inference may not be applicable to the population of all cases, especially with a smaller number of complete cases. Some SAS procedures use all the available cases in an analysis, that is, cases with available information. For example, PROC CORR estimates a variable mean by using all cases with nonmissing values on this variable, ignoring the possible missing values in other variables. PROC CORR a

Question: How to impute missing values in SAS?


Biology and therapy of fibromyalgia. New therapies in fibromyalgia. Fibromyalgia is a chronic, musculoskeletal pain condition that predominately affects women. Although fibromyalgia is common and associated with substantial morbidity and disability, there are no US Food and Drug Administration-approved treatments. However, progress has been made in identifying pharmacological and non-pharmacological treatments for fibromyalgia. Recent pharmacological treatment studies have focused on selective serotonin and norepinephrine reuptake inhibitors, which enhance serotonin and norepinephrine neurotransmission in the descending pain pathways and lack many of the adverse side effects associated with tricyclic medications. Promising results have also been reported for medications that bind to the alpha2delta subunit of voltage-gated calcium channels, resulting in decreased calcium influx at nerve terminals and subsequent reduction in the release of several neurotransmitters thought to play a role in pain processing. There is also evidence to support exercise, cognitive behavioral therapy, education, and social support in the management of fibromyalgia. It is likely that many patients would benefit from combinations of pharmacological and non-pharmacological treatments, but more study is needed.

Question: What are the symptoms of fibromyalgia?


Maximizing Throughput of Overprovisioned HPC Data Centers Under a Strict Power Budget. Building future generation supercomputers while constraining their power consumption is one of the biggest challenges faced by the HPC community. For example, US Department of Energy has set a goal of 20 MW for an exascale (1018 flops) supercomputer. To realize this goal, a lot of research is being done to revolutionize hardware design to build power efficient computers and network interconnects. In this work, we propose a software-based online resource management system that leverages hardware facilitated capability to constrain the power consumption of each node in order to optimally allocate power and nodes to a job. Our scheme uses this hardware capability in conjunction with an adaptive runtime system that can dynamically change the resource configuration of a running job allowing our resource manager to re-optimize allocation decisions to running jobs as new jobs arrive, or a running job terminates.  We also propose a performance modeling scheme that estimates the essential power characteristics of a job at any scale. The proposed online resource manager uses these performance characteristics for making scheduling and resource allocation decisions that maximize the job throughput of the supercomputer under a given power budget. We demonstrate the benefits of our approach by using a mix of jobs with different power-response characteristics. We show that with a power budget of 4.75 MW, we can obtain up to 5.2X improvement in job throughput when compared with the SLURM scheduling policy that is power-unaware. We corroborate our results with real experiments on a relatively small scale cluster, in which we obtain a 1.7X improvement.

Question: How to maximize throughput of a supercomputer under a strict power budget?


Preserving Relation Privacy in Online Social Network Data. Online social networks routinely publish data of interest to third parties, but in so doing often reveal relationships, such as a friendship or contractual association, that an attacker can exploit. This systematic look at existing privacy-preservation techniques highlights the vulnerabilities of users even in networks that completely anonymize identities. Through a taxonomy that categorizes techniques according to the degree of user identity exposure, the authors examine the ways that existing approaches compromise relation privacy and offer more secure alternatives.

Question: What is the taxonomy of privacy preserving techniques?


Reducing Patient Mortality in Hospitals : The Role of Human Resource. Developing effective health care organizations is increasingly complex as a result of demographic changes, globalization and developments in medicine. This study examines the potential contribution of organizational behavior theory and research by investigating the relationship between systems of human resource management (HRM) practices and effectiveness of patient care in hospitals. Relatively little research has been conducted to explore these issues in health care settings. In a sample of 52 hospitals in England, we examine the relationship between the HRM system and health care outcome. Specifically, we study the association between high performance HRM policies and practices and standardized patient mortality rates. The research reveals that, after controlling for prior mortality and other potentially confounding factors such as the ratio of doctors to patients, greater use of a complementary set of HRM practices has a statistically and practically significant relationship with patient mortality. The findings suggest that managers and policy makers should focus sharply on improving the functioning of relevant HR management systems in health care organizations as one important means by which to improve patient care.

Question: What is the relationship between the human resource management system and patient mortality?


A programmable and virtualized network & IT infrastructure for the internet of things: How can NFV & SDN help for facing the upcoming challenges. The Internet of Things (IoT) revolution has major impacts on the network & Information Technology (IT) infrastructure. As IT in the past decade, network virtualization is simultaneously on its way with for instance Network Functions Virtualization (NFV) and Software-Defined Networking (SDN). NFV and SDN are approaches enhancing the infrastructure agility thus facilitating the design, delivery and operation of network services in a dynamic and scalable manner. IoT will push the infrastructure to its limit with numerous and diverse requirements to fulfill, we therefore believe that the agility brought by the combination of NFV and SDN is essential to face the IoT revolution. In this article, we first highlight some IoT challenges that the network & IT infrastructure will face. The NFV and SDN benefits are presented from a network operator point of view. Following a description of the IoT ecosystem and a recall of some of the IoT stakeholders expectations, a new multi-layered IoT architecture involving SDN and NFV and based upon network & IT resources is put forward. Finally, the article illustrates how the proposed architecture is able to cope with the identified IoT challenges.

Question: How can NFV & SDN help for facing the upcoming challenges.


Configuration Tool for ARINC 653 Operating Systems Eu. ARINC 653 Specification defines a standardized interface of real-time operating systems and an Application Executive (APEX) to develop the reliable applications for avionics based on Integrated Modular Avionics (IMA). The requirements of system platform based on ARINC 653 Standard are defined as configuration data and are integrated to the XML configuration file(s) in the real-time operating system. Unfortunately, existing configuration tools for integrating requirements do not provide checking the syntax errors of XML and verifying the integrity of input data for partitioning. This paper presents a configuration tool for ARINC 653 OS that consist of Wizard module which generates the basic configuration data for IMA based on XML Scheme of ARINC 653 Standard, XML and Partition Editor for the partitioning system of IMA, and Verification module which checks the integrity of input data and XML syntax with its visualization.

Question: What is the configuration tool for ARINC 653 OS?


Semi-supervised auto-encoder based on manifold learning. Auto-encoder is a popular representation learning technique which can capture the generative model of data via a encoding and decoding procedure typically driven by reconstruction errors in an unsupervised way. In this paper, we propose a semi-supervised manifold learning based auto-encoder (named semAE). semAE is based on a regularized auto-encoder framework which leverages semi-supervised manifold learning to impose regularization based on the encoded representation. Our proposed approach suits more practical scenarios in which a small number of labeled data are available in addition to a large number of unlabeled data. Experiments are conducted on several well-known benchmarking datasets to validate the efficacy of semAE from the aspects of both representation and classification. The comparisons to state-of-the-art representation learning methods on classification performance in semi-supervised settings demonstrate the superiority of our approach.

Question: What is a semi-supervised auto-encoder?


Building a Question-Answering Chatbot using Forum Data in the Semantic Space. We build a conversational agent which knowledge base is an online forum for parents of autistic children. We collect about 35,000 threads totalling some 600,000 replies, and label 1% of them for usefulness using Amazon Mechanical Turk. We train a Random Forest Classifier using sent2vec features to label the remaining thread replies. Then, we use word2vec to match user queries conceptually with a thread, and then a reply with a predefined context window.

Question: How to build a question answering chatbot using forum data in the semantic space?


Severely suppressed bone turnover: a potential complication of alendronate therapy.. Alendronate, an inhibitor of bone resorption, is widely used in osteoporosis treatment. However, concerns have been raised about potential oversuppression of bone turnover during long-term use. We report on nine patients who sustained spontaneous nonspinal fractures while on alendronate therapy, six of whom displayed either delayed or absent fracture healing for 3 months to 2 yr during therapy. Histomorphometric analysis of the cancellous bone showed markedly suppressed bone formation, with reduced or absent osteoblastic surface in most patients. Osteoclastic surface was low or low-normal in eight patients, and eroded surface was decreased in four. Matrix synthesis was markedly diminished, with absence of double-tetracycline label and absent or reduced single-tetracycline label in all patients. The same trend was seen in the intracortical and endocortical surfaces. Our findings raise the possibility that severe suppression of bone turnover may develop during long-term alendronate therapy, resulting in increased susceptibility to, and delayed healing of, nonspinal fractures. Although coadministration of estrogen or glucocorticoids appears to be a predisposing factor, this apparent complication can also occur with monotherapy. Our observations emphasize the need for increased awareness and monitoring for the potential development of excessive suppression of bone turnover during long-term alendronate therapy.

Question: What is the potential complication of alendronate therapy?


Automated segmentation of 3D anatomical structures on CT images by using a deep convolutional network based on end-to-end learning approach. We have proposed an end-to-end learning approach that trained a deep convolutional neural network (CNN) for automatic CT image segmentation, which accomplished a voxel-wised multiple classification to directly map each voxel on 3D CT images to an anatomical label automatically. The novelties of our proposed method were (1) transforming the anatomical structures segmentation on 3D CT images into a majority voting of the results of 2D semantic image segmentation on a number of 2D-slices from different image orientations, and (2) using "convolution" and "deconvolution" networks to achieve the conventional "coarse recognition" and "fine extraction" functions which were integrated into a compact all-in-one deep CNN for CT image segmentation. The advantage comparing to previous works was its capability to accomplish real-time image segmentations on 2D slices of arbitrary CT-scan-range (e.g. body, chest, abdomen) and produced correspondingly-sized output. In this paper, we propose an improvement of our proposed approach by adding an organ localization module to limit CT image range for training and testing deep CNNs. A database consisting of 240 3D CT scans and a human annotated ground truth was used for training (228 cases) and testing (the remaining 12 cases). We applied the improved method to segment pancreas and left kidney regions, respectively. The preliminary results showed that the accuracies of the segmentation results were improved significantly (pancreas was 34% and kidney was 8% increased in Jaccard index from our previous results). The effectiveness and usefulness of proposed improvement for CT image segmentations were confirmed.

Question: How to segment pancreas and left kidney regions?


Question: What is a classification and regression tree?


Multispectral Deep Neural Networks for Pedestrian Detection. Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet-based pedestrian detectors trained by color or thermal images separately provide complementary information in discriminating human instances. Thus there is a large potential to improve pedestrian detection by using color and thermal images in DNNs simultaneously. We carefully design four ConvNet fusion architectures that integrate two-branch ConvNets on different DNNs stages, all of which yield better performance compared with the baseline detector. Our experimental results on KAIST pedestrian benchmark show that the Halfway Fusion model that performs fusion on the middle-level convolutional features outperforms the baseline method by 11% and yields a missing rate 3.5% lower than the other proposed architectures.

Question: What is the difference between Faster R-CNN and ConvNet?


Adolescent Internet Addiction: Testing the Association Between Self-Esteem, the Perception of Internet Attributes, and Preference for Online Social Interactions. There is a general consensus that Internet addiction (IA) is mainly related to social aspects of the Web, especially among adolescents. The empirical link between poor social skills and IA is well documented; however, theoretical explanations for this relationship are less developed. One possibility is that people with poor social skills are especially prone to develop a preference for online social interaction (POSI), which, in turn, predicts problematic usage. This hypothesis has been tested for loneliness and social anxiety, but not for self-esteem (SE; one of the main antecedents of IA). Furthermore, the mediating role of the perceived relevance of some Internet features (e.g., anonymity) in the relationship between SE and POSI has never been investigated. A cross-sectional study was conducted with 257 adolescents. Using mediation analyses, we found evidence among females for the mediating role of (a) POSI in the relationship between SE and IA, and (b) the subjective relevance of some Internet features in the association between SE and POSI. No significant effects were found for males.

Question: What is the mediating role of the perceived relevance of some Internet features in the relationship between self-esteem and internet addiction?


The importance of drawing in the mechanical design process. -This paper is a study on the importance of drawing ( both formal drafting and informal sketching) during the process of mechanical design. Five hypotheses, focused on the t~es of drawings, their necessity in mechanical problem solving, and their relation to the external representation medium, are presented and supported. Support is through referenced studies in other domains and the results of protocol studies performed on five mechanical designers. Videotapes of all the marks-on-paper made by designers in representative sections of the design process were studied in detail for their type and purpose. The resulting data is supportive of the hypotheses. These results also give requirements for future computer aided design tools and graphics education, and goals for further studies. I. I N T R O D U C T I O N The goal of this paper is to study the importance of drawing (both formal drafting and informal sketching) during the process of mechanical design. This goal can be extended to state that we intend to show the necessio" of drawing during all the developmental stages of a mechanical design. Through the information presented here, the requirements for future computer aided design tools, graphics education, and further studies will be developed. All mechanical engineers are taught drafting. Thus, most engineers are skilled at making and interpreting these formal mechanical drawings. These drawings are representations of a final design (the end product of the design process) and they are intended to archive the completed design and communicate it to other designers and manufacturing personnel. Additionally, engineers are notorious for not being able to think without making "'back-of-the-envelope" sketches of rough ideas. Sometimes these informal sketches serve to communicate a concept to a colleague, but more often they just help the idea take shape on paper. It is in considering how these sketches help an

Question: What is the importance of drawing in the mechanical design process?


Entry into Platform-Based Markets ∗. We develop a theoretical model to examine the relative importance of platform quality, indirect network effects, and consumer expectations on the success of entrants in platform-based markets. We find that an entrant's success depends on the strength of indirect network effects and on the consumers' discount factor for future applications. We illustrate the model's applicability by examining Xbox's entry into the video game industry. We find that Xbox had a small quality advantage over the incumbent, PlayStation 2, and the strength of indirect network effects and the consumers' discount factor, while statistically significant, fall in the region where PlayStation 2's position is unsustainable.

Question: What is the relative importance of platform quality, indirect network effects, and consumer expectations on the success of entrants in platform-based markets?


Econometric Modelling of Markov-Switching Vector Autoregressions using MSVAR for Ox.

Question: What is the difference between a vector autoregression and a vector error correction model?


Enabling ECN in Multi-Service Multi-Queue Data Centers. Recent proposals have leveraged Explicit Congestion Notification (ECN) to achieve high throughput low latency data center network (DCN) transport. However, most of them implicitly assume each switch port has one queue, making the ECN schemes they designed inapplicable to production DCNs where multiple service queues per port are employed to isolate different traffic classes through weighted fair sharing. In this paper, we reveal this problem by leveraging extensive testbed experiments to explore the intrinsic tradeoffs between throughput, latency, and weighted fair sharing in multi-queue scenarios. Using the guideline learned from the exploration, we design MQ-ECN, a simple yet effective solution to enable ECN for multi-service multiqueue production DCNs. Through a series of testbed experiments and large-scale simulations, we show that MQ-ECN breaks the tradeoffs by delivering both high throughput and low latency simultaneously, while still preserving weighted fair sharing.

Question: What is the tradeoff between throughput, latency, and weighted fair sharing in multi-queue scenarios?


Research on high-frequency induction heating load-matched based on LLC resonant circuit. Modern high-frequency induction heating technology puts forward higher requirements for load matching of the induction heating power supply. LLC resonant load circuit can replace the traditional induction heating load matching transformer, and increase power density. This paper analyzes the load matching characteristics of LLC load resonant circuit in high frequency induction heating power supply, and gives the parameter selection method of matching components. At last, combining with phase-shifting pulse-width modulation (PWM) control method, the phase-shifting induction heating experiment platform based on LLC load resonant circuit is established. Experimental results show that the performance of LLC circuit in high frequency induction heating power supply load matching is excellent.

Question: What is the load matching of LLC resonant circuit?


Computer-Mediated Communication Conflict Management in Groups that Work in Two Different Communication Contexts : Face-To-Face and. can be found at: Small Group Research Additional services and information for http://sgr.sagepub.com/cgi/alerts  Email Alerts: http://sgr.sagepub.com/subscriptions  Subscriptions: http://www.sagepub.com/journalsReprints.nav Reprints: http://www.sagepub.com/journalsPermissions.nav Permissions: http://sgr.sagepub.com/cgi/content/refs/33/5/481 SAGE Journals Online and HighWire Press platforms): (this article cites 36 articles hosted on the Citations

Question: What is the difference between face-to-face and computer-mediated communication?


An induction furnace employing with half bridge series resonant inverter. In this paper, an induction furnace employing with half bridge series resonant inverter built around IGBT as its switching devices suitable for melting 500 grams of brass is presented. Melting times of 10 minutes were achieved at a power level of approximately 4 kW. The operating frequency is automatically tracked to maintain a small constant lagging phase angle using dual phase lock loop when brass is melted. The coil voltage is controlled to protect the resonant capacitors. The experimental results are presented.

Question: How to melt 500 grams of brass?


The long-term sequelae of child and adolescent abuse: a longitudinal community study.. The purpose of the present study was to examine the relationship between childhood and adolescent physical and sexual abuse before the age of 18 and psychosocial functioning in mid-adolescence (age 15) and early adulthood (age 21) in a representative community sample of young adults. Subjects were 375 participants in an ongoing 17-years longitudinal study. At age 21, nearly 11% reported physical or sexual abuse before age 18. Psychiatric disorders based on DSM-III-R criteria were assessed utilizing the NIMH Diagnostic Interview Schedule, Revised Version (DIS-III-R). Approximately 80% of the abused young adults met DSM-III-R criteria for at least one psychiatric disorder at age 21. Compared to their nonabused counterparts, abused subjects demonstrated significant impairments in functioning both at ages 15 and at 21, including more depressive symptomatology, anxiety, psychiatric disorders, emotional-behavioral problems, suicidal ideation, and suicide attempts. While abused individuals were functioning significantly more poorly overall at ages 15 and 21 than their nonabused peers, gender differences and distinct patterns of impaired functioning emerged. These deficits underscore the need for early intervention and prevention strategies to forestall or minimize the serious consequences of child abuse.

Question: What is the long-term sequelae of child and adolescent abuse?


OptoCOMM: Introducing a new optical underwater wireless communication modem. OptoCOMM aims at demonstrating the potential, at physical level, of a communication facility for the SUNRISE platform constituted by an Optical Underwater Wireless Communication (OUWC) module with target performance of 10 Mb/s transmission rate at 10 meters range in shallow medium/high turbidity harbour waters. The module, which is based on blue Light Emitting Diode (LED) units and common photodiodes, is an evolution of the proof-of-principle prototype already proven in laboratory (pool). It will constitute an additional node integrated in the Littoral Ocean Observatory Network (LOON) test-bed of the SUNRISE infrastructures, providing a high speed and short-range communication node, which will complete the capability of acoustic modems already present in the test-bed. Three modules (nodes) will be developed and experimentally demonstrated: one for direct integration with the LOON infrastructures, one, battery powered, to be potentially installed on buoys, Remotely Operated Vehicles (ROVs), etc., and one to be installed on the eFolaga Autonomous Underwater Vehicle (AUV) of the proponents. The paper describes in detail the development of the modems as well as the first lab experiments, where the core technology has been successfully tested.

Question: What is the optical underwater wireless communication modem?