Learning to rank academic experts in the DBLP dataset. Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state-of-the-art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph-structure of the citation patterns for the community of experts, and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combing all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state-of-the-art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.

Question: What is the state-of-the-art in expert finding?


dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning. Learning graph representations is a fundamental task aimed at capturing various properties of graphs in vector space. The most recent methods learn such representations for static networks. However, real world networks evolve over time and have varying dynamics. Capturing such evolution is key to predicting the properties of unseen networks. To understand how the network dynamics affect the prediction performance, we propose an embedding approach which learns the structure of evolution in dynamic graphs and can predict unseen links with higher precision. Our model, dyngraph2vec, learns the temporal transitions in the network using a deep architecture composed of dense and recurrent layers. We motivate the need of capturing dynamics for prediction on a toy data set created using stochastic block models. We then demonstrate the efficacy of dyngraph2vec over existing state-of-the-art methods on two real world data sets. We observe that learning dynamics can improve the quality of embedding and yield better performance in link prediction.

Question: What is the difference between a static and a dynamic graph?


Cu pillar bumps as a lead-free drop-in replacement for solder-bumped, flip-chip interconnects. We evaluated Cu pillar bumps with a lead-free SnAg solder cap as 1st-level soldered interconnects in a standard organic BGA (ball grid array) flip chip package. Various pillar heights and solder volumes were investigated at a minimum pad pitch of 150 mum. Flip chip assembly followed the exact process as in the case of collapsible SnAg solder bumps, using an identical material set and identical equipment. Thanks to the properties of Cu pillar bumps, substrate design tolerances could be relaxed compared to solder bump interconnects of the same pitch. This, together with the fact that no solder-on-pad (SOP) is required for Cu pillar bumps, allows for lower substrate cost, which is a major factor in flip chip packaging cost. Cu pillar bumps also offer significantly higher current carrying capability and better thermal performance compared to solder bumps. We found that flip chip assembly with Cu pillar bumps is a robust process with regard to variations in assembly parameters, such as solder cap volume, flux depth, chip placement accuracy and substrate pad size. It is possible to attach the Cu pillar bumps to the metal lines of the non-SOP substrates with line spacing down to 40 mum plusmn15 mum without any solder bridging. It is shown that flip chip packages with suitable 150 mum-pitch Cu pillar bumps provide high mechanical and electrical reliability. No corrosion on the Cu pillars was found after humidity tests (MSL2 level and unbiased HAST for 192 h). Thermal cycling passed 2000 cycles, even at JEDEC JESD22-A104-B condition C (-65 degC to 150 degC). High Temperature Storage passed 2000 h at 150 degC. Cross sections reveal that after 1000 h at 150degC all Sn in the solder is transformed into Cu-Sn intermetallic compounds (IMCs). Preliminary electromigration test results at highly accelerated conditions (Tbump ap 177 degC, I = 0.8 A) show almost 2 orders of magnitude longer

Question: What is the difference between Cu pillar bumps and solder bumps?


A Review of Ensemble Methods in Bioinformatics. Ensemble learning is an intensively studies technique in machine learning and pattern recognition. Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complexity data structures. The aim of this article is two-fold. First, it is to provide a review of the most widely used ensemble learning methods and their application in various bioinformatics problems, including the main topics of gene expression, mass spectrometry-based proteomics, gene-gene interaction identification from genome-wide association studies, and prediction of regulatory elements from DNA and protein sequences. Second, we try to identify and summarize future trends of ensemble methods in bioinformatics. Promising directions such as ensemble of support vector machine, meta-ensemble, and ensemble based feature selection are discussed.

Question: What is the difference between ensemble learning and machine learning?


Integration of an On-line Kaldi Speech Recogniser to the Alex Dialogue Systems Framework. This paper describes the integration of an on-line Kaldi speech recogniser into the Alex Dialogue Systems Framework (ADSF). As the Kaldi OnlineLatgenRecogniser is written in C++, we first developed a Python wrapper for the recogniser so that the ADSF, written in Python, could interface with it. Training scripts for acoustic and language modelling were developed and integrated into ADSF, and acoustic and language models were build. Finally, optimal recogniser parameters were determined and evaluated. The dialogue system Alex with the new speech recogniser is evaluated on Public Transport Information (PTI) domain.

Question: How to integrate a speech recogniser into a dialogue system?


Three-dimensional bin packing problem with variable bin height. 0377-2217/$ see front matter 2009 Elsevier B.V. A doi:10.1016/j.ejor.2009.05.040 * Corresponding author. Address: Institute for Logi fax: +61 3 9689 4859. E-mail addresses: wuyong@pmail.ntu.edu.sg (Y. W This paper studies a variant of the three-dimensional bin packing problem (3D-BPP), where the bin height can be adjusted to the cartons it packs. The bins and cartons to be packed are assumed rectangular in shape. The cartons are allowed to be rotated into any one of the six positions that keep the carton edges parallel to the bin edges. This greatly increases the difficulty of finding a good solution since the search space expands significantly comparing to the 3D-BPP where the cartons have fixed orientations. A mathematical (mixed integer programming) approach is modified based on [Chen, C. S., Lee, S. M., Shen, Q. S., 1995. An analytical model for the container loading problem. European Journal of Operational Research 80 (1), 68–76] and numerical experiments indicate that the mathematical approach is not suitable for the variable bin height 3D-BPP. A special bin packing algorithm based on packing index is designed to utilize the special problem feature and is used as a building block for a genetic algorithm designed for the 3DBPP. The paper also investigates the situation where more than one type of bin are used and provides a heuristic for packing a batch of cartons using the genetic algorithm. Numerical experiments show that our proposed method yields quick and satisfactory results when benchmarked against the actual packing practice and the MIP model with the latest version of CPLEX. 2009 Elsevier B.V. All rights reserved.

Question: What is the difference between the 3D-BPP and the variable bin height 3D-BPP?


Question: How to express the interaction between states and constraints?


Spatio-Temporal Join on Apache Spark. Effective processing of extremely large volumes of spatial data has led to many organizations employing distributed processing frameworks. Apache Spark is one such open-source framework that is enjoying widespread adoption. Within this data space, it is important to note that most of the observational data (i.e., data collected by sensors, either moving or stationary) has a temporal component, or timestamp. In order to perform advanced analytics and gain insights, the temporal component becomes equally important as the spatial and attribute components. In this paper, we detail several variants of a spatial join operation that addresses both spatial, temporal, and attribute-based joins. Our spatial join technique differs from other approaches in that it combines spatial, temporal, and attribute predicates in the join operator.  In addition, our spatio-temporal join algorithm and implementation differs from others in that it runs in commercial off-the-shelf (COTS) application. The users of this functionality are assumed to be GIS analysts with little if any knowledge of the implementation details of spatio-temporal joins or distributed processing. They are comfortable using simple tools that do not provide the ability to tweak the configuration of the

Question: What is a spatio-temporal join?


What's Wrong with the Diffusion of Innovation Theory. This paper examines the usefulness of the diffusion of innovation research in developing theoretical accounts of the adoption of complex and networked IT solutions. We contrast six conjectures underlying DOI research with field data obtained from the study of the diffusion of EDI. Our analysis shows that DOI based analyses miss some important facets in the diffusion of complex technologies. We suggest that complex IT solutions should be understood as socially constructed and learning intensive artifacts, which can be adopted for varying reasons within volatile diffusion arenas. Therefore DOI researchers should carefully recognize the complex, networked, and learning intensive features of technology; understand the role of institutional regimes, focus on process features (including histories) and key players in the diffusion arena, develop multi-layered theories that factor out mappings between different layers and locales, use multiple perspectives including political models, institutional models and theories of team behavior, and apply varying time scales while crafting accounts of what happened and why. In general the paper calls for a need to develop DOI theories at the site by using multiple levels of

Question: What is the diffusion of innovation theory?


A Metric for Distributions with Applications to Image Databases. Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India We introduce a new distance between two distributions that we call the Earth Mover's Distance (EMD), which reflects the minimal amount of work that must be performed to transform one distribution into the other by moving "distribution mass" around. This is a special case of the transportation problem from linear optimization, for which efficient algorithms are available. The EMD also allows for partial matching. When used to compare distributions that have the same overall mass, the EMD is a true metric, and has easy-to-compute lower bounds. In this paper we focus on applications to image databases, especially color and texture. We use the EMD to exhibit the structure of color-distribution and texture spaces by means of MultiDimensional Scaling displays. We also propose a novel approach to the problem of navigating through a collection of color images, which leads to a new paradigm for image database search.

Question: What is the earth mover's distance?


A Priority based Round Robin CPU Scheduling Algorithm for Real Time Systems. The main objective of this paper is to develop a new approach for round robin C P U scheduling a l g o r i t h m which improves the performance of CPU in real time operating system. The proposed Priority based Round-Robin CPU Scheduling algorithm is based on the integration of round-robin and priority scheduling algorithm. It retains the advantage of round robin in reducing starvation and also integrates the advantage of priority scheduling. The proposed algorithm also implements the concept of aging by assigning new priorities to the processes. Existing round robin CPU scheduling algorithm cannot be implemented in real time operating system due to their high context switch rates, large waiting time, large response time, large turnaround time and less throughput. The proposed algorithm improves all the drawbacks of round robin C P U scheduling algorithm. The paper also presents the comparative analysis of proposed algorithm with existing round robin scheduling algorithm on the basis of varying time quantum, average waiting time, average turnaround time and number of context switches.

Question: What is the priority based round robin CPU scheduling algorithm?


A three-level, T-type, power electronics building block using Si-SiC hybrid switch for high-speed drives. This paper presents the design and implementation of a 100 kW three-level T-type (3L-T2), single-phase power electronics building block (PEBB). The PEBB features switching power devices with associated gate drivers, DC-link capacitors and interconnection busbar. A hybrid switch (HyS), which includes a Si IGBT and a SiC MOSFET in parallel, forms the switching power device in the PEBB. An low-inductive multi-layer laminated busbar was designed to have symmetrical current commutation loops. A major contribution is that this is achieved while no three-level T-type power module was used. A loop inductance of 29 nH was achieved which is lower than the existing state-of-the-art three-level designs in literature. The fabricated prototype PEBB achieves a specific power density of 27.7 kW/kg and a volumetric power density of 308.61 W/in3. Single-phase operation of the PEBB has been demonstrated at the switching frequency of 28 kHz.

Question: What is a T-type power module?


A Novel Approach for Addressing Wandering Off Elderly Using Low Cost Passive RFID Tags. Wandering (e.g. elopement) by elderly persons at acute hospitals and nursing homes poses a significant problem to providing patient care as such incidents can lead to injury and even accidental morbidity. These problems are particularly serious given aging populations around the world. While various technologies exit (such as door alarms), they are expensive and the reliability of such systems have not been evaluated in the past. In this article we propose two novel methods for a very low cost solution to address the problem of wandering off patients using body worn low cost passive Radio Frequency Identification (RFID) tags using phase based measurements. Our approach requires no modification to the air interface protocols, firmware or hardware. Results from extensive experiments show that: i) the proposed algorithms can accurately identify whether a person is moving into or out of, for example, a room; and ii) it can be implemented in real-time to develop a low cost wandering off alarm.

Question: How to detect if a person is moving into or out of a room?


Teaching a lay theory before college narrows achievement gaps at scale.. Previous experiments have shown that college students benefit when they understand that challenges in the transition to college are common and improvable and, thus, that early struggles need not portend a permanent lack of belonging or potential. Could such an approach-called a lay theory intervention-be effective before college matriculation? Could this strategy reduce a portion of racial, ethnic, and socioeconomic achievement gaps for entire institutions? Three double-blind experiments tested this possibility. Ninety percent of first-year college students from three institutions were randomly assigned to complete single-session, online lay theory or control materials before matriculation (n > 9,500). The lay theory interventions raised first-year full-time college enrollment among students from socially and economically disadvantaged backgrounds exiting a high-performing charter high school network or entering a public flagship university (experiments 1 and 2) and, at a selective private university, raised disadvantaged students' cumulative first-year grade point average (experiment 3). These gains correspond to 31-40% reductions of the raw (unadjusted) institutional achievement gaps between students from disadvantaged and nondisadvantaged backgrounds at those institutions. Further, follow-up surveys suggest that the interventions improved disadvantaged students' overall college experiences, promoting use of student support services and the development of friendship networks and mentor relationships. This research therefore provides a basis for further tests of the generalizability of preparatory lay theories interventions and of their potential to reduce social inequality and improve other major life transitions.

Question: What is the difference between a lay theory and a theory of change?


Automatic Labelling of Tabla Signals. Most of the recent developments in the field of music indexing and music information retrieval are focused on western music. In this paper, we present an automatic music transcription system dedicated to Tabla a North Indian percussion instrument. Our approach is based on three main steps: firstly, the audio signal is segmented in adjacent segments where each segment represents a single stroke. Secondly, rhythmic information such as relative durations are calculated using beat detection techniques. Finally, the transcription (recognition of the strokes) is performed by means of a statistical model based on Hidden Markov Model (HMM). The structure of this model is designed in order to represent the time dependencies between successives strokes and to take into account the specificities of the tabla score notation (transcription symbols may be context dependent). Realtime transcription of Tabla soli (or performances) with an error rate of 6.5% is made possible with this transcriber. The transcription system, along with some additional features such as sound synthesis or phrase correction, are integrated in a user-friendly environment called Tablascope.

Question: What is the tabla?


ModSpec: An open, flexible specification framework for multi-domain device modelling. We describe ModSpec, a MATLAB/Octave based specification format suitable for modelling devices across a wide variety of application domains, including circuits, optics, fluidics and biology. The ModSpec format and associated API are centered around describing the nonlinear differential equations at the core of any device model. The format is open, general and easy to use, and is supported by toolchains that translate and automatically differentiate models, set up equations for systems of interacting devices, and provide simulation facilities. We illustrate the use of ModSpec for modelling semiconductor, photovoltaic, fluidic and neuronal devices and systems.

Question: What is a specification framework?


A Novel Myoelectric Pattern Recognition Strategy for Hand Function Restoration After Incomplete Cervical Spinal Cord Injury. This study presents a novel myoelectric pattern recognition strategy towards restoration of hand function after incomplete cervical spinal cord Injury (SCI). High density surface electromyogram (EMG) signals comprised of 57 channels were recorded from the forearm of nine subjects with incomplete cervical SCI while they tried to perform six different hand grasp patterns. A series of pattern recognition algorithms with different EMG feature sets and classifiers were implemented to identify the intended tasks of each SCI subject. High average overall accuracies (>; 97%) were achieved in classification of seven different classes (six intended hand grasp patterns plus a hand rest pattern), indicating that substantial motor control information can be extracted from partially paralyzed muscles of SCI subjects. Such information can potentially enable volitional control of assistive devices, thereby facilitating restoration of hand function. Furthermore, it was possible to maintain high levels of classification accuracy with a very limited number of electrodes selected from the high density surface EMG recordings. This demonstrates clinical feasibility and robustness in the concept of using myoelectric pattern recognition techniques toward improved function restoration for individuals with spinal injury.

Question: What is the novel myoelectric pattern recognition strategy?


MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach. Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-base agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG on the other hand achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.

Question: What is the best performing approach to entity linking?


Attribute-Guided Face Generation Using Conditional CycleGAN. We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a highres face image for the low-res input that satisfies the given attributes. To address this problem, we condition the CycleGAN and propose conditional CycleGAN, which is designed to 1) handle unpaired training data because the training low/high-res and high-res attribute images may not necessarily align with each other, and to 2) allow easy control of the appearance of the generated face via the input attributes. We demonstrate high-quality results on the attribute-guided conditional CycleGAN, which can synthesize realistic face images with appearance easily controlled by user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses). Using the attribute image as identity to produce the corresponding conditional vector and by incorporating a face verification network, the attribute-guided network becomes the identity-guided conditional CycleGAN which produces high-quality and interesting results on identity transfer. We demonstrate three applications on identityguided conditional CycleGAN: identity-preserving face superresolution, face swapping, and frontal face generation, which consistently show the advantage of our new method.

Question: How to generate a high-res face image given a low-res face image?


A rare case of mesh infection 3 years after a laparoscopic totally extraperitoneal (TEP) inguinal hernia repair.. Late complications after a laparoscopic inguinal hernia repair are extremely rare and have only recently entered into the literature. One such late complication is mesh infection, of which there have been a handful of cases reported in the literature. Mesh infections occurring many years after inguinal hernia repairs are not only of significance because they are not well documented in the literature, and the pathogenesis and risk factors contributing to their development are not well understood. This report details a rare case of mesh infection 3 years after a laparoscopic totally extraperitoneal inguinal hernia repair, describes our management of the condition, highlights the current options for management, and attempts to define its pathophysiology.

Question: What is the risk of mesh infection after a laparoscopic inguinal hernia repair?


Monolithic Low Cost Ka-Band Wilkinson Power Dividers on Flexible Organic Substrates. A millimeter wave Wilkinson power divider is presented on a thin and durable organic substrate, along with an analysis of bend topologies to reduce coupling between the output paths. The divider provides excellent performance across the entire Ka-band from 27 to 40 GHz. Insertion loss is approximately 0.3 dB and isolation between the output ports is around 30 dB at the design frequency.

Question: What is a millimeter wave Wilkinson power divider?


Value of Information Systems and Products : Understanding the Users ' Perspective and Values. Developers aim at providing value through their systems and products. However, value is not financial only, but depends on usage and users' perceptions of value. In this paper, we clarify the concept of value from the users' perspective and the role of user involvement in providing value. First, theories and approaches of psychology, marketing and human-computer interaction are reviewed. Secondly, the concept of 'user values' is suggested to clarify the concept of value from the user's point of view and a category framework of user values is presented to make them more concrete and easier to identify. Thirdly, the activities and methods for adopting user values in development work are discussed. The analysis of the literature shows that value has been considered in multiple ways in development. However, users' perspectives have received less attention. As a conclusion, we draw future research directions for value-centered design and propose that user involvement is essential in identifying user values, interpreting the practical meaning of the values and implementing them in development work.

Question: What is the value of information systems and products?


Optimal management of shoulder impingement syndrome. Shoulder impingement is a progressive orthopedic condition that occurs as a result of altered biomechanics and/or structural abnormalities. An effective nonoperative treatment for impingement syndrome is aimed at addressing the underlying causative factor or factors that are identified after a complete and thorough evaluation. The clinician devises an effective rehabilitation program to regain full glenohumeral range of motion, reestablish dynamic rotator cuff stability, and implement a progression of resistive exercises to fully restore strength and local muscular endurance in the rotator cuff and scapular stabilizers. The clinician can introduce stresses and forces via sport-specific drills and functional activities to allow a return to activity.

Question: What is the optimal management of shoulder impingement syndrome?


What can quantum theory bring to information retrieval. The probabilistic formalism of quantum physics is said to provide a sound basis for building a principled information retrieval framework. Such a framework can be based on the notion of information need vector spaces where events, such as document relevance or observed user interactions, correspond to subspaces. As in quantum theory, a probability distribution over these subspaces is defined through weighted sets of state vectors (density operators), and used to represent the current view of the retrieval system on the user information need. Tensor spaces can be used to capture different aspects of information needs. Our evaluation shows that the framework can lead to acceptable performance in an ad-hoc retrieval task. Going beyond this, we discuss the potential of the framework for three active challenges in information retrieval, namely, interaction, novelty and diversity.

Question: What is the potential of quantum theory in information retrieval?


An Efficient Participant's Selection Algorithm for Crowdsensing. With the advancement of mobile technology the use of Smartphone is greatly increased. Everyone has the mobile phones and it becomes the necessity of life. Today, smart devices are flooding the internet data at every time and in any form that cause the mobile crowdsensing (MCS). One of the key challenges in mobile crowd sensing system is how to effectively identify and select the well-suited participants in recruitments from a large user pool. This research work presents the concept of crowdsensing along with the selection process of participants from a large user pool. MCS provides the efficient selection process for participants that how well suited participant's selects/recruit from a large user pool. For this, the proposed selection algorithm plays our role in which the recruitment of participants takes place with the availability status from the large user pool. At the end, the graphical result presented with the suitable location of the participants and their time slot. Keywords—Mobile crowdsensing (MCS); Mobile Sensing Platform (MSP]); crowd sensing; participant; user pool; crowdsourcing

Question: How to select participants from a large user pool?


An Introduction to Active Shape Models ∗. Biomedical images usually contain complex objects, which will vary in appearance significantly from one image to another. Attempting to measure or detect the presence of particular structures in such images can be a daunting task. The inherent variability will thwart naive schemes. However, by using models which can cope with the variability it is possible to successfully analyse complex images. Here we will consider a number of methods where the model represents the expected shape and local grey-level structure of a target object in an image. Model-based methods make use of a prior model of what is expected in the image, and typically attempt to find the best match of the model to the data in a new image. Having matched the model, one can then make measurements or test whether the target is actually present. This approach is a 'top-down' strategy, and differs significantly from 'bottomup' methods. In the latter the image data is examined at a low level, looking for local structures such as edges or regions, which are assembled into groups in an attempt to identify objects of interest. Without a global model of what to expect, this approach is difficult and prone to failure. A wide variety of model based approaches have been explored (see the review below). This chapter will concentrate on a statistical approach, in which a model is built from analysing the appearance of a set of labelled examples. Where structures vary in shape or texture, it is possible to learn what are plausible variations and what are not. A new image can be interpreted by finding the best plausible match of the model to the image data. The advantages of such a method are that

Question: What is a statistical approach?


Nasal Duplication Combined with Cleft Lip and Palate: Surgical Correction and Long-Term Follow-Up. Background Diprosopus dirrhinus, or nasal duplication, is a rare entity of partial craniofacial duplication.   Methods The case we present is the first report of diprosopus dirrhinus associated with complete cleft lip and palate. The baby was born in Cambodia at full term by normal vaginal delivery with no significant perinatal and family history. Physical examination revealed significant facial deformity due to the duplicated nose and the left complete cleft lip/palate on the right subset.   Results There were 4 nostrils; both medial apertures including the cleft site were found to be 10-15 mm deep cul-de-sac structures without communication to the nasopharynx. The upper third of the face was notable for hypertelorism with a duplication of the soft-tissue nasion and glabella. Between the 2 nasal dorsums, there was a small cutaneous depression with a lacrimal fistula in the midline. Surgical treatment included the first stage of primary lip and nose repair and the second stage of palatoplasty.   Conclusions The patient was followed up at the age of 10 years showing satisfactory results for both aesthetic and functional aspects. Further management in the future will be required for the hypertelorism and nasal deformity.

Question: What is the first stage of primary lip and nose repair?


Dynamic Action Repetition for Deep Reinforcement Learning. One of the long standing goals of Artificial Intelligence (AI) is to build cognitive agents which can perform complex tasks from raw sensory inputs without explicit supervision (Lake et al. 2016). Recent progress in combining Reinforcement Learning objective functions and Deep Learning architectures has achieved promising results for such tasks. An important aspect of such sequential decision making problems, which has largely been neglected, is for the agent to decide on the duration of time for which to commit to actions. Such action repetition is important for computational efficiency, which is necessary for the agent to respond in real-time to events (in applications such as self-driving cars). Action Repetition arises naturally in real life as well as simulated environments. The time scale of executing an action enables an agent (both humans and AI) to decide the granularity of control during task execution. Current state of the art Deep Reinforcement Learning models, whether they are off-policy (Mnih et al. 2015; Wang et al. 2015) or on-policy (Mnih et al. 2016), consist of a framework with a static action repetition paradigm, wherein the action decided by the agent is repeated for a fixed number of time steps regardless of the contextual state while executing the task. In this paper, we propose a new framework Dynamic Action Repetition which changes Action Repetition Rate (the time scale of repeating an action) from a hyper-parameter of an algorithm to a dynamically learnable quantity. At every decision-making step, our models allow the agent to commit to an action and the time scale of executing the action. We show empirically that such a dynamic time scale mechanism improves the performance on relatively harder games in the Atari 2600 domain, independent of the underlying Deep Reinforcement Learning algorithm used.

Question: What is the dynamic action repetition framework?


devices and apps for health care professionals : uses and benefits table 1 uses for Mobile devices and apps by health care professionals.

Question: What are the uses of mobile devices and apps by health care professionals?


Fingerprinting Electronic Control Units for Vehicle Intrusion Detection. As more software modules and external interfaces are getting added on vehicles, new attacks and vulnerabilities are emerging. Researchers have demonstrated how to compromise in-vehicle Electronic Control Units (ECUs) and control the vehicle maneuver. To counter these vulnerabilities, various types of defense mechanisms have been proposed, but they have not been able to meet the need of strong protection for safety-critical ECUs against in-vehicle network attacks. To mitigate this deficiency, we propose an anomaly-based intrusion detection system (IDS), called Clock-based IDS (CIDS). It measures and then exploits the intervals of periodic in-vehicle messages for fingerprinting ECUs. The thusderived fingerprints are then used for constructing a baseline of ECUs' clock behaviors with the Recursive Least Squares (RLS) algorithm. Based on this baseline, CIDS uses Cumulative Sum (CUSUM) to detect any abnormal shifts in the identification errors — a clear sign of intrusion. This allows quick identification of in-vehicle network intrusions with a low false-positive rate of 0.055%. Unlike state-of-the-art IDSs, if an attack is detected, CIDS's fingerprinting of ECUs also facilitates a rootcause analysis; identifying which ECU mounted the attack. Our experiments on a CAN bus prototype and on real vehicles have shown CIDS to be able to detect a wide range of in-vehicle network attacks.

Question: How to fingerprint electronic control units for vehicle intrusion detection?


Differentially Private Stochastic Gradient Descent for in-RDBMS Analytics. This paper studies differential privacy for stochastic gradient descent (SGD) in an in-RDBMS system. While significant progress has been made separately on in-RDBMS SGD and private SGD, none of the major in-RDBMS machine learning frameworks have incorporated differentially private SGD. There are two inter-related issues for this disconnect between research and practice: (1) low model accuracy due to added noise to guarantee privacy, and (2) high development and runtime overhead of the private algorithms. This paper takes a first step to remedy this disconnect and proposes a private SGD algorithm to address both issues in an integrated manner. In contrast to the white-box approach adopted by previous algorithms, we revisit and use the classical technique of output perturbation. While using output perturbation trivially addresses (2), it gives rise to challenges in addressing (1). We address this challenge by providing a novel analysis of the L2-sensitivity of SGD, which allows, under the same privacy guarantees, better convergence of SGD when only a constant number of passes can be made over the data. We then integrate this algorithm, along with the state-of-the-art differentially private SGD, into Bismarck, an inRDBMS analytics system. Extensive experiments demonstrate that our algorithm can be easily integrated, incurs virtually no overhead, scales well, and most importantly, for a number of datasets yields substantially better (up to 4X) test accuracy than the state-of-the-art algorithms.

Question: What is the in-RDBMS analytics system?


Efficient Ordered Combinatorial Semi-Bandits for Whole-Page Recommendation. Multi-Armed Bandit (MAB) framework has been successfully applied in many web applications. However, many complex real-world applications that involve multiple content recommendations cannot fit into the traditional MAB setting. To address this issue, we consider an ordered combinatorial semi-bandit problem where the learner recommends S actions from a base set of K actions, and displays the results in S (out of M ) different positions. The aim is to maximize the cumulative reward with respect to the best possible subset and positions in hindsight. By the adaptation of a minimumcost maximum-flow network, a practical algorithm based on Thompson sampling is derived for the (contextual) combinatorial problem, thus resolving the problem of computational intractability. With its potential to work with whole-page recommendation and any probabilistic models, to illustrate the effectiveness of our method, we focus on Gaussian process optimization and a contextual setting where click-throughrate is predicted using logistic regression. We demonstrate the algorithms' performance on synthetic Gaussian process problems and on large-scale news article recommendation datasets from Yahoo! Front Page Today Module.

Question: What is the minimumcost maximum-flow network?


Be Appropriate and Funny: Automatic Entity Morph Encoding. Internet users are keen on creating different kinds of morphs to avoid censorship, express strong sentiment or humor. For example, in Chinese social media, users often use the entity morph "方便面 (Instant Noodles)" to refer to "周永康 (Zhou Yongkang)" because it shares one character "康 (Kang)" with the well-known brand of instant noodles "康师傅 (Master Kang)". We developed a wide variety of novel approaches to automatically encode proper and interesting morphs, which can effectively pass decoding tests 1.

Question: What is the meaning of "Instant Noodles"?


A research manifesto for services science. The services sector has grown over the last 50 years to dominate economic activity in most advanced industrial economies, yet scientific understanding of modern services is rudimentary. Here, we argue for a services science discipline to integrate across academic silos and advance service innovation more rapidly.

Question: What is a services science discipline?


Angular Brushing of Extended Parallel Coordinates. In this paper we present angularbrushingfor parallel coordinates (PC) as a new approach to high-light rational dataproperties, i.e., features which depend on two data dimensions (instead of one). We also demonstrate smoothbrushing of PC as an intuitive tool to specify non-binary degreeof-interest functions (then used for F+C visualization). We also shortly describe our implementation as well as its application to the visualization of CFD data.

Question: What is angular brushing?


On Training Targets for Supervised Speech Separation. Formulation of speech separation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised learning algorithm, typically a deep neural network, is trained to learn a mapping from noisy features to a time-frequency representation of the target of interest. Traditionally, the ideal binary mask (IBM) is used as the target because of its simplicity and large speech intelligibility gains. The supervised learning framework, however, is not restricted to the use of binary targets. In this study, we evaluate and compare separation results by using different training targets, including the IBM, the target binary mask, the ideal ratio mask (IRM), the short-time Fourier transform spectral magnitude and its corresponding mask (FFT-MASK), and the Gammatone frequency power spectrum. Our results in various test conditions reveal that the two ratio mask targets, the IRM and the FFT-MASK, outperform the other targets in terms of objective intelligibility and quality metrics. In addition, we find that masking based targets, in general, are significantly better than spectral envelope based targets. We also present comparisons with recent methods in non-negative matrix factorization and speech enhancement, which show clear performance advantages of supervised speech separation.

Question: What is the difference between the ideal binary mask and the target binary mask?


Image Based Camera Localization: an Overview. Virtual reality, augmented reality, robotics, and autonomous driving, have recently attracted much attention from both academic and industrial communities, in which image-based camera localization is a key task. However, there has not been a complete review on image-based camera localization. It is urgent to map this topic to enable individuals enter the field quickly. In this paper, an overview of image-based camera localization is presented. A new and complete classification of image-based camera localization approaches is provided and the related techniques are introduced. Trends for future development are also discussed. This will be useful not only to researchers, but also to engineers and other individuals interested in this field.

Question: What is the difference between image-based camera localization and image-based camera tracking?


A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving. Driving through dynamically changing traffic scenarios is a highly challenging task for autonomous vehicles, especially on urban roadways. Prediction of surrounding vehicles' driving behaviors plays a crucial role in autonomous vehicles. Most traditional driving behavior prediction models work only for a specific traffic scenario and cannot be adapted to different scenarios. In addition, priori driving knowledge was never considered sufficiently. This study proposes a novel scenario-adaptive approach to solve these problems. A novel ontology model was developed to model traffic scenarios. Continuous features of driving behavior were learned by Hidden Markov Models (HMMs). Then, a knowledge base was constructed to specify the model adaptation strategies and store priori probabilities based on the scenario's characteristics. Finally, the target vehicle's future behavior was predicted considering both a posteriori probabilities and a priori probabilities. The proposed approach was sufficiently evaluated with a real autonomous vehicle. The application scope of traditional models can be extended to a variety of scenarios, while the prediction performance can be improved by the consideration of priori knowledge. For lane-changing behaviors, the prediction time horizon can be extended by up to 56% (0.76 s) on average. Meanwhile, long-term prediction precision can be enhanced by over 26%.

Question: How to predict the future driving behavior of a vehicle?


Simplified high-accuracy calculation of eddy-current loss in round-wire windings. It has recently been shown that the most commonly used methods for calculating high-frequency eddy-current loss in round-wire windings can have substantial error, exceeding 60%. Previous work includes a formula based on a parametric set of finite-element analysis (FEA) simulations that gives proximity-effect loss for a large range of frequencies, using the parameters from a lookup table based on winding geometry. We improve the formula by decreasing the number of parameters in the formula and also, more importantly, by using simple functions to get the parameters from winding geometry such that a large lookup table is not needed. The function we present is exact in the low frequency limit (diameter much smaller than skin depth) and has error less than 4% at higher frequencies. We make our new model complete by examining the field expression needed to get the total proximity-effect loss and by including the skin-effect loss. We also present experimental results confirming the validity of the model and its superiority to standard methods.

Question: What is the eddy-current loss in a round-wire winding?


Test Results and Torque Improvement of the 50-kW Switched Reluctance Motor Designed for Hybrid Electric Vehicles. A switched reluctance motor (SRM) has been developed as one of the possible candidates of rare-earth-free electric motors. A prototype machine has been built and tested. It has competitive dimensions, torque, power, and efficiency with respect to the 50-kW interior permanent magnet synchronous motor employed in the hybrid electric vehicles (Toyota Prius 2003). It is found that competitive power of 50-kW rating and efficiency of 95% are achieved. The prototype motor provided 85% of the target torque. Except the maximum torque, the most speed-torque region is found to be covered by the test SRM. The cause of discrepancy in the measured and calculated torque values is examined. An improved design is attempted, and a new experimental switched reluctance machine is designed and built for testing. The results are given in this paper.

Question: What is the cause of discrepancy in the measured and calculated torque values?


Automatic Collocation Suggestion in Academic Writing. In recent years, collocation has been widely acknowledged as an essential characteristic to distinguish native speakers from non-native speakers. Research on academic writing has also shown that collocations are not only common but serve a particularly important discourse function within the academic community. In our study, we propose a machine learning approach to implementing an online collocation writing assistant. We use a data-driven classifier to provide collocation suggestions to improve word choices, based on the result of classification. The system generates and ranks suggestions to assist learners' collocation usages in their academic writing with satisfactory results. *

Question: What is a data-driven classifier?


Ladder Variational Autoencoders. Variational autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these highly expressive models. We propose a new inference model, the Ladder Variational Autoencoder, that recursively corrects the generative distribution by a data dependent approximate likelihood in a process resembling the recently proposed Ladder Network. We show that this model provides state of the art predictive log-likelihood and tighter log-likelihood lower bound compared to the purely bottom-up inference in layered Variational Autoencoders and other generative models. We provide a detailed analysis of the learned hierarchical latent representation and show that our new inference model is qualitatively different and utilizes a deeper more distributed hierarchy of latent variables. Finally, we observe that batch normalization and deterministic warm-up (gradually turning on the KL-term) are crucial for training variational models with many stochastic layers.

Question: What is a ladder variational autoencoder?


SDN/NFV-enabled satellite communications networks: Opportunities, scenarios and challenges. In the context of next generation 5G networks, the satellite industry is clearly committed to revisit and revamp the role of satellite communications. As major drivers in the evolution of (terrestrial) fixed and mobile networks, Software Defined Networking (SDN) and Network Function Virtualisation (NFV) technologies are also being positioned as central technology enablers towards improved and more flexible integration of satellite and terrestrial segments, providing satellite network further service innovation and business agility by advanced network resources management techniques. Through the analysis of scenarios and use cases, this paper provides a description of the benefits that SDN/NFV technologies can bring into satellite communications towards 5G. Three scenarios are presented and analysed to delineate different potential improvement areas pursued through the introduction of SDN/NFV technologies in the satellite ground segment domain. Within each scenario, a number of use cases are developed to gain further insight into specific capabilities and to identify the technical challenges stemming from them.

Question: What is the difference between SDN and NFV?


Multiple templates auto exposure control based on luminance histogram for onboard camera. In order to adjust the exposure of high resolution and frequency images from an on-board camera in real time, a simple exposure control approach based on luminance histogram analysis is presented to realize light control precisely and rapidly. The algorithm divides the initial image into nine blocks and calculates the mean brightness of every part. It uses the luminance histogram to analyze the degree of exposure of both: the global image and every part, especially interest areas. According to the area size of the neighborhood on the two edges of the histogram, the different weight matrix can be decided to quickly calculate the optimal brightness for the next frame, and then the optimal exposure time can be determined accurately. The algorithm does not need to determine an expensive quadratic function to decide the weight of every part so as to fast perform auto exposure control. The experimental results show that the algorithm can correctly and fast adjust the exposure time of the on-board camera on an autonomous robot, and has strong adaptability for various shootings outdoor.

Question: How to adjust the exposure of high resolution and frequency images from an on-board camera in real time?


Implementation of PID controller and pre-filter to control non-linear ball and plate system. In this paper, the authors try to make PID controller with Pre-filter that is implemented at ball and plate system. Ball and plate system will control the position of ball's axis in pixels value by using servo motor as its actuator and webcam as its sensor of position. PID controller with Pre-filter will have a better response than conventional PID controller. Eventhough the response of PID with Pre-filter is slower than conventional PID, the effect of Pre-filter in the system will give the less overshoot response.

Question: How to control the position of ball's axis in pixels value by using servo motor as its actuator and webcam as its sensor of position?


Co-regularized least square regression for multi-view multi-class classification. Many classification problems involve instances that are unlabeled, multi-view and multi-class. However, few technique has been benchmarked for this complex scenario, with a notable exception that combines co-trained naive bayes (CoT-NB) with BCH coding. In this paper, we benchmark the performance of co-regularized least square regression (CoR-LS) for semi-supervised multi-view multi-class classification. We find it performed consistently and significantly better than CoT-NB over eight data sets at different scales. We also find for CoR-LS identity coding is optimal on large data sets and BCH coding is optimal on small data sets. Optimal scoring, a data-dependent coding scheme, often provides near-optimal performance.

Question: What is the difference between CoT-NB and CoR-LS?


Analysis and optimization of software requirements prioritization techniques. Prioritizing requirements helps the project team to understand which requirements are most important and most urgent. Based on this finding a software engineer can decide what to develop/implement in the first release and what on the coming releases. Prioritization is also a useful activity for decision making in other phases of software engineering like development, testing, and implementation. There are a number of techniques available to prioritize the requirements with their associated strengths and limitations. In this paper we will examine state of the art techniques and analyze their applicability on software requirements domain. At the end we present a framework that will help the software engineer of how to perform prioritization process by combining existing techniques and approaches.

Question: What is the state of the art in software requirements prioritization?


Automated Irrigation System-IoT Based Approach. Agriculture is a major source of earning of Indians and agriculture has made a big impact on India's economy. The development of crops for a better yield and quality deliver is exceptionally required. So suitable conditions and suitable moisture in beds of crop can play a major role for production.. Mostly irrigation is done by tradition methods of stream flows from one end to other. Such supply may leave varied moisture levels in filed. The administration of the water system can be enhanced utilizing programmed watering framework This paper proposes a programmed water system with framework for the terrains which will reduce manual labour and optimizing water usage increasing productivity of crops. For formulating the setup, Arduino kit is used with moisture sensor with Wi-Fi module. Our experimental setup is connected with cloud framework and data is acquisition is done. Then data is analysed by cloud services and appropriate recommendations are given.

Question: What is the difference between a traditional irrigation system and an IoT based irrigation system?


SeemGo: Conditional Random Fields Labeling and Maximum Entropy Classification for Aspect Based Sentiment Analysis. This paper describes our SeemGo system for the task of Aspect Based Sentiment Analysis in SemEval-2014. The subtask of aspect term extraction is cast as a sequence labeling problem modeled with Conditional Random Fields that obtains the F-score of 0.683 for Laptops and 0.791 for Restaurants by exploiting both word-based features and context features. The other three subtasks are solved by the Maximum Entropy model, with the occurrence counts of unigram and bigram words of each sentence as features. The subtask of aspect category detection obtains the best result when applying the Boosting method on the Maximum Entropy model, with the precision of 0.869 for Restaurants. The Maximum Entropy model also shows good performance in the subtasks of both aspect term and aspect category polarity classification.

Question: What is the best aspect based sentiment analysis system?


On the modelling of publish/subscribe communication systems. This paper presents a formal framework of a distributed computation based on a publish/subscribe system. The framework abstracts the system through two delays, namely the subscription/unsubscription delay and the diffusion delay. This abstraction allows one to model concurrent execution of publication and subscription operations without waiting for the stability of the system state and to define a Liveness property which gives the conditions for the presence of a notification event in the global history of the system. This formal framework allows us to analytically define a measure of the effectiveness of a publish/subscribe system, which reflects the percentage of notifications guaranteed by the system to subscribers. A simulation study confirms the validity of the analytical measurements. Copyright c © 2005 John Wiley & Sons, Ltd.

Question: What is the difference between a publish/subscribe system and a message queue?


Anomaly Detection in Noisy Images. Title of dissertation: Anomaly Detection in Noisy Images Xavier Gibert Serra, Ph.D. Examination, Fall 2015 Dissertation directed by: Professor Rama Chellappa Department of Electrical and Computer Engineering Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as

Question: What is the difference between a crack and an anomaly?


Role of affective self-regulatory efficacy in diverse spheres of psychosocial functioning.. This prospective study with 464 older adolescents (14 to 19 years at Time 1; 16 to 21 years at Time 2) tested the structural paths of influence through which perceived self-efficacy for affect regulation operates in concert with perceived behavioral efficacy in governing diverse spheres of psychosocial functioning. Self-efficacy to regulate positive and negative affect is accompanied by high efficacy to manage one's academic development, to resist social pressures for antisocial activities, and to engage oneself with empathy in others' emotional experiences. Perceived self-efficacy for affect regulation essentially operated mediationally through the latter behavioral forms of self-efficacy rather than directly on prosocial behavior, delinquent conduct, and depression. Perceived empathic self-efficacy functioned as a generalized contributor to psychosocial functioning. It was accompanied by prosocial behavior and low involvement in delinquency but increased vulnerability to depression in adolescent females.

Question: What is the role of affective self-regulatory efficacy in diverse spheres of psychosocial functioning?


Power grids reliability appraisal with intrusion tolerant capability in SCADA systems. By intruding into the substations and control center of the supervisory control and data acquisition (SCADA) system, trip commands can be sent to intelligent electronic devices (IEDs) which control the system breakers. Reliability of the power system can be impacted through the cyber attacks. In this paper, a modified semi-Markov process (SMP) model is used to describe the procedures of normal and penetration attacks against the intrusion tolerant system. By modeling the transition probabilities between the SMP states and the sojourn time of each SMP state, the mean times to compromise (MTTCs) of the normal and penetration attacks are calculated. With increased probabilities of breaker trips resulted from the cyber attacks, the loss of load probabilities (LOLP) are evaluated based on IEEE reliability test system 79 (RTS79). When the level of attack increases or the level of defense in the system decreases, the simulation results demonstrate that the power system becomes less reliable.

Question: How to calculate the mean time to compromise (MTTC) of the normal and penetration attacks?


Misconceptions about real-time computing: a serious problem for next-generation systems. The author defines real-time computing and states and dispels the most common misconceptions about it. He discusses the fundamental technical issues of real-time computing. He examines specification and verification, scheduling theory, operating systems, programming languages and design methodology, distributed databases, artificial intelligence, fault tolerance, architectures, and communication.<<ETX>>

Question: What is ETX?


An Application of Reinforcement Learning to Aerobatic Helicopter Flight. Autonomous helicopter flight is widely regarded to be a highly challenging control problem. This paper presents the first successful autonomous completion on a real RC helicopter of the following four aerobatic maneuvers: forward flip and sideways roll at low speed, tail-in funnel, and nose-in funnel. Our experimental results significantly extend the state of the art in autonomous helicopter flight. We used the following approach: First we had a pilot fly the helicopter to help us find a helicopter dynamics model and a reward (cost) function. Then we used a reinforcement learning (optimal control) algorithm to find a controller that is optimized for the resulting model and reward function. More specifically, we used differential dynamic programming (DDP), an extension of the linear quadratic regulator (LQR).

Question: What is reinforcement learning?


Management of a Large Frontal Encephalocoele With Supraorbital Bar Remodeling and Advancement.. Of all the craniofacial abnormalities, facial clefts are the most disfiguring. Facial clefts are classified according to the affected anatomical area as described by Tessier. Through this classification, the location and extent of the cleft can be designated numerically.A 2-month-old male infant was referred to authors' craniofacial unit, from a hospital in a rural province of South Africa, with a problem of a supranasal encephalocoele. Bilateral raised eyebrows were noted as was a right-sided upper lid central third coloboma. Computed tomography and magnetic resonance imaging scans confirmed the presence of a supranasal encephalocoele with a large frontal bone defect and splayed nasal bones. Bilateral enlarged orbits were noted with tented orbital roofs that we classified as Tessier number 10 facial clefts. The child was booked for an encephalocoele excision and calvarial reconstruction at 4 months of age.As a result of the encephalocoele, the supraorbital bar with its adjacent nasal bones was cleaved in 2, resulting in a significant frontal bone defect. Osteotomies were performed to remove the supraorbital bar and nasal bones from the calvarium. The supraorbital bar segment was remodeled and plated with absorbable poly-L-lactic acid plates. Osteotomies of the nasal bones allowed them to be united centrally, also with absorbable plates. This entire construct was transferred and secured to the calvarium, but in a more caudal position thereby obliterating the frontal bone and Tessier number 10 facial cleft defects with a naturally contoured construct.

Question: What is a Tessier number 10 facial cleft?


Culture and Conformity : A Meta-Analysis of Studies Using Asch ' s ( 1952 b , 1956 ) Line Judgment Task. A meta-analysis of conformity studies using an Asch-type line judgment task (1952b, 1956) was conducted to investigate whether the level of conformity has changed over time and whether it is related crogs-culturally to individualism-collectivism. The fiterature search produced 133 studies drawn from 17 countries. An analysis of U.S. studies found that conformity has declined since the 1950s. Results from 3 surveys were used to assess a country's individualism-collectivism, and for each survey the measures were found to be significantly related to conformity. Collectivist countries tended to show higher levels of conformity than individualist countries. Conformity research must attend more to cultural variables and to their role in the processes involved in social influence.

Question: What is the relationship between conformity and individualism-collectivism?


Total parser combinators. A monadic parser combinator library which guarantees termination of parsing, while still allowing many forms of left recursion, is described. The library's interface is similar to those of many other parser combinator libraries, with two important differences: one is that the interface clearly specifies which parts of the constructed parsers may be infinite, and which parts have to be finite, using dependent types and a combination of induction and coinduction; and the other is that the parser type is unusually informative.  The library comes with a formal semantics, using which it is proved that the parser combinators are as expressive as possible. The implementation is supported by a machine-checked correctness proof.

Question: What is a parser combinator library?


Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men.. OBJECTIVE To investigate the relation between obesity, fat distribution, and weight gain through adulthood and the risk of non-insulin-dependent diabetes mellitus (NIDDM).   RESEARCH DESIGN AND METHODS We analyzed data from a cohort of 51,529 U.S. male health professionals, 40-75 years of age in 1986, who completed biennial questionnaires sent out in 1986, 1988, 1990, and 1992. During 5 years of follow-up (1987-1992), 272 cases of NIDDM were diagnosed among men without a history of diabetes, heart disease, and cancer in 1986 and who provided complete health information. Relative risks (RRs) associated with different anthropometric measures were calculated controlling for age, and multivariate RRs were calculated controlling for smoking, family history of diabetes, and age.   RESULTS We found a strong positive association between overall obesity as measured by body mass index (BMI) and risk of diabetes. Men with a BMI of > or = 35 kg/m2 had a multivariate RR of 42.1 (95% confidence interval [CI] 22.0-80.6) compared with men with a BMI < 23.0 kg/m2. BMI at age 21 and absolute weight gain throughout adulthood were also significant independent risk factors for diabetes. Fat distribution, measured by waist-to-hip ratio (WHR), was a good predictor of diabetes only among the top 5%, while waist circumference was positively associated with the risk of diabetes among the top 20% of the cohort.   CONCLUSIONS These data suggest that waist circumference may be a better indicator than WHR of the relationship between abdominal adiposity and risk of diabetes. Although early obesity, absolute weight gain throughout adulthood, and waist circumference were good predictors of diabetes, attained BMI was the dominant risk factor for NIDDM; even men of average relative weight had significantly elevated RRs.

Question: What is the difference between BMI and WHR?


Chain Replication for Supporting High Throughput and Availability. Chain replication is a new approach to coordinating clusters of fail-stop storage servers. The approach is intended for supporting large-scale storage services that exhibit high throughput and availability without sacrificing strong consistency guarantees. Besides outlining the chain replication protocols themselves, simulation experiments explore the performance characteristics of a prototype implementation. Throughput, availability, and several objectplacement strategies (including schemes based on distributed hash table routing) are discussed.

Question: What is chain replication?


Incremental tessellation of trimmed parametric surfaces. This paper presents an incremental approach for tessellating trimmed parametric surfaces. A database is built to keep the tessellation of a CAD model. With the topological information provided, the database is efficiently updated when the model is modified. This enhances the tessellation process as only a slight change of the existing tessellation is required. The tessellation which involves the insertion and removal of trimlines is performed completely in the parametric space. The topological information also facilitates the triangles classification and the merging of boundaries between surfaces. The algorithm is particularly suitable for the generation of a valid triangulation of a complex CAD model for many downstream applications, e.g. in rapid prototyping (RP). q 2000 Elsevier Science Ltd. All rights reserved.

Question: What is the incremental tessellation of trimmed parametric surfaces?


INTELLIGENT SYSTEMS IN TOURISM A Social Science Perspective. Intelligent systems sense their environment and learn from the actions they implement to reach specific goals. They are increasingly used to support tourist information search and decision making as well as work processes. In order to model the tourism domain, these systems require a profound understanding of its nature. Looking at existing literature in tourism, this paper discusses critical gaps in the knowledge of the field to be filled so that intelligent system design can be informed and impacts understood. Specifically, it discusses the need to better conceptualize technology in tourism research and argues for a focus on uses and interactions. It challenges simplistic views of tourist information search and decisionmaking processes and calls for more research on potential impacts.

Question: What is the impact of tourism on the environment?


The pains and pleasures of parenting: when, why, and how is parenthood associated with more or less well-being?. The relationship between parenthood and well-being has become a hot topic among scholars, media, and general public alike. The research, however, has been mixed-some studies indicate that parents are happier than nonparents, whereas others suggest the reverse. We suggest that the question of whether parents are more or less happy than their childless peers is not the most meaningful one. To reconcile the conflicting literature and expand understanding of the emotional experience of parenthood, we present a model of parents' well-being that describes why and how parents experience more or less happiness than nonparents (i.e., mediators of the link between parenthood and well-being). We then apply this model to explain when parents are more likely to experience more or less happiness (i.e., moderators of parents' well-being, such as parent age or child temperament). Supporting our model, we review 3 primary methodological approaches: studies comparing parents and nonparents, studies examining changes in well-being across the transition to parenthood, and studies comparing parents' experiences while with their children to their other daily activities. Our review suggests that the relationship between parenthood and well-being is highly complex. We propose that parents are unhappy to the extent that they encounter relatively greater negative emotions, magnified financial problems, more sleep disturbance, and troubled marriages. By contrast, when parents experience greater meaning in life, satisfaction of their basic needs, greater positive emotions, and enhanced social roles, they are met with happiness and joy.

Question: How does parenthood affect well-being?


gSLICr: SLIC superpixels at over 250Hz. We introduce a parallel GPU implementation of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Using a single graphic card, our implementation achieves speedups of up to 83× from the standard sequential implementation. Our implementation is fully compatible with the standard sequential implementation and the software is now available online and is open source.

Question: What is a superpixel?


Emotion, plasticity, context, and regulation: perspectives from affective neuroscience.. The authors present an overview of the neural bases of emotion. They underscore the role of the prefrontal cortex (PFC) and amygdala in 2 broad approach- and withdrawal-related emotion systems. Components and measures of affective style are identified. Emphasis is given to affective chronometry and a role for the PFC in this process is proposed. Plasticity in the central circuitry of emotion is considered, and implications of data showing experience-induced changes in the hippocampus for understanding psychopathology and stress-related symptoms are discussed. Two key forms of affective plasticity are described--context and regulation. A role for the hippocampus in context-dependent normal and dysfunctional emotional responding is proposed. Finally, implications of these data for understanding the impact on neural circuitry of interventions to promote positive affect and on mechanisms that govern health and disease are considered.

Question: What is the role of the prefrontal cortex in emotion?


Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data. Growing concern over traffic safety has led to research efforts directed towards predicting freeway crashes in Advanced Traffic Management and Information Systems (ATMIS) environment. This paper aims at developing a crash-likelihood prediction model using real-time traffic-flow variables (measured through series of underground sensors) and rain data (collected at weather stations) potentially associated with crash occurrence. Archived loop detector and rain data and historical crash data have been used to calibrate the model. This model can be implemented using an online loop and rain data to identify high crash potential in real-time. Principal component analysis (PCA) and logistic regression (LR) have been used to estimate a weather model that determines a rain index based on the rain readings at the weather station in the proximity of the freeway. A matched case-control logit model has also been used to model the crash potential based on traffic loop data and the rain index. The 5-min average occupancy and standard deviation of volume observed at the downstream station, and the 5-min coefficient of variation in speed at the station closest to the crash, all during 5-10 min prior to the crash occurrence along with the rain index have been found to affect the crash occurrence most significantly.

Question: How can we predict a crash?


Large-Scale Named Entity Disambiguation Based on Wikipedia Data. This paper presents a large-scale system for the recognition and semantic disambiguation of named entities based on information extracted from a large encyclopedic collection and Web search results. It describes in detail the disambiguation paradigm employed and the information extraction process from Wikipedia. Through a process of maximizing the agreement between the contextual information extracted from Wikipedia and the context of a document, as well as the agreement among the category tags associated with the candidate entities, the implemented system shows high disambiguation accuracy on both news stories and Wikipedia articles.

Question: What is the disambiguation paradigm employed?


Blockchain Based Access Control. Access Control systems are used in computer security to regulate the access to critical or valuable resources. The rights of subjects to access such resources are typically expressed through access control policies, which are evaluated at access request time against the current access context. This paper proposes a new approach based on blockchain technology to publish the policies expressing the right to access a resource and to allow the distributed transfer of such right among users. In our proposed protocol the policies and the rights exchanges are publicly visible on the blockchain, consequently any user can know at any time the policy paired with a resource and the subjects who currently have the rights to access the resource. This solution allows distributed auditability, preventing a party from fraudulently denying the rights granted by an enforceable policy. We also show a possible working implementation based on XACML policies, deployed on the Bitcoin blockchain.

Question: What is a blockchain based access control system?


Penetration Testing with Improved Input Vector Identification. Penetration testing is widely used to help ensure the security of web applications. It discovers vulnerabilities by simulating attacks from malicious users on a target application. Identifying the input vectors of a web application and checking the results of an attack are important parts of penetration testing, as they indicate where an attack could be introduced and whether an attempted attack was successful. Current techniques for identifying input vectors and checking attack results are typically ad-hoc and incomplete, which can cause parts of an application to be untested and leave vulnerabilities undiscovered. In this paper, we propose a new approach to penetration testing that addresses these limitations by leveraging two recently-developed analysis techniques. The first is used to identify a web application's possible input vectors, and the second is used to automatically check whether an attack resulted in an injection. To empirically evaluate our approach, we compare it against a state-of-the-art, alternative technique. Our results show that our approach performs a more thorough penetration testing and leads to the discovery of more vulnerabilities.

Question: What is a web application?


Neuronal Specificity of Acupuncture in Alzheimer's Disease and Mild Cognitive Impairment Patients: A Functional MRI Study. Although acupuncture is considered to be effective and safe for Alzheimer's disease (AD) and mild cognitive impairment (MCI), the mechanism underlying its therapeutic effect is still unknown. Most studies clarifying the neuronal pathway produced by acupuncture were still applied to healthy subjects with limited single acupuncture point stimulation, which was inconsistency with clinical practice. Thus, in our present study, we investigate the differences between brain activity changes in AD and MCI patients caused by multi-acupuncture point Siguan (four gates), in order to provide visualized evidence for neuronal specificity of clinical acupuncture. Forty-nine subjects were recruited, including 21 AD patients, 14 MCI patients, and 14 healthy controls (HC). AD and MCI patients were randomly divided into two groups, respectively: real acupuncture point group (14 AD and 8 MCI) and sham acupuncture point group (7 AD and 6 MCI). We adopted a 16-minute, single-block, experimental design for acquiring functional MRI images. We found, in AD and MCI patients, Siguan (four gates) elicited extensive activations and deactivations in cognitive-related areas, visual-related areas, the sensorimotor-related area, basal ganglia, and cerebellum. Compared with HC, AD and MCI patients showed similar activations in cognitive-related brain areas (inferior frontal gyrus, supramarginal gyrus, and rolandic operculum) as well as deactivations in cognitive-related areas, visual-related areas, basal ganglia, and cerebellum, which were not found in HC. Compared with sham acupuncture points, real acupuncture points produced more specific brain changes with both activated and deactivated brain activities in AD and MCI. The preliminary results in our study verified the objective evidence for neuronal specificity of acupuncture in AD and MCI patients.

Question: What is the difference between real and sham acupuncture?


Nocardia seriolae infection in the three striped tigerfish, Terapon jarbua (Forsskål).. An epizootic in pond cultured three striped tigerfish, Terapon jarbua, in Taiwan was caused by Nocardia seriolae. Diseased fish first showed clinical signs and mortalities in February and March 2003. The cumulative mortality within 2 months was 2.4% (1200 of 50 000) and affected fish were 7 months old with total lengths from 18 to 25 cm. Most affected fish were pale and lethargic with haemorrhages and ulcers on the skin. The most significant gross pathological changes were varying degrees of ascites and enlargement of the spleen, kidney and liver. Obvious white nodules, varying in size, were found in these organs. Bacteria were either coccal or filamentous in appearance, with bead-like forms. Isolates from diseased fish were characterized using the API ZYM (Analytical profile index; Bio Mérieux, France) systems and conventional tests and identified as Nocardia sp. The isolate was designated NS127 and was confirmed as N. seriolae by a polymerase chain reaction assay that gave the expected specific 432 bp amplicon. In addition, its 16S rDNA sequence gave 100% sequence identity with N. seriolae. A partial sequence of the 16S rRNA gene, heat shock protein gene and RNA polymerase gene (rpo B) of NS127 and the type strain of N. seriolae BCRC 13745 formed a monophyletic clade with a high sequence similarity and bootstrap value of 99.9%. White nodules induced in experimental fish were similar to naturally infected cases and N. seriolae was re-isolated on brain heart infusion agar. This is the first report of N. seriolae-infection in three striped tigerfish in aquaculture.

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


Automatically clustering similar units for unit selection in speech synthesis. This paper describes a new method for synthesizing speech by concatenating sub-word units from a database of labelled speech. A large unit inventory is created by automatically clustering units of the same phone class based on their phonetic and prosodic context. The appropriate cluster is then selected for a target unit offering a small set of candidate units. An optimal path is found through the candidate units based on their distance from the cluster center and an acoustically based join cost. Details of the method and justification are presented. The results of experiments using two different databases are given, optimising various parameters within the system. Also a comparison with other existing selection based synthesis techniques is given showing the advantages this method has over existing ones. The method is implemented within a full text-to-speech system offering efficient natural sounding speech synthesis.

Question: How to cluster similar units for unit selection in speech synthesis?


The Vertica Analytic Database: C-Store 7 Years Later. This paper describes the system architecture of the Vertica Analytic Database (Vertica), a commercialization of the design of the C-Store research prototype. Vertica demonstrates a modern commercial RDBMS system that presents a classical relational interface while at the same time achieving the high performance expected from modern "web scale" analytic systems by making appropriate architectural choices. Vertica is also an instructive lesson in how academic systems research can be directly commercialized into a successful product.

Question: What is Vertica?


Quantitative analysis of small-plastic debris on beaches in the Hawaiian Archipelago.. Small-plastic beach debris from nine coastal locations throughout the Hawaiian Archipelago was analyzed. At each beach, replicate 20 l samples of sediment were collected, sieved for debris between 1 and 15 mm in size, sorted by type, counted and weighed. Small-plastic debris occurred on all of the beaches, but the greatest quantity was found at three of the most remote beaches on Midway Atoll and Moloka'i. Of the debris analyzed, 72% by weight was plastic. A total of 19100 pieces of plastic were collected from the nine beaches, 11% of which was pre-production plastic pellets. This study documents for the first time the presence of small-plastic debris on Hawaiian beaches and corroborates estimates of the abundance of plastics in the marine environment in the North Pacific.

Question: What is the largest plastic debris found on a beach?


An approach to discriminate GNSS spoofing from multipath fading. GNSS signals are vulnerable to various types of interference including jamming and spoofing attacks. Spoofing signals are designed to deceive target GNSS receivers without being detected by conventional receiver quality monitoring metrics. This paper focuses on detecting an overlapped spoofing attack where the correlation peaks of the authentic and spoofing signals interact during the attack. Several spoofing detection and signal quality monitoring (SQM) metrics are introduced. This paper proposes a spoofing detection architecture utilizing combination of different metrics to detect spoofing signals and distinguish them from multipath signals. Experimental results show that the pre-despreading spoofing detection metrics such as variance analysis are not sensitive to multipath propagation and can be used in conjunction with post-despreading methods to correctly detect spoofing signals. Several test scenarios based on different spoofing and multipath cases are performed to demonstrate the effectiveness of the proposed architecture to correctly detect spoofing attack ands distinguish them from multipath.

Question: How to detect spoofing signals?


Context-Independent Claim Detection for Argument Mining. Argumentation mining aims to automatically identify structured argument data from unstructured natural language text. This challenging, multifaceted task is recently gaining a growing attention, especially due to its many potential applications. One particularly important aspect of argumentation mining is claim identification. Most of the current approaches are engineered to address specific domains. However, argumentative sentences are often characterized by common rhetorical structures, independently of the domain. We thus propose a method that exploits structured parsing information to detect claims without resorting to contextual information, and yet achieve a performance comparable to that of state-of-the-art methods that heavily rely on the context.

Question: How to detect claims without resorting to contextual information?


Revised Estimates for the Number of Human and Bacteria Cells in the Body. Reported values in the literature on the number of cells in the body differ by orders of magnitude and are very seldom supported by any measurements or calculations. Here, we integrate the most up-to-date information on the number of human and bacterial cells in the body. We estimate the total number of bacteria in the 70 kg "reference man" to be 3.8·1013. For human cells, we identify the dominant role of the hematopoietic lineage to the total count (≈90%) and revise past estimates to 3.0·1013 human cells. Our analysis also updates the widely-cited 10:1 ratio, showing that the number of bacteria in the body is actually of the same order as the number of human cells, and their total mass is about 0.2 kg.

Question: How many human cells are there in the body?


Review of Dercum's disease and proposal of diagnostic criteria, diagnostic methods, classification and management. UNLABELLED DEFINITION AND CLINICAL PICTURE: We propose the minimal definition of Dercum's disease to be generalised overweight or obesity in combination with painful adipose tissue. The associated symptoms in Dercum's disease include fatty deposits, easy bruisability, sleep disturbances, impaired memory, depression, difficulty concentrating, anxiety, rapid heartbeat, shortness of breath, diabetes, bloating, constipation, fatigue, weakness and joint aches.   CLASSIFICATION We suggest that Dercum's disease is classified into: I. Generalised diffuse form A form with diffusely widespread painful adipose tissue without clear lipomas, II. Generalised nodular form - a form with general pain in adipose tissue and intense pain in and around multiple lipomas, and III. Localised nodular form - a form with pain in and around multiple lipomas IV. Juxtaarticular form - a form with solitary deposits of excess fat for example at the medial aspect of the knee.   EPIDEMIOLOGY Dercum's disease most commonly appears between the ages of 35 and 50 years and is five to thirty times more common in women than in men. The prevalence of Dercum's disease has not yet been exactly established.   AETIOLOGY Proposed, but unconfirmed aetiologies include: nervous system dysfunction, mechanical pressure on nerves, adipose tissue dysfunction and trauma. DIAGNOSIS AND DIAGNOSTIC METHODS: Diagnosis is based on clinical criteria and should be made by systematic physical examination and thorough exclusion of differential diagnoses. Advisably, the diagnosis should be made by a physician with a broad experience of patients with painful conditions and knowledge of family medicine, internal medicine or pain management. The diagnosis should only be made when the differential diagnoses have been excluded.   DIFFERENTIAL DIAGNOSIS Differential diagnoses include: fibromyalgia, lipoedema, panniculitis

Question: What is the definition of Dercum's disease?


ERP Projects: Key Success Factors and Risk of Failure A Proposed Model of Governance of Enterprise Resource Planning. The ERP (Enterprise Resource Planning) is a major innovation in the Field of Information Systems and Organization Management. Including their ability to make the organization more integrated and consistent, and absolute control of information. But the implementation of these projects remains a disturbing adventure that may lead to success or deadly failure of the organization that is embarking on this project. The aims of this article are to determine the necessary critical success factors for the ERP and causes of failures,through a comparative study of large organizations that have adopted ERP , for addressing the convergence and divergence points in implementation ERP. Such a study will allow us to develop a governance model for ERP

Question: What is the ERP?


MATLAB functions to analyze directional (azimuthal) data - II: Correlation. Data that represent azimuthal directions cannot be analyzed in the same manner as ordinary linear variables that measure, for example, size or quality. This is especially true for correlations between variables. Although many statistical techniques have been developed for dealing with directional data, software is scarce for the unique calculations. This paper describes a MATLAB script, located on the IAMG server, that calculates measures of association (correlations) between two circular variables, or between a circular and a linear variable. The calculations include tests of significance of the associations. These tests use large-sample approximations or resampling methods. r 2005 Elsevier Ltd. All rights reserved.

Question: How to calculate the correlation between two circular variables?


LEARNING PHYSICAL DYNAMICS. We present the Neural Physics Engine (NPE), a framework for learning simulators of intuitive physics that naturally generalize across variable object count and different scene configurations. We propose a factorization of a physical scene into composable object-based representations and a neural network architecture whose compositional structure factorizes object dynamics into pairwise interactions. Like a symbolic physics engine, the NPE is endowed with generic notions of objects and their interactions; realized as a neural network, it can be trained via stochastic gradient descent to adapt to specific object properties and dynamics of different worlds. We evaluate the efficacy of our approach on simple rigid body dynamics in two-dimensional worlds. By comparing to less structured architectures, we show that the NPE's compositional representation of the structure in physical interactions improves its ability to predict movement, generalize across variable object count and different scene configurations, and infer latent properties of objects such as mass.

Question: How does the NPE learn to predict movement?


Design and Simulation of Bridgeless PFC Boost Rectifiers. This work presents new three-level unidirectional single-phase PFC rectifier topologies well-suited for applications targeting high efficiency and/or high power density. The characteristics of a selected novel rectifier topology, including its principles of operation, modulation strategy, PID control scheme, and a power circuit design related analysis are presented. Finally, a 220-V/3-kW laboratory prototype is constructed and used in order to verify the characteristics of the new converter, which include remarkably low switching losses and single ac-side boost inductor, that allow for a 98.6% peak efficiency with a switching frequency of 140 kHz.

Question: What is the power of the converter?


Walking Robots and the Central and Peripheral Control of Locomotion in Insects. This paper outlines aspects of locomotor control in insects that may serve as the basis for the design of controllers for autonomous hexapod robots. Control of insect walking can be considered hierarchical and modular. The brain determines onset, direction, and speed of walking. Coordination is done locally in the ganglia that control leg movements. Typically, networks of neurons capable of generating alternating contractions of antagonistic muscles (termed central pattern generators, or CPGs) control the stepping movements of individual legs. The legs are coordinated by interactions between the CPGs and sensory feedback from the moving legs. This peripheral feedback provides information about leg load, position, velocity, and acceleration, as well as information about joint angles and foot contact. In addition, both the central pattern generators and the sensory information that feeds them may be modulated or adjusted according to circumstances. Consequently, locomotion in insects is extraordinarily robust and adaptable.

Question: What is the difference between a central pattern generator and a peripheral feedback?


Protecting Your Right: Verifiable Attribute-Based Keyword Search with Fine-Grained Owner-Enforced Search Authorization in the Cloud. Search over encrypted data is a critically important enabling technique in cloud computing, where encryption-before-outsourcing is a fundamental solution to protecting user data privacy in the untrusted cloud server environment. Many secure search schemes have been focusing on the single-contributor scenario, where the outsourced dataset or the secure searchable index of the dataset are encrypted and managed by a single owner, typically based on symmetric cryptography. In this paper, we focus on a different yet more challenging scenario where the outsourced dataset can be contributed from multiple owners and are searchable by multiple users, i.e., multi-user multi-contributor case. Inspired by attribute-based encryption (ABE), we present the first attribute-based keyword search scheme with efficient user revocation (ABKS-UR) that enables scalable fine-grained (i.e., file-level) search authorization. Our scheme allows multiple owners to encrypt and outsource their data to the cloud server independently. Users can generate their own search capabilities without relying on an always online trusted authority. Fine-grained search authorization is also implemented by the owner-enforced access policy on the index of each file. Further, by incorporating proxy re-encryption and lazy re-encryption techniques, we are able to delegate heavy system update workload during user revocation to the resourceful semi-trusted cloud server. We formalize the security definition and prove the proposed ABKS-UR scheme selectively secure against chosen-keyword attack. To build confidence of data user in the proposed secure search system, we also design a search result verification scheme. Finally, performance evaluation shows the efficiency of our scheme.

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