On Resampling Algorithms for Particle Filters. In this paper a comparison is made between four frequently encountered resampling algorithms for particle filters. A theoretical framework is introduced to be able to understand and explain the differences between the resampling algorithms. This facilitates a comparison of the algorithms with respect to their resampling quality and computational complexity. Using extensive Monte Carlo simulations the theoretical results are verified. It is found that systematic resampling is favourable, both in terms of resampling quality and computational complexity.

Question: What is a particle filter?


All-Digital Background Calibration of a Successive Approximation ADC Using the "Split ADC" Architecture. The "split ADC" architecture enables fully digital calibration and correction of nonlinearity errors due to capacitor mismatch in a successive approximation (SAR) ADC. The die area of a single ADC design is split into two independent halves, each converting the same input signal. Total area and power is unchanged, resulting in minimal increase in analog complexity. For each conversion, the half-sized ADCs generate two independent outputs which are digitally corrected using estimates of capacitor mismatch errors for each ADC. The ADC outputs are averaged to produce the ADC output code. The difference of the two outputs is used in a background calibration algorithm which estimates the error in the correction parameters. Any nonzero difference drives an LMS feedback loop toward zero difference which can only occur when the average error in each correction parameter is zero. A novel segmentation and shuffling scheme in the SAR capacitive DAC enables background calibration for a wide range of input signals including dc. Simulation of a 16 bit 1 Msps SAR ADC in 180 nm CMOS shows calibration convergence within 200 000 samples.

Question: What is the difference between a SAR ADC and a SAR DAC?


X CLAIM : Interoperability with Cryptocurrency-Backed Tokens ?. Building trustless cross-blockchain trading protocols is challenging. Therefore, centralized liquidity providers remain the preferred route to execute transfers across chains — which fundamentally contradicts the purpose of permissionless ledgers to replace trusted intermediaries. Enabling crossblockchain trades could not only enable currently competing blockchain projects to better collaborate, but seems of particular importance to decentralized exchanges as those are currently limited to the trade of digital assets within their respective blockchain ecosystem. In this paper we systematize the notion of cryptocurrencybacked tokens, an approach towards trustless cross-chain communication. We propose XCLAIM, a protocol for issuing, trading, and redeeming e.g. Bitcoin-backed tokens on Ethereum. We provide implementations for three possible protocol versions and evaluate their security and on-chain costs. With XCLAIM, it costs at most USD 1.17 to issue an arbitrary amount of Bitcoinbacked tokens on Ethereum, given current blockchain transaction fees. Our protocol requires no modifications to Bitcoin's and Ethereum's consensus rules and is general enough to support other cryptocurrencies.

Question: What is a cryptocurrencybacked token?


Graph construction and b-matching for semi-supervised learning. Graph based semi-supervised learning (SSL) methods play an increasingly important role in practical machine learning systems. A crucial step in graph based SSL methods is the conversion of data into a weighted graph. However, most of the SSL literature focuses on developing label inference algorithms without extensively studying the graph building method and its effect on performance. This article provides an empirical study of leading semi-supervised methods under a wide range of graph construction algorithms. These SSL inference algorithms include the Local and Global Consistency (LGC) method, the Gaussian Random Field (GRF) method, the Graph Transduction via Alternating Minimization (GTAM) method as well as other techniques. Several approaches for graph construction, sparsification and weighting are explored including the popular k-nearest neighbors method (kNN) and the b-matching method. As opposed to the greedily constructed kNN graph, the b-matched graph ensures each node in the graph has the same number of edges and produces a balanced or regular graph. Experimental results on both artificial data and real benchmark datasets indicate that b-matching produces more robust graphs and therefore provides significantly better prediction accuracy without any significant change in computation time.

Question: What is the difference between the k-nearest neighbors method and the b-matching method?


Deep neural network for manufacturing quality prediction. Expected product quality is affected by multi-parameter in complex manufacturing processes. Product quality prediction can offer the possibility of designing better system parameters at the early production stage. Many existing approaches fail at providing favorable results duo to shallow architecture in prediction model that can not learn multi-parameter's features insufficiently. To address this issue, a deep neural network (DNN), consisting of a deep belief network (DBN) in the bottom and a regression layer on the top, is proposed in this paper. The DBN uses a greedy algorithm for unsupervised feature learning. It could learn effective features for manufacturing quality prediction in an unsupervised pattern which has been proven to be effective for many fields. Then the learned features are inputted into the regression tool, and the quality predictions are obtained. One type of manufacturing system with multi-parameter is investigated by the proposed DNN model. The experiments show that the DNN has good performance of the deep architecture, and overwhelms the peer shallow models. It is recommended from this study that the deep learning technique is more promising in manufacturing quality prediction.

Question: What is the difference between a deep belief network and a deep neural network?


Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots. Exploration in an unknown environment is the core functionality for mobile robots. Learning-based exploration methods, including convolutional neural networks, provide excellent strategies without human-designed logic for the feature extraction [1]. But the conventional supervised learning algorithms cost lots of efforts on the labeling work of datasets inevitably. Scenes not included in the training set are mostly unrecognized either. We propose a deep reinforcement learning method for the exploration of mobile robots in an indoor environment with the depth information from an RGB-D sensor only. Based on the Deep Q-Network framework [2], the raw depth image is taken as the only input to estimate the Q values corresponding to all moving commands. The training of the network weights is end-to-end. In arbitrarily constructed simulation environments, we show that the robot can be quickly adapted to unfamiliar scenes without any man-made labeling. Besides, through analysis of receptive fields of feature representations, deep reinforcement learning motivates the convolutional networks to estimate the traversability of the scenes. The test results are compared with the exploration strategies separately based on deep learning [1] or reinforcement learning [3]. Even trained only in the simulated environment, experimental results in real-world environment demonstrate that the cognitive ability of robot controller is dramatically improved compared with the supervised method. We believe it is the first time that raw sensor information is used to build cognitive exploration strategy for mobile robots through end-to-end deep reinforcement learning.

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


Understanding PubMed® user search behavior through log analysis. This article reports on a detailed investigation of PubMed users' needs and behavior as a step toward improving biomedical information retrieval. PubMed is providing free service to researchers with access to more than 19 million citations for biomedical articles from MEDLINE and life science journals. It is accessed by millions of users each day. Efficient search tools are crucial for biomedical researchers to keep abreast of the biomedical literature relating to their own research. This study provides insight into PubMed users' needs and their behavior. This investigation was conducted through the analysis of one month of log data, consisting of more than 23 million user sessions and more than 58 million user queries. Multiple aspects of users' interactions with PubMed are characterized in detail with evidence from these logs. Despite having many features in common with general Web searches, biomedical information searches have unique characteristics that are made evident in this study. PubMed users are more persistent in seeking information and they reformulate queries often. The three most frequent types of search are search by author name, search by gene/protein, and search by disease. Use of abbreviation in queries is very frequent. Factors such as result set size influence users' decisions. Analysis of characteristics such as these plays a critical role in identifying users' information needs and their search habits. In turn, such an analysis also provides useful insight for improving biomedical information retrieval.Database URL:http://www.ncbi.nlm.nih.gov/PubMed.

Question: What is the difference between a gene and a protein?


IMPROVING WEB SERVICE CLUSTERING THROUGH POST FILTERING TO BOOTSTRAP THE SERVICE DISCOVERY. Web service clustering is one of a very efficient approach to discover Web services efficiently. Current approaches use similarity-distance measurement methods such as string-based, corpus-based, knowledge-based and hybrid methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information, shortage of high-quality ontologies and encoding fine-grained information. Thus, the approaches couldn't identify the correct clusters for some services and placed them in wrong clusters. As a result of this, cluster performance is reduced. This paper proposes post-filtering approach to increase the performance of clusters by rearranging services incorrectly clustered. Our approach uses context aware similarity method that learns domain context by machine learning to produce models of context for terms retrieved from the Web in the filtering process to calculate the service similarity. We applied post filtering approach to hybrid term similarity based clustering approach that we proposed in our previous work. Experimental results show that our post-filtering approach works efficiently.

Question: What is the context aware similarity method?


Search , Obfuscation , and Price Elasticities on the Internet 1. We examine the competition between a group of Internet retailers who operate in an environment where a price search engine plays a dominant role. We show that for some products in this environment, the easy price search makes demand tremendously pricesensitive. Retailers, though, engage in obfuscation—practices that frustrate consumer search or make it less damaging to firms—resulting in much less price sensitivity on some other products. We discuss several models of obfuscation and examine its effects on demand and markups empirically.

Question: What is price elasticity?


Wearable driver drowsiness detection using electrooculography signal. Every year, more than 100,000 automobile crashes are caused by driver drowsiness. Various technologies have been developed to address this issue, including vehicle-based measurements, behavior change detection, and physiological signal analysis. Both vehicle-based measurements and behavior change detection require bulky components. They also identify the driver's drowsiness too late for effective accident prevention. The physiological signal changes in an early stage and can be used to detect the on-set of driver drowsiness. In this paper, the development of a wearable drowsiness detection system is introduced. This system measures the electrooculography (EOG) signal; transmits the signal to a smartphone wirelessly; and could alarm the driver based on a prediction algorithm that can estimate 0.5-second-ahead EOG signal behavior. This system is compact, comfortable, and cost effective. The 0.5-second-ahead estimation capability provides the critical time for a driver to correct the behavior, and ultimately saves lives.

Question: What is the development of a wearable drowsiness detection system?


Nasal bone length throughout gestation: normal ranges based on 3537 fetal ultrasound measurements.. OBJECTIVE To establish normal ranges for nasal bone length measurements throughout gestation and to compare measurements in two subsets of patients of different race (African-American vs. Caucasian) to determine whether a different normal range should be used in these populations.   METHOD Normal nasal bone length reference ranges were generated using prenatal measurements by a standardized technique in 3537 fetuses.   RESULTS The nasal bone lengths were found to correlate positively with advancing gestation (R(2) = 0.77, second-order polynomial). No statistical difference was found between African-American and Caucasian subjects.   CONCLUSION These reference ranges may prove to be useful in prenatal screening and diagnosis of syndromes known to be associated with nasal hypoplasia. Different normal ranges for African-American and Caucasian women are not required.

Question: What is the normal range for nasal bone length?


Efficient Algorithms for Convolutional Sparse Representations. When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but results in a representation that is multi-valued and not optimized with respect to the entire image. An alternative representation structure is provided by a convolutional sparse representation, in which a sparse representation of an entire image is computed by replacing the linear combination of a set of dictionary vectors by the sum of a set of convolutions with dictionary filters. The resulting representation is both single-valued and jointly optimized over the entire image. While this form of a sparse representation has been applied to a variety of problems in signal and image processing and computer vision, the computational expense of the corresponding optimization problems has restricted application to relatively small signals and images. This paper presents new, efficient algorithms that substantially improve on the performance of other recent methods, contributing to the development of this type of representation as a practical tool for a wider range of problems.

Question: What is the difference between a convolutional sparse representation and a linear sparse representation?


Semantics matters : cognitively plausible delineation of city centres from point of interest data. We sketch a workflow for cognitively plausible recognition of vague geographical concepts, such as a city centre. Our approach imitates a pedestrian strolling through the streets, and comparing his/her internal cognitive model of a city centre with the stimulus from the external world to decide whether he/she is in the city centre or outside. The cognitive model of a British city centre is elicited through an online questionnaire survey and used to delineate referents of city centre from point of interest data. We first compute a measure of ''city centre-ness'' at each location within a city, and then merge the area of high city centre-ness to a contiguous region. The process is illustrated on the example of the City of Bristol, and the computed city centre area for Bristol is evaluated by comparison to reference areas derived from alternative sources. The evaluation suggests that our approach performs well and produces a representation of a city centre that is near to people''s conceptualisation. The benefits of our work are better (and user-driven) descriptions of complex geographical concepts. We see such models as a prerequisite for generalisation over large changes in detail, and for very specific purposes.

Question: How to delineate city centres from point of interest data?


Load balancing and OpenMP implementation of nested parallelism. Many problems have multiple layers of parallelism. The outer-level may consist of few and coarse-grained tasks. Next, each of these tasks may also be rich in parallelism, and be split into a number of fine-grained tasks, which again may consist of even finer subtasks, and so on. Here we argue and demonstrate by examples that utilizing multiple layers of parallelism may give much better scaling than if one restricts oneself to only one level of parallelism. Two non-trivial issues for multi-level parallelism are load balancing and implementation. In this paper we provide an algorithm for finding good distributions of threads to tasks and discuss how to implement nested parallelism in OpenMP. 2005 Elsevier B.V. All rights reserved.

Question: What is the difference between load balancing and OpenMP implementation of nested parallelism?


Beyond pixels: Exploiting camera metadata for photo classification. Semantic scene classification based only on low-level vision cues has had limited success on unconstrained image sets. On the other hand, camera metadata related to capture conditions provides cues independent of the captured scene content that can be used to improve classification performance. We consider three problems, indoor-outdoor classification, sunset detection, and manmade-natural classification. Analysis of camera metadata statistics for images of each class revealed that metadata fields, such as exposure time, flash fired, and subject distance, are most discriminative for each problem. A Bayesian network is employed to fuse content-based and metadata cues in the probability domain and degrades gracefully even when specific metadata inputs are missing (a practical concern). Finally, we provide extensive experimental results on the three problems using content-based and metadata cues to demonstrate the efficacy of the proposed integrated scene classification scheme.

Question: How to classify images based on metadata?


Area and length minimizing flows for shape segmentation. A number of active contour models have been proposed that unify the curve evolution framework with classical energy minimization techniques for segmentation, such as snakes. The essential idea is to evolve a curve (in two dimensions) or a surface (in three dimensions) under constraints from image forces so that it clings to features of interest in an intensity image. The evolution equation has been derived from first principles as the gradient flow that minimizes a modified length functional, tailored to features such as edges. However, because the flow may be slow to converge in practice, a constant (hyperbolic) term is added to keep the curve/surface moving in the desired direction. We derive a modification of this term based on the gradient flow derived from a weighted area functional, with image dependent weighting factor. When combined with the earlier modified length gradient flow, we obtain a partial differential equation (PDE) that offers a number of advantages, as illustrated by several examples of shape segmentation on medical images. In many cases the weighted area flow may be used on its own, with significant computational savings.

Question: What is the area functional?


Overview of Control and Grid Synchronization for Distributed Power Generation Systems. Renewable energy sources like wind, sun, and hydro are seen as a reliable alternative to the traditional energy sources such as oil, natural gas, or coal. Distributed power generation systems (DPGSs) based on renewable energy sources experience a large development worldwide, with Germany, Denmark, Japan, and USA as leaders in the development in this field. Due to the increasing number of DPGSs connected to the utility network, new and stricter standards in respect to power quality, safe running, and islanding protection are issued. As a consequence, the control of distributed generation systems should be improved to meet the requirements for grid interconnection. This paper gives an overview of the structures for the DPGS based on fuel cell, photovoltaic, and wind turbines. In addition, control structures of the grid-side converter are presented, and the possibility of compensation for low-order harmonics is also discussed. Moreover, control strategies when running on grid faults are treated. This paper ends up with an overview of synchronization methods and a discussion about their importance in the control

Question: What is the difference between a grid-side converter and a grid-connected converter?


Security Threats to Automotive CAN Networks - Practical Examples and Selected Short-Term Countermeasures. The IT security of automotive systems is an evolving area of research. To analyse the current situation and the potentially growing tendency of arising threats we performed several practical tests on recent automotive technology. With a focus on automotive systems based on CAN bus technology, this article summarises the results of four selected tests performed on the control systems for the window lift, warning light and airbag control system as well as the central gateway. These results are supplemented in this article by a classification of these four attack scenarios using the established CERT taxonomy and an analysis of underlying security vulnerabilities, and especially, potential safety implications. With respect to the results of these tests, in this article we further discuss two selected countermeasures to address basic weaknesses exploited in our tests. These are adaptations of intrusion detection (discussing three exemplary detection patterns) and IT-forensic measures (proposing proactive measures based on a forensic model). This article discusses both looking at the four attack scenarios introduced before, covering their capabilities and restrictions. While these reactive approaches are short-term measures, which could already be added to today's automotive IT architecture, long-term concepts also are shortly introduced, which are mainly preventive but will require a major redesign. Beneath a short overview on respective research approaches, we discuss their individual requirements, potential and restrictions. & 2010 Elsevier Ltd. All rights reserved.

Question: What is the CAN bus?


Derivation of Human Trophoblast Stem Cells.. Trophoblast cells play an essential role in the interactions between the fetus and mother. Mouse trophoblast stem (TS) cells have been derived and used as the best in vitro model for molecular and functional analysis of mouse trophoblast lineages, but attempts to derive human TS cells have so far been unsuccessful. Here we show that activation of Wingless/Integrated (Wnt) and EGF and inhibition of TGF-β, histone deacetylase (HDAC), and Rho-associated protein kinase (ROCK) enable long-term culture of human villous cytotrophoblast (CT) cells. The resulting cell lines have the capacity to give rise to the three major trophoblast lineages, which show transcriptomes similar to those of the corresponding primary trophoblast cells. Importantly, equivalent cell lines can be derived from human blastocysts. Our data strongly suggest that the CT- and blastocyst-derived cell lines are human TS cells, which will provide a powerful tool to study human trophoblast development and function.

Question: How do we derive human trophoblast stem cells?


Kd-Jump: a Path-Preserving Stackless Traversal for Faster Isosurface Raytracing on GPUs. Stackless traversal techniques are often used to circumvent memory bottlenecks by avoiding a stack and replacing return traversal with extra computation. This paper addresses whether the stackless traversal approaches are useful on newer hardware and technology (such as CUDA). To this end, we present a novel stackless approach for implicit kd-trees, which exploits the benefits of index-based node traversal, without incurring extra node visitation. This approach, which we term Kd-Jump, enables the traversal to immediately return to the next valid node, like a stack, without incurring extra node visitation (kd-restart). Also, Kd-Jump does not require global memory (stack) at all and only requires a small matrix in fast constant-memory. We report that Kd-Jump outperforms a stack by 10 to 20% and kd-restar t by 100%. We also present a Hybrid Kd-Jump, which utilizes a volume stepper for leaf testing and a run-time depth threshold to define where kd-tree traversal stops and volume-stepping occurs. By using both methods, we gain the benefits of empty space removal, fast texture-caching and realtime ability to determine the best threshold for current isosurface and view direction.

Question: What is the difference between a stackless traversal and a stack?


System-level Max POwer (SYMPO) - a systematic approach for escalating system-level power consumption using synthetic benchmarks. To effectively design a computer system for the worst case power consumption scenario, system architects often use hand-crafted maximum power consuming benchmarks at the assembly language level. These stressmarks, also called power viruses, are very tedious to generate and require significant domain knowledge. In this paper, we propose SYMPO, an automatic SYstem level Max POwer virus generation framework, which maximizes the power consumption of the CPU and the memory system using genetic algorithm and an abstract workload generation framework. For a set of three ISAs, we show the efficacy of the power viruses generated using SYMPO by comparing the power consumption with that of MPrime torture test, which is widely used by industry to test system stability. Our results show that the usage of SYMPO results in the generation of power viruses that consume 14-41% more power compared to MPrime on SPARC ISA. The genetic algorithm achieved this result in about 70 to 90 generations in 11 to 15 hours when using a full system simulator. We also show that the power viruses generated in the Alpha ISA consume 9-24% more power compared to the previous approach of stressmark generation. We measure and provide the power consumption of these benchmarks on hardware by instrumenting a quad-core AMD Phenom II X4 system. The SYMPO power virus consumes more power compared to various industry grade power viruses on x86 hardware. We also provide a microarchitecture independent characterization of various industry standard power viruses.

Question: How does SYMPO compare to MPrime?


Logic Programming for Software-Defined Networks.

Question: What is a software-defined network?


News Sentiment Analysis Based on Cross-Domain Sentiment Word Lists and Content Classifiers. The main task of sentiment classification is to automatically judge sentiment polarity (positive or negative) of published sentiment data (e.g. news or reviews). Some researches have shown that supervised methods can achieve good performance for blogs or reviews. However, the polarity of a news report is hard to judge. Web news reports are different from other web documents. The sentiment features in news are less than the features in other Web documents. Besides, the same words in different domains have different polarity. So we propose a selfgrowth algorithm to generate a cross-domain sentiment word list, which is used in sentiment classification of Web news. This paper considers some previously undescribed features for automatically classifying Web news, examines the effectiveness of these techniques in isolation and when aggregated using classification algorithms, and also validates the selfgrowth algorithm for the cross-domain word list.

Question: How to classify news?


A data-oriented (and beyond) network architecture. The Internet has evolved greatly from its original incarnation. For instance, the vast majority of current Internet usage is data retrieval and service access, whereas the architecture was designed around host-to-host applications such as telnet and ftp. Moreover, the original Internet was a purely transparent carrier of packets, but now the various network stakeholders use middleboxes to improve security and accelerate applications. To adapt to these changes, we propose the Data-Oriented Network Architecture (DONA), which involves a clean-slate redesign of Internet naming and name resolution.

Question: What is a data-oriented network architecture?


Using Associative Classifiers for Predictive Analysis in Health Care Data Mining. Association rule mining is one of the most important and well researched techniques of data mining for descriptive task, initially used for market basket analysis. It finds all the rules existing in the transactional database that satisfy some minimum support and minimum confidence constraints. Classification using Association rule mining is another major Predictive analysis technique that aims to discover a small set of rule in the database that forms an accurate classifier. In this paper, we introduce the combined approach that integrates association rule mining and classification rule mining called Associative Classification (AC). This is new classification approach. The integration is done by focusing on mining a special subset of association rules called classification association rule (CAR). And then classification is being performed using these CAR. Using association rule mining for constructing classification systems is a promising approach. Given the readability of the associative classifiers, they are especially fit to applications were the model may assist domain experts in their decisions. Medical field is a good example was such applications may appear. Consider an example were

Question: What is the difference between association rule mining and classification rule mining?


Chapter 8 Location-Based Social Networks : Users. In this chapter, we introduce and define the meaning of location-based social network (LBSN) and discuss the research philosophy behind LBSNs from the perspective of users and locations. Under the circumstances of trajectory-centric LBSN, we then explore two fundamental research points concerned with understanding users in terms of their locations. One is modeling the location history of an individual using the individual's trajectory data. The other is estimating the similarity between two different people according to their location histories. The inferred similarity represents the strength of connection between two users in a locationbased social network, and can enable friend recommendations and community discovery. The general approaches for evaluating these applications are also presented.

Question: What is the difference between location-based social networks and location-based social networks?


Silk - Generating RDF Links while Publishing or Consuming Linked Data. The central idea of the Web of Data is to interlink data items using RDF links. However, in practice most data sources are not sufficiently interlinked with related data sources. The Silk Link Discovery Framework addresses this problem by providing tools to generate links between data items based on user-provided link specifications. It can be used by data publishers to generate links between data sets as well as by Linked Data consumers to augment Web data with additional RDF links. In this poster we present the Silk Link Discovery Framework and report on two usage examples in which we employed Silk to generate links between two data sets about movies as well as to find duplicate persons in a stream of data items that is crawled from the Web.

Question: What is the silk link discovery framework?


Social-information-processing factors in reactive and proactive aggression in children's peer groups.. We examined social-information-processing mechanisms (e.g., hostile attributional biases and intention-cue detection deficits) in chronic reactive and proactive aggressive behavior in children's peer groups. In Study 1, a teacher-rating instrument was developed to assess these behaviors in elementary school children (N = 259). Reactive and proactive scales were found to be internally consistent, and factor analyses partially supported convergent and discriminant validities. In Study 2, behavioral correlates of these forms of aggression were examined through assessments by peers (N = 339). Both types of aggression related to social rejection, but only proactively aggressive boys were also viewed as leaders and as having a sense of humor. In Study 3, we hypothesized that reactive aggression (but not proactive aggression) would occur as a function of hostile attributional biases and intention-cue detection deficits. Four groups of socially rejected boys (reactive aggressive, proactive aggressive, reactive-proactive aggressive, and nonaggressive) and a group of average boys were presented with a series of hypothetical videorecorded vignettes depicting provocations by peers and were asked to interpret the intentions of the provocateur (N = 117). Only the two reactive-aggressive groups displayed biases and deficits in interpretations. In Study 4, attributional biases and deficits were found to be positively correlated with the rate of reactive aggression (but not proactive aggression) displayed in free play with peers (N = 127). These studies supported the hypothesis that attributional biases and deficits are related to reactive aggression but not to proactive aggression.

Question: What is the difference between reactive and proactive aggression?


A randomized controlled trial of postcrisis suicide prevention.. OBJECTIVE This study tested the hypothesis that professionals' maintenance of long-term contact with persons who are at risk of suicide can exert a suicide-prevention influence. This influence was hypothesized to result from the development of a feeling of connectedness and to be most pertinent to high-risk individuals who refuse to remain in the health care system.   METHODS A total of 3,005 persons hospitalized because of a depressive or suicidal state, populations known to be at risk of subsequent suicide, were contacted 30 days after discharge about follow-up treatment. A total of 843 patients who had refused ongoing care were randomly divided into two groups; persons in one group were contacted by letter at least four times a year for five years. The other group-the control group-received no further contact. A follow-up procedure identified patients who died during the five-year contact period and during the subsequent ten years. Suicide rates in the contact and no-contact groups were compared.   RESULTS Patients in the contact group had a lower suicide rate in all five years of the study. Formal survival analyses revealed a significantly lower rate in the contact group (p=.04) for the first two years; differences in the rates gradually diminished, and by year 14 no differences between groups were observed.   CONCLUSIONS A systematic program of contact with persons who are at risk of suicide and who refuse to remain in the health care system appears to exert a significant preventive influence for at least two years. Diminution of the frequency of contact and discontinuation of contact appear to reduce and eventually eliminate this preventive influence.

Question: What is the difference between a randomized controlled trial and a randomized controlled trial?


A Hotel Recommendation System Based on Collaborative Filtering and Rankboost Algorithm. A hotel recommendation system based on collaborative filtering method of clustering and Rankboost algorithm proposed in this paper, which can avoid the cold-start and scalability problems existing in traditional collaborative filtering. One can find a hotel quickly and efficiently when he uses this hotel recommendation system.

Question: What is the hotel recommendation system based on collaborative filtering and rankboost algorithm?


Urethrocutaneous fistula repair after hypospadias surgery.. OBJECTIVE To evaluate and compare the success rates of simple and layered repairs of urethrocutaneous fistulae after hypospadias repair.   PATIENTS AND METHODS The charts of 72 children who developed fistulae after hypospadias repair were reviewed; 39 had a simple closure of the fistula, whereas 32 had a 'pants over vest' repair, in all cases after excluding an impairment of urine outflow.   RESULTS The success rate at the first attempt was 74% for simple closure and 94% for the layered repair; at the second attempt it was 80% and 100%, the difference being statistically significant for both repairs.   CONCLUSIONS Although probably far from an optimal technique for repairing urethrocutaneous fistulae, the pants-over-vest repair allows a good success rate for penile shaft fistulae.

Question: What is the success rate of urethrocutaneous fistula repair after hypospadias repair?


Programming challenges of chatbot: Current and future prospective. In the modern Era of technology, Chatbots is the next big thing in the era of conversational services. Chatbots is a virtual person who can effectively talk to any human being using interactive textual skills. Currently, there are many cloud base Chatbots services which are available for the development and improvement of the chatbot sector such as IBM Watson, Microsoft bot, AWS Lambda, Heroku and many others. A virtual person is based on machine learning and Artificial Intelligence (AI) concepts and due to dynamic nature, there is a drawback in the design and development of these chatbots as they have built-in AI, NLP, programming and conversion services. This paper gives an overview of cloud-based chatbots technologies along with programming of chatbots and challenges of programming in current and future Era of chatbot.

Question: What is the programming of chatbots?


Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning. We combine three methods which significantly improve the OCR accuracy of OCR models trained on early printed books: (1) The pretraining method utilizes the information stored in already existing models trained on a variety of typesets (mixed models) instead of starting the training from scratch. (2) Performing cross fold training on a single set of ground truth data (line images and their transcriptions) with a single OCR engine (OCRopus) produces a committee whose members then vote for the best outcome by also taking the top-N alternatives and their intrinsic confidence values into account. (3) Following the principle of maximal disagreement we select additional training lines which the voters disagree most on, expecting them to offer the highest information gain for a subsequent training (active learning). Evaluations on six early printed books yielded the following results: On average the combination of pretraining and voting improved the character accuracy by 46% when training five folds starting from the same mixed model. This number rose to 53% when using different models for pretraining, underlining the importance of diverse voters. Incorporating active learning improved the obtained results by another 16% on average (evaluated on three of the six books). Overall, the proposed methods lead to an average error rate of 2.5% when training on only 60 lines. Using a substantial ground truth pool of 1,000 lines brought the error rate down even further to less than 1% on average.

Question: How do you improve OCR accuracy on early printed books?


Path Planning with Phased Array SLAM and Voronoi Tessellation. Autonomous vehicles must often navigate environments that are at least partially unknown. They are faced with the tasks of creating a coordinate system to localize themselves on, identify the positions of obstacles, and chart safe paths through the environment. This process is known as Simultaneous Localization and Mapping, or SLAM. SLAM has traditionally been executed using measurements of distance to features in the environment. We propose an angle based methodology using a single phased array antenna and DSP.aimed to reduce this requirement to a single path for each data type. Additionally, our method makes use of rudimentary echo-location to discover reflective obstacles. Finally, our method uses Voronoi Tessellation for path planning.

Question: What is the difference between a phased array and a single phased array?


Topic-aware Social Influence Minimization. In this paper, we address the problem of minimizing the negative influence of undesirable things in a network by blocking a limited number of nodes from a topic modeling perspective. When undesirable thing such as a rumor or an infection emerges in a social network and part of users have already been infected, our goal is to minimize the size of ultimately infected users by blocking $k$ nodes outside the infected set. We first employ the HDP-LDA and KL divergence to analysis the influence and relevance from a topic modeling perspective. Then two topic-aware heuristics based on betweenness and out-degree for finding approximate solutions to this problem are proposed. Using two real networks, we demonstrate experimentally the high performance of the proposed models and learning schemes.

Question: How to minimize the negative influence of undesirable things in a social network?


Feature Selection using Multiobjective Optimization for Aspect based Sentiment Analysis. In this paper, we propose a system for aspect-based sentiment analysis (ABSA) by incorporating the concepts of multi-objective optimization (MOO), distributional thesaurus (DT) and unsupervised lexical induction. The task can be thought of as a sequence of processes such as aspect term extraction, opinion target expression identification and sentiment classification. We use MOO for selecting the most relevant features, and demonstrate that classification with the resulting feature set can improve classification accuracy on many datasets. As base learning algorithms we make use of Support Vector Machines (SVM) for sentiment classification and Conditional Random Fields (CRF) for aspect term and opinion target expression extraction tasks. Distributional thesaurus and unsupervised DT prove to be effective with enhanced performance. Experiments on benchmark setups of SemEval-2014 and SemEval-2016 shared tasks show that we achieve the state of the art on aspect-based sentiment analysis for several languages.

Question: What is the state of the art for aspect-based sentiment analysis?


Optimization of Control Parameters for Genetic Algorithms. The task of optimizing a complex system presents at least two levels of problems for the system designer. First, a class of optimization algorithms must be chosen that is suitable for application to the system. Second, various parameters of the optimization algorithm need to be tuned for efficiency. A class of adaptive search procedures called genetic algorithms (GA) has been used to optimize a wide variety of complex systems. GA's are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems. The results are validated on an image registration problem. GA's are shown to be effective for both levels of the systems optimization problem.

Question: What is a genetic algorithm?


A linguistic ontology of space for natural language processing. a r t i c l e i n f o a b s t r a c t We present a detailed semantics for linguistic spatial expressions supportive of computational processing that draws substantially on the principles and tools of ontological engineering and formal ontology. We cover language concerned with space, actions in space and spatial relationships and develop an ontological organization that relates such expressions to general classes of fixed semantic import. The result is given as an extension of a linguistic ontology, the Generalized Upper Model, an organization which has been used for over a decade in natural language processing applications. We describe the general nature and features of this ontology and show how we have extended it for working particularly with space. Treaitng the semantics of natural language expressions concerning space in this way offers a substantial simplification of the general problem of relating natural spatial language to its contextualized interpretation. Example specifications based on natural language examples are presented, as well as an evaluation of the ontology's coverage, consistency, predictive power, and applicability.

Question: What is the ontology of space?


A Database of Narrative Schemas. This paper describes a new language resource of events and semantic roles that characterize real-world situations. Narrative schemas contain sets of related events (edit and publish), a temporal ordering of the events (edit before publish), and the semantic roles of the participants (authors publish books). This type of world knowledge was central to early research in natural language understanding. Scripts were one of the main formalisms, representing common sequences of events that occur in the world. Unfortunately, most of this knowledge was hand-coded and time consuming to create. Current machine learning techniques, as well as a new approach to learning through coreference chains, has allowed us to automatically extract rich event structure from open domain text in the form of narrative schemas. The narrative schema resource described in this paper contains approximately 5000 unique events combined into schemas of varying sizes. We describe the resource, how it is learned, and a new evaluation of the coverage of these schemas over unseen documents.

Question: What is a narrative schema?


Design of prototype humanoid robotics platform for HRP. This paper presents a prototype humanoid robotics platform developed for HRP-2. HRP-2 is a new humanoid robotics platform, which we have been developing in phase two of HRP. HRP is a humanoid robotics project, which has been lunched by Ministry of Economy, Trade and Industry (METI) of Japan from 1998FY to 2002FY for five years. The ability of the biped locomotion of HRP-2 is improved so that HRP-2 can cope with rough terrain in the open air and can prevent the possible damages to a humanoid robot's own self in the event of tipping over. The ability of whole body motion of HRP-2 is also improved so that HRP-2 can get up by a humanoid robot's own self even tough HRP-2 tips over. In this paper, the mechanisms and specifications of developed prototype humanoid robotics platform, and its electrical system are introduced.

Question: What is the prototype humanoid robotics platform for HRP-2?


Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records. BACKGROUND The ability to identify the risk factors related to an adverse condition, e.g., heart failures (HF) diagnosis, is very important for improving care quality and reducing cost. Existing approaches for risk factor identification are either knowledge driven (from guidelines or literatures) or data driven (from observational data). No existing method provides a model to effectively combine expert knowledge with data driven insight for risk factor identification.   METHODS We present a systematic approach to enhance known knowledge-based risk factors with additional potential risk factors derived from data. The core of our approach is a sparse regression model with regularization terms that correspond to both knowledge and data driven risk factors.   RESULTS The approach is validated using a large dataset containing 4,644 heart failure cases and 45,981 controls. The outpatient electronic health records (EHRs) for these patients include diagnosis, medication, lab results from 2003-2010. We demonstrate that the proposed method can identify complementary risk factors that are not in the existing known factors and can better predict the onset of HF. We quantitatively compare different sets of risk factors in the context of predicting onset of HF using the performance metric, the Area Under the ROC Curve (AUC). The combined risk factors between knowledge and data significantly outperform knowledge-based risk factors alone. Furthermore, those additional risk factors are confirmed to be clinically meaningful by a cardiologist.   CONCLUSION We present a systematic framework for combining knowledge and data driven insights for risk factor identification. We demonstrate the power of this framework in the context of predicting onset of HF, where our approach can successfully identify intuitive and predictive risk factors beyond a set of known HF risk factors.

Question: How to combine knowledge and data driven insights for risk factor identification?


What does research reproducibility mean?. The language and conceptual framework of "research reproducibility" are nonstandard and unsettled across the sciences. In this Perspective, we review an array of explicit and implicit definitions of reproducibility and related terminology, and discuss how to avoid potential misunderstandings when these terms are used as a surrogate for "truth."

Question: What does research reproducibility mean?


Training Spiking ConvNets by STDP and Gradient Descent. This paper proposes a new method for training multi-layer spiking convolutional neural networks (CNNs). Training a multi-layer spiking network poses difficulties because the output spikes do not have derivatives and the commonly use backpropagation method for non-spiking networks is not easily applied. Our method uses a novel version of layered spike-timing- dependent plasticity (STDP) that incorporates supervised and unsupervised components. Our method starts with conventional learning methods and converts them to spatio-temporally local rules suited for spiking neural networks (SNNs). The training process uses two components for unsupervised feature extraction and supervised classification. The first component is a new STDP rule for spike-based representation learning which trains convolutional filters. The second introduces a new STDP-based supervised learning rule for spike pattern classification via an approximation to gradient descent. Stacking these components implements a novel spiking CNN of integrate-and-fire (IF) neurons with performances comparable with the state-of-the-art deep SNNs. The experimental results show the success of the proposed model for the MNIST handwritten digit classification. Our network architecture is the only high performance, spiking CNN which provides bio-inspired STDP rules in a hierarchy of feature extraction and classification in an entirely spike-based framework.

Question: How does the proposed method compare to other methods?


Relationships between handwriting performance and organizational abilities among children with and without dysgraphia: a preliminary study.. Organizational ability constitutes one executive function (EF) component essential for common everyday performance. The study aim was to explore the relationship between handwriting performance and organizational ability in school-aged children. Participants were 58 males, aged 7-8 years, 30 with dysgraphia and 28 with proficient handwriting. Group allocation was based on children's scores in the Handwriting Proficiency Screening Questionnaire (HPSQ). They performed the Hebrew Handwriting Evaluation (HHE), and their parents completed the Questionnaire for Assessing Students' Organizational Abilities-for Parents (QASOA-P). Significant differences were found between the groups for handwriting performance (HHE) and organizational abilities (QASOA-P). Significant correlations were found in the dysgraphic group between handwriting spatial arrangement and the QASOA-P mean score. Linear regression indicated that the QASOA-P mean score explained 42% of variance of handwriting proficiency (HPSQ). Based on one discriminant function, 81% of all participants were correctly classified into groups. Study results strongly recommend assessing organizational difficulties in children referred for therapy due to handwriting deficiency.

Question: What is the relationship between handwriting and organizational abilities?


The evolution of human skin coloration.. Skin color is one of the most conspicuous ways in which humans vary and has been widely used to define human races. Here we present new evidence indicating that variations in skin color are adaptive, and are related to the regulation of ultraviolet (UV) radiation penetration in the integument and its direct and indirect effects on fitness. Using remotely sensed data on UV radiation levels, hypotheses concerning the distribution of the skin colors of indigenous peoples relative to UV levels were tested quantitatively in this study for the first time. The major results of this study are: (1) skin reflectance is strongly correlated with absolute latitude and UV radiation levels. The highest correlation between skin reflectance and UV levels was observed at 545 nm, near the absorption maximum for oxyhemoglobin, suggesting that the main role of melanin pigmentation in humans is regulation of the effects of UV radiation on the contents of cutaneous blood vessels located in the dermis. (2) Predicted skin reflectances deviated little from observed values. (3) In all populations for which skin reflectance data were available for males and females, females were found to be lighter skinned than males. (4) The clinal gradation of skin coloration observed among indigenous peoples is correlated with UV radiation levels and represents a compromise solution to the conflicting physiological requirements of photoprotection and vitamin D synthesis. The earliest members of the hominid lineage probably had a mostly unpigmented or lightly pigmented integument covered with dark black hair, similar to that of the modern chimpanzee. The evolution of a naked, darkly pigmented integument occurred early in the evolution of the genus Homo. A dark epidermis protected sweat glands from UV-induced injury, thus insuring the integrity of somatic thermoregulation. Of greater significance to individual reproductive success was that highly melanized skin protect

Question: What is the evolution of human skin coloration?


Vision based fire detection. Vision based fire detection is potentially a useful technique. With the increase in the number of surveillance cameras being installed, a vision based fire detection capability can be incorporated in existing surveillance systems at relatively low additional cost. Vision based fire detection offers advantages over the traditional methods. It will thus complement the existing devices. In this paper, we present spectral, spatial and temporal models of fire regions in visual image sequences. The spectral model is represented in terms of the color probability density of fire pixels. The spatial model captures the spatial structure within a fire region. The shape of a fire region is represented in terms of the spatial frequency content of the region contour using its Fourier coefficients. The temporal changes in these coefficients are used as the temporal signatures of the fire region. Specifically, an auto regressive model of the Fourier coefficient series is used. Experiments with a large number of scenes show that our method is capable of detecting fire reliably.

Question: What is the difference between a fire and a fire region?


High-Speed Parallel Decimal Multiplication with Redundant Internal Encodings. The decimal multiplication is one of the most important decimal arithmetic operations which have a growing demand in the area of commercial, financial, and scientific computing. In this paper, we propose a parallel decimal multiplication algorithm with three components, which are a partial product generation, a partial product reduction, and a final digit-set conversion. First, a redundant number system is applied to recode not only the multiplier, but also multiples of the multiplicand in signed-digit (SD) numbers. Furthermore, we present a multioperand SD addition algorithm to reduce the partial product array. Finally, a digit-set conversion algorithm with a hybrid prefix network to decrease the number of the logic gates on the critical path is discussed. An analysis of the timing delay and an HDL model synthesized under 90 nm technology show that by considering the tradeoff of designs among three components, the overall delay of the proposed 16 × 16-digit multiplier takes about 11 percent less timing delay with 2 percent less area compared to the current fastest design.

Question: What is the decimal multiplication?


Integrating occlusion culling with parallel LOD for rendering complex 3D environments on GPU. In many research domains, such as mechanical engineering, game development and virtual reality, a typical output model is usually produced in a multi-object manner for efficient data management. To have a complete description of the whole model, tens of thousands of, or even millions of, such objects are created, which makes the entire dataset exceptionally complex. Consequently, visualizing the model becomes a computationally intensive process that impedes a real-time rendering and interaction.

Question: How to integrate occlusion culling with parallel LOD for rendering complex 3D environments on GPU.


A self-boost charge pump topology for a gate drive high-side power supply. A self-boost charge pump topology is presented for a floating high-side gate drive power supply that features high voltage and current capabilities for use in integrated power electronic modules (IPEMs). The transformerless topology uses a small capacitor to transfer energy to the high-side switch from a single power supply referred to the negative rail. Unlike conventional bootstrap power supplies, no switching of the main phase-leg switches is required to provide power continuously to the high-side gate drive, even if the high-side switch is permanently on. Additional advantages include low parts-count and simple control requirements. A piecewise linear model of the self-boost charge pump is derived and the circuit's operating characteristics are analyzed. Simulation and experimental results are provided to verify the desired operation of the new charge pump circuit. Guidelines are provided to assist with circuit component selection in new applications.

Question: What is a self-boost charge pump topology?


A recursive approach to low complexity codes. A bstruciA method is described for constructing long error-correcting codes from one or more shorter error-correcting codes, referred to as subcodes, and a bipartite graph. A graph is shown which specifies carefully chosen subsets of the digits of the new codes that must be codewords in one of the shorter subcodes. Lower bounds to the rate and the minimum distance of the new code are derived in terms of the parameters of the graph and the subcodes. Both the encoders and decoders proposed are shown to take advantage of the code's explicit decomposition into subcodes to decompose and simplify the associated computational processes. Bounds on the performance of two specific decoding algorithms are established, and the asymptotic growth of the complexity of decoding for two types of codes and decoders is analyzed. The proposed decoders are able to mahe effective use of probabilistic information supplied by the channel receiver, e.g., reliability information, without greatly increasing the number of computations required. It is shown that choosing a transmission order for the digits that is appropriate for the graph and the subcodes can give the code excellent burst-error correction abilities. The construction principles are illustrated by several examples.

Question: What is a recursive approach to low complexity codes?


A New Approach to Linear Filtering and Prediction Problems. AN IMPORTANT class of theoretical and practical problems in communication and control is of a statistical nature. Such problems are: (i) Prediction of random signals; (ii) separation of random signals from random noise; (iii) detection of signals of known form (pulses, sinusoids) in the presence of random noise. In his pioneering work, Wiener [1]3 showed that problems (i) and (ii) lead to the so-called Wiener-Hopf integral equation; he also gave a method (spectral factorization) for the solution of this integral equation in the practically important special case of stationary statistics and rational spectra. Many extensions and generalizations followed Wiener's basic work. Zadeh and Ragazzini solved the finite-memory case [2]. Concurrently and independently of Bode and Shannon [3], they also gave a simplified method [2) of solution. Booton discussed the nonstationary Wiener-Hopf equation [4]. These results are now in standard texts [5-6]. A somewhat different approach along these main lines has been given recently by Darlington [7]. For extensions to sampled signals, see, e.g., Franklin [8], Lees [9]. Another approach based on the eigenfunctions of the WienerHopf equation (which applies also to nonstationary problems whereas the preceding methods in general don't), has been pioneered by Davis [10] and applied by many others, e.g., Shinbrot [11], Blum [12], Pugachev [13], Solodovnikov [14]. In all these works, the objective is to obtain the specification of a linear dynamic system (Wiener filter) which accomplishes the prediction, separation, or detection of a random signal.4 ——— 1 This research was supported in part by the U. S. Air Force Office of Scientific Research under Contract AF 49 (638)-382. 2 7212 Bellona Ave. 3 Numbers in brackets designate References at end of paper. 4 Of course, in general these tasks may be done better by nonlinear filters. At present, however, little or nothing is known about

Question: What is the difference between Wiener-Hopf equation and Wiener filter?


Design and analysis of cross-fed rectangular array antenna; an X-band microstrip array antenna, operating at 11 GHz. In this paper, an X-Band Microstrip Patch antenna termed as the Cross fed rectangular array antenna has been presented with an enhanced radiation efficiency. The proposed antenna is designed and simulated using FEKO 5.5 suite. It is then fabricated on a 40 × 40mm2 Rogers RT-Duroid 5880LZ dielectric material board. This antenna is composed of a rectangular patch array in a cross fashion with four patches, each with a dimension of 12.3 mm × 9.85 mm excited using a wire-port. The antenna operates at a frequency of 11.458 GHz in the X-Band (8-12 GHz). It has achieved stable radiation efficiency of 80.71% with a gain of 7.4 dB at the operating frequency. It is thus inferred that this antenna can be used for X-Band applications such as in maritime navigation and airborne radars.

Question: What is the operating frequency of this antenna?


A Deep Reinforcement Learning Algorithm with Expert Demonstrations and Supervised Loss and its application in Autonomous Driving. In this paper, we propose a deep reinforcement learning(DRL) algorithm which combines Deep Deterministic Policy Gradient (DDPG) with expert demonstrations and supervised loss for decision making for autonomous driving. Training DRL agent with supervised learning is adopted to accelerate the exploration process and increase the stability. A supervised loss function is introduced in the algorithm to update the actor networks. In addition, reward construction is combined to make the training process more stable and efficient. The proposed algorithm is applied to a popular autonomous driving simulator called TORCS. The experimental results show that the training efficiency and stability are improved by utilizing our algorithm in autonomous driving.

Question: What is a deep reinforcement learning algorithm?


Developing Competency Models to Promote Integrated Human Resource Practices • 309 DEVELOPING COMPETENCY MODELS TO PROMOTE INTEGRATED HUMAN RESOURCE PRACTICES. Today, competencies are used in many facets of human resource management, ranging from individual selection, development, and performance management to organizational strategic planning. By incorporating competencies into job analysis methodologies, the Office of Personnel Management (OPM) has developed robust competency models that can form the foundation for each of these initiatives. OPM has placed these models into automated systems to ensure access for employees, human resources professionals, and managers. Shared access to the data creates a shared frame of reference and a common language of competencies that have provided the basis for competency applications in public sector agencies. © 2002 Wiley Periodicals, Inc.

Question: What is the difference between competency models and competency frameworks?


Machine Learning under Attack: Vulnerability Exploitation and Security Measures. Learning to discriminate between secure and hostile patterns is a crucial problem for species to survive in nature. Mimetism and camouflage are well-known examples of evolving weapons and defenses in the arms race between predators and preys. It is thus clear that all of the information acquired by our senses should not be considered necessarily secure or reliable. In machine learning and pattern recognition systems, however, we have started investigating these issues only recently. This phenomenon has been especially observed in the context of adversarial settings like malware detection and spam filtering, in which data can be purposely manipulated by humans to undermine the outcome of an automatic analysis. As current pattern recognition methods are not natively designed to deal with the intrinsic, adversarial nature of these problems, they exhibit specific vulnerabilities that an attacker may exploit either to mislead learning or to evade detection. Identifying these vulnerabilities and analyzing the impact of the corresponding attacks on learning algorithms has thus been one of the main open issues in the novel research field of adversarial machine learning, along with the design of more secure learning algorithms.  In the first part of this talk, I introduce a general framework that encompasses and unifies previous work in the field, allowing one to systematically evaluate classifier security against different, potential attacks. As an example of application of this framework, in the second part of the talk, I discuss evasion attacks, where malicious samples are manipulated at test time to evade detection. I then show how carefully-designed poisoning attacks can mislead some learning algorithms by manipulating only a small fraction of their training data. In addition, I discuss some defense mechanisms against both attacks in the context of real-world applications, including biometr

Question: What is the difference between machine learning and pattern recognition?


Source Localization in Wireless Sensor Networks From Signal Time-of-Arrival Measurements. Recent advances in wireless sensor networks have led to renewed interests in the problem of source localization. Source localization has broad range of applications such as emergency rescue, asset inventory, and resource management. Among various measurement models, one important and practical source signal measurement is the received signal time of arrival (TOA) at a group of collaborative wireless sensors. Without time-stamp at the transmitter, in traditional approaches, these received TOA measurements are subtracted pairwise to form time-difference of arrival (TDOA) data for source localization, thereby leading to a 3-dB loss in signal-to-noise ratio (SNR). We take a different approach by directly applying the original measurement model without the subtraction preprocessing. We present two new methods that utilize semidefinite programming (SDP) relaxation for direct source localization. We further address the issue of robust estimation given measurement errors and inaccuracy in the locations of receiving sensors. Our results demonstrate some potential advantages of source localization based on the direct TOA data over time-difference preprocessing.

Question: How to localize a source in a wireless sensor network?


How Envy Influences SNS Intentions to Use. Social networking sites (SNS) have grown to be one of the most prevalent technologies, providing users a variety of benefits. However, SNS also provide user sufficient opportunities to access others' positive information. This would elicit envy. In the current study, we develop a theoretical framework that elaborates the mechanism through which online envy is generated and influences SNS usage. We specify that online users experience two types of envy and each one could have distinct influences on continuance intention to use of SNS. Our findings provide valuable implications for both academic researchers and IS practitioners.

Question: How envy influences SNS usage?


A trust framework model for situational contexts. Trust is a widely studied and acknowledged concept including a diversity of operationalizations. A trust framework model for situational contexts is presented to communicate a clearer and better understanding of the trust situation studied. The framework model includes the situational trust model, the trust transaction, and the trust equation. The situational trust model relates the trustor, the trustee, the trust object, and the trust environment. The application of the trust model is exemplified to define the trust situation of consumer trust in e-commerce.

Question: What is a trust framework model?


The Complementarity of Information Technology Infrastructure and E-Commerce Capability: A Resource-Based Assessment of Their Business Value. This study seeks to assess the business value of e-commerce capability and information technology (IT) infrastructure in the context of electronic business at the firm level. Grounded in the IT business-value literature and enhanced by the resource-based theory of the firm, we developed a research framework in which both the main effects and the interaction effects of e-commerce and IT on firm performance were tested. Within this theoretical framework, we formulated several hypotheses. We then developed a multidimensional e-commerce capability construct, and after establishing its validity and reliability, tested the hypotheses with empirical data from 114 companies in the retail industry. Controlling for variations of firm size and subindustry effects, our empirical analysis found a strong positive interaction effect between IT infrastructure and e-commerce capability. This suggests that their complementarity positively contributes to firm performance in terms of sales per employee, inventory turnover, and cost reduction. The results are consistent with the resource-based theory, and provide empirical evidence to the complementary synergy between front-end e-commerce capability and back-end IT infrastructure. Combined together, they become more effective in producing business value. Yet the value of this synergy has not been recognized in the IT payoff literature. The "productivity paradox" observed in various studies has been attributed to variation in methods and measures, yet we offer an additional explanation: ignoring complementarities in business value measurement implies that the impact of IT was seriously underestimated. Our results emphasized the integration of resources as a feasible path to e-commerce value—companies need to enhance the integration between front-end e-commerce capability and back-end IT infrastructu

Question: What is the complementarity of information technology infrastructure and e-commerce capability?


Episodic and semantic memory in mild cognitive impairment. Little is known about episodic and semantic memory in the early predementia stage of Alzheimer's disease (AD), which is referred to as mild cognitive impairment (MCI). To explore person knowledge, item recognition and spatial associative memory, we designed the Face Place Test (FPT). A total of 75 subjects participated: 22 patients with early AD, 24 with MCI and 29 matched controls. As predicted, AD patients showed significant deficits in person naming, item recognition and recall of spatial location (placing). Surprisingly, subjects with MCI were also impaired on all components. There was no significant difference between AD and MCI except on the placing component. Analysis of the relationship between semantic (naming) and episodic (recognition and placing) components of the FPT revealed a significant association between the two episodic tasks, but not between episodic and semantic performance. Patients with MCI show deficits of episodic and semantic memory. The extent of impairment suggests dysfunction beyond the medial temporal lobe. The FPT might form the basis of a sensitive early indicator of AD.

Question: What is the difference between episodic and semantic memory?


Gossip-based Causal Order Delivery Protocol Respecting Deadline Constraints in Publish/Subscribe Systems. Publish/subscribe systems based on gossip protocols are elastically to scale in and out and provides suitable consistency guarantees for data safety and high availability but, does not deal with end-to-end message delay and message order-based consistency. Especially in real-time collaborative applications, it is possible for the messages to take each a different time to arrive at end users. So, these applications should be based on P/S infrastructure including dealing with message deadlines and message ordering consistencies. Gossip communication is becoming one of the promising solutions for addressing P/S scalability problems in providing information propagation functionality by exploiting a mixture of diverse consistency options. In this paper, we present a new causal order protocol based on scalable P/S architecture for real-time collaborative applications in social web platforms to guarantee causally ordered message delivery, respecting deadline-constraints from brokers to subscribers. In the proposed protocol, every broker manages a 2-dimensional vector, representing its knowledge of the last message sent by each broker at a certain time. But, every broker disseminates a multicast message with a 1-dimensional vector, the time-stamped information that represents the maximum number of gossip rounds to subscribers because all messages disseminated by brokers have the same deadline as the maximum number of gossip rounds. Therefore, the proposed protocol for P/S based on gossiping results in very low communication overhead from brokers to subscribers in the context of respecting deadline-constraints.

Question: What is gossip based causal order delivery protocol?


The benefits of being present: mindfulness and its role in psychological well-being.. Mindfulness is an attribute of consciousness long believed to promote well-being. This research provides a theoretical and empirical examination of the role of mindfulness in psychological well-being. The development and psychometric properties of the dispositional Mindful Attention Awareness Scale (MAAS) are described. Correlational, quasi-experimental, and laboratory studies then show that the MAAS measures a unique quality of consciousness that is related to a variety of well-being constructs, that differentiates mindfulness practitioners from others, and that is associated with enhanced self-awareness. An experience-sampling study shows that both dispositional and state mindfulness predict self-regulated behavior and positive emotional states. Finally, a clinical intervention study with cancer patients demonstrates that increases in mindfulness over time relate to declines in mood disturbance and stress.

Question: What is the role of mindfulness in psychological well-being?


A spatio-temporal reference model of the aging brain. Both normal aging and neurodegenerative disorders such as Alzheimer's disease (AD) cause morphological changes of the brain. It is generally difficult to distinguish these two causes of morphological change by visual inspection of magnetic resonance (MR) images. To facilitate making this distinction and thus aid the diagnosis of neurodegenerative disorders, we propose a method for developing a spatio-temporal model of morphological differences in the brain due to normal aging. The method utilizes groupwise image registration to characterize morphological variation across brain scans of people with different ages. To extract the deformations that are due to normal aging we use partial least squares regression, which yields modes of deformations highly correlated with age, and corresponding scores for each input subject. Subsequently, we determine a distribution of morphologies as a function of age by fitting smooth percentile curves to these scores. This distribution is used as a reference to which a person's morphology score can be compared. We validate our method on two different datasets, using images from both cognitively normal subjects and patients with Alzheimer disease (AD). Results show that the proposed framework extracts the expected atrophy patterns. Moreover, the morphology scores of cognitively normal subjects are on average lower than the scores of AD subjects, indicating that morphology differences between AD subjects and healthy subjects can be partly explained by accelerated aging. With our methods we are able to assess accelerated brain aging on both population and individual level. A spatio-temporal aging brain model derived from 988 T1-weighted MR brain scans from a large population imaging study (age range 45.9-91.7y, mean age 68.3y) is made publicly available at www.agingbrain.nl.

Question: How old is the average canadian?


Unsupervised Induction of Semantic Roles within a Reconstruction-Error Minimization Framework. We introduce a new approach to unsupervised estimation of feature-rich semantic role labeling models. Our model consists of two components: (1) an encoding component: a semantic role labeling model which predicts roles given a rich set of syntactic and lexical features; (2) a reconstruction component: a tensor factorization model which relies on roles to predict argument fillers. When the components are estimated jointly to minimize errors in argument reconstruction, the induced roles largely correspond to roles defined in annotated resources. Our method performs on par with most accurate role induction methods on English and German, even though, unlike these previous approaches, we do not incorporate any prior linguistic knowledge about the languages.

Question: What is a tensor factorization model?


Evolutionary Computation Meets Machine Learning: A Survey. Evolutionary computation (EC) is a kind of optimization methodology inspired by the mechanisms of biological evolution and behaviors of living organisms. In the literature, the terminology evolutionary algorithms is frequently treated the same as EC. This article focuses on making a survey of researches based on using ML techniques to enhance EC algorithms. In the framework of an ML-technique enhanced-EC algorithm (MLEC), the main idea is that the EC algorithm has stored ample data about the search space, problem features, and population information during the iterative search process, thus the ML technique is helpful in analyzing these data for enhancing the search performance. The paper presents a survey of five categories: ML for population initialization, ML for fitness evaluation and selection, ML for population reproduction and variation, ML for algorithm adaptation, and ML for local search.

Question: What is the difference between evolutionary computation and machine learning?


Measuring dimensions of intergenerational contact: factor analysis of the Queen's University Scale.. OBJECTIVES Intergenerational contact has been linked to a range of health outcomes, including greater engagement and lower depression. Measures of contact are limited. Informed by Allport's contact theory, the Queen's University Scale consists of items rating contact with elders. We administered the survey to a young adult sample (N = 606) to identify factors that may optimize intervention programming and enhance young persons' health as they age.   METHODS We conducted exploratory factor analysis (EFA) in the structural equation modeling framework and then confirmatory factor analysis with items pertaining to the general elder population.   RESULTS EFAs did not yield an adequate factor structure. We tested two alternative confirmatory models based on findings from the EFA. Neither a second-order model nor a first-order model allowing double loadings and correlated errors proved adequate.   CONCLUSION Difficulty finding an adequate factor solution reflects challenges to measuring intergenerational contact with this scale. Items reflect relevant topics but subscale models are limited in interpretability. Knox and colleagues' analyses led them to recommend a brief, global scale, but we did not find empirical support for such a measure. Next steps include development and testing of a reliable, valid scale measuring dimensions of contact as perceived by both youth and elders.

Question: How do you measure intergenerational contact?


5G 3GPP-Like Channel Models for Outdoor Urban Microcellular and Macrocellular Environments. For the development of new 5G systems to operate in bands up to 100 GHz, there is a need for accurate radio propagation models at these bands that currently are not addressed by existing channel models developed for bands below 6 GHz. This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.

Question: What is the difference between a macrocell and a microcell?


Fusing Social Media Cues: Personality Prediction from Twitter and Instagram. Incorporating users' personality traits has shown to be instrumental in many personalized retrieval and recommender systems. Analysis of users' digital traces has become an important resource for inferring personality traits. To date, the analysis of users' explicit and latent characteristics is typically restricted to a single social networking site (SNS). In this work, we propose a novel method that integrates text, image, and users' meta features from two different SNSs: Twitter and Instagram. Our preliminary results indicate that the joint analysis of users' simultaneous activities in two popular SNSs seems to lead to a consistent decrease of the prediction errors for each personality trait.

Question: How to fuse social media cues?


CNN vs. SIFT for Image Retrieval: Alternative or Complementary?. In the past decade, SIFT is widely used in most vision tasks such as image retrieval. While in recent several years, deep convolutional neural networks (CNN) features achieve the state-of-the-art performance in several tasks such as image classification and object detection. Thus a natural question arises: for the image retrieval task, can CNN features substitute for SIFT? In this paper, we experimentally demonstrate that the two kinds of features are highly complementary. Following this fact, we propose an image representation model, complementary CNN and SIFT (CCS), to fuse CNN and SIFT in a multi-level and complementary way. In particular, it can be used to simultaneously describe scene-level, object-level and point-level contents in images. Extensive experiments are conducted on four image retrieval benchmarks, and the experimental results show that our CCS achieves state-of-the-art retrieval results.

Question: What is the state-of-the-art image retrieval method?


Sentence level emotion recognition based on decisions from subsentence segments. Emotion recognition from speech plays an important role in developing affective and intelligent systems. This study investigates sentence-level emotion recognition. We propose to use a two-step approach to leverage information from subsentence segments for sentence level decision. First we use a segment level emotion classifier to generate predictions for segments within a sentence. A second component combines the predictions from these segments to obtain a sentence level decision. We evaluate different segment units (words, phrases, time-based segments) and different decision combination methods (majority vote, average of probabilities, and a Gaussian Mixture Model (GMM)). Our experimental results on two different data sets show that our proposed method significantly outperforms the standard sentence-based classification approach. In addition, we find that using time-based segments achieves the best performance, and thus no speech recognition or alignment is needed when using our method, which is important to develop language independent emotion recognition systems.

Question: How to recognize emotion from speech?


Eight-Band Antenna With A Small Ground Clearance for LTE Metal-Frame Mobile Phone Applications. This letter presents a compact eight-band monopole antenna whose size is 70 mm × 7 mm × 6 mm for LTE metal-frame mobile phones. The mainly radiating part of the proposed antenna is the metal frame. There are three gaps with the same size of 1 mm in the frame, and these gaps divide the antenna into two parts. Besides, a match circuit is used to enlarge the bandwidth and make the lower band matched. The advantage of the proposed antenna is that eight bands can be covered with a metal frame under the condition of only a 7 mm ground clearance. A prototype is manufactured and tested. The measured –6 dB impedance bandwidths are 375 MHz (0.675–1.05 GHz) and 1.2 GHz (1.6–2.8 GHz). The LTE700, GSM850, GSM900, GSM1800, GSM1900, UMTS, LTE2300, and LTE2500 bands are covered. The measured efficiencies are 52.7%–78.7% at the lower band and 45.6%–81% at the higher band. The measured gains are 0–2.1 dBi at the lower band and 1.6–3.6 dBi at the higher band.

Question: What is the size of the antenna?


A Practical Attack to De-anonymize Social Network Users. Social networking sites such as Facebook, LinkedIn, and Xing have been reporting exponential growth rates and have millions of registered users. In this paper, we introduce a novel de-anonymization attack that exploits group membership information that is available on social networking sites. More precisely, we show that information about the group memberships of a user (i.e., the groups of a social network to which a user belongs) is sufficient to uniquely identify this person, or, at least, to significantly reduce the set of possible candidates. That is, rather than tracking a user's browser as with cookies, it is possible to track a person. To determine the group membership of a user, we leverage well-known web browser history stealing attacks. Thus, whenever a social network user visits a malicious website, this website can launch our de-anonymization attack and learn the identity of its visitors. The implications of our attack are manifold, since it requires a low effort and has the potential to affect millions of social networking users. We perform both a theoretical analysis and empirical measurements to demonstrate the feasibility of our attack against Xing, a medium-sized social network with more than eight million members that is mainly used for business relationships. Furthermore, we explored other, larger social networks and performed experiments that suggest that users of Facebook and LinkedIn are equally vulnerable.

Question: How to de-anonymize social network users?


Pedagogy-Based-Technology and Chemistry Students' Performance in Higher Institutions: A Case of Debre Berhan University.. Many students have difficulty in learning abstract and complex lessons of chemistry. This study investigated how students develop their understandings of abstract and complex lessons in chemistry with the aid of visualizing tools: animation, simulation and video that allow them to build clear concepts. Animation, simulation and video enable learning by doing and provide opportunity to explore the abstract and complex lessons of chemistry. They also enable to present information in more dynamic, compelling and interactive way with engaging environment. The study applied animation, simulation and video supporting, on student-centered learning activities, in electrochemistry for second year students organized with state of art technology flash and micro media player and evaluated for its effectiveness. A particular focus of this investigation was to what extent the uses of animation, simulation and video influenced student-centered learning. Preand posttests were administrated on the target group and concurrent embedded mixed case study design was employed. Total quality of the developed package was evaluated by administrating a separate schedule containing open and closed ended question. The comments and ratings obtained in the learners' insights provided the basis for the learning impact of the study. The result obtained from the experiment and responses of the schedule showed that technology integrated with appropriate pedagogy improves the performance of students.

Question: What is the role of technology in teaching?


Demonstration of MOS capacitor measurement for wafer manufacturing using a Direct Charge Measurement. Direct Charge Measurement (DCM) has a capability to improve the capacitance measurement time in parametric test. Through an actual wafer measurement, we have successfully verified that DCM can measure MOS capacitor much faster than an LCR meter while keeping good correlations for wafer manufacturing.

Question: How can I measure capacitance?


Safe Control under Uncertainty. Controller synthesis for hybrid systems that satisfy temporal specifications expressing various system properties is a challenging problem that has drawn the attention of many researchers. However, making the assumption that such temporal properties are deterministic is far from the reality. For example, many of the properties the controller has to satisfy are learned through machine learning techniques based on sensor input data. In this paper, we propose a new logic, Probabilistic Signal Temporal Logic (PrSTL), as an expressive language to define the stochastic properties, and enforce probabilistic guarantees on them. We further show how to synthesize safe controllers using this logic for cyber-physical systems under the assumption that the stochastic properties are based on a set of Gaussian random variables. One of the key distinguishing features of PrSTL is that the encoded logic is adaptive and changes as the system encounters additional data and updates its beliefs about the latent random variables that define the safety properties. We demonstrate our approach by synthesizing safe controllers under the PrSTL specifications for multiple case studies including control of quadrotors and autonomous vehicles in dynamic environments.

Question: How to synthesize safe controllers for cyber-physical systems under the assumption that the stochastic properties are based on a set of Gaussian random variables?


TinyECC: A Configurable Library for Elliptic Curve Cryptography in Wireless Sensor Networks. Public Key Cryptography (PKC) has been the enabling technology underlying many security services and protocols in traditional networks such as the Internet. In the context of wireless sensor networks, elliptic curve cryptography (ECC), one of the most efficient types of PKC, is being investigated to provide PKC supportin sensor network applications so that the existing PKC-based solutions can be exploited. This paper presents the design, implementation, and evaluation of TinyECC, a configurable library for ECC operations in wireless sensor networks. The primary objective of TinyECC is to provide a ready-to-use, publicly available software package for ECC-based PKC operations that can be flexibly configured and integrated into sensor network applications. TinyECC provides a number of optimization switches, which can turn specific optimizations on or off based on developers' needs. Different combinations of the optimizations have different execution time andresource consumptions, giving developers great flexibility in integrating TinyECC into sensor network applications. This paperalso reports the experimental evaluation of TinyECC on several common sensor platforms, including MICAz, Tmote Sky, and Imote2. The evaluation results show the impacts of individual optimizations on the execution time and resource consumptions, and give the most computationally efficient and the most storage efficient configuration of TinyECC.

Question: What is the size of the canadian military?


FORWARD AND INVERSE KINEMATICS STUDY OF INDUSTRIAL ROBOTS TAKING INTO ACCOUNT CONSTRUCTIVE AND FUNCTIONAL PARAMETER ' S MODELING. Forward and inverse kinematic studies of industrial robots (IR) have been developed and presented in a large number of papers. However, eve n g neral mathematic formalization is usually almost correct, (basically following up general Har tenberg Denavit (H-D) conventions and associated homogenous transformation matrix), only few papers presents kinematic models ready to be directly implemented on a real scale industrial robot or as well able to evaluate kinematics behavior of a real scale IR specific model. That is usually due to som e inconsistencies in modeling, the most frequently of these referring on: the incomplete formalization of the full set of constructive and functional parame ters (that mandatory need to be considered in case of a specific real IR's model), avoidance of considering IR's specific design features, (as joint dimensions a d links dimensions are) leading to wrongly locat ing the reference frames used for expressing homogenous coordinate transformations, as well as missing of the validation procedures able to check the correct itude of the mathematical models, previously to its implementing in a real scale IR's controller. That is why present paper shows first a completely new approach for IR's forward an inverse kinematics, in terms of IR's analytical modeling by taking into account the full set of IR's constructive and funct ional parameters of two different IR's models. Then , for both direct and inverse mathematical models complet e symbolic formalization and full set of solutions for forward and inverse kinematics are presented for bo th IR types. In order to study mathematical models applicability on the real scale IR, two specific IR models were studied: an ABB serial-link open chain kinematics IR and a Fanuc serial-link closed chain ki ematics IR. Numerical results were verified by cross validation using bot

Question: What is the difference between a serial link and a parallel link?


Natural methods for robot task learning: instructive demonstrations, generalization and practice. Among humans, teaching various tasks is a complex process which relies on multiple means for interaction and learning, both on the part of the teacher and of the learner. Used together, these modalities lead to effective teaching and learning approaches, respectively. In the robotics domain, task teaching has been mostly addressed by using only one or very few of these interactions. In this paper we present an approach for teaching robots that relies on the key features and the general approach people use when teaching each other: first give a demonstration, then allow the learner to refine the acquired capabilities by practicing under the teacher's supervision, involving a small number of trials. Depending on the quality of the learned task, the teacher may either demonstrate it again or provide specific feedback during the learner's practice trial for further refinement. Also, as people do during demonstrations, the teacher can provide simple instructions and informative cues, increasing the performance of learning. Thus, instructive demonstrations, generalization over multiple demonstrations and practice trials are essential features for a successful human-robot teaching approach. We implemented a system that enables all these capabilities and validated these concepts with a Pioneer 2DX mobile robot learning tasks from multiple demonstrations and teacher feedback.

Question: What is the difference between a demonstration and a practice trial?


Machine learning applications in genetics and genomics. The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

Question: What is machine learning?


The potential environmental impact of engineered nanomaterials. With the increased presence of nanomaterials in commercial products, a growing public debate is emerging on whether the environmental and social costs of nanotechnology outweigh its many benefits. To date, few studies have investigated the toxicological and environmental effects of direct and indirect exposure to nanomaterials and no clear guidelines exist to quantify these effects.

Question: What is the potential environmental impact of engineered nanomaterials?


Crowd-Squared: Amplifying the Predictive Power of Search Trend Data. Table A1. Search Terms Generated by the Crowd (a) Influenza – Crowd-Squared Search Terms and % of Turkers Mentioning Each Term Search Term % Mention Search Term % Mention Search Term % Mention Search Term % Mention sick 68% influenza 4% achy 2% fatigue 1% fever 39% coughing 4% weak 2% home 1% cough 21% germs 4% bad 2% hospital 1% cold 16% headache 4% shots 2% nyquil 1% shot 13% ache 4% body aches 2% season 1% vomit 11% runny nose 4% flu shot 2% tea 1%

Question: What is the crowd-squared search term?