Continuous software engineering: A roadmap and agenda. Throughout its short history, software development has been characterized by harmful disconnects between important activities such as planning, development and implementation. The problem is further exacerbated by the episodic and infrequent performance of activities such as planning, testing, integration and releases. Several emerging phenomena reflect attempts to address these problems. For example, Continuous Integration is a practice which has emerged to eliminate discontinuities between development and deployment. In a similar vein, the recent emphasis on DevOps recognizes that the integration between software development and its operational deployment needs to be a continuous one. We argue a similar continuity is required between business strategy and development, BizDev being the term we coin for this. These disconnects are even more problematic given the need for reliability and resilience in the complex and data-intensive systems being developed today. We identify a number of continuous activities which together we label as 'Continuous ∗' (i.e. Continuous Star) which we present as part of an overall roadmap for Continuous Software engineering. We argue for a continuous (but not necessarily rapid) software engineering delivery pipeline. We conclude the paper with a research agenda. © 2015 Elsevier Inc. All rights reserved.

Question: What is continuous software engineering?


Ultrasonography for clinical decision-making and intervention in airway management: from the mouth to the lungs and pleurae. OBJECTIVES To create a state-of-the-art overview of the new and expanding role of ultrasonography in clinical decision-making, intervention and management of the upper and lower airways, that is clinically relevant, up-to-date and practically useful for clinicians.   METHODS This is a narrative review combined with a structured Medline literature search.   RESULTS Ultrasonography can be utilised to predict airway difficulty during induction of anaesthesia, evaluate if the stomach is empty or possesses gastric content that poses an aspiration risk, localise the essential cricothyroid membrane prior to difficult airway management, perform nerve blocks for awake intubation, confirm tracheal or oesophageal intubation and facilitate localisation of tracheal rings for tracheostomy. Ultrasonography is an excellent diagnostic tool in intraoperative and emergency diagnosis of pneumothorax. It also enables diagnosis and treatment of interstitial syndrome, lung consolidation, atelectasis, pleural effusion and differentiates causes of acute breathlessness during pregnancy. Patient safety can be enhanced by performing procedures under ultrasound guidance, e.g. thoracocentesis, vascular line access and help guide timing of removal of chest tubes by quantification of residual pneumothorax size.   CONCLUSIONS Ultrasonography used in conjunction with hands-on management of the upper and lower airways has multiple advantages. There is a rapidly growing body of evidence showing its benefits.   TEACHING POINTS • Ultrasonography is becoming essential in management of the upper and lower airways. • The tracheal structures can be identified by ultrasonography, even when unidentifiable by palpation. • Ultrasonography is the primary diagnostic approach in suspicion of intraoperative pneumothorax. • Point-of-care ultrasonography of the airways has a steep learning curv

Question: What is the role of ultrasonography in airway management?


Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks. We present an approach that exploits hierarchical Recurrent Neural Networks (RNNs) to tackle the video captioning problem, i.e., generating one or multiple sentences to describe a realistic video. Our hierarchical framework contains a sentence generator and a paragraph generator. The sentence generator produces one simple short sentence that describes a specific short video interval. It exploits both temporal-and spatial-attention mechanisms to selectively focus on visual elements during generation. The paragraph generator captures the inter-sentence dependency by taking as input the sentential embedding produced by the sentence generator, combining it with the paragraph history, and outputting the new initial state for the sentence generator. We evaluate our approach on two large-scale benchmark datasets: YouTubeClips and TACoS-MultiLevel. The experiments demonstrate that our approach significantly outperforms the current state-of-the-art methods with BLEU@4 scores 0.499 and 0.305 respectively.

Question: How large is the canadian military?


Fast and robust fixed-point algorithms for independent component analysis. Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon's information-theoretic approach and the projection pursuit approach. Using maximum entropy approximations of differential entropy, we introduce a family of new contrast (objective) functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model, and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally, we introduce simple fixed-point algorithms for practical optimization of the contrast functions. These algorithms optimize the contrast functions very fast and reliably.

Question: What is the difference between the projection pursuit and the information-theoretic approach?


Challenges for the Self-Safety in Autonomous Vehicles. The combination of multiple functions having different and complementary capabilities enables the emergence of Autonomous Vehicles. Their deployment is limited by the level of complexity they represent together with the challenges encountered in real environments with strong safety concerns. Thus a major concern prior to massive deployment is on how to ensure the safety of autonomous vehicles despite likely internal (e.g. malfunctions) and external (e.g., aggressive behaviors) disturbances they might undergo. This paper presents the challenges that undergoes the design and development of autonomous vehicles with respect to their functional architecture and adaptive behaviors from a safety perspective. For the purpose of the rationales, we define needs and requirements that lead to the formulation of an architectural framework. Our approach is based on paradigms and technologies from non-automotive domains to address non-functional system properties like safety, reliability and security. The notion of micro-services is also introduced for the self-safety of autonomous vehicles. These are part of the proposed framework that should facilitate the analysis, design, development and validation for the adequate composition and orchestration of services aimed to warrant the required non-functional properties, such as safety. In the present paper, we introduce the structural and behavioral adaptations of the framework to offer a holistic and scalable vision of the safety over the system.

Question: What is the difference between autonomous vehicles and self-driving cars?


Structured low-rank approximation and its applications. Fitting data by a bounded complexity linear model is equivalent to low-rank approximation of a matrix constructed from the data. The data matrix being Hankel structured is equivalent to the existence of a linear time-invariant system that fits the data and the rank constraint is related to a bound on the model complexity. In the special case of fitting by a static model, the data matrix and its low-rank approximation are unstructured. We outline applications in system theory (approximate realization, model reduction, output error, and errors-in-variables identification), signal processing (harmonic retrieval, sum-of-damped exponentials, and finite impulse response modeling), and computer algebra (approximate common divisor). Algorithms based on heuristics and local optimization methods are presented. Generalizations of the low-rank approximation problem result from different approximation criteria (e.g., weighted norm) and constraints on the data matrix (e.g., nonnegativity). Related problems are rank minimization and structured pseudospectra. 2007 Elsevier Ltd. All rights reserved.

Question: What is the difference between a low-rank approximation and a structured low-rank approximation?


Statistical Nested Sensor Array Signal Processing. OF THE DISSERTATION Statistical Nested Sensor Array Signal Processing by Keyong Han Doctor of Philosophy in Electrical Engineering Washington University in St. Louis, 2015 Professor Arye Nehorai, Chair Source number detection and direction-of-arrival (DOA) estimation are two major applications of sensor arrays. Both applications are often confined to the use of uniform linear arrays (ULAs), which is expensive and difficult to yield wide aperture. Besides, a ULA with N scalar sensors can resolve at most N − 1 sources. On the other hand, a systematic approach was recently proposed to achieve O(N) degrees of freedom (DOFs) using O(N) sensors based on a nested array, which is obtained by combining two or more ULAs with successively increased spacing. This dissertation will focus on a fundamental study of statistical signal processing of nested arrays. Five important topics are discussed, extending the existing nested-array strategies to more practical scenarios. Novel signal models and algorithms are proposed. First, based on the linear nested array, we consider the problem for wideband Gaussian sources. To employ the nested array to the wideband case, we propose effective strategies to apply nested-array processing to each frequency component, and combine all the spectral xi information of various frequencies to conduct the detection and estimation. We then consider the practical scenario with distributed sources, which considers the spreading phenomenon of sources. Next, we investigate the self-calibration problem for perturbed nested arrays, for which existing works require certain modeling assumptions, for example, an exactly known array geometry, including the sensor gain and phase. We propose corresponding robust algorithms to estimate both the model errors and the DOAs. The partial Toeplitz structure of the covariance matrix is employed to estimate the gain errors, and the sparse total least squares is used to de

Question: What is the difference between the Canadian Armed Forces and the Canadian Army?


Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-Expression Spotting and Recognition Methods. Micro-expressions (MEs) are rapid, involuntary facial expressions which reveal emotions that people do not intend to show. Studying MEs is valuable as recognizing them has many important applications, particularly in forensic science and psychotherapy. However, analyzing spontaneous MEs is very challenging due to their short duration and low intensity. Automatic ME analysis includes two tasks: ME spotting and ME recognition. For ME spotting, previous studies have focused on posed rather than spontaneous videos. For ME recognition, the performance of previous studies is low. To address these challenges, we make the following contributions: (i) We propose the first method for spotting spontaneous MEs in long videos (by exploiting feature difference contrast). This method is training free and works on arbitrary unseen videos. (ii) We present an advanced ME recognition framework, which outperforms previous work by a large margin on two challenging spontaneous ME databases (SMIC and CASMEII). (iii) We propose the first automatic ME analysis system (MESR), which can spot and recognize MEs from spontaneous video data. Finally, we show our method outperforms humans in the ME recognition task by a large margin, and achieves comparable performance to humans at the very challenging task of spotting and then recognizing spontaneous MEs.

Question: What is the first method for spotting spontaneous micro-expressions in long videos?


Reduced-Order Model and Stability Analysis of Low-Voltage DC Microgrid. Depleting fossil fuels, increasing energy demand, and need for high-reliability power supply motivate the use of dc microgrids. This paper analyzes the stability of low-voltage dc microgrid systems. Sources are controlled using a droop-based decentralized controller. Various components of the system have been modeled. A linearized system model is derived using small-signal approximation. The stability of the system is analyzed by identifying the eigenvalues of the system matrix. The sufficiency condition for stable operation of the system is derived. It provides upper bound on droop constants and is useful during planning and designing of dc microgrids. Furthermore, the sensitivity of system poles to variation in cable resistance and inductance is identified. It is proved that the poles move further inside the negative real plane with a decrease in inductance or an increase in resistance. The method proposed in this paper is applicable to any interconnecting structure of sources and loads. The results obtained by analysis are verified by detailed simulation study. Root locus plots are included to confirm the movement of system poles. The viability of the model is confirmed by experimental results from a scaled-down laboratory prototype of a dc microgrid developed for the purpose.

Question: How to analyze the stability of a dc microgrid?


Discovering Reader's Emotions Triggered from News Articles. The study of sentiment analysis aims to explore the occurrences of opinion words in a document, and their orientation and strength. Most existing researches focus on writer's emotions, which are the feelings that authors were expressing. In contrast, news articles are usually described in objective terms, in which opinion words are rare. In this paper, we propose to discover the reader's feelings when they read news articles. First, different feature units are extracted such as unigrams, bigrams, and segmented words. We compare several feature selection methods including document frequency variation, information gain, mutual information, and chi-square test, to select the candidate features for sentiment classification. Then, the performance for multi-class classification and multiple binary classification are evaluated. The experimental results on real news articles show the effectiveness of the proposed method for discovering reader's emotions triggered from news articles. Further investigation is needed to validate the performance in larger scales.

Question: How to discover reader's emotions triggered from news articles?


VQM-based QoS/QoE mapping for streaming video. Numerous new broadband and multimedia applications have emerged in recent years, in which streaming video services are particularly of interest. Unlike data communication, streaming video requires a subjective measure of quality referred to as the Quality of Experience (QoE), but the subjective evaluation is very time consuming and complex. We are proposing an objective approach to provide mapping between QoE and Quality of Service (QoS), in which Video Quality Metric (VQM) is utilized to indicate QoE. We have analyzed the emulation results and derived a simple formula to estimate QoE from QoS parameters for streaming video under certain conditions. This method is non-intrusive and quick to use.

Question: What is the quality of experience of streaming video?


Anonymity and Online Commenting: The Broken Windows Effect and the End of Drive-by Commenting. In this study we ask how regulations about commenter identity affect the quantity and quality of discussion on commenting fora. In December 2013, the Huffington Post changed the rules for its comment forums to require participants to authenticate their accounts through Facebook. This enabled a large-scale 'before and after' analysis. We collected over 42m comments on 55,000 HuffPo articles published in the period January 2013 to June 2014 and analysed them to determine how changes in identity disclosure impacted on discussions in the publication's comment pages. We first report our main results on the quantity of online commenting, where we find both a reduction and a shift in its distribution from politicised to blander topics. We then discuss the quality of discussion. Here we focus on the subset of 18.9m commenters who were active both before and after the change, in order to disentangle the effects of the worst offenders withdrawing and the remaining commenters modifying their tone. We find a 'broken windows' effect, whereby comment quality improves even when we exclude interaction with trolls and spammers.

Question: How does anonymity affect online commenting?


The Alternating Decision Tree Learning Algorithm. The application of boosting procedures to decision tree algorithms has been shown to produce very accurate classi ers. These classiers are in the form of a majority vote over a number of decision trees. Unfortunately, these classi ers are often large, complex and diÆcult to interpret. This paper describes a new type of classi cation rule, the alternating decision tree, which is a generalization of decision trees, voted decision trees and voted decision stumps. At the same time classi ers of this type are relatively easy to interpret. We present a learning algorithm for alternating decision trees that is based on boosting. Experimental results show it is competitive with boosted decision tree algorithms such as C5.0, and generates rules that are usually smaller in size and thus easier to interpret. In addition these rules yield a natural measure of classi cation con dence which can be used to improve the accuracy at the cost of abstaining from predicting examples that are hard to classify.

Question: What is the alternating decision tree?


Gate sizing: finFETs vs 32nm bulk MOSFETs. FinFET devices promise to replace traditional MOSFETs because of superior ability in controlling leakage and minimizing short channel effects while delivering a strong drive current. We investigate in this paper gate sizing of finFET devices, and we provide a comparison with 32nm bulk CMOS. Wider finFET devices are built utilizing multiple parallel fins between the source and drain. Independent gating of the finFET's double gates allows significant reduction in leakage current. We perform temperature-aware circuit optimization by modeling delay using temperature-dependent parameters, and by imposing constraints that limit the maximum allowable number of parallel fins. We show that finFET circuits are superior in performance and produce less static power when compared to 32nm circuits.

Question: What is the difference between finFETs and bulk MOSFETs?


Survey on Performance of CMOS Sensors. This paper presents a survey on CMOS Image Sensors (CIS) based on active Pixel Sensors (APS). Due to its properties, the APS is used in low voltage, low power and its applications in portable imaging systems such as disposable cameras and on-chip autonomous security cameras are random access, faster readout and low noise with peripheral circuitry.

Question: What is the difference between CIS and APS?


Random projections fuzzy k-nearest neighbor(RPFKNN) for big data classification. As the number of features in pattern recognition applications continuously grows, new algorithms are necessary to reduce the dimensionality of the feature space while producing comparable results. For example, a dynamic area of research, activity recognition, produces large quantities of high-velocity, high-dimensionality data that require real time classification. While dimensionality reduction approaches such as principle component analysis (PCA) and feature selection work well for datasets of reasonable size and dimensionality, they fail on big data. A possible approach to classification of high-dimensionality datasets is to combine a typical classifier, fuzzy k-nearest neighbor in our case (FKNN), with feature reduction by random projection (RP). As opposed to PCA where one projection matrix is computed based on least square optimality, in RP, a projection matrix is chosen at random multiple times. As the random projection procedure is repeated many times, the question is how to aggregate the values of the classifier obtained in each projection. In this paper we present a fusion strategy for RP FKNN, denoted as RPFKNN. The fusion strategy is based on the class membership values produced by FKNN and classification accuracy in each projection. We test RPFKNN on several synthetic and activity recognition datasets.

Question: What is the difference between RPFKNN and RPFKNN?


Business Analytics in the Context of Big Data: A Roadmap for Research. This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems (SIGDSS) workshop, respectively. Recognizing the potential of "big data" to offer new insights for decision making and innovation, panelists at the two events discussed how organizations can use and manage big data for competitive advantage. In addition, expert panelists helped to identify research gaps. While emerging research in the academic community identifies some of the issues in acquiring, analyzing, and using big data, many of the new developments are occurring in the practitioner community. We bridge the gap between academic and practitioner research by presenting a big data analytics framework that depicts a process view of the components needed for big data analytics in organizations. Using practitioner interviews and literature from both academia and practice, we identify the current state of big data research guided by the framework and propose potential areas for future research to increase the relevance of academic research to practice.

Question: What is the difference between academic and practitioner research?


Standard-Compliant Multiview Video Coding and Streaming for Virtual Reality Applications. Virtual reality (VR) systems employ multiview cameras or camera rigs to capture a scene from the entire 360-degree perspective. Due to computational or latency constraints, it might not be possible to stitch multiview videos into a single video sequence prior to encoding. In this paper we investigate the coding and streaming of multiview VR video content. We present a standard-compliant method where we first divide the camera views into two types: Primary views represent a subset of camera views with lower resolution and non-overlapping (minimally overlapping) content which cover the entire 360-degree field-of-view to guarantee immediate monoscopic viewing during very rapid head movements. Auxiliary views consist of remaining camera views with higher resolution which produce overlapping content with the primary views and are additionally used for stereoscopic viewing. Based on this categorization, we propose a coding arrangement in which, the primary views are independently coded in the base layer and the additional auxiliary views are coded as an enhancement layer, using inter-layer prediction from primary views. The proposed system not only meets the low latency requirements of VR systems, but also conforms to the existing multilayer extensions of the High Efficiency Video Coding standard. Simulation results show that the coding and streaming performance of the proposed scheme is significantly improved compared to earlier methods.

Question: What is the standard-compliant multiview video coding and streaming for virtual reality applications?


BYOD security challenges: control and protect your most sensitive data. According to a recent survey by Harris Interactive and ESET, more than 80% of employed adults use some kind of personally owned electronic device for work-related functions.1 One of the primary advantages in allowing employees to bring and use their personal devices for work is that it increases efficiency and flexibility. By giving access to the corporate network, and therefore corporate information via unmanaged devices, work can be done from anywhere, at any time, using any endpoint. It also provides cost savings to organisations, since personal device usage means they don't need to provide or manage mobile devices for their employees. The Harris survey also determined that 47% of employees use personal desktop computers to access or store company information, while 41% do this with personal laptops, 24% with smartphones and 10% with tablets. However, with less than half of these devices being protected by basic security measures, some organisations may start to worry that the security challenges associated with BYOD far outweigh the benefits.

Question: What is the security challenges associated with BYOD?


An Overview of BioCreative II.5. We present the results of the BioCreative II.5 evaluation in association with the FEBS Letters experiment, where authors created Structured Digital Abstracts to capture information about protein-protein interactions. The BioCreative II.5 challenge evaluated automatic annotations from 15 text mining teams based on a gold standard created by reconciling annotations from curators, authors, and automated systems. The tasks were to rank articles for curation based on curatable protein-protein interactions; to identify the interacting proteins (using UniProt identifiers) in the positive articles (61); and to identify interacting protein pairs. There were 595 full-text articles in the evaluation test set, including those both with and without curatable protein interactions. The principal evaluation metrics were the interpolated area under the precision/recall curve (AUC iP/R), and (balanced) F-measure. For article classification, the best AUC iP/R was 0.70; for interacting proteins, the best system achieved good macroaveraged recall (0.73) and interpolated area under the precision/recall curve (0.58), after filtering incorrect species and mapping homonymous orthologs; for interacting protein pairs, the top (filtered, mapped) recall was 0.42 and AUC iP/R was 0.29. Ensemble systems improved performance for the interacting protein task.

Question: What is the best BioCreative II.5 evaluation metric?


Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks. We propose a method for embedding twodimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset.

Question: How to embed locations in a continuous vector space?


Motion estimation with non-local total variation regularization. State-of-the-art motion estimation algorithms suffer from three major problems: Poorly textured regions, occlusions and small scale image structures. Based on the Gestalt principles of grouping we propose to incorporate a low level image segmentation process in order to tackle these problems. Our new motion estimation algorithm is based on non-local total variation regularization which allows us to integrate the low level image segmentation process in a unified variational framework. Numerical results on the Mid-dlebury optical flow benchmark data set demonstrate that we can cope with the aforementioned problems.

Question: How to solve the problems of motion estimation?


PROCEDURAL GENERATION OF IMAGINATIVE TREES USING A SPACE COLONIZATION ALGORITHM. The modeling of trees is challenging due to their complex branching structures. Three different ways to generate trees are using real world data for reconstruction, interactive modeling methods and modeling with procedural or rule-based systems. Procedural content generation is the idea of using algorithms to automate content creation processes, and it is useful in plant modeling since it can generate a wide variety of plants that can adapt and react to the environment and changing conditions. This thesis focuses on and extends a procedural tree generation technique that uses a space colonization algorithm to model the tree branches' competition for space, and shifts the previous works' focus from realism to fantasy. The technique satisfied the idea of using interaction between the tree's internal and external factors to determine its final shape, by letting the designer control the where and the how of the tree's growth process. The implementation resulted in a tree generation application where the user's imagination decides the limit of what can be produced, and if that limit is reached can the application be used to randomly generate a wide variety of trees and tree-like structures. A motivation for many researchers in the procedural content generation area is how it can be used to augment human imagination. The result of this thesis can be used for that, by stepping away from the restrictions of realism, and with ease let the user generate widely diverse trees, that are not necessarily realistic but, in most cases, adapts to the idea of a tree.

Question: What is procedural content generation?


THE AUDIT RISK MODEL UNDER THE RISK OF FRAUD. In this article, we derive an audit risk formula for a simple situation. This formula closely resembles the SAS 47 model when we assume that no material misstatement due to fraud exists. A simple case illustrates how the risk of material misstatement due to management fraud impacts audit risk and how performing special audit procedures to detect such irregularities can decrease overall audit risk. While SAS 53 requires auditors to assess the risk of errors and irregularities and plan the audit to provide reasonable assurance of detecting errors and irregularities, the audit risk model of SAS 47 does not directly address the risk of management fraud. This article develops an audit risk model using the belief-function framework that considers the risks faced by auditors due to random errors, defalcations (employee fraud) and management fraud. We consider two cases. In the first, we consider only affirmative items of evidence and derive an analytical formula for the audit risk. In the second, we consider mixed items of evidence (both affirmative and negative), which models the situation more frequently faced by auditors. We demonstrate that a serious underestimation of audit risk can occur if the audit risk model is used without specifically considering the risks associated with management fraud.

Question: What is the audit risk model?


Automatic Generation of Efficient Domain-Optimized Planners from Generic Parametrized Planners. When designing state-of-the-art, domain-independent planning systems, many decisions have to be made with respect to the domain analysis or compilation performed during preprocessing, the heuristic functions used during search, and other features of the search algorithm. These design decisions can have a large impact on the performance of the resulting planner. By providing many alternatives for these choices and exposing them as parameters, planning systems can in principle be configured to work well on different domains. However, usually planners are used in default configurations that have been chosen because of their good average performance over a set of benchmark domains, with limited experimentation of the potentially huge range of possible configurations. In this work, we propose a general framework for automatically configuring a parameterized planner, showing that substantial performance gains can be achieved. We apply the framework to the well-known LPG planner, which has 62 parameters and over 6.5 × 10 possible configurations. We demonstrate that by using this highly parameterized planning system in combination with the off-the-shelf, state-of-the-art automatic algorithm configuration procedure ParamILS, the planner can be specialized obtaining significantly improved performance.

Question: How to configure a planner?


High Dimensional Semiparametric Gaussian Copula Graphical Models. We propose a semiparametric approach called the nonparanormal SKEPTIC for efficiently and robustly estimating high-dimensional undirected graphical models. To achieve modeling flexibility, we consider the nonparanormal graphical models proposed by Liu, Lafferty and Wasserman [J. Mach. Learn. Res. 10 (2009) 2295–2328]. To achieve estimation robustness, we exploit nonparametric rank-based correlation coefficient estimators, including Spearman's rho and Kendall's tau. We prove that the nonparanormal SKEPTIC achieves the optimal parametric rates of convergence for both graph recovery and parameter estimation. This result suggests that the nonparanormal graphical models can be used as a safe replacement of the popular Gaussian graphical models, even when the data are truly Gaussian. Besides theoretical analysis, we also conduct thorough numerical simulations to compare the graph recovery performance of different estimators under both ideal and noisy settings. The proposed methods are then applied on a large-scale genomic data set to illustrate their empirical usefulness. The R package huge implementing the proposed methods is available on the Comprehensive R Archive Network: http://cran.r-project.org/.

Question: What is the difference between the nonparanormal and Gaussian graphical models?


Semi-supervised Semantic Pattern Discovery with Guidance from Unsupervised Pattern Clusters. We present a simple algorithm for clustering semantic patterns based on distributional similarity and use cluster memberships to guide semi-supervised pattern discovery. We apply this approach to the task of relation extraction. The evaluation results demonstrate that our novel bootstrapping procedure significantly outperforms a standard bootstrapping. Most importantly, our algorithm can effectively prevent semantic drift and provide semi-supervised learning with a natural stopping criterion.

Question: What is the bootstrapping algorithm?


Statistical Edge Detection: Learning and Evaluating Edge Cues. We formulate edge detection as statistical inference. This statistical edge detection is data driven, unlike standard methods for edge detection which are model based. For any set of edge detection filters (implementing local edge cues) we use pre-segmented images to learn the probability distributions of filter responses conditioned on whether they are evaluated on or off an edge. Edge detection is formulated as a discrimination task specified by a likelihood ratio test on the filter responses. This approach emphasizes the necessity of modeling the image background (the off-edges). We represent the conditional probability distributions non-parametrically and learn them on two different datasets of 100 (Sowerby) and 50 (South Florida) images. Multiple edges cues, including chrominance and multiple-scale, are combined by using their joint distributions. Hence this cue combination is optimal in the statistical sense. We evaluate the effectiveness of different visual cues using the Chernoff information and Receiver Operator Characteristic (ROC) curves. This shows that our approach gives quantitatively better results than the Canny edge detector when the image background contains

Question: What is the difference between statistical edge detection and standard edge detection?


Learning Distributed Representations of Sentences from Unlabelled Data. Unsupervised methods for learning distributed representations of words are ubiquitous in today's NLP research, but far less is known about the best ways to learn distributed phrase or sentence representations from unlabelled data. This paper is a systematic comparison of models that learn such representations. We find that the optimal approach depends critically on the intended application. Deeper, more complex models are preferable for representations to be used in supervised systems, but shallow log-bilinear models work best for building representation spaces that can be decoded with simple spatial distance metrics. We also propose two new unsupervised representation-learning objectives designed to optimise the trade-off between training time, domain portability and performance.

Question: What is the best way to learn distributed representations of sentences?


BUILDING GENERALIZABLE AGENTS. Towards bridging the gap between machine and human intelligence, it is of utmost importance to introduce environments that are visually realistic and rich in content. In such environments, one can evaluate and improve a crucial property of practical intelligent systems, namely generalization. In this work, we build House3D, a rich, extensible and efficient environment that contains 45,622 human-designed 3D scenes of houses, ranging from single-room studios to multistoreyed houses, equipped with a diverse set of fully labeled 3D objects, textures and scene layouts, based on the SUNCG dataset (Song et al., 2017). With an emphasis on semantic-level generalization, we study the task of concept-driven navigation, RoomNav, using a subset of houses in House3D. In RoomNav, an agent navigates towards a target specified by a semantic concept. To succeed, the agent learns to comprehend the scene it lives in by developing perception, understand the concept by mapping it to the correct semantics, and navigate to the target by obeying the underlying physical rules. We train RL agents with both continuous and discrete action spaces and show their ability to generalize in new unseen environments. In particular, we observe that (1) training is substantially harder on large house sets but results in better generalization, (2) using semantic signals (e.g. segmentation mask) boosts the generalization performance, and (3) gated networks on semantic input signal lead to improved training performance and generalization. We hope House3D1, including the analysis of the RoomNav task, serves as a building block towards designing practical intelligent systems and we wish it to be broadly adopted by the community.

Question: What is the size of the house3d dataset?


Paired Learners for Concept Drift. To cope with concept drift, we paired a stable online learner with a reactive one. A stable learner predicts based on all of its experience, whereas are active learner predicts based on its experience over a short, recent window of time. The method of paired learning uses differences in accuracy between the two learners over this window to determine when to replace the current stable learner, since the stable learner performs worse than does there active learner when the target concept changes. While the method uses the reactive learner as an indicator of drift, it uses the stable learner to predict, since the stable learner performs better than does the reactive learner when acquiring target concept. Experimental results support these assertions. We evaluated the method by making direct comparisons to dynamic weighted majority, accuracy weighted ensemble, and streaming ensemble algorithm (SEA) using two synthetic problems, the Stagger concepts and the SEA concepts, and three real-world data sets: meeting scheduling, electricity prediction, and malware detection. Results suggest that, on these problems, paired learners outperformed or performed comparably to methods more costly in time and space.

Question: What is concept drift?


Mail versus Mall: A Strategic Analysis of Competition between Direct Marketers and Conventional Retailers. Consumers now purchase several offerings from direct sellers, including catalog and Internet marketers. These direct channels exist in parallel with the conventional retail stores. The availability of multiple channels has significant implications for the performance of consumer markets. The literature in marketing and economics has, however, been dominated by a focus on the conventional retail sector. This paper is an effort toward modeling competition in the multiplechannel environment from a strategic viewpoint. At the outset, a parsimonious model that accommodates the following consumer and market characteristics is introduced. First, the relative attractiveness of retail shopping varies across consumers. Second, the fit with the direct channel varies across product categories. Third, the strength of existing retail presence in local markets moderates competition. Fourth, in contrast with the fixed location of the retail store that anchors its localized market power, the location of the direct marketer is irrelevant to the competitive outcome. The model is first applied in a setting where consumers have complete knowledge of product availability and prices in all channels. In the resulting equilibrium, the direct marketer acts as a competitive wedge between retail stores. The direct presence is so strong that each retailer competes against the remotely located direct marketer, rather than against neighboring retailers. This outcome has implications for the marketing mix of retailers, which has traditionally been tuned to attract consumers choosing between retail stores. In the context of market entry, conditions under which a direct channel can access a local market in retail entry equilibrium are derived. Our analysis suggests that the traditional focus on retail entry equilibria may not yield informative or relevant findings when direct channels are a str

Question: What is the difference between mail and mall?


Attention-based Pyramid Aggregation Network for Visual Place Recognition. Visual place recognition is challenging in the urban environment and is usually viewed as a large scale image retrieval task. The intrinsic challenges in place recognition exist that the confusing objects such as cars and trees frequently occur in the complex urban scene, and buildings with repetitive structures may cause over-counting and the burstiness problem degrading the image representations. To address these problems, we present an Attention-based Pyramid Aggregation Network (APANet), which is trained in an end-to-end manner for place recognition. One main component of APANet, the spatial pyramid pooling, can effectively encode the multi-size buildings containing geo-information. The other one, the attention block, is adopted as a region evaluator for suppressing the confusing regional features while highlighting the discriminative ones. When testing, we further propose a simple yet effective PCA power whitening strategy, which significantly improves the widely used PCA whitening by reasonably limiting the impact of over-counting. Experimental evaluations demonstrate that the proposed APANet outperforms the state-of-the-art methods on two place recognition benchmarks, and generalizes well on standard image retrieval datasets.

Question: What is the name of the algorithm?


A Multi-scale Approach to Gesture Detection and Recognition. We propose a generalized approach to human gesture recognition based on multiple data modalities such as depth video, articulated pose and speech. In our system, each gesture is decomposed into large-scale body motion and local subtle movements such as hand articulation. The idea of learning at multiple scales is also applied to the temporal dimension, such that a gesture is considered as a set of characteristic motion impulses, or dynamic poses. Each modality is first processed separately in short spatio-temporal blocks, where discriminative data-specific features are either manually extracted or learned. Finally, we employ a Recurrent Neural Network for modeling large-scale temporal dependencies, data fusion and ultimately gesture classification. Our experiments on the 2013 Challenge on Multimodal Gesture Recognition dataset have demonstrated that using multiple modalities at several spatial and temporal scales leads to a significant increase in performance allowing the model to compensate for errors of individual classifiers as well as noise in the separate channels.

Question: What is a gesture recognition system?


Robust Distributed Spectrum Sensing in Cognitive Radio Networks. Distributed spectrum sensing (DSS) enables a Cognitive Radio (CR) network to reliably detect licensed users and avoid causing interference to licensed communications. The data fusion technique is a key component of DSS. We discuss the Byzantine failure problem in the context of data fusion, which may be caused by either malfunctioning sensing terminals or Spectrum Sensing Data Falsification (SSDF) attacks. In either case, incorrect spectrum sensing data will be reported to a data collector which can lead to the distortion of data fusion outputs. We investigate various data fusion techniques, focusing on their robustness against Byzantine failures. In contrast to existing data fusion techniques that use a fixed number of samples, we propose a new technique that uses a variable number of samples. The proposed technique, which we call Weighted Sequential Probability Ratio Test (WSPRT), introduces a reputation-based mechanism to the Sequential Probability Ratio Test (SPRT). We evaluate WSPRT by comparing it with a variety of data fusion techniques under various network operating conditions. Our simulation results indicate that WSPRT is the most robust against the Byzantine failure problem among the data fusion techniques that were considered.

Question: What is the Byzantine failure problem?


Characterization of log periodic planar dipole array antenna. This paper proposes a analysis approach for characterizing the broadband Log Periodic Planar Dipole Array (LPPDA) antenna useful for C-band applications. A simplest geometry of the edge fed Log Periodic Planar Dipole Array is designed and developed. Parameters like length, width of the elements, and spacing between elements are the varied using defined scale factor (τ) and a spacing factor (σ), keeping others constant and their impact was noted down. A parametric analysis is performed. using CST software. A transmission line equivalent circuit of the proposed LPPDA is constructed. Log periodicity behaviour of the proposed antenna is checked by plotting input impedance with respect to logarithm of frequency. An antenna is fabricated and measured in laboratory.

Question: How to characterize an antenna?


Robust Visual Tracking for Non-Instrumented Augmented Reality. This paper presents a robust and flexible framework for augmented reality which does not require instrumenting either the environment or the workpiece. A model-based visual tracking system is combined with with rate gyroscopes to produce a system which can track the rapid camera rotations generated by a head-mounted camera, even if images are substantially degraded by motion blur. This tracking yields estimates of head position at video field rate (50Hz) which are used to align computer-generated graphics on an optical see-through display. Nonlinear optimisation is used for the calibration of display parameters which include a model of optical distortion. Rendered visuals are pre-distorted to correct the optical distortion of the display.

Question: How to track a moving object in a video?


Flash memory cells-an overview. The aim of this paper is to give a thorough overview of Flash memory cells. Basic operations and charge-injection mechanisms that are most commonly used in actual Flash memory cells are reviewed to provide an understanding of the underlying physics and principles in order to appreciate the large number of device structures, processing technologies, and circuit designs presented in the literature. New cell structures and architectural solutions have been surveyed to highlight the evolution of the Flash memory technology, oriented to both reducing cell size and upgrading product functions. The subject is of extreme interest: new concepts involving new materials, structures, principles, or applications are being continuously introduced. The worldwide semiconductor memory market seems ready to accept many new applications in fields that are not specific to traditional nonvolatile memories.

Question: What is the flash memory cell?


Steerable Higher Order Mode Dielectric Resonator Antenna With Parasitic Elements for 5G Applications. This paper presents the findings of a steerable higher order mode (TE<inline-formula> <tex-math notation="LaTeX">$^{\mathrm {y}}_{1\delta 3}$ </tex-math></inline-formula>) dielectric resonator antenna with parasitic elements. The beam steering was successfully achieved by switching the termination capacitor on the parasitic element. In this light, all of the dielectric resonator antennas (DRAs) have the same dielectric permittivity similar to that of ten and excited by a <inline-formula> <tex-math notation="LaTeX">$50\Omega $ </tex-math></inline-formula> microstrip with a narrow aperture. The effect of the mutual coupling on the radiation pattern and the reflection coefficient, as well as the array factor, was investigated clearly using MATLAB version 2014b and ANSYS HFSS version 16. As the result, the antenna beam of the proposed DRA array managed to steer from −32° to +32° at 15 GHz. Furthermore, the measured antenna array showed the maximum gain of 9.25 dBi and the reflection coefficients which are less than −10 dB with the bandwidth more than 1.3 GHz, which is viewed as desirable for device-to-device communication in 5G Internet of Things applications.

Question: What is the difference between a steerable higher order mode dielectric resonator antenna and a steerable higher order mode dielectric resonator antenna with parasitic elements?


Genetic Evidence That Carbohydrate-Stimulated Insulin Secretion Leads to Obesity.. BACKGROUND A fundamental precept of the carbohydrate-insulin model of obesity is that insulin secretion drives weight gain. However, fasting hyperinsulinemia can also be driven by obesity-induced insulin resistance. We used genetic variation to isolate and estimate the potentially causal effect of insulin secretion on body weight.   METHODS Genetic instruments of variation of insulin secretion [assessed as insulin concentration 30 min after oral glucose (insulin-30)] were used to estimate the causal relationship between increased insulin secretion and body mass index (BMI), using bidirectional Mendelian randomization analysis of genome-wide association studies. Data sources included summary results from the largest published metaanalyses of predominantly European ancestry for insulin secretion (n = 26037) and BMI (n = 322154), as well as individual-level data from the UK Biobank (n = 138541). Data from the Cardiology and Metabolic Patient Cohort study at Massachusetts General Hospital (n = 1675) were used to validate genetic associations with insulin secretion and to test the observational association of insulin secretion and BMI.   RESULTS Higher genetically determined insulin-30 was strongly associated with higher BMI (β = 0.098, P = 2.2 × 10-21), consistent with a causal role in obesity. Similar positive associations were noted in sensitivity analyses using other genetic variants as instrumental variables. By contrast, higher genetically determined BMI was not associated with insulin-30.   CONCLUSIONS Mendelian randomization analyses provide evidence for a causal relationship of glucose-stimulated insulin secretion on body weight, consistent with the carbohydrate-insulin model of obesity.

Question: What is the difference between Mendelian randomization and observational studies?


The inferior parietal lobule and temporoparietal junction: A network perspective. Information processing in specialized, spatially distributed brain networks underlies the diversity and complexity of our cognitive and behavioral repertoire. Networks converge at a small number of hubs - highly connected regions that are central for multimodal integration and higher-order cognition. We review one major network hub of the human brain: the inferior parietal lobule and the overlapping temporoparietal junction (IPL/TPJ). The IPL is greatly expanded in humans compared to other primates and matures late in human development, consistent with its importance in higher-order functions. Evidence from neuroimaging studies suggests that the IPL/TPJ participates in a broad range of behaviors and functions, from bottom-up perception to cognitive capacities that are uniquely human. The organization of the IPL/TPJ is challenging to study due to the complex anatomy and high inter-individual variability of this cortical region. In this review we aimed to synthesize findings from anatomical and functional studies of the IPL/TPJ that used neuroimaging at rest and during a wide range of tasks. The first half of the review describes subdivisions of the IPL/TPJ identified using cytoarchitectonics, resting-state functional connectivity analysis and structural connectivity methods. The second half of the article reviews IPL/TPJ activations and network participation in bottom-up attention, lower-order self-perception, undirected thinking, episodic memory and social cognition. The central theme of this review is to discuss how network nodes within the IPL/TPJ are organized and how they participate in human perception and cognition.

Question: What is the inferior parietal lobule?


TicTac: Accelerating Distributed Deep Learning with Communication Scheduling.. State-of-the-art deep learning systems rely on iterative distributed training to tackle the increasing complexity of models and input data. The iteration time in these communication-heavy systems depends on the computation time, communication time and the extent of overlap of computation and communication. In this work, we identify a shortcoming in systems with graph representation for computation, such as TensorFlow and PyTorch, that result in high variance in iteration time — random order of received parameters across workers. We develop a system, TicTac, to improve the iteration time by fixing this issue in distributed deep learning with Parameter Servers while guaranteeing near-optimal overlap of communication and computation. TicTac identifies and enforces an order of network transfers which improves the iteration time using prioritization. Our system is implemented over TensorFlow and requires no changes to the model or developer inputs. TicTac improves the throughput by up to 37.7% in inference and 19.2% in training, while also reducing straggler effect by up to 2.3×. Our code is publicly available.

Question: How does TicTac improve the iteration time?


Issues in Stacked Generalization. Stacked generalization is a general method of using a high-level model to combine lowerlevel models to achieve greater predictive accuracy. In this paper we address two crucial issues which have been considered to be a `black art' in classi cation tasks ever since the introduction of stacked generalization in 1992 by Wolpert: the type of generalizer that is suitable to derive the higher-level model, and the kind of attributes that should be used as its input. We nd that best results are obtained when the higher-level model combines the con dence (and not just the predictions) of the lower-level ones. We demonstrate the e ectiveness of stacked generalization for combining three di erent types of learning algorithms for classi cation tasks. We also compare the performance of stacked generalization with majority vote and published results of arcing and bagging.

Question: What is stacked generalization?


Review of Farm Management Information Systems ( FMIS ). There have been considerable advancements in the field of MIS over the years, and it continues to grow and develop in response to the changing needs of the business and marketing environment. Professionals and academicians are contributing and serving the field of MIS through the dissemination of their knowledge and ideas in professional journals. Thus, changes and trends that likely have an impact on MIS concepts, processes, and implementation can be determined by reviewing the articles published in journals. Content of the articles published in journals can also give us an idea about the types of research and themes that are popular during a given period. To see the evolutionary change in the field of MIS, the present study examined the content of articles published in business and marketing journals. [New York Science Journal 2010;3(5):87-95]. (ISSN 1554 – 0200).

Question: What is the content of the articles published in business and marketing journals?


Park Here! a smart parking system based on smartphones' embedded sensors and short range Communication Technologies. Nowadays, the convergence of Internet of Things (IoT) networking and mobile applications is favoring the deployment of novel and advanced smart parking systems through which users can be informed in real-time about the presence of vacant parking spots close to their destinations. In this paper, we provide an example of such opportunity, by describing Park Here!, a novel mobile application that aims at mitigating the overhead caused by parking spot seeking operations in urban areas. Our solution targets common city environments, where no per-spot sensors are available, and there is no remote service allowing the reservation in-advance of a parking spot. For this scenario, we propose a novel algorithm for the automatic detection of parking actions performed by the user, through the analysis of smartphone embedded sensors' (accelerometer/gyroscope), and of the Bluetooth connectivity. Once a parking event has been detected, an adaptive strategy allows disseminating the information over the target scenario, using a combination of Internet connection to a remote server, and Device-to-Device (D2D) connections over WiFi Direct links. Preliminary experiments demonstrate the accuracy of the proposed algorithm in correctly identifying parking events in an automatic way, and hence in notifying information to other potentially interested users.

Question: How does Park Here! work?


Neural Networks applied to wireless communications. This paper presents a time-delayed neural network (TDNN) model that has the capability of learning and predicting the dynamic behavior of nonlinear elements that compose a wireless communication system. This model could help speeding up system deployment by reducing modeling time. This paper presents results of effective application of the TDNN model to an amplifier, part of a wireless transmitter.

Question: What is a neural network?


Ballistocardiography and Seismocardiography: A Review of Recent Advances. In the past decade, there has been a resurgence in the field of unobtrusive cardiomechanical assessment, through advancing methods for measuring and interpreting ballistocardiogram (BCG) and seismocardiogram (SCG) signals. Novel instrumentation solutions have enabled BCG and SCG measurement outside of clinical settings, in the home, in the field, and even in microgravity. Customized signal processing algorithms have led to reduced measurement noise, clinically relevant feature extraction, and signal modeling. Finally, human subjects physiology studies have been conducted using these novel instruments and signal processing tools with promising results. This paper reviews the recent advances in these areas of modern BCG and SCG research.

Question: What is the ballistocardiogram?


A Review of Sleep EEG Patterns . Part I : A Compilation of Amended Rules for Their Visual Recognition according to Rechtschaffen and Kales. Task Force 'Scoring of Polysomnographic Recordings' of the German Sleep Society (DGSM): Andrea Rodenbeck, Ralf Binder, Peter Geisler, Heidi Danker-Hopfe, Reimer Lund, Friedhart Raschke, Hans-Günther Weeß, and Hartmut Schulz (Chairman) Department of Psychiatry, University of Göttingen, Göttingen, Germany Department of Psychiatry, Pfalzklinikum, Klingenmünster, Germany Department of Psychiatry, University of Regensburg, Regensburg, Germany Department of Psychiatry, Charité-CBF, Berlin, Germany Grüntenstr. 11, 80686 München (formerly Dept. of Pneumology, ASKLEPIOS Clinic Gauting, Germany) Institute of Rehabilitation Norderney, Norderney, Germany Department of Neurology, HELIOS-Klinikum Erfurt, and Free University of Berlin, Biopsychology, Germany

Question: What is the name of the German Sleep Society?


Discovering users' topics of interest on twitter: a first look. Twitter, a micro-blogging service, provides users with a framework for writing brief, often-noisy postings about their lives. These posts are called "Tweets." In this paper we present early results on discovering Twitter users' topics of interest by examining the entities they mention in their Tweets. Our approach leverages a knowledge base to disambiguate and categorize the entities in the Tweets. We then develop a "topic profile," which characterizes users' topics of interest, by discerning which categories appear frequently and cover the entities. We demonstrate that even in this early work we are able to successfully discover the main topics of interest for the users in our study.

Question: How do you discover topics of interest on twitter?


Fetal facial profile markers of Down syndrome in the second and third trimesters of pregnancy.. OBJECTIVES To investigate the use of the maxilla-nasion-mandible (MNM) angle and fetal profile (FP) line to assess the degree of midfacial hypoplasia in Down-syndrome fetuses in the second and third trimesters of pregnancy.   METHODS The MNM angle and FP line were measured retrospectively in stored two-dimensional images or three-dimensional volumes of fetuses with Down syndrome. Data collected from January 2006 to July 2013 were retrieved from the digital databases of participating units. The MNM angle was expressed as a continuous variable (degrees) and the FP line as positive, negative or zero. Measurements were obtained from stored images in the midsagittal plane by two experienced examiners and compared with our previously reported normal ranges for euploid fetuses. A MNM angle below the 5(th) centile of the reference range and a positive or negative FP line were considered as abnormal.   RESULTS A total of 133 fetuses with Down syndrome were available for analysis, eight of which were subsequently excluded because of inadequate images. The MNM angle was not influenced by gestational age (P = 0.48) and was significantly smaller in Down-syndrome fetuses than in euploid fetuses (mean, 12.90° vs 13.53°, respectively; P = 0.015). The MNM angle was below the 5th centile for euploid fetuses in 16.8% of fetuses with Down syndrome (P < 0.01). In the cohort of Down-syndrome fetuses, a positive FP line was present in 41.6% of cases (with a false-positive rate (FPR) of 6.3%) and was positively correlated with Down syndrome and gestational age (P < 0.01). There was no case with a negative FP line. In cases of Down syndrome, a positive FP line was correlated with a small MNM angle (P < 0.01).   CONCLUSIONS A small MNM angle and a positive FP line can be regarded as novel markers for Down syndrome. The FP line is an easy marker to measure, has a low FPR, does not require knowledge

Question: What is the false positive rate of the fetal facial profile line?


The Effect of Noise on Sofware Engineers' Performance. Background: Noise, de€ned as an unwanted sound, is one of the commonest factors that could a'ect people's performance in their daily work activities. Œe so‰ware engineering research community has marginally investigated the e'ects of noise on so‰ware engineers' performance. Aims: We studied if noise a'ects so‰ware engineers' performance in: (i) comprehending functional requirements and (ii) €xing faults in source code. Method: We conducted two experiments with €nal-year undergraduate students in Computer Science. In the €rst experiment, we asked 55 students to comprehend functional requirements exposing them or not to noise, while in the second experiment 42 students were asked to €x faults in Java code. Results: Œe participants in the second experiment, when exposed to noise, had signi€cantly worse performance in €xing faults in source code. On the other hand, we did not observe any statistically signi€cant di'erence in the €rst experiment. Conclusions: Fixing faults in source code seems to be more vulnerable to noise than comprehending functional requirements.

Question: What is the difference between comprehending functional requirements and €xing faults in source code?


PCA-based feature selection scheme for machine defect classification. The sensitivity of various features that are characteristic of a machine defect may vary considerably under different operating conditions. Hence it is critical to devise a systematic feature selection scheme that provides guidance on choosing the most representative features for defect classification. This paper presents a feature selection scheme based on the principal component analysis (PCA) method. The effectiveness of the scheme was verified experimentally on a bearing test bed, using both supervised and unsupervised defect classification approaches. The objective of the study was to identify the severity level of bearing defects, where no a priori knowledge on the defect conditions was available. The proposed scheme has shown to provide more accurate defect classification with fewer feature inputs than using all features initially considered relevant. The result confirms its utility as an effective tool for machine health assessment.

Question: What is the purpose of the study?


A Novel Miniature YIG Tuned Oscillator Achieves Octave Tuning Bandwidth with Ultra Low Phase Noise in X and Ku Bands. Traditional YIG tuned oscillators use negative resistance type circuits and large magnetic structure to produce excellent phase noise with octave plus tuning ranges, but at the expense of size and weight. This paper describes a revolutionary approach to YIG tuned oscillator design which makes use of a unique combination of a miniaturized magnetic structure, and a GaAs HBT ring oscillator circuit topology, working through a YIG tuned filter to produce octave tuning ranges in X and Ku bands with phase noise less than -125 dBC per Hz at 100 KHz. Frequency pulling is less than 10 KHz into a 2:1 VSWR load over all phases. Simulations and measured data are in excellent agreement

Question: What is a novel miniature YIG tuned oscillator?


COLOUR AND SHAPE ANALYSIS TECHNIQUES FOR WEED DETECTION IN CEREAL FIELDS. Information on weed distribution within the field is necessary to implement spatially variable herbicide application. This paper deals with the development of near-ground image capture and processing techniques in order to detect broad leaf weeds in cereal crops, under actual field conditions. The proposed methods use both colour and shape analysis techniques for discriminating crop, weeds and soil. The performance of algorithms was assessed by comparing the results with a human classification, providing a good success rate. The study shows the potential of using image processing techniques to generate weed maps.

Question: What is the potential of using image processing techniques to generate weed maps?


The CMU Pose, Illumination, and Expression (PIE) Database. Between October 2000 and December 2000 we collected a database of over 40,000 facial images of 68 people. Using the CMU 3D Room we imaged each person across 13 different poses, under 43 different illumination conditions, and with 4 different expressions. We call this database the CMU Pose, Illumination, and Expression (PIE) database. In this paper we describe the imaging hardware, the collection procedure, the organization of the database, several potential uses of the database, and how to obtain the database.

Question: What is the CMU Pose, Illumination, and Expression (PIE) database?


Constraint Propagation Algorithms for Temporal Reasoning. This paper considers computational aspects of several temporal representation languages. It investigates an interval-based representation, and a point-based one. Computing the consequences of temporal assertions is shown to be computational/y intractable in the interval-based representation, but not in the poinfbased one. However, a fragment of the interval language can be expressed using the point language and benefits from the tractability of the latter.' The representation of time has been a recurring concern of Artificial Intelligence researchers. Many representation schemes have been proposed for temporal reasoning; of these, one of the most attractive is James Allen's algebra of temporal intervals [Allen 831. This representation scheme is particularly appealing for its simplicity and for its ease of implementation with constraint propagation algorithms. Reasoners based on this algebra have been put to use in several ways. For example, the planning system of Allen and Koomen [1983] relies heavily on the temporal algebra to perform reasoning about the ordering of actions. Elegant approaches such as this one may be compromised, however, by computational characteristics of the interval algebra. This paper concerns itself with these computational aspects of Allen's algebra, and of a simpler algebra of time points. Our perspective here is primarily computation-theoretic. We approach the problem of temporal representation by asking questions of complexity and tractability. In this light, this paper examines Allen's interval algebra, and the simpler algebra of time points. The bulk of the paper establishes some formal results about the temporal algebras. In brief these results are: l Determining consistency of statements in the interval algebra is NP-hard, as is determining all consequences of these statements. Allen's polynomial-time constraint propagation algorithm is sound but not complete for these tasks. l

Question: What is the complexity of determining consistency of statements in the interval algebra?


Design and evaluation of a Twitter hashtag recommendation system. Twitter has evolved into a powerful communication and information sharing tool used by millions of people around the world to post what is happening now. A hashtag, a keyword prefixed with a hash symbol (#), is a feature in Twitter to organize tweets and facilitate effective search among a massive volume of data. In this paper, we propose an automatic hashtag recommendation system that helps users find new hashtags related to their interests.  We propose the Hashtag Frequency-Inverse Hashtag Ubiquity (HF-IHU) ranking scheme, which is a variation of the well-known TF-IDF, that considers hashtag relevancy, as well as data sparseness. Experiments on a large Twitter data set demonstrate that our method successfully yields relevant hashtags for user's interest and that recommendations more stable and reliable than ranking tags based on tweet content similarity. Our results show that HF-IHU can achieve over 30% hashtag recall when asked to identify the top 10 relevant hashtags for a particular tweet.

Question: How to design a hashtag recommendation system?


The lexical constituency model: some implications of research on Chinese for general theories of reading.. The authors examine the implications of research on Chinese for theories of reading and propose the lexical constituency model as a general framework for word reading across writing systems. Word identities are defined by 3 interlinked constituents (orthographic, phonological, and semantic). The implemented model simulates the time course of graphic, phonological, and semantic priming effects, including immediate graphic facilitation followed by graphic inhibition with simultaneous phonological facilitation, a pattern unique to the Chinese writing system. Pseudocharacter primes produced only facilitation, supporting the model's assumption that lexical thresholds determine phonological and semantic, but not graphic, effects. More generally, both universal reading processes and writing system constraints exist. Although phonology is universal, its activation process depends on how the writing system structures graphic units.

Question: What is the lexical constituency model?


Advances in geometric modeling and feature extraction on pots. This paper outlines progress on the 3DK Knowledge and Distributed Intelligence project undertaken by the Partnership for Research In Stereo Modeling (PRISM) at Arizona State University. Three of the six 3DK pilot projects of this National Science Foundation funded project involve archaeological and biological material, namely ceramic vessels, lithic artifacts, and bones. These projects are: (1) "3D Morphology of Ceramic Vessels" which aims to learn about vessel uniformity and proportionality as indicators of developing craft specialization and complex social organization among prehistoric cultures; (2) "Lithic Refitting" which intends to develop the algorithms required to (partially) automate the refitting process through 3D scanning and surface modeling; and finally (3) "3D Topography of Joint Surfaces" which investigates the ability of human ancestors to make tools and walk upright by developing software that will automate segmentation of osteological features and quantify the surface area and curvature of joint surfaces and the congruency between reciprocal joint surfaces, allowing for the development of biomechanical models. This paper discusses the links between these three unique projects and explores the underlying research aspects common to all: geometric modeling, feature recognition, and the development of a database structure aimed at making 3D models of these artifacts available online for query.

Question: What is the purpose of the 3DK Knowledge and Distributed Intelligence project?


PRET DRAM controller: Bank privatization for predictability and temporal isolation. Hard real-time embedded systems employ high-capacity memories such as Dynamic RAMs (DRAMs) to cope with increasing data and code sizes of modern designs. However, memory controller design has so far largely focused on improving average-case performance. As a consequence, the latency of memory accesses is unpredictable, which complicates the worst-case execution time analysis necessary for hard real-time embedded systems.  Our work introduces a novel DRAM controller design that is predictable and that significantly reduces worst-case access latencies. Instead of viewing the DRAM device as one resource that can only be shared as a whole, our approach views it as multiple resources that can be shared between one or more clients individually. We partition the physical address space following the internal structure of the DRAM device, i.e., its ranks and banks, and interleave ac- cesses to the blocks of this partition. This eliminates contention for shared resources within the device, making accesses temporally predictable and temporally isolated. This paper describes our DRAM controller design and its integration with a precision-timed (PRET) architecture called PTARM. We present analytical bounds on the latency and throughput of the proposed controller, and confirm these via simulation.

Question: What is the worst case latency of a DRAM controller?


Achieving high navigation accuracy using inertial navigation systems in autonomous underwater vehicles. This paper presents a summary of the current state-of-the-art in INS-based navigation systems in AUVs manufactured by Bluefin Robotics Corporation. A detailed description of the successful integrations of the Kearfott T-24 Ring Laser Gyro and the IXSEA PHINS III Fiber Optic Gyro into recent Bluefin Robotics AUVs is presented. Both systems provide excellent navigation accuracy for high quality data acquisition. This paper provides a comprehensive assessment of the primary advantages and disadvantages of each INS, paying particular attention to navigation accuracy, power draw, physical size, and acoustic radiated noise. Additionally, a brief presentation of a recently integrated Synthetic Aperture Sonar system will be used to highlight how critical a high-performance INS is to hydrographic, mine countermeasures, and other SAS applications.

Question: What is the difference between a gyro and a gyroscope?


Can virtual reality simulations be used as a research tool to study empathy, problems solving and perspective taking of educators?: theory, method and application. Simulations in virtual environments are becoming an important research tool for educators. These simulations can be used in a variety of areas from the study of emotions and psychological disorders to more effective training. The current study uses animated narrative vignette simulation technology to observe a classroom situation depicting a low level aggressive peer-to-peer victim dyad. Participants were asked to respond to this situation as if they were the teacher, and these responses were then coded and analyzed. Consistent with other literature, the pre-service teachers expressed very little empathic concern, problem-solving or management of the situation with the victim. Future direction and educational implications are presented.

Question: What is the current study?


Neural Machine Translation with Gumbel-Greedy Decoding. Previous neural machine translation models used some heuristic search algorithms (e.g., beam search) in order to avoid solving the maximum a posteriori problem over translation sentences at test phase. In this paper, we propose the GumbelGreedy Decoding which trains a generative network to predict translation under a trained model. We solve such a problem using the Gumbel-Softmax reparameterization, which makes our generative network differentiable and trainable through standard stochastic gradient methods. We empirically demonstrate that our proposed model is effective for generating sequences of discrete words.

Question: How to train a neural machine translation model?


Language, thought and reference.. How should we best analyse the meaning of proper names, indexicals, demonstratives, both simple and complex, and definite descriptions? In what relation do such expressions stand to the objects they designate? In what relation do they stand to mental representations of those objects? Do these expressions form a semantic class, or must we distinguish between those that arc referential and those that are quantificational? Such questions have constituted one of the core research areas in the philosophy of language for much of the last century, yet consensus remains elusive: the field is still divided, for instance, between those who hold that all such expressions are semantically descriptive and those who would analyse most as the natural language counterparts of logical individual constants. The aim of this thesis is to cast new light on such questions by approaching them from within the cognitive framework of Sperber and Wilson's Relevance Theory. Relevance Theory offers not just an articulated pragmatics but also a broad conception of the functioning of natural language which differs radically from that presupposed within (most of) the philosophy of language. The function of linguistic expressions, on this conception, is not to determine propositional content, but rather to provide inferential premises which, in parallel with context and general pragmatic principles, will enable a bearer to reach the speaker's intended interpretation. Working within this framework, I shall argue that the semantics of the expressions discussed should best be analysed not in terms of their relation to those objects which, on occasions of use, they may designate, but rather in terms of the indications they offer a hearer concerning the mental representation which constitutes the content of a speaker's informative intention. Such an analysis can, I shall claim, capture certain key data on reference which have proved notoriously problematic, while respec

Question: What is the difference between a referential and a quantificational expression?


Model-Driven Approach for Body Area Network Application Development. This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application.

Question: What is the difference between a model-driven approach and a model-based approach?


Multi-Granularity Chinese Word Embedding. This paper considers the problem of learning Chinese word embeddings. In contrast to English, a Chinese word is usually composed of characters, and most of the characters themselves can be further divided into components such as radicals. While characters and radicals contain rich information and are capable of indicating semantic meanings of words, they have not been fully exploited by existing word embedding methods. In this work, we propose multi-granularity embedding (MGE) for Chinese words. The key idea is to make full use of such word-character-radical composition, and enrich word embeddings by further incorporating finer-grained semantics from characters and radicals. Quantitative evaluation demonstrates the superiority of MGE in word similarity computation and analogical reasoning. Qualitative analysis further shows its capability to identify finer-grained semantic meanings of words.

Question: What is the difference between character embedding and word embedding?


Electricity-producing bacterial communities in microbial fuel cells.. Microbial fuel cells (MFCs) are not yet commercialized but they show great promise as a method of water treatment and as power sources for environmental sensors. The power produced by these systems is currently limited, primarily by high internal (ohmic) resistance. However, improvements in the system architecture will soon result in power generation that is dependent on the capabilities of the microorganisms. The bacterial communities that develop in these systems show great diversity, ranging from primarily delta-Proteobacteria that predominate in sediment MFCs to communities composed of alpha-, beta-, gamma- or delta-Proteobacteria, Firmicutes and uncharacterized clones in other types of MFCs. Much remains to be discovered about the physiology of these bacteria capable of exocellular electron transfer, collectively defined as a community of "exoelectrogens". Here, we review the microbial communities found in MFCs and the prospects for this emerging bioenergy technology.

Question: What is the difference between a microbial fuel cell and a microbial electrolysis cell?


Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models.. This paper identifies several serious problems with the widespread use of ANOVAs for the analysis of categorical outcome variables such as forced-choice variables, question-answer accuracy, choice in production (e.g. in syntactic priming research), et cetera. I show that even after applying the arcsine-square-root transformation to proportional data, ANOVA can yield spurious results. I discuss conceptual issues underlying these problems and alternatives provided by modern statistics. Specifically, I introduce ordinary logit models (i.e. logistic regression), which are well-suited to analyze categorical data and offer many advantages over ANOVA. Unfortunately, ordinary logit models do not include random effect modeling. To address this issue, I describe mixed logit models (Generalized Linear Mixed Models for binomially distributed outcomes, Breslow & Clayton, 1993), which combine the advantages of ordinary logit models with the ability to account for random subject and item effects in one step of analysis. Throughout the paper, I use a psycholinguistic data set to compare the different statistical methods.

Question: What is the difference between ordinary logit models and mixed logit models?


Multi-accent deep neural network acoustic model with accent-specific top layer using the KLD-regularized model adaptation. We propose a multi-accent deep neural network acoustic model with an accent-specific top layer and shared bottom hidden layers. The accent-specific top layer is used to model the distinct accent specific patterns. The shared bottom hidden layers allow maximum knowledge sharing between the native and the accent models. This design is particularly attractive when considering deploying such a system to a live speech service due to its computational efficiency. We applied the KL-divergence (KLD) regularized model adaptation to train the accent-specific top layer. On the mobile short message dictation task (SMD), with 1K, 10K, and 100K British or Indian accent adaptation utterances, the proposed approach achieves 18.1%, 26.0%, and 28.5% or 16.1%, 25.4%, and 30.6% word error rate reduction (WERR) for the British and the Indian accent respectively against a baseline cross entropy (CE) model trained from 400 hour data. On the 100K utterance accent adaptation setup, comparable performance gain can be obtained against a baseline CE model trained with 2000 hour data. We observe smaller yet significant WER reduction on a baseline model trained using the MMI sequence-level criterion.

Question: How to train a multi-accent deep neural network acoustic model?


A Systematic Mapping Study on Software Reuse. Context: Software reuse is considered as the key to a successful software development because of its potential to reduce the time to market, increase quality and reduce costs. This increase in demand made the software organizations to envision the use of software reusable assets which can also help in solving recurring problems. Till now, software reuse is confined to reuse of source code in the name of code scavenging. Now a day, software organizations are extending the concepts of software reuse to other life cycle objects as they realized that reuse of source code alone does not save money. The academia has put forward some assets as reusable and presented methods or approaches for reusing them. Also, for a successful software reuse the organizations should assess the value of reuse and keep track on their reuse programs. The other area which is vital for software reuse is the maintenance. Maintenance of reusable software has direct impact on the cost of the software. In this regard, academia has presented a number of techniques, methods, metrics and models for assessing the value of reuse and for maintaining the reusable software. Objectives: In our thesis, we investigate on the reusable assets and the methods/ approaches that are put forward by the academia for reusing those assets. Also a systematic mapping study is performed to investigate what techniques, methods, models and metrics for assessing the value of reuse and for maintaining the reused software are proposed and we also investigate their validation status as well. Methods: The databases like IEEE Xplore, ACM digital library, Inspec, Springer and Google scholar were used to search for the relevant studies for our systematic mapping study. We followed basic inclusion criteria along with detailed inclusion/exclusion criteria for selecting the appropriate article. Results: Through our systematic mapping study, we could summarize the list of 14 reusable asset

Question: What is a systematic mapping study?


Brief Announcement: Dynamic Determinacy Race Detection for Task Parallelism with Futures. Existing dynamic determinacy race detectors for task-parallel programs are limited to programs with strict computation graphs, where a task can only wait for its descendant tasks to complete. In this paper, we present the first known determinacy race detector for non-strict computation graphs with futures. The space and time complexity of our algorithm are similar to those of the classical SP-bags algorithm, when using only structured parallel constructs such as spawn-sync and async-finish. In the presence of point-to-point synchronization using futures, the complexity of the algorithm increases by a factor determined by the number of future operations, which includes future task creation and future get operations. The experimental results show that the slowdown factor observed for our algorithm relative to the sequential version is in the range of 1.00x to 9.92x, which is very much in line with slowdowns experienced for fully strict computation graphs.

Question: What is the space complexity of the algorithm?


Entailment above the word level in distributional semantics. We introduce two ways to detect entailment using distributional semantic representations of phrases. Our first experiment shows that the entailment relation between adjective-noun constructions and their head nouns (big cat |= cat), once represented as semantic vector pairs, generalizes to lexical entailment among nouns (dog |= animal). Our second experiment shows that a classifier fed semantic vector pairs can similarly generalize the entailment relation among quantifier phrases (many dogs|=some dogs) to entailment involving unseen quantifiers (all cats|=several cats). Moreover, nominal and quantifier phrase entailment appears to be cued by different distributional correlates, as predicted by the type-based view of entailment in formal semantics.

Question: What is entailment?


Frequency compensation of high-speed, low-voltage CMOS multistage amplifiers. This paper presents the frequency compensation of high-speed, low-voltage multistage amplifiers. Two frequency compensation techniques, the Nested Miller Compensation with Nulling Resistors (NMCNR) and Reversed Nested Indirect Compensation (RNIC), are discussed and employed on two multistage amplifier architectures. A four-stage pseudo-differential amplifier with CMFF and CMFB is designed in a 1.2 V, 65-nm CMOS process. With NMCNR, it achieves a phase margin (PM) of 59° with a DC gain of 75 dB and unity-gain frequency (fug) of 712 MHz. With RNIC, the same four-stage amplifier achieves a phase margin of 84°, DC gain of 76 dB and fug of 2 GHz. Further, a three-stage single-ended amplifier is designed in a 1.1-V, 40-nm CMOS process. The three-stage OTA with RNIC achieves PM of 81°, DC gain of 80 dB and fug of 770 MHz. The same OTA achieves PM of 59° with NMCNR, while maintaining a DC gain of 75 dB and fug of 262 MHz. Pole-splitting, to achieve increased stability, is illustrated for both compensation schemes. Simulations illustrate that the RNIC scheme achieves much higher PM and fug for lower values of compensation capacitance compared to NMCNR, despite the growing number of low voltage amplifier stages.

Question: What is the frequency compensation of high-speed, low-voltage CMOS multistage amplifiers?


Minimum Steiner Tree Construction. In optimizing the area of Very Large Scale Integrated (VLSI) layouts, circuit interconnections should generally be realized with minimum total interconnect. This chapter addresses several variations of the corresponding fundamental Steiner minimal tree (SMT) problem, where a given set of pins is to be connected using minimum total wirelength. Steiner trees are important in global routing and wirelength estimation [15], as well as in various non-VLSI applications such as phylogenetic tree reconstruction in biology [48], network routing [61], and civil engineering, among many other areas [21, 25, 26, 29, 51, 74].

Question: What is the minimum steiner tree?


A compendium of RNA-binding motifs for decoding gene regulation. RNA-binding proteins are key regulators of gene expression, yet only a small fraction have been functionally characterized. Here we report a systematic analysis of the RNA motifs recognized by RNA-binding proteins, encompassing 205 distinct genes from 24 diverse eukaryotes. The sequence specificities of RNA-binding proteins display deep evolutionary conservation, and the recognition preferences for a large fraction of metazoan RNA-binding proteins can thus be inferred from their RNA-binding domain sequence. The motifs that we identify in vitro correlate well with in vivo RNA-binding data. Moreover, we can associate them with distinct functional roles in diverse types of post-transcriptional regulation, enabling new insights into the functions of RNA-binding proteins both in normal physiology and in human disease. These data provide an unprecedented overview of RNA-binding proteins and their targets, and constitute an invaluable resource for determining post-transcriptional regulatory mechanisms in eukaryotes.

Question: What is the function of RNA-binding proteins?


An Extension on " Statistical Comparisons of Classifiers over Multiple Data Sets " for all Pairwise Comparisons. In a recently published paper in JMLR, Demšar (2006) recommends a set of non-parametric statistical tests and procedures which can be safely used for comparing the performance of classifiers over multiple data sets. After studying the paper, we realize that the paper correctly introduces the basic procedures and some of the most advanced ones when comparing a control method. However, it does not deal with some advanced topics in depth. Regarding these topics, we focus on more powerful proposals of statistical procedures for comparing n×n classifiers. Moreover, we illustrate an easy way of obtaining adjusted and comparable p-values in multiple comparison procedures.

Question: How to compare multiple classifiers?


Quantifying social group evolution. The rich set of interactions between individuals in society results in complex community structure, capturing highly connected circles of friends, families or professional cliques in a social network. Thanks to frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. Our knowledge of the mechanisms governing the underlying community dynamics is limited, but is essential for a deeper understanding of the development and self-optimization of society as a whole. We have developed an algorithm based on clique percolation that allows us to investigate the time dependence of overlapping communities on a large scale, and thus uncover basic relationships characterizing community evolution. Our focus is on networks capturing the collaboration between scientists and the calls between mobile phone users. We find that large groups persist for longer if they are capable of dynamically altering their membership, suggesting that an ability to change the group composition results in better adaptability. The behaviour of small groups displays the opposite tendency—the condition for stability is that their composition remains unchanged. We also show that knowledge of the time commitment of members to a given community can be used for estimating the community's lifetime. These findings offer insight into the fundamental differences between the dynamics of small groups and large institutions.

Question: How can we quantify the evolution of social groups?


A Key Characteristic of Sex Differences in the Developing Brain: Greater Variability in Brain Structure of Boys than Girls.. In many domains, including cognition and personality, greater variability is observed in males than in females in humans. However, little is known about how variability differences between sexes are represented in the brain. The present study tested whether there is a sex difference in variance in brain structure using a cohort of 643 males and 591 females aged between 3 and 21 years. The broad age-range of the sample allowed us to test if variance differences in the brain differ across age. We observed significantly greater male than female variance for several key brain structures, including cerebral white matter and cortex, hippocampus, pallidum, putamen, and cerebellar cortex volumes. The differences were observed at both upper and lower extremities of the distributions and appeared stable across development. These findings move beyond mean levels by showing that sex differences were pronounced for variability, thereby providing a novel perspective on sex differences in the developing brain.

Question: What is the difference between the sexes in the developing brain?


Active-Constructive-Interactive: A Conceptual Framework for Differentiating Learning Activities. Active, constructive, and interactive are terms that are commonly used in the cognitive and learning sciences. They describe activities that can be undertaken by learners. However, the literature is actually not explicit about how these terms can be defined; whether they are distinct; and whether they refer to overt manifestations, learning processes, or learning outcomes. Thus, a framework is provided here that offers a way to differentiate active, constructive, and interactive in terms of observable overt activities and underlying learning processes. The framework generates a testable hypothesis for learning: that interactive activities are most likely to be better than constructive activities, which in turn might be better than active activities, which are better than being passive. Studies from the literature are cited to provide evidence in support of this hypothesis. Moreover, postulating underlying learning processes allows us to interpret evidence in the literature more accurately. Specifying distinct overt activities for active, constructive, and interactive also offers suggestions for how learning activities can be coded and how each kind of activity might be elicited.

Question: What is the difference between active, constructive, and interactive learning activities?