Mobile Media Use, Multitasking and Distractibility. Portable media devices are ubiquitous and their use has become a core component of many people's daily experience, but to what effect? In this paper, the authors review research on the ways in which media use and multitasking relate to distraction, distractibility and impulsivity. They review recent research on the effects of media multitasking on driving, walking, work, and academic performance. The authors discuss earlier research concerning the nature of media's impact on attention and review cognitive and neuropsychological findings on the effects of divided attention. Research provides clear evidence that mobile media use is distracting, with consequences for safety, efficiency and learning. Greater use of media is correlated with higher levels of trait impulsivity and distractibility, but the direction of causality has not been established. Individuals may become more skilled at media multitasking over time, but intervention is currently required to improve the safe and effective use of mobile media. DOI: 10.4018/ijcbpl.2012070102 16 International Journal of Cyber Behavior, Psychology and Learning, 2(3), 15-29, July-September 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. and neuropsychological research on the effects of divided attention. Finally, we explore how high levels of media multitasking might alter our general responses to experience in ways that are marked by changes in distractibility and impulsivity. THE DISTRACTING EFFECTS OF MULTITASKING WITH MOBILE MEDIA

Question: What is the effect of media multitasking on driving?


Session-based Recommendations with Recurrent Neural Networks. Session-based recommendation Permanent cold start: where personalized recommendations fail • User identification: Many sites (e.g. classifieds, video services) don't require users to log in. Although some form of identification is possible, it is not reliable. • Intent/theme: Sessions usually have a goal or a specific theme. Different sessions of the same user center around different concepts. The entire user history may not help much in identifying the user's current needs. • Never/rarely returning users: High percentage of the users of webshops come from search engines in search for some products and rarely return. Workaround in practice • Item-to-item recommendations: Recommend similar or frequently co-occurring items. We explore item-to-session recommendations. By modeling the whole session, more accurate recommendations can be provided. We propose an RNN-based approach to model the session and provide session-based recommendations. domonkos.tikk@gravityrd.com @domonkostikk balazs.hidasi@gravityrd.com @balazshidasi Balázs Hidasi

Question: What is the difference between a session-based recommendation and a session-based recommendation?


Semantic-Based Location Recommendation With Multimodal Venue Semantics. In recent years, we have witnessed a flourishing of location -based social networks. A well-formed representation of location knowledge is desired to cater to the need of location sensing, browsing, navigation and querying. In this paper, we aim to study the semantics of point-of-interest (POI) by exploiting the abundant heterogeneous user generated content (UGC) from different social networks. Our idea is to explore the text descriptions, photos, user check-in patterns, and venue context for location semantic similarity measurement. We argue that the venue semantics play an important role in user check-in behavior. Based on this argument, a unified POI recommendation algorithm is proposed by incorporating venue semantics as a regularizer. In addition to deriving user preference based on user-venue check-in information, we place special emphasis on location semantic similarity. Finally, we conduct a comprehensive performance evaluation of location semantic similarity and location recommendation over a real world dataset collected from Foursquare and Instagram. Experimental results show that the UGC information can well characterize the venue semantics, which help to improve the recommendation performance.

Question: What is the venue semantics?


Interactions with big data analytics. Increasingly in the 21st century, our daily lives leave behind a detailed digital record: our shifting thoughts and opinions shared on Twitter, our social relationships, our purchasing habits, our information seeking, our photos and videos—even the movements of our bodies and cars. Naturally, for those interested in human behavior, this bounty of personal data is irresistible. Decision makers of all kinds, from company executives to government agencies to researchers and scientists, would like to base their decisions and actions on this data. In response, a new discipline of big data analytics is forming. Fundamentally, big data analytics is a workflow that distills terabytes of low-value data (e.g., every tweet) down to, in some cases, a single bit of high-value data (Should Company X acquire Company Y? Can we reject the null hypothesis?). The goal is to see the big picture from the minutia of our digital lives. It is no surprise today that big data is useful for HCI researchers and user interface design. As one example, A/B testing is a standard practice in the usability community to help determine relative differences in user performance using different interfaces. For many years, we have used strict laboratory conditions to evaluate interfaces, but more recently we have seen the ability to implement those tests quickly and on a large population by running controlled Interactions with Big Data Analytics

Question: What is A/B testing?


A Compact 90-degree Twist using Novel Ridged Waveguide for Integrated Waveguide Subsystems. A compact waveguide twist using a single step of novel ridged structure for integrated waveguide subsystems is proposed. The cross-section of the ridged waveguide has the shape of two squares partially overlapped. The advantages of this configuration are the facilitation of manufacturing and the reduction of overall dimensions. In this paper, the broadband operation principle is described by using equivalent circuit, moreover the its design using HFSS and the experimental results are presented. In the result, the return loss in excess of 30dB could be obtained over 22 percent of frequency band without additional waveguide steps. The twist length is 0.22 times the wavelength of the standard rectangular waveguides

Question: What is the return loss of the compact 90-degree twist?


Chapter 7 Towards Automatically-Tuned Deep Neural Networks. Recent advances in AutoML have led to automated tools that can compete with machine learning experts on supervised learning tasks. In this work, we present two versions of Auto-Net, which provide automatically-tuned deep neural networks without any human intervention. The first version, Auto-Net 1.0, builds upon ideas from the competition-winning system Auto-sklearn by using the Bayesian Optimization method SMAC and uses Theano as the underlying deep learning (DL) framework. The more recent Auto-Net 2.0 builds upon a recent combination of Bayesian Optimization and HyperBand, called BOHB, and uses PyTorch as DL framework. To the best of our knowledge, Auto-Net 1.0 was the first automatically-tuned neural network to win competition datasets against human experts (as part of the first AutoML challenge). Further empirical results show that ensembling Auto-Net 1.0 with Auto-sklearn can perform better than either approach alone, and that Auto-Net 2.0 can perform better yet.

Question: What is the first automatically-tuned deep neural network to win a competition?


Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset. The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this, realistic protection and investigation countermeasures need to be developed. Such countermeasures include network intrusion detection and network forensic systems. For that purpose, a well-structured and representative dataset is paramount for training and validating the credibility of the systems. Although there are several network, in most cases, not much information is given about the Botnet scenarios that were used. This paper, proposes a new dataset, Bot-IoT, which incorporates legitimate and simulated IoT network traffic, along with various types of attacks. We also present a realistic testbed environment for addressing the existing dataset drawbacks of capturing complete network information, accurate labeling, as well as recent and complex attack diversity. Finally, we evaluate the reliability of the BoT-IoT dataset using different statistical and machine learning methods for forensics purposes compared with the existing datasets. This work provides the baseline for allowing botnet identificaiton across IoT-specific networks. The Bot-IoT dataset can be accessed at [1].

Question: What is the botnet dataset?


The Challenge of Optical Music Recognition. This article describes the challenges posed by optical music recognition – a topic in computer science that aims to convert scanned pages of music into an on-line format. First, the problem is described; then a generalised framework for software is presented that emphasises key stages that must be solved: staff line identification, musical object location, musical feature classification, and musical semantics. Next, significant research projects in the area are reviewed, showing how each fits the generalised framework. The article concludes by discussing perhaps the most open question in the field: how to compare the accuracy and success of rival systems, highlighting certain steps that help ease the task.

Question: What is optical music recognition?


Analyzing the Held-Karp TSP Bound: A Monotonicity Property with Application. In their 1971 paper on the Traveling Salesman Problem and Minimum Spanning Trees, Held and Karp showed that finding an optimally weighted 1-tree is equivalent to solving a linear program for the Traveling Salesman Problem (TSP) with only node-degree constraints and subtour elimination constraints. In this paper we show that the Held-Karp 1-trees have a certain monotonicity property: given a particular instance of the symmetric TSP with triangle inequality, the cost of the minimum weighted 1-tree is monotonic with respect to the set of nodes included. As a consequence, we obtain an alternate proof of a result of Wolsey and show that linear programs with node-degree and subtour elimination constraints must have a cost at least 23OPT , where OPT is the cost of the optimum solution to the TSP instance. The traveling salesman problem is one of the most notorious in the field of combinatorial optimization, and one of the most well-studied [7]. Currently, the most successful approach to finding optimal solutions to large-scale problems is based on formulating the problem as a linear program and finding explicit partial descriptions of this linear polytope [5], [8]. The most natural constraints are derived from an integer linear programming formulation that uses nodedegree constraints and subtour elimination constraints. We focus our attention on symmetric instances of the TSP that obey the triangle inequality. Let V = {1, 2, . . . , n} denote the set of nodes. For any distinct i and j, assign a cost cij such that cij = cji, and for any k distinct from i and j, cij ≤ cik + ckj . Then the Subtour LP on this instance is B = min ∑ 1≤i<j≤n cijxij subject to: ∑ j>i xij + ∑ j<i xji = 2, i = 1, 2, . . . , n, ∑ i∈S,j∈S,i<j xij ≤ |S|− 1, for any proper subsetS ⊂ V, 0 ≤ xij ≤ 1, 1 ≤ i < j ≤ n. (1) ∗This research was supported in part by Air Force Contract AFOSR-86-0078 and by the National Science Foundation

Question: What is the cost of the minimum weighted 1-tree?


Job performance prediction in a call center using a naive Bayes classifier. This study presents an approach to predict the performance of sales agents of a call center dedicated exclusively to sales and telemarketing activities. This approach is based on a naive Bayesian classifier. The objective is to know what levels of the attributes are indicative of individuals who perform well. A sample of 1037 sales agents was taken during the period between March and September of 2009 on campaigns related to insurance sales and service pre-paid phone services, to build the naive Bayes network. It has been shown that, socio-demographic attributes are not suitable for predicting performance. Alternatively, operational records were used to predict production of sales agents, achieving satisfactory results. In this case, the classifier training and testing is done through a stratified tenfold cross-validation. It classified the instances correctly 80.60% of times, with the proportion of false positives of 18.1% for class no (does not achieve minimum) and 20.8% for the class yes (achieves equal or above minimum acceptable). These results suggest that socio-demographic attributes has no predictive power on performance, while the operational information of the activities of the sale agent can predict the future performance of the

Question: What is the purpose of this study?


A segmented claw-pole motor for traction applications considering recycling aspects. With the expansion of the fleet of electric and hybrid electric vehicles worldwide, it is of interest to consider recycling aspects of the parts that are introduced in these new vehicles. This paper focuses on the design of an electrical machine, a claw-pole motor, considering recycling of its components. The suggested design has segmented core parts of soft magnetic composites which yield a design very suitable for recycling as the core material is brittle and it thus simplifies the access of the copper winding. The windings are pre-formed ring-coils with a high fill-factor and with no end-windings. Through the use of direct water cooling, employing space between the claws, the current density can be high, and the machine can be compact. The results of finite element simulations of the electromagnetic properties (previously validated against measurements) show that a high performance recyclable motor with ratings above 10 kW can be made. In comparison with a commercial motor, the claw-pole motor has similar performance, a higher core and magnet weight and a lower copper weight. It also has an expected advantage regarding manufacturing cost and cost of recycling and it has lower copper loss.

Question: What is a claw-pole motor?


Bioinspired engineering of thermal materials.. In the development of next-generation materials with enhanced thermal properties, biological systems in nature provide many examples that have exceptional structural designs and unparalleled performance in their thermal or nonthermal functions. Bioinspired engineering thus offers great promise in the synthesis and fabrication of thermal materials that are difficult to engineer through conventional approaches. In this review, recent progress in the emerging area of bioinspired advanced materials for thermal science and technology is summarized. State-of-the-art developments of bioinspired thermal-management materials, including materials for efficient thermal insulation and heat transfer, and bioinspired materials for thermal/infrared detection, are highlighted. The dynamic balance of bioinspiration and practical engineering, the correlation of inspiration approaches with the targeted applications, and the coexistence of molecule-based inspiration and structure-based inspiration are discussed in the overview of the development. The long-term outlook and short-term focus of this critical area of advanced materials engineering are also presented.

Question: What is the long-term outlook of bioinspired engineering of thermal materials?


Rational Choice Theory : Toward a Psychological , Social , and Material Contextualization of Human Choice Behavior. The main purpose of this paper is to provide a brief overview of the rational choice approach, followed by an identification of several of the major criticisms of RCT and its conceptual and empirical limitations. It goes on to present a few key initiatives to develop alternative, more realistic approaches which transcend some of the limitations of Rational Choice Theory (RCT). Finally, the article presents a few concluding reflections and a table comparing similarities and differences between the mainstream RCT and some of the initial components of an emerging choice theory. Our method has been to conduct a brief selective review of rational choice theoretical formulations and applications as well as a review of diverse critical literature in the social sciences where rational choice has been systematically criticized. We have focused on a number of leading contributors (among others, several Nobel Prize Recipients in economics, who have addressed rational choice issues). So this article makes no claim for completeness. The review maps a few key concepts and assumptions underpinning the conceptual model and empirical applications of RCT. It reviews also a range of critical arguments and evidence of limitations. It identifies selected emerging concepts and theoretical revisions and adaptations to choice theory and what they entail. The results obtained, based on our literature reviews and analyses, are the identification of several major limitations of RCT as well as selected modifications and adaptations of choice theory which overcome or promise to overcome some of the RCT limitations. Thus, the article with Table 1 in hand provides a point of departure for follow-up systematic reviews and more precise questions for future theory development. The criticisms and adaptations of RCT have contributed to greater realism, empirical relevance, and increased

Question: What is the difference between rational choice theory and rational choice theory?


Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach. The exponential increase in the availability of online reviews and recommendations makes sentiment classification an interesting topic in academic and industrial research. Reviews can span so many different domains that it is difficult to gather annotated training data for all of them. Hence, this paper studies the problem of domain adaptation for sentiment classifiers, hereby a system is trained on labeled reviews from one source domain but is meant to be deployed on another. We propose a deep learning approach which learns to extract a meaningful representation for each review in an unsupervised fashion. Sentiment classifiers trained with this high-level feature representation clearly outperform state-of-the-art methods on a benchmark composed of reviews of 4 types of Amazon products. Furthermore, this method scales well and allowed us to successfully perform domain adaptation on a larger industrial-strength dataset of 22 domains.

Question: What is a benchmark composed of reviews of 4 types of Amazon products?


Change from baseline and analysis of covariance revisited.. The case for preferring analysis of covariance (ANCOVA) to the simple analysis of change scores (SACS) has often been made. Nevertheless, claims continue to be made that analysis of covariance is biased if the groups are not equal at baseline. If the required equality were in expectation only, this would permit the use of ANCOVA in randomized clinical trials but not in observational studies. The discussion is related to Lord's paradox. In this note, it is shown, however that it is not a necessary condition for groups to be equal at baseline, not even in expectation, for ANCOVA to provide unbiased estimates of treatment effects. It is also shown that although many situations can be envisaged where ANCOVA is biased it is very difficult to imagine circumstances under which SACS would then be unbiased and a causal interpretation could be made.

Question: What is Lord's paradox?


The Human Hippocampus and Spatial and Episodic Memory. Finding one's way around an environment and remembering the events that occur within it are crucial cognitive abilities that have been linked to the hippocampus and medial temporal lobes. Our review of neuropsychological, behavioral, and neuroimaging studies of human hippocampal involvement in spatial memory concentrates on three important concepts in this field: spatial frameworks, dimensionality, and orientation and self-motion. We also compare variation in hippocampal structure and function across and within species. We discuss how its spatial role relates to its accepted role in episodic memory. Five related studies use virtual reality to examine these two types of memory in ecologically valid situations. While processing of spatial scenes involves the parahippocampus, the right hippocampus appears particularly involved in memory for locations within an environment, with the left hippocampus more involved in context-dependent episodic or autobiographical memory.

Question: What is the role of the hippocampus in spatial memory?


An Intrusion Detection System Based on KDD-99 Data using Data Mining Techniques and Feature Selection. Internet and internet users are increasing day by day. Also due to rapid development of internet technology , security is becoming big issue. Intruders are monitoring comput er network continuously for attacks. A sophisticated firewall with efficient intrusion detection system (IDS) is required to pre vent computer network from attacks. A comprehensive study of litera tures proves that data mining techniques are more powerful techni que to develop IDS as a classifier. Performance of classif ier is a crucial issue in terms of its efficiency, also number of fe ature to be scanned by the IDS should also be optimized. In thi s paper two techniques C5.0 and artificial neural network (ANN) ar e utilized with feature selection. Feature selection techniques will discard some irrelevant features while C5.0 and ANN acts as a classifier to classify the data in either normal type or one of t he five types of attack.KDD99 data set is used to train and test the models ,C5.0 model with numbers of features is producing better r sults with all most 100% accuracy. Performances were also verified in terms of data partition size.

Question: What is the difference between C5.0 and ANN?


Online Parameter Estimation Technique for Adaptive Control Applications of Interior PM Synchronous Motor Drives. This paper proposes an online parameter estimation method based on a discrete-time dynamic model for the interior permanent-magnet synchronous motors (IPMSMs). The proposed estimation technique, which takes advantage of the difference in dynamics of motor parameters, consists of two affine projection algorithms. The first one is designed to accurately estimate the stator inductances, whereas the second one is designed to precisely estimate the stator resistance, rotor flux linkage, and load torque. In this paper, the adaptive decoupling proportional-integral (PI) controllers with the maximum torque per ampere operation, which utilize the previously identified parameters in real time, are chosen to verify the effectiveness of the proposed parameter estimation scheme. The simulation results via MATLAB/Simulink and the experimental results via a prototype IPMSM drive system with a TI TMS320F28335 DSP are presented under various conditions. A comparative study with the conventional decoupling PI control method is carried out to demonstrate the better performances (i.e., faster dynamic response, less steady-state error, more robustness, etc.) of the adaptive decoupling PI control scheme based on the proposed online parameter estimation technique.

Question: How to estimate the parameters of a synchronous motor?


A Novel Feature Selection Approach Based on FODPSO and SVM. A novel feature selection approach is proposed to address the curse of dimensionality and reduce the redundancy of hyperspectral data. The proposed approach is based on a new binary optimization method inspired by fractional-order Darwinian particle swarm optimization (FODPSO). The overall accuracy (OA) of a support vector machine (SVM) classifier on validation samples is used as fitness values in order to evaluate the informativity of different groups of bands. In order to show the capability of the proposed method, two different applications are considered. In the first application, the proposed feature selection approach is directly carried out on the input hyperspectral data. The most informative bands selected from this step are classified by the SVM. In the second application, the main shortcoming of using attribute profiles (APs) for spectral-spatial classification is addressed. In this case, a stacked vector of the input data and an AP with all widely used attributes are created. Then, the proposed feature selection approach automatically chooses the most informative features from the stacked vector. Experimental results successfully confirm that the proposed feature selection technique works better in terms of classification accuracies and CPU processing time than other studied methods without requiring the number of desired features to be set a priori by users.

Question: What is the difference between the proposed feature selection approach and the other studied methods?


Classification and Prediction of Future Weather by using Back Propagation Algorithm-An Approach. Weather forecasting is the process of recording the parameters of weather like wind direction, wind speed, humidity, rainfall, temperature etc. Back Propagation Algorithm can be applied on these parameters in order to predict the future weather. In "Classification and Prediction of Future Weather by using Back Propagation Algorithm" technique, one parameter say temperature varies by some unit and the variation on other parameters say humidity, temperature and rainfall will be predicted with respect to temperature. In this paper, different models which were used in the past for weather forecasting are discussed. This paper is focusing on three parts, first is different models which were used in weather forecasting, second part is introducing a new wireless kit use for weather forecasting and third part includes the Back Propagation Algorithm can be applied on different parameters of weather forecast. Keywords--Back Propagation Algorithm, Artificial Neural Network, Wireless kit, Classification and Prediction

Question: What is the difference between weather forecasting and weather prediction?


Battery Pack Modeling , Simulation , and Deployment on a Multicore Real Time Target. Battery Management System (BMS) design is a complex task requiring sophisticated models that mimic the electrochemical behavior of the battery cell under a variety of operating conditions. Equivalent circuits are well-suited for this task because they offer a balance between fidelity and simulation speed, their parameters reflect direct experimental observations, and they are scalable. Scalability is particularly important at the real time simulation stage, where a model of the battery pack runs on a real-time simulator that is physically connected to the peripheral hardware in charge of monitoring and control. With modern battery systems comprising hundreds of cells, it is important to employ a modeling and simulation approach that is capable of handling numerous simultaneous instances of the basic unit cell while maintaining real time performance. In previous publications we presented a technique for the creation of a battery cell model that contains the electrochemical fingerprints of a battery cell based on equivalent circuit model fitting to experimental data. In this work we extend our previous model to represent a battery pack, featuring cell creation, placement, and connection using automation scripts, thus facilitating the design of packs of arbitrary size and electrical topology. In addition, we present an assessment of model partitioning schemes for real time execution on multicore targets to ensure efficient use of hardware resources, a balanced computational load, and a study of the potential impact of the calculation latencies inherent to distributed systems on solver accuracy. Prior to C code generation for real time execution, a model profiler assesses the model partitioning and helps determine the multicore configuration that results in the lowest average turnaround time, the time elapsed between task start and finish. The resulting model is useful in the generation

Question: Battery pack modeling, simulation, and deployment on a multicore real time target.


Using WordNet to Disambiguate Word Senses for Text Retrieval. This paper describes an automatic indexing procedure that uses the "IS-A" relations contained within WordNet and the set of nouns contained in a text to select a sense for each plysemous noun in the text. The result of the indexing procedure is a vector in which some of the terms represent word senses instead of word stems. Retrieval experiments comparing the effectivenss of these sense-based vectors vs. stem-based vectors show the stem-based vectors to be superior overall, although the sense-based vectors do improve the performance of some queries. The overall degradation is due in large part to the difficulty of disambiguating senses in short query statements. An analysis of these results suggests two conclusions: the IS-A links define a generalization/specialization hierarchy that is not sufficient to reliably select the correct sense of a noun from the set of fine sense distinctions in WordNet; and missing correct matches because of incorrect sense resolution has a much more deleterious effect on retrieval performance than does making spurious matches.

Question: What is the difference between a generalization and a specialization?


Workshop on novelty and diversity in recommender systems - DiveRS 2011. Novelty and diversity have been identified as key dimensions of recommendation utility in real scenarios, and a fundamental research direction to keep making progress in the field. Yet recommendation novelty and diversity remain a largely open area for research. The DiveRS workshop gathered researchers and practitioners interested in the role of these dimensions in recommender systems. The workshop seeks to advance towards a better understanding of what novelty and diversity are, how they can improve the effectiveness of recommendation methods and the utility of their outputs. The workshop pursued the identification of open problems, relevant research directions, and opportunities for innovation in the recommendation business.

Question: What is the divers workshop?


Omega-3 Fatty Acids and Inflammatory Processes. Long chain fatty acids influence inflammation through a variety of mechanisms; many of these are mediated by, or at least associated with, changes in fatty acid composition of cell membranes. Changes in these compositions can modify membrane fluidity, cell signaling leading to altered gene expression, and the pattern of lipid mediator production. Cell involved in the inflammatory response are typically rich in the n-6 fatty acid arachidonic acid, but the contents of arachidonic acid and of the n-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) can be altered through oral administration of EPA and DHA. Eicosanoids produced from arachidonic acid have roles in inflammation. EPA also gives rise to eicosanoids and these often have differing properties from those of arachidonic acid-derived eicosanoids. EPA and DHA give rise to newly discovered resolvins which are anti-inflammatory and inflammation resolving. Increased membrane content of EPA and DHA (and decreased arachidonic acid content) results in a changed pattern of production of eicosanoids and resolvins. Changing the fatty acid composition of cells involved in the inflammatory response also affects production of peptide mediators of inflammation (adhesion molecules, cytokines etc.). Thus, the fatty acid composition of cells involved in the inflammatory response influences their function; the contents of arachidonic acid, EPA and DHA appear to be especially important. The anti-inflammatory effects of marine n-3 PUFAs suggest that they may be useful as therapeutic agents in disorders with an inflammatory component.

Question: What is the difference between omega-3 and omega-6 fatty acids?


Analytical loss model of power MOSFET. An accurate analytical model is proposed in this paper to calculate the power loss of a metal-oxide semiconductor field-effect transistor. The nonlinearity of the capacitors of the devices and the parasitic inductance in the circuit, such as the source inductor shared by the power stage and driver loop, the drain inductor, etc., are considered in the model. In addition, the ringing is always observed in the switching power supply, which is ignored in the traditional loss model. In this paper, the ringing loss is analyzed in a simple way with a clear physical meaning. Based on this model, the circuit power loss could be accurately predicted. Experimental results are provided to verify the model. The simulation results match the experimental results very well, even at 2-MHz switching frequency.

Question: What is the analytical loss model of power MOSFET?


Epidemiological features of chronic low-back pain. Although the literature is filled with information about the prevalence and incidence of back pain in general, there is less information about chronic back pain, partly because of a lack of agreement about definition. Chronic back pain is sometimes defined as back pain that lasts for longer than 7-12 weeks. Others define it as pain that lasts beyond the expected period of healing, and acknowledge that chronic pain may not have well-defined underlying pathological causes. Others classify frequently recurring back pain as chronic pain since it intermittently affects an individual over a long period. Most national insurance and industrial sources of data include only those individuals in whom symptoms result in loss of days at work or other disability. Thus, even less is known about the epidemiology of chronic low-back pain with no associated work disability or compensation. Chronic low-back pain has also become a diagnosis of convenience for many people who are actually disabled for socioeconomic, work-related, or psychological reasons. In fact, some people argue that chronic disability in back pain is primarily related to a psychosocial dysfunction. Because the validity and reliability of some of the existing data are uncertain, caution is needed in an assessment of the information on this type of pain.

Question: What is the prevalence of chronic low-back pain?


UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.

Question: What is the UK Biobank?


Two cortical systems for memory-guided behaviour. Although the perirhinal cortex (PRC), parahippocampal cortex (PHC) and retrosplenial cortex (RSC) have an essential role in memory, the precise functions of these areas are poorly understood. Here, we review the anatomical and functional characteristics of these areas based on studies in humans, monkeys and rats. Our Review suggests that the PRC and PHC–RSC are core components of two separate large-scale cortical networks that are dissociable by neuroanatomy, susceptibility to disease and function. These networks not only support different types of memory but also appear to support different aspects of cognition.

Question: What are the functions of the perirhinal cortex?


Estimating Attributes: Analysis and Extensions of RELIEF. In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them. Kira and Rendell (1992a,b) developed an algorithm called RELIEF, which was shown to be very eecient in estimating attributes. Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems. In this paper RELIEF is analysed and extended to deal with noisy, incomplete, and multi-class data sets. The extensions are veriied on various artiicial and one well known real-world problem.

Question: What is the RELIEF algorithm?


Gender differences in the relationship between internet addiction and depression: A cross-lagged study in Chinese adolescents. The present study explored the role of gender in the association between Internet addiction and depression. Three-wave longitudinal panel data were collected from self-reported questionnaires that were completed by 1715 adolescents in grades 6e8 in China. Cross-lagged structural equation modeling was used to examine the relationship between Internet addiction and depression. In male adolescents, depression was found to significantly predict subsequent Internet addiction, suggesting that depression was the cause of Internet addiction and supporting the mood enhancement hypothesis. In female adolescents, Internet addiction was found to significantly predict subsequent depression, indicating that Internet addiction leads to depression and supporting the social displacement hypothesis. These results indicate that the relationship between Internet addiction and depression depended on gender. In addition, it was found that males and females exhibit different behavioral patterns and motivations of Internet usage. Males were more likely to use the Internet for pleasure and less likely to surf the Internet to search for information, compared with females. Although both males and females were prone to surfing the Internet alone, males were more likely to go online with friends compared with females. These findings suggest that gender-specific preventative and interventional strategies should be developed to reduce Internet addiction. © 2016 Elsevier Ltd. All rights reserved.

Question: What is the difference between male and female adolescents?


TUT: a statistical model for detecting trends, topics and user interests in social media. The rapid development of online social media sites is accompanied by the generation of tremendous web contents. Web users are shifting from data consumers to data producers. As a result, topic detection and tracking without taking users' interests into account is not enough. This paper presents a statistical model that can detect interpretable trends and topics from document streams, where each trend (short for trending story) corresponds to a series of continuing events or a storyline. A topic is represented by a cluster of words frequently co-occurred. A trend can contain multiple topics and a topic can be shared by different trends. In addition, by leveraging a Recurrent Chinese Restaurant Process (RCRP), the number of trends in our model can be determined automatically without human intervention, so that our model can better generalize to unseen data. Furthermore, our proposed model incorporates user interest to fully simulate the generation process of web contents, which offers the opportunity for personalized recommendation in online social media. Experiments on three different datasets indicated that our proposed model can capture meaningful topics and trends, monitor rise and fall of detected trends, outperform baseline approach in terms of perplexity on held-out dataset, and improve the result of user participation prediction by leveraging users' interests to different trends.

Question: What is a statistical model for detecting trends, topics and user interests in social media?


Pfinder: Real-Time Tracking of the Human Body. Pfinder is a real-time system for tracking people and interpreting their behavior. It runs at 10Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions. Pfinder has been successfully used in a wide range of applications including wireless interfaces, video databases, and low-bandwidth coding. Index Terms —Blobs, blob tracking, real-time, person tracking, 3D person tracking, segmentation, gesture recognition, mixture model, MDL.

Question: What is the name of the system?


Service-oriented middleware for the Future Internet: state of the art and research directions. Service-oriented computing is now acknowledged as a central paradigm for Internet computing, supported by tremendous research and technology development over the last 10 years. However, the evolution of the Internet, and in particular, the latest Future Internet vision, challenges the paradigm. Indeed, service-oriented computing has to face the ultra large scale and heterogeneity of the Future Internet, which are orders of magnitude higher than those of today's service-oriented systems. This article aims at contributing to this objective by identifying the key research directions to be followed in light of the latest state of the art. This article more specifically focuses on research challenges for service-oriented middleware design, therefore, investigating service description, discovery, access, and composition in the Future Internet of services.

Question: What is service-oriented middleware?


An early fire-detection method based on image processing. The paper presents an early fire-alarm raising method based on video processing. The basic idea of the proposed of fire-detection is to adopt a RGB (red, green, blue) model based chromatic and disorder measurement for extracting fire-pixels and smoke-pixels. The decision function of fire-pixels is mainly deduced by the intensity and saturation of R component. The extracted fire-pixels will be verified if it is a real fire by both dynamics of growth and disorder, and further smoke. Based on iterative checking on the growing ratio of flames, a fire-alarm is given when the alarm-raising condition is met. Experimental results show that the developed technique can achieve fully automatic surveillance of fire accident with a lower false alarm rate and thus is very attractive for the important military, social security, commercial applications, and so on, at a general cost.

Question: What is the fire-alarm raising method based on image processing?


What do exploratory searchers look at in a faceted search interface?. This study examined how searchers interacted with a web-based, faceted library catalog when conducting exploratory searches. It applied eye tracking, stimulated recall interviews, and direct observation to investigate important aspects of gaze behavior in a faceted search interface: what components of the interface searchers looked at, for how long, and in what order. It yielded empirical data that will be useful for both practitioners (e.g., for improving search interface designs), and researchers (e.g., to inform models of search behavior). Results of the study show that participants spent about 50 seconds per task looking at (fixating on) the results, about 25 seconds looking at the facets, and only about 6 seconds looking at the query itself. These findings suggest that facets played an important role in the exploratory search process.

Question: What do exploratory searchers look at in a faceted search interface?


Uptane: Security and Customizability of Software Updates for Vehicles. A widely accepted premise is that complex software frequently contains bugs that can be remotely exploited by attackers. When this software is on an electronic control unit (ECU) in a vehicle, exploitation of these bugs can have life or death consequences. Since software for vehicles is likely to proliferate and grow more complex in time, the number of exploitable vulnerabilities will increase. As a result, manufacturers are keenly aware of the need to quickly and efficiently deploy updates so that software vulnerabilities can be remedied as soon as possible.

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


The neurophysiology of unmyelinated tactile afferents. CT (C tactile) afferents are a distinct type of unmyelinated, low-threshold mechanoreceptive units existing in the hairy but not glabrous skin of humans and other mammals. Evidence from patients lacking myelinated tactile afferents indicates that signaling in these fibers activate the insular cortex. Since this system is poor in encoding discriminative aspects of touch, but well-suited to encoding slow, gentle touch, CT fibers in hairy skin may be part of a system for processing pleasant and socially relevant aspects of touch. CT fiber activation may also have a role in pain inhibition. This review outlines the growing evidence for unique properties and pathways of CT afferents.

Question: What is the role of CT fibers in pain inhibition?


An Investigation into Neural Net Optimization via Hessian Eigenvalue Density. To understand the dynamics of optimization in deep neural networks, we develop a tool to study the evolution of the entire Hessian spectrum throughout the optimization process. Using this, we study a number of hypotheses concerning smoothness, curvature, and sharpness in the deep learning literature. We then thoroughly analyze a crucial structural feature of the spectra: in non-batch normalized networks, we observe the rapid appearance of large isolated eigenvalues in the spectrum, along with a surprising concentration of the gradient in the corresponding eigenspaces. In batch normalized networks, these two effects are almost absent. We characterize these effects, and explain how they affect optimization speed through both theory and experiments. As part of this work, we adapt advanced tools from numerical linear algebra that allow scalable and accurate estimation of the entire Hessian spectrum of ImageNet-scale neural networks; this technique may be of independent interest in other applications.

Question: What is the name of the tool?


Existential consistency: measuring and understanding consistency at Facebook. Replicated storage for large Web services faces a trade-off between stronger forms of consistency and higher performance properties. Stronger consistency prevents anomalies, i.e., unexpected behavior visible to users, and reduces programming complexity. There is much recent work on improving the performance properties of systems with stronger consistency, yet the flip-side of this trade-off remains elusively hard to quantify. To the best of our knowledge, no prior work does so for a large, production Web service.  We use measurement and analysis of requests to Facebook's TAO system to quantify how often anomalies happen in practice, i.e., when results returned by eventually consistent TAO differ from what is allowed by stronger consistency models. For instance, our analysis shows that 0.0004% of reads to vertices would return different results in a linearizable system. This in turn gives insight into the benefits of stronger consistency; 0.0004% of reads are potential anomalies that a linearizable system would prevent. We directly study local consistency models---i.e., those we can analyze using requests to a sample of objects---and use the relationships between models to infer bounds on the others.  We also describe a practical consistency monitoring system that tracks &phis;-consistency, a new consistency metric ideally suited for health monitoring. In addition, we give insight into the increased programming complexity of weaker consistency by discussing bugs our monitoring uncovered, and anti-patterns we teach developers to avoid.

Question: What is the difference between linearizability and eventual consistency?


Relevance-Based Language Models. We explore the relation between classical probabilistic models of information retrieval and the emerging language modeling approaches. It has long been recognized that the primary obstacle to effective performance of classical models is the need to estimate a relevance model: probabilities of words in the relevant class. We propose a novel technique for estimating these probabilities using the query alone. We demonstrate that our technique can produce highly accurate relevance models, addressing important notions of synonymy and polysemy. Our experiments show relevance models outperforming baseline language modeling systems on TREC retrieval and TDT tracking tasks. The main contribution of this work is an effective formal method for estimating a relevance model with no training data.

Question: What is the relation between classical probabilistic models of information retrieval and the emerging language modeling approaches?


CRISPR interference (CRISPRi) for sequence-specific control of gene expression. Sequence-specific control of gene expression on a genome-wide scale is an important approach for understanding gene functions and for engineering genetic regulatory systems. We have recently described an RNA-based method, CRISPR interference (CRISPRi), for targeted silencing of transcription in bacteria and human cells. The CRISPRi system is derived from the Streptococcus pyogenes CRISPR (clustered regularly interspaced palindromic repeats) pathway, requiring only the coexpression of a catalytically inactive Cas9 protein and a customizable single guide RNA (sgRNA). The Cas9-sgRNA complex binds to DNA elements complementary to the sgRNA and causes a steric block that halts transcript elongation by RNA polymerase, resulting in the repression of the target gene. Here we provide a protocol for the design, construction and expression of customized sgRNAs for transcriptional repression of any gene of interest. We also provide details for testing the repression activity of CRISPRi using quantitative fluorescence assays and native elongating transcript sequencing. CRISPRi provides a simplified approach for rapid gene repression within 1–2 weeks. The method can also be adapted for high-throughput interrogation of genome-wide gene functions and genetic interactions, thus providing a complementary approach to RNA interference, which can be used in a wider variety of organisms.

Question: How to design a sgRNA for transcriptional repression?


Fast Priority Queues for Cached Memory. The cache hierarchy prevalent in todays high performance processors has to be taken into account in order to design algorithms that perform well in practice. This paper advocates the adaption of external memory algorithms to this purpose. This idea and the practical issues involved are exemplified by engineering a fast priority queue suited to external memory and cached memory that is based on <i>k</i>-way merging. It improves previous external memory algorithms by constant factors crucial for transferring it to cached memory. Running in the cache hierarchy of a workstation the algorithm is at least two times faster than an optimized implementation of binary heaps and 4-ary heaps for large inputs.

Question: What is the cache hierarchy?


A CSRR Loaded MIMO Antenna System for ISM Band Operation. A 2 × 2 (four-element) multiple-input multiple-output (MIMO) patch antenna system is designed and fabricated for a 2.45-GHz ISM band operation. It uses complementary split-ring resonator (CSRR) loading on its ground plane for antenna miniaturization. This reduces the single-element antenna size by 76%. The total board size of the proposed MIMO antenna system, including the GND plane is 100 × 50 × 0.8 mm3, while the single-patch antenna element has a size of 14 × 18 mm2. The antenna is fabricated and tested. Measured results are in good agreement with simulations. A minimum measured isolation of 10 dB is obtained given the close interelement spacing of 0.17λ. The maximum measured gain for a single operating element is -0.8 dBi.

Question: What is the size of the antenna?


Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences. We present a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences. We solicit and verify questions and answers for this challenge through a 4-step crowdsourcing experiment. Our challenge dataset contains ∼6k questions for +800 paragraphs across 7 different domains (elementary school science, news, travel guides, fiction stories, etc) bringing in linguistic diversity to the texts and to the questions wordings. On a subset of our dataset, we found human solvers to achieve an F1-score of 86.4%. We analyze a range of baselines, including a recent state-of-art reading comprehension system, and demonstrate the difficulty of this challenge, despite a high human performance. The dataset is the first to study multi-sentence inference at scale, with an open-ended set of question types that requires reasoning skills.

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


Survey of STT-MRAM Cell Design Strategies: Taxonomy and Sense Amplifier Tradeoffs for Resiliency. Spin-Transfer Torque Random Access Memory (STT-MRAM) has been explored as a post-CMOS technology for embedded and data storage applications seeking non-volatility, near-zero standby energy, and high density. Towards attaining these objectives for practical implementations, various techniques to mitigate the specific reliability challenges associated with STT-MRAM elements are surveyed, classified, and assessed in this article. Cost and suitability metrics assessed include the area of nanomagmetic and CMOS components per bit, access time and complexity, sense margin, and energy or power consumption costs versus resiliency benefits. Solutions to the reliability issues identified are addressed within a taxonomy created to categorize the current and future approaches to reliable STT-MRAM designs. A variety of destructive and non-destructive sensing schemes are assessed for process variation tolerance, read disturbance reduction, sense margin, and write polarization asymmetry compensation. The highest resiliency strategies deliver a sensing margin above 300mV while incurring low power and energy consumption on the order of picojoules and microwatts, respectively, and attaining read sense latency of a few nanoseconds down to hundreds of picoseconds for non-destructive and destructive sensing schemes, respectively.

Question: What is the difference between a non-destructive and a destructive sensing scheme?


Concept Detection on Medical Images using Deep Residual Learning Network. Medical images are often used in clinical diagnosis. However, interpreting the insights gained from them is often a time-consuming task even for experts. For this reason, there is a need for methods that can automatically approximate the mapping from medical images to condensed textual descriptions. For identifying the presence of relevant biomedical concepts in medical images for the ImageCLEF 2017 Caption concept detection subtask we propose the use of a pretrained residual deep neural network. Specifically, a 50-layered resNet was used and retrained on the medical images. The proposed method achieved F1 Score 0.1583 on the test data.

Question: What is the ImageCLEF 2017 Caption concept detection subtask?


IoT-based occupancy monitoring techniques for energy-efficient smart buildings. With the proliferation of Internet of Things (IoT) devices such as smartphones, sensors, cameras, and RFIDs, it is possible to collect massive amount of data for localization and tracking of people within commercial buildings. Enabled by such occupancy monitoring capabilities, there are extensive opportunities for improving the energy consumption of buildings via smart HVAC control. In this respect, the major challenges we envision are 1) to achieve occupancy monitoring in a minimally intrusive way, e.g., using the existing infrastructure in the buildings and not requiring installation of any apps in the users' smart devices, and 2) to develop effective data fusion techniques for improving occupancy monitoring accuracy using a multitude of sources. This paper surveys the existing works on occupancy monitoring and multi-modal data fusion techniques for smart commercial buildings. The goal is to lay down a framework for future research to exploit the spatio-temporal data obtained from one or more of various IoT devices such as temperature sensors, surveillance cameras, and RFID tags that may be already in use in the buildings. A comparative analysis of existing approaches and future predictions for research challenges are also provided.

Question: What is the future of IoT in smart buildings?


Female Pattern Hair Loss: a clinical and pathophysiological review*. Female Pattern Hair Loss or female androgenetic alopecia is the main cause of hair loss in adult women and has a major impact on patients' quality of life. It evolves from the progressive miniaturization of follicles that lead to a subsequent decrease of the hair density, leading to a non-scarring diffuse alopecia, with characteristic clinical, dermoscopic and histological patterns. In spite of the high frequency of the disease and the relevance of its psychological impact, its pathogenesis is not yet fully understood, being influenced by genetic, hormonal and environmental factors. In addition, response to treatment is variable. In this article, authors discuss the main clinical, epidemiological and pathophysiological aspects of female pattern hair loss.

Question: What is the main cause of hair loss in adult women?


Smalltalk-80: The Language and Its Implementation. Covers Smalltalk language and concepts from Xerox PARC. This need to a valid in with hexadecimal number before. Dwight thanks adele goldberg david robson copyright addison wesley isbn edition used was at compile. Smalltalk 80 syntax uses punctuation in, the language they can be significantly more popular. There is not this may be written as artifacts such messages. By storing them are parsed according to evaluate? The boolean receiver as lisp often even the virtual machine. One left smalltalk 80 is, the modern open. The increasing usage visualworks has recently been used was an image. Alan kay one thing developers to a transcript window would. Even primitives such interleaving of the, language and dec objectstudio each restart. Enfin was an object holds is, extremely important. Also be recreated explicitly define the, book presents a copy of the agile methods messages.

Question: What is the name of the book?


Comparing Children's Crosshair and Finger Interactions in Handheld Augmented Reality: Relationships Between Usability and Child Development. Augmented reality technology is a unique medium that can be helpful for young children's entertainment and education, but in order to achieve the benefits of this technology, augmented reality experiences need to be appropriately designed for young children's developing physical and cognitive skills. In the present study we investigated how 5-10 year-old children react to typical handheld augmented reality interaction techniques such as crosshair selection and finger selection, in AR environments that require them to change perspective or not. Our analysis shows significant impacts of age upon AR performance, with young children having slower selection times, more tracking losses, and taking longer to recover tracking. Significant differences were also found between AR interaction technique conditions, with finger selection being faster than crosshair selection, and interactions which required changes in perspective taking longer, generating more tracking losses, and more errors in selection. Furthermore, by analyzing children's performance in relation to metrics of physical and cognitive development, we identified correlations between AR interaction techniques performance and developmental tests of spatial relations, block construction and visuomotor precision. Gender differences were analyzed but no significant effects were detected.

Question: How do children react to augmented reality?


Testing and evaluation of a wearable augmented reality system for natural outdoor environments. This paper describes performance evaluation of a wearable augmented reality system for natural outdoor environments. Applied Research Associates (ARA), as prime integrator on the DARPA ULTRA-Vis (Urban Leader Tactical, Response, Awareness, and Visualization) program, is developing a soldier-worn system to provide intuitive 'heads-up' visualization of tactically-relevant geo-registered icons. Our system combines a novel pose estimation capability, a helmet-mounted see-through display, and a wearable processing unit to accurately overlay geo-registered iconography (e.g., navigation waypoints, sensor points of interest, blue forces, aircraft) on the soldier's view of reality. We achieve accurate pose estimation through fusion of inertial, magnetic, GPS, terrain data, and computer-vision inputs. We leverage a helmet-mounted camera and custom computer vision algorithms to provide terrain-based measurements of absolute orientation (i.e., orientation of the helmet with respect to the earth). These orientation measurements, which leverage mountainous terrain horizon geometry and mission planning landmarks, enable our system to operate robustly in the presence of external and body-worn magnetic disturbances. Current field testing activities across a variety of mountainous environments indicate that we can achieve high icon geo-registration accuracy (<10mrad) using these vision-based methods.

Question: How does the system work?


High Performance Computing for Hyperspectral Remote Sensing. Advances in sensor and computer technology are revolutionizing the way remotely sensed data is collected, managed and analyzed. In particular, many current and future applications of remote sensing in Earth science, space science, and soon in exploration science will require real- or near real-time processing capabilities. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) models to remote sensing missions. A relevant example of a remote sensing application in which the use of HPC technologies (such as parallel and distributed computing) is becoming essential is hyperspectral remote sensing, in which an imaging spectrometer collects hundreds or even thousands of measurements (at multiple wavelength channels) for the same area on the surface of the Earth. In this paper, we review recent developments in the application of HPC techniques to hyperspectral imaging problems, with particular emphasis on commodity architectures such as clusters, heterogeneous networks of computers, and specialized hardware devices such as field programmable gate arrays (FPGAs) and commodity graphic processing units (GPUs). A quantitative comparison across these architectures is given by analyzing performance results of different parallel implementations of the same hyperspectral unmixing chain, delivering a snapshot of the state-of-the-art in this area and a thoughtful perspective on the potential and emerging challenges of applying HPC paradigms to hyperspectral remote sensing problems.

Question: What is the state of the art in hyperspectral remote sensing?


The Adaptive Lasso and Its Oracle Properties. The lasso is a popular technique for simultaneous estimation and variable selection. Lasso variable selection has been shown to be consistent under certain conditions. In this work we derive a necessary condition for the lasso variable selection to be consistent. Consequently, there exist certain scenarios where the lasso is inconsistent for variable selection. We then propose a new version of the lasso, called the adaptive lasso, where adaptive weights are used for penalizing different coefficients in the 1 penalty. We show that the adaptive lasso enjoys the oracle properties; namely, it performs as well as if the true underlying model were given in advance. Similar to the lasso, the adaptive lasso is shown to be near-minimax optimal. Furthermore, the adaptive lasso can be solved by the same efficient algorithm for solving the lasso. We also discuss the extension of the adaptive lasso in generalized linear models and show that the oracle properties still hold under mild regularity conditions. As a byproduct of our theory, the nonnegative garotte is shown to be consistent for variable selection.

Question: What is the adaptive lasso?


Design Considerations of Charge Pump for Antenna Switch Controller With SOI CMOS Technology. An enhanced charge pump for the antenna switch controller using the silicon-on-insulator (SOI) CMOS technology is presented in this brief. The pseudo cross-coupled technique is proposed to reduce parasitic capacitances at charging/discharging nodes through charge transferring paths, which improves the current drive capability and provides better accuracy of the output voltage. Furthermore, the codesign between the gate control voltages of power MOS transistors and the clock drive signals of pumping capacitors has been investigated to eliminate the reversion loss and reduce the ripple voltage. The pseudo cross-coupled charge pump has been fabricated in the 0.18-  $\mu\text{m}$ SOI CMOS technology with an area of 0.065 mm2. According to the comparison results of the conventional and enhanced charge pumps, the start-up time and the recovery time are typically shortened by 71.4% and 21.7%, owing to the improvement of the current drive capability, and the ripple voltage at no-load condition is greatly reduced by 46.1%.

Question: How to design a charge pump for an antenna switch controller?


Enhancing well-being and alleviating depressive symptoms with positive psychology interventions: a practice-friendly meta-analysis.. Do positive psychology interventions-that is, treatment methods or intentional activities aimed at cultivating positive feelings, positive behaviors, or positive cognitions-enhance well-being and ameliorate depressive symptoms? A meta-analysis of 51 such interventions with 4,266 individuals was conducted to address this question and to provide practical guidance to clinicians. The results revealed that positive psychology interventions do indeed significantly enhance well-being (mean r=.29) and decrease depressive symptoms (mean r=.31). In addition, several factors were found to impact the effectiveness of positive psychology interventions, including the depression status, self-selection, and age of participants, as well as the format and duration of the interventions. Accordingly, clinicians should be encouraged to incorporate positive psychology techniques into their clinical work, particularly for treating clients who are depressed, relatively older, or highly motivated to improve. Our findings also suggest that clinicians would do well to deliver positive psychology interventions as individual (versus group) therapy and for relatively longer periods of time.

Question: What is the effect of positive psychology interventions on well-being and depressive symptoms?


MOA: Massive Online Analysis. MassiveOnline Analysis (MOA) is a software environment for implementing al orithms and running experiments for online learning from evolving data str eams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Na ı̈ve Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license.

Question: What is MOA?


Mediated parent-child contact in work-separated families. Parents and children in families living with regular separation due to work develop strategies to manage being apart. We interviewed 14 pairs of parents and children (ages 7 -- 13) from work-separated families to understand their experiences and the strategies that they use to keep their family together. Parents focus on combining scheduled synchronous and spontaneous asynchronous communication to maintain a constant presence in the life of the child. Children, on the other hand, focus on other sources of support, on other activities, and on the eventual reunion. Both the remote parent and the child rely heavily on a collocated adult to maintain aware-ness and contact. We compare work-separated families with other types of separation and highlight opportunities for new designs.

Question: How do work-separated families differ from other types of separation?


SYSTEM DYNAMICS : SYSTEMIC FEEDBACK MODELING FOR POLICY ANALYSIS.

Question: What is a systemic feedback model?


An ultra-low power capacitor-less LDO for always-on domain in NB-IoT applications. This paper presents an ultra-low power 55 nm CMOS capacitor-less Low-Dropout (LDO) voltage regulator for power management of an Always-On domain module for implementation of NB-IoT SoC applications. Compared with traditional IoT SoC, the power consumption specification of NB-IoT SoC is lower. One effective way to achieve low power application is to reduce the power of the Always-On domain module in the SoC, where the LDO plays a critical role. According to the tape-out, results are validated with a 0.89V output voltage from 1.8V to 3.3V supply voltage, delivering a load current of 100uA. The peak-peak variance of output voltage is less than 100mV from 1.8V to 3.3V supply voltage. The quiescent current is lower than 61.86nA, including a 2-Transistor reference voltage, which can implement NB-IoT SoC application, with yield as high as 87.88%.

Question: What is the always-on domain?


The Practical Use of BI Visualizations in Decision-making. Type of thesis: Master Degree Project in Accounting, 30 credits University: University of Gothenburg, School of Business, Economics and Law Semester: Spring 2017 Author: Kevin Larsson & Philip Kaber Supervisor: Elisabeth Frisk Title: The Practical Use of BI Visualizations in Decision-making Background and problem: Due to the constantly altering business environment, firms are trying to find ways of combining information technology (IT) and decision-making in order to grasp information of the complex and dynamic business environment. As a solution to this strenuousness; business intelligence (BI) systems serve to conduct more efficient and reliable decisions by the means of visualizations. One existing gap in the research of BI concerns the sociotechnical effects involved in the decision-making, in which knowledge of how BI visualizations are used and may be used in practice to support the decision-making have remained limited. Research aim: The research aim of the study is to investigate how BI visualizations are and may be used to support strategic and operational decisions. Further, the study addresses how BI visualizations are used in various decision contexts. Research questions: The following questions are addressed in order to achieve the research aim: (1) How are and may BI visualizations be used in strategic and operational decisions? (1a) Which visual elements are used depending on the task and decision category? (2) How are BI visualizations used in different decision contexts? Research design: A qualitative study was made through semi-structured interviews. In-depth interviews with six people from the purchasing company were conducted in order to understand how BI and visualizations are and may be used. Two people from the consultancy company were interviewed in order to understand how BI and visualizations may be used. Discussion and conclusion: The primary purpose of BI systems in the purchasing

Question: How are BI visualizations used in strategic and operational decisions?


An Overview of Smart Home Environments: Architectures, Technologies and Applications. The aim of this research survey note is to provide a comprehensive overview of Smart Home Environments with a focus on their architectures and application areas, as well as utilized technologies, infrastructures, and standards. The main source of information was provided by past and existing research and development projects carried out in this area, while the main result of our survey is a classification of Smart Home Environments, as revealed by this projects' survey.

Question: What is a smart home environment?


A Feature-Based, Robust, Hierarchical Algorithm for Registering Pairs of Images of the Curved Human Retina. ÐThis paper describes a robust hierarchical algorithm for fully-automatic registration of a pair of images of the curved human retina photographed by a fundus microscope. Accurate registration is essential for mosaic synthesis, change detection, and design of computer-aided instrumentation. Central to the new algorithm is a 12-parameter interimage transformation derived by modeling the retina as a rigid quadratic surface with unknown parameters, imaged by an uncalibrated weak perspective camera. The parameters of this model are estimated by matching vascular landmarks extracted by an algorithm that recursively traces the blood vessel structure. The parameter estimation technique, which could be generalized to other applications, is a hierarchy of models and methods: an initial match set is pruned based on a zeroth order transformation estimated as the peak of a similarity-weighted histogram; a first order, affine transformation is estimated using the reduced match set and least-median of squares; and the final, second order, 12-parameter transformation is estimated using an M-estimator initialized from the first order estimate. This hierarchy makes the algorithm robust to unmatchable image features and mismatches between features caused by large interframe motions. Before final convergence of the M-estimator, feature positions are refined and the correspondence set is enhanced using normalized sum-of-squared differences matching of regions deformed by the emerging transformation. Experiments involving 3,000 image pairs (I; HPR Â I; HPR pixels) from 16 different healthy eyes were performed. Starting with as low as 20 percent overlap between images, the algorithm improves its success rate exponentially and has a negligible failure rate above 67 percent overlap. The experiments also quantify the reduction in errors as the model complexities increase. Final regist

Question: How large is the canadian military?


Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches. The naïve Bayes classifier is built on the assumpti on of conditional independence between the attributes given the class. The algorithm has been shown to be surprisingly robust to obvious violations of this condition, but it is natural to ask if it is possib le to further improve the accuracy by relaxing this assumption. We examine an approach where naïve Bayes is augmented by the addition of correlation a rcs between attributes. We explore two methods for finding the set of augmenting arcs, a greedy hillclimbing search, and a novel, more computationally efficient algorithm that we call SuperParent. We compare these methods to TAN; a state-of the-art distribution-based approach to finding the augmenti ng arcs.

Question: What is the difference between a greedy hillclimbing search and a novel, more computationally efficient algorithm that we call SuperParent?


The Anatomy of Motivation: An Evolutionary-Ecological Approach. There have been few attempts to bring evolutionary theory to the study of human motivation. From this perspective motives can be considered psychological mechanisms to produce behavior that solves evolutionarily important tasks in the human niche. From the dimensions of the human niche we deduce eight human needs: optimize the number and survival of gene copies; maintain bodily integrity; avoid external threats; optimize sexual, environmental, and social capital; and acquire reproductive and survival skills. These needs then serve as the foundation for a necessary and sufficient list of 15 human motives, which we label: lust, hunger, comfort, fear, disgust, attract, love, nurture, create, hoard, affiliate, status, justice, curiosity, and play. We show that these motives are consistent with evidence from the current literature. This approach provides us with a precise vocabulary for talking about motivation, the lack of which has hampered progress in behavioral science. Developing testable theories about the structure and function of motives is essential to the project of understanding the organization of animal cognition and learning, as well as for the applied behavioral sciences.

Question: What is the human niche?


Diverse and Controllable Image Captioning with Part-of-Speech Guidance. Automatically describing an image is an important capability for virtual assistants. Significant progress has been achieved in recent years on this task of image captioning. However, classical prediction techniques based on maximum likelihood trained LSTM nets don't embrace the inherent ambiguity of image captioning. To address this concern, recent variational auto-encoder and generative adversarial network based methods produce a set of captions by sampling from an abstract latent space. But, this latent space has limited interpretability and therefore, a control mechanism for captioning remains an open problem. This paper proposes a captioning technique conditioned on part-of-speech. Our method provides human interpretable control in form of part-of-speech. Importantly, part-of-speech is a language prior, and conditioning on it provides: (i) more diversity as evaluated by counting n-grams and the novel sentences generated, (ii) achieves high accuracy for the diverse captions on standard captioning metrics.

Question: How to generate diverse captions?


Evaluation of Business Processes for Business Process Standardization. Companies often implement multiple process variants in their organizations at the same time. Often these variants differ widely in terms of efficiency, quality and cycle time. In times of highly volatile global economic markets, companies cannot afford unnecessary redundant business processes. Business Process Standardization has therefore become a major topic in the field of Business Process Management, both in research and practice. Management decisions concerning standardization are difficult and complex, due to limited project budgets, organizational and technological changes as well as the increasing challenges of globalization. Choosing suitable processes for standardization is the essential precondition for streamlining business processes in the first place and therefore for reaping the full benefits of Business Process Standardization. However, there is hardly any tool available that supports the evaluation of business processes for standardization. To close this research gap, we develop an instrument for the evaluation of business processes in the context of Business Process Standardization with the aim to help practitioners in their day-to-day operations. Our research aims at providing the necessary preconditions for companies to profit from the advantages of business process standardization and to support decision-making.

Question: How to evaluate business processes for standardization?


Integrated bioethanol and protein production from brown seaweed Laminaria digitata.. A wild-growing glucose-rich (i.e. 56.7% glucose content) brown seaweed species Laminaria digitata, collected from the North Coast of Denmark in August 2012, was used as the feedstock for an integrated bioethanol and protein production. Glutamic acid and aspartic acid are the two most abundant amino acids in the algal protein, both with proportional content of 10% in crude protein. Only minor pretreatment of milling was used on the biomass to facilitate the subsequent enzymatic hydrolysis and fermentation. The Separate Hydrolysis and Fermentation (SHF) resulted in obviously higher ethanol yield than the Simultaneous Saccharification and Fermentation (SSF). High conversion rate at maximum of 84.1% glucose recovery by enzymatic hydrolysis and overall ethanol yield at maximum of 77.7% theoretical were achieved. Protein content in the solid residues after fermentation was enriched by 2.7 fold, with similar distributions of amino acids, due to the hydrolysis of polymers in the seaweed cell wall matrix.

Question: What is the ethanol yield of the integrated bioethanol and protein production from brown seaweed Laminaria digitata?


Type shifting in construction grammar : An integrated approach to aspectual coercion *. Implicit type shifting, or coercion, appears to indicate a modular grammatical architecture, in which the process of semantic composition may add meanings absent from the syntax in order to ensure that certain operators, e.g., the progressive, receive suitable arguments (Jackendo¤ 1997; De Swart 1998). I will argue that coercion phenomena actually provide strong support for a sign-based model of grammar, in which rules of morphosyntactic combination can shift the designations of content words with which they combine. On this account, enriched composition is a by-product of the ordinary referring behavior of constructions. Thus, for example, the constraint which requires semantic concord between the syntactic sisters in the string a bottle is also what underlies the coerced interpretation found in a beer. If this concord constraint is stated for a rule of morphosyntactic combination, we capture an important generalization: a single combinatory mechanism, the construction, is responsible for both coerced and compositional meanings. Since both type-selecting constructions (e.g., the French Imparfait) and type-shifting constructions (e.g., English progressive aspect) require semantic concord between syntactic sisters, we account for the fact that constructions of both types perform coercion. Coercion data suggest that aspectual sensitivity is not merely a property of formally di¤erentiated past tenses, as in French and Latin, but a general property of tense constructions, including the English present and past tenses.

Question: What is the difference between a type-selecting construction and a type-shifting construction?


Combining geometry and combinatorics: A unified approach to sparse signal recovery. There are two main algorithmic approaches to sparse signal recovery: geometric and combinatorial. The geometric approach utilizes geometric properties of the measurement matrix Phi. A notable example is the Restricted Isometry Property, which states that the mapping Phi preserves the Euclidean norm of sparse signals; it is known that random dense matrices satisfy this constraint with high probability. On the other hand, the combinatorial approach utilizes sparse matrices, interpreted as adjacency matrices of sparse (possibly random) graphs, and uses combinatorial techniques to recover an approximation to the signal. In this paper we present a unification of these two approaches. To this end, we extend the notion of Restricted Isometry Property from the Euclidean lscr2 norm to the Manhattan lscr1 norm. Then we show that this new lscr1 -based property is essentially equivalent to the combinatorial notion of expansion of the sparse graph underlying the measurement matrix. At the same time we show that the new property suffices to guarantee correctness of both geometric and combinatorial recovery algorithms. As a result, we obtain new measurement matrix constructions and algorithms for signal recovery which, compared to previous algorithms, are superior in either the number of measurements or computational efficiency of decoders.

Question: What is the unified approach to sparse signal recovery?


Kinematic and dynamic vehicle models for autonomous driving control design. We study the use of kinematic and dynamic vehicle models for model-based control design used in autonomous driving. In particular, we analyze the statistics of the forecast error of these two models by using experimental data. In addition, we study the effect of discretization on forecast error. We use the results of the first part to motivate the design of a controller for an autonomous vehicle using model predictive control (MPC) and a simple kinematic bicycle model. The proposed approach is less computationally expensive than existing methods which use vehicle tire models. Moreover it can be implemented at low vehicle speeds where tire models become singular. Experimental results show the effectiveness of the proposed approach at various speeds on windy roads.

Question: What is the difference between kinematic and dynamic vehicle models?


The Multi-User Security of Authenticated Encryption: AES-GCM in TLS 1.3. We initiate the study of multi-user (mu) security of authenticated encryption (AE) schemes as a way to rigorously formulate, and answer, questions about the "randomized nonce" mechanism proposed for the use of the AE scheme GCM in TLS 1.3. We (1) Give definitions of mu ind (indistinguishability) and mu kr (key recovery) security for AE (2) Characterize the intent of nonce randomization as being improved mu security as a defense against mass surveillance (3) Cast the method as a (new) AE scheme RGCM (4) Analyze and compare the mu security of both GCM and RGCM in the model where the underlying block cipher is ideal, showing that the mu security of the latter is indeed superior in many practical contexts to that of the former, and (5) Propose an alternative AE scheme XGCM having the same efficiency as RGCM but better mu security and a more simple and modular design.

Question: What is the difference between the AE scheme GCM and the AE scheme RGCM?


Does Microfinance Really Help the Poor ? New Evidence from Flagship Programs in Bangladesh. The microfinance movement has built on innovations in financial intermediation that reduce the costs and risks of lending to poor households. Replications of the movement's flagship, the Grameen Bank of Bangladesh, have now spread around the world. While programs aim to bring social and economic benefits to clients, few attempts have been made to quantify benefits rigorously. This paper draws on a new cross-sectional survey of nearly 1800 households, some of which are served by the Grameen Bank and two similar programs, and some of which have no access to programs. Households that are eligible to borrow and have access to the programs do not have notably higher consumption levels than control households, and, for the most part, their children are no more likely to be in school. Men also tend to work harder, and women less. More favorably, relative to controls, households eligible for programs have substantially (and significantly) lower variation in consumption and labor supply across seasons. The most important potential impacts are thus associated with the reduction of vulnerability, not of poverty per se. The consumption-smoothing appears to be driven largely by income-smoothing, not by borrowing and lending. The evaluation holds lessons for studies of other programs in low-income countries. While it is common to use fixed effects estimators to control for unobservable variables correlated with the placement of programs, using fixed effects estimators can exacerbate biases when, as here, programs target their programs to specific populations within larger communities.

Question: Does microfinance really help the poor?


Supporting Private Data on Hyperledger Fabric with Secure Multiparty Computation. Hyperledger Fabric is a "permissioned" blockchain architecture, providing a consistent distributed ledger, shared by a set of "peers." As with every blockchain architecture, the core principle of Hyperledger Fabric is that all the peers must have the same view of the shared ledger, making it challenging to support private data for the different peers. Extending Hyperledger Fabric to support private data (that can influence transactions) would open the door to many exciting new applications, in areas from healthcare to commerce, insurance, finance, and more. In this work we explored adding private-data support to Hyperledger Fabric using secure multiparty computation (MPC). Specifically, in our solution the peers store on the chain encryption of their private data, and use secure MPC whenever such private data is needed in a transaction. This solution is very general, allowing in principle to base transactions on any combination of public and private data. We created a demo of our solution over Hyperledger Fabric v1.0, implementing a bidding system where sellers can list assets on the ledger with a secret reserve price, and bidders publish their bids on the ledger but keep secret the bidding price itself. We implemented a smart contract (aka "chaincode") that runs the auction on this secret data, using a simple secure-MPC protocol that was built using the EMP-toolkit library. The chaincode itself was written in Go, and we used the SWIG library to make it possible to call our protocol implementation in C++. We identified two basic services that should be added to Hyperledger Fabric to support our solution, and are now working on implementing them.

Question: What is secure multiparty computation?


Universal Language Model Fine-tuning for Text Classification. Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 1824% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100× more data. We opensource our pretrained models and code1.

Question: What is the state-of-the-art in text classification?


Semi-Supervised Text Classification Using EM. For several decades, statisticians have advocated using a c ombination of labeled and unlabeled data to train classifiers by estimating parameters o f a generative model through iterative Expectation-Maximization (EM) techniques. Thi s chapter explores the effectiveness of this approach when applied to the domain of text class ification. Text documents are represented here with a bag-of-words model, which leads to a generative classification model based on a mixture of multinomials. This model is an ext remely simplistic representation of the complexities of written text. This chapter explains and illustrates three key points about semi-supervised learning for text classifi cat on with generative models. First, despite the simplistic representation, some text do mains have a high positive correlation between generative model probability and classifica tion accuracy. In these domains, a straightforward application of EM with the naive Bayes tex t model works well. Second, some text domains do not have this correlation. Here we can ad opt a more expressive and appropriate generative model that does have a positive c orrelation. In these domains, semi-supervised learning again improves classification ac curacy. Finally, EM suffers from the problem of local maxima, especially in high dimension do mains such as text classification. We demonstrate that deterministic annealing, a varia nt of EM, can help overcome the problem of local maxima and increase classification accurac y further when the generative model is appropriate.

Question: What is the difference between supervised and semi-supervised learning?


Autonomous Intersection Management: A Heuristic Approach. Self-driving cars do not sound like a part of science fiction movies anymore. With most of the automobile giants committed to launching their autonomous cars as soon as possible, it is expected that self-driving cars will hit the road in a very short time. Increasing the penetration factor of autonomous vehicles will need algorithms to control them in an efficient way in different scenarios. One such scenario is intersection management (IM). We propose an intuitive heuristic to resolve space–time conflicts in vehicle's trip, enabling vehicles to cross the intersection safely, and with minimum delay. The intersection is modeled as a group of conflict points where intersecting internal links cross each other. For safe and efficient scheduling of vehicles, heuristics are proposed separately for: 1) entering in approach lane; 2) safe traversal in the approach lane; 3) safe and efficient crossing of intersection; and (4) safe departure along depart lane. Vehicle scheduling is done using a three-leveled heuristic in which each level serves a distinct purpose and together guarantee collision-free passing of vehicles. Performance evaluation of the proposed scheme is done in Simulation of Urban MObility simulator and a comparative analysis is done with three other IM schemes. Experiments show that the proposed scheme gives a smaller average trip delay of vehicles compared with other IM schemes.

Question: How to model an intersection?


Bayesian Classification (AutoClass): Theory and Results. We describe AutoClass, an approach to unsupervised classiication based upon the classical mixture model, supplemented by a Bayesian method for determining the optimal classes. We include a moderately detailed exposition of the mathematics behind the AutoClass system. We emphasize that no current unsupervised classiication system can produce maximally useful results when operated alone. It is the interaction between domain experts and the machine searching over the model space, that generates new knowledge. Both bring unique information and abilities to the database analysis task, and each enhances the others' eeectiveness. We illustrate this point with several applications of AutoClass to complex real world databases, and describe the resulting successes and failures. 6.1 Introduction This chapter is a summary of our experience in using an automatic classiication program (AutoClass) to extract useful information from databases. It also gives an outline of the principles that underlie automatic classiication in general, and AutoClass in particular. We are concerned with the problem of automatic discovery of classes in data (sometimes called clustering, or unsupervised learning), rather than the generation of class descriptions from labeled examples (called supervised learning). In some sense, automatic classiication aims at discovering the \natural" classes in the data. These classes reeect basic causal mechanisms that makes some cases look more like each other than the rest of the cases. The causal mechanisms may be as boring as sample biases in the data, or could reeect some major new discovery in the domain. Sometimes, these classes were well known to experts in the eld, but unknown to AutoClass, and other times

Question: What is the difference between unsupervised and supervised learning?


A Survey of Point-of-interest Recommendation in Location-based Social Networks. Point-of-interest (POI) recommendation that suggests new places for users to visit arises with the popularity of location-based social networks (LBSNs). Due to the importance of POI recommendation in LBSNs, it has attracted much academic and industrial interest. In this paper, we offer a systematic review of this field, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges in POI recommendation, compared with traditional recommendation problems, e.g., movie recommendation. Then, we present a comprehensive review in three aspects: influential factors for POI recommendation, methodologies employed for POI recommendation, and different tasks in POI recommendation. Specifically, we propose three taxonomies to classify POI recommendation systems. First, we categorize the systems by the influential factors check-in characteristics, including the geographical information, social relationship, temporal influence, and content indications. Second, we categorize the systems by the methodology, including systems modeled by fused methods and joint methods. Third, we categorize the systems as general POI recommendation and successive POI recommendation by subtle differences in the recommendation task whether to be bias to the recent check-in. For each category, we summarize the contributions and system features, and highlight the representative work. Moreover, we discuss the available data sets and the popular metrics. Finally, we point out the possible future directions in this area and conclude this survey.

Question: What is a systematic review?


Guidelines for conducting and reporting case study research in software engineering. Case study is a suitable research methodology for software engineering research since it studies contemporary phenomena in its natural context. However, the understanding of what constitutes a case study varies, and hence the quality of the resulting studies. This paper aims at providing an introduction to case study methodology and guidelines for researchers conducting case studies and readers studying reports of such studies. The content is based on the authors' own experience from conducting and reading case studies. The terminology and guidelines are compiled from different methodology handbooks in other research domains, in particular social science and information systems, and adapted to the needs in software engineering. We present recommended practices for software engineering case studies as well as empirically derived and evaluated checklists for researchers and readers of case study research.

Question: What is a case study?


BranchScope: A New Side-Channel Attack on Directional Branch Predictor. We present BranchScope - a new side-channel attack where the attacker infers the direction of an arbitrary conditional branch instruction in a victim program by manipulating the shared directional branch predictor. The directional component of the branch predictor stores the prediction on a given branch (taken or not-taken) and is a different component from the branch target buffer (BTB) attacked by previous work. BranchScope is the first fine-grained attack on the directional branch predictor, expanding our understanding of the side channel vulnerability of the branch prediction unit. Our attack targets complex hybrid branch predictors with unknown organization. We demonstrate how an attacker can force these predictors to switch to a simple 1-level mode to simplify the direction recovery. We carry out BranchScope on several recent Intel CPUs and also demonstrate the attack against an SGX enclave.

Question: What is a branch predictor?