An Autonomous Reliability-Aware Negotiation Strategy for Cloud Computing Environments. Cloud computing paradigm allows subscription-based access to computing and storages services over the Internet. Since with advances of Cloud technology, operations such as discovery, scaling, and monitoring are accomplished automatically, negotiation between Cloud service requesters and providers can be a bottleneck if it is carried out by humans. Therefore, our objective is to offer a state-of-the-art solution to automate the negotiation process in Cloud environments. In previous works in the SLA negotiation area, requesters trust whatever QoS criteria values providers offer in the process of negotiation. However, the proposed negotiation strategy for requesters in this work is capable of assessing reliability of offers received from Cloud providers. In addition, our proposed negotiation strategy for Cloud providers considers utilization of resources when it generates new offers during negotiation and concedes more on the price of less utilized resources. The experimental results show that our strategy helps Cloud providers to increase their profits when they are participating in parallel negotiation with multiple requesters.

Question: How to negotiate a SLA for cloud computing?


Local Feature Based Multiple Object Instance Identification Using Scale and Rotation Invariant Implicit Shape Model. In this paper, we propose a Scale and Rotation Invariant Implicit Shape Model (SRIISM), and develop a local feature matching based system using the model to accurately locate and identify large numbers of object instances in an image. Due to repeated instances and cluttered background, conventional methods for multiple object instance identification suffer from poor identification results. In the proposed SRIISM, we model the joint distribution of object centers, scale, and orientation computed from local feature matches in Hough voting, which is not only invariant to scale changes and rotation of objects, but also robust to false feature matches. In the multiple object instance identification system using SRIISM, we apply a fast 4D bin search method in Hough space with complexity O(n), where n is the number of feature matches, in order to segment and locate each instance. Futhermore, we apply maximum likelihood estimation (MLE) for accurate object pose detection. In the evaluation, we created datasets simulating various industrial applications such as pick-and-place and inventory management. Experiment results on the datasets show that our method outperforms conventional methods in both accuracy (5%-30% gain) and speed (2x speed up).

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


Un Método para definir la Arquitectura de Procesos. In this paper we present a method for systematically defining the process architecture of any organization. We begin by presenting a detailed interpretation of the upper levels of Zachman's framework, providing an answer to the cell formed by the intersection of the perspective of the Planner (row 1) and the dimension of Function (column 2). Our method consists of four steps: capturing the organizational structure; exhaustively analyzing the flow of information between the different organizational units, customers, and providers, allowing for a high-level understanding of the organization's operation; identifying and modeling the configuration of value that best adapts itself to the creation of value for the organization (value chain, workshop, or network); and, given the specified value configuration, identifying, defining, and interrelating the different essential processes.

Question: What is the process architecture of an organization?


Arabic Opinion Mining Using Parallel Decision Trees. Opinion mining is an interested area of research, which epitomize the customer reviews of a product or service and express whether the opinions are positive or negative. Various methods have been proposed as classifiers for opinion mining such as Naïve Bayesian, and Support vector machine, these methods classify opinion without giving us the reasons about why the instance opinion is classified to certain class. Therefore, in our work, we investigate opinion mining of Arabic text at the document level, by applying decision trees classification classifier to have clear, understandable rule, also we apply parallel decision trees classifiers to have efficient results. We applied parallel decision trees on two Arabic corpus of text documents by using parallel implementation of RapidMiner tools. In case of applying parallel decision tree family on OCA we get the best results of accuracy (93.83%), f-measure (93.22) and consumed time 42 Sec at thread 4, one of the resulted rule is Urdu language lines. In case of applying parallel decision tree family on BHA we get the best results of accuracy (90.63%), f-measure (82.29) and consumed time 219 Sec at thread 4, one of the resulted rule is Urdu language lines.

Question: What is opinion mining?


Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking. Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. We present a benchmark for Multiple Object Tracking launched in the late 2014, with the goal of creating a framework for the standardized evaluation of multiple object tracking methods. This paper collects the two releases of the benchmark made so far, and provides an in-depth analysis of almost 50 state-of-the-art trackers that were tested on over 11000 frames. We show the current trends and weaknesses of multiple people tracking methods, and provide pointers of what researchers should be focusing on to push the field forward.

Question: What is the state of the art in multiple object tracking?


Latent syntactic structure-based sentiment analysis. People share their opinions about things like products, movies and services using social media channels. The analysis of these textual contents for sentiments is a gold mine for marketing experts, thus automatic sentiment analysis is a popular area of applied artificial intelligence. We propose a latent syntactic structure-based approach for sentiment analysis which requires only sentence-level polarity labels for training. Our experiments on three domains (movie, IT products, restaurant) show that a sentiment analyzer that exploits syntactic parses and has access only to sentence-level polarity annotation for in-domain sentences can outperform state-of-the-art models that were trained on out-domain parse trees with sentiment annotation for each node of the trees. In practice, millions of sentence-level polarity annotations are usually available for a particular domain thus our approach is applicable for training a sentiment analyzer for a new domain while it can exploit the syntactic structure of sentences as well.

Question: What is a latent syntactic structure-based approach for sentiment analysis?


ACIQ: Analytical Clipping for Integer Quantization of neural networks. Unlike traditional approaches that focus on the quantization at the network level, in this work we propose to minimize the quantization effect at the tensor level. We analyze the trade-off between quantization noise and clipping distortion in low precision networks. We identify the statistics of various tensors, and derive exact expressions for the mean-square-error degradation due to clipping. By optimizing these expressions, we show marked improvements over standard quantization schemes that normally avoid clipping. For example, just by choosing the accurate clipping values, more than 40% accuracy improvement is obtained for the quantization of VGG16-BN to 4-bits of precision. Our results have many applications for the quantization of neural networks at both training and inference time. One immediate application is for a rapid deployment of neural networks to low-precision accelerators without time-consuming fine tuning or the availability of the full datasets.

Question: What is the trade-off between quantization noise and clipping distortion?


Validating Formative Partial Least Squares (PLS) Models: Methodological Review and Empirical Illustration. The issue of formative constructs, as opposed to th e more frequently used reflective ones, has recently gained momentum among IS and Management re searchers. Most researchers maintain that formative constructs have been understudied, a n that there is paucity in methodological literature to guide researchers on how such constru cts should be developed and estimated. A survey of IS research has revealed that about 29% o f constructs were misspecified as reflective rather than formative constructs. Furthermore, guid elines about how models containing formative constructs should be indentified and estimated are fragmented and inconsistent. Thus, this paper aims to present a methodological review of formativ e model identification and evaluation. We bring a brief theoretical overview of formative con structs, and put together a guideline for estimating formative measurement and structural mod els. We then present a simplified model composed of three formative constructs and illustra te how it is assessed and estimated using

Question: How to validate a formative model?


Comparing Blockchain and Cloud Services for Business Process Execution. Blockchain is of rising importance as a technology for engineering applications in cross-organizational settings, avoiding reliance on central trusted third-parties. The use of blockchain, instead of traditional databases or services, is an architectural choice in the development of a software system. The costs of execution and storage are important non-functional qualities, but as yet very little has been done to study them for blockchain-based systems. We investigate the cost of using blockchain using business process execution as a lens. Specifically, we compare the cost for computation and storage of business process execution on blockchain vs. a popular cloud service. First, we capture the cost models for both alternatives. Second, we implemented and measured the cost of business process execution on blockchain and cloud services for an example business process model from the literature. We observe two orders of magnitude difference in this cost.

Question: What is the cost of business process execution on blockchain?


A Survey of Machine Learning Techniques for Spam Filtering. Email spam or junk e-mail (unwanted e-mail "usually of a commercial nature sent out in bulk") is one of the major problems of the today's Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is an important and popular one. Common uses for mail filters include organizing incoming email and removal of spam and computer viruses. A less common use is to inspect outgoing email at some companies to ensure that employees comply with appropriate laws. Users might also employ a mail filter to prioritize messages, and to sort them into folders based on subject matter or other criteria. Mail filters can be installed by the user, either as separate programs, or as part of their email program (email client). In email programs, users can make personal, "manual" filters that then automatically filter mail according to the chosen criteria. In this paper, we present a survey of the performance of five commonly used machine learning methods in spam filtering. Most email programs now also have an automatic spam filtering function.

Question: What is the difference between a mail filter and a spam filter?


Characterizing Cloud Applications on a Google Data Center. In this paper, we characterize Google applications, based on a one-month Google trace with over 650k jobs running across over 12000 heterogeneous hosts from a Google data center. On one hand, we carefully compute the valuable statistics about task events and resource utilization for Google applications, based on various types of resources (such as CPU, memory) and execution types (e.g., whether they can run batch tasks or not). Resource utilization per application is observed with an extremely typical Pareto principle. On the other hand, we classify applications via a K-means clustering algorithm with optimized number of sets, based on task events and resource usage. The number of applications in the K-means clustering sets follows a Pareto-similar distribution. We believe our work is very interesting and valuable for the further investigation of Cloud environment.

Question: What is the size of the google data center?


Classification of Artefacts in EEG Signal Recordings and Overview of Removing Techniques. EEG is a record of brain activity from various sites of the brain. Artefacts are unwanted noise signals in an EEG record. Classification of artefacts based on source of its generation like physiological artefacts and external artefacts. Body of the subjects are main source of Physiological artifacts, while external artefacts are from outside the body due to the environment or measuring devices. Recognition, identification and elimination of artifacts is an important process to minimize the chance of misinterpretation of EEG, not only for clinical and non-clinical fields such as brain computer interface, intelligent control system robotics etc. This paper classifies the artefacts from the database collected at Dr. R. N. Cooper Mun. General Hospital Mumbai India.

Question: What is the classification of artefacts in EEG signal recordings?


Time and cognitive load in working memory.. According to the time-based resource-sharing model (P. Barrouillet, S. Bernardin, & V. Camos, 2004), the cognitive load a given task involves is a function of the proportion of time during which it captures attention, thus impeding other attention-demanding processes. Accordingly, the present study demonstrates that the disruptive effect on concurrent maintenance of memory retrievals and response selections increases with their duration. Moreover, the effect on recall performance of concurrent activities does not go beyond their duration insofar as the processes are attention demanding. Finally, these effects are not modality specific, as spatial processing was found to disrupt verbal maintenance. These results suggest a sequential and time-based function of working memory in which processing and storage rely on a single and general purpose attentional resource needed to run executive processes devoted to constructing, maintaining, and modifying ephemeral representations.

Question: What is the time-based resource-sharing model?


Methods of suicide: international suicide patterns derived from the WHO mortality database.. OBJECTIVE Accurate information about preferred suicide methods is important for devising strategies and programmes for suicide prevention. Our knowledge of the methods used and their variation across countries and world regions is still limited. The aim of this study was to provide the first comprehensive overview of international patterns of suicide methods.   METHODS Data encoded according to the International Classification of Diseases (10th revision) were derived from the WHO mortality database. The classification was used to differentiate suicide methods. Correspondence analysis was used to identify typical patterns of suicide methods in different countries by providing a summary of cross-tabulated data.   FINDINGS Poisoning by pesticide was common in many Asian countries and in Latin America; poisoning by drugs was common in both Nordic countries and the United Kingdom. Hanging was the preferred method of suicide in eastern Europe, as was firearm suicide in the United States and jumping from a high place in cities and urban societies such as Hong Kong Special Administrative Region, China. Correspondence analysis demonstrated a polarization between pesticide suicide and firearm suicide at the expense of traditional methods, such as hanging and jumping from a high place, which lay in between.   CONCLUSION This analysis showed that pesticide suicide and firearm suicide replaced traditional methods in many countries. The observed suicide pattern depended upon the availability of the methods used, in particular the availability of technical means. The present evidence indicates that restricting access to the means of suicide is more urgent and more technically feasible than ever.

Question: What is the most common method of suicide in the world?


A taxonomy and rating system to measure situation awareness in resuscitation teams. Team SA involves a common perspective between two or more individuals regarding current environmental events, their meaning, and projected future status. Team SA has been theorized to be important for resuscitation team effectiveness. Accordingly, multidimensional frameworks of observable behaviors relevant to resuscitation teams are needed to understand more deeply the nature of team SA, its implications for team effectiveness, and whether it can be trained. A seven-dimension team resuscitation SA framework was developed following a literature review and consensus process using a modified Delphi approach with a group of content experts. We applied a pre-post design within a day-long team training program involving four video-recorded simulated resuscitation events and 42 teams across Canada. The first and fourth events represented "pre" and "post" training events, respectively. Teams were scored on SA five times within each 15-minute event. Distractions were introduced to investigate whether SA scores would be affected. The current study provides initial construct validity evidence for a new measure of SA and explicates SA's role in resuscitation teams.

Question: What is the role of situation awareness in resuscitation teams?


Analysis of jitter due to power-supply noise in phase-locked loops. Phase-locked loops (PLL) in RF and mixed signal VLSI circuits experience supply noise which translates to a timing jitter. In this paper an analysis of the timing jitter due to the noise on the power supply rails is presented. Stochastic models of the power supply noise in VLSI circuits for different values of on-chip decoupling capacitances are presented first. This is followed by calculation of the phase noise of the voltage-controlled oscillator (VCO) in terms of the statistical properties of supply noise. Finally the timing jitter of PLL is predicted in response to the VCO phase noise. A PLL circuit has been designed in 0.35μ CMOS process, and our mathematical model was applied to determine the timing jitter. Experimental results prove the accuracy of the predicted model.

Question: What is the difference between a phase-locked loop and a phase-locked oscillator?


Efficient Large Message Broadcast using NCCL and CUDA-Aware MPI for Deep Learning. Emerging paradigms like High Performance Data Analytics (HPDA) and Deep Learning (DL) pose at least two new design challenges for existing MPI runtimes. First, these paradigms require an efficient support for communicating unusually large messages across processes. And second, the communication buffers used by HPDA applications and DL frameworks generally reside on a GPU's memory. In this context, we observe that conventional MPI runtimes have been optimized over decades to achieve lowest possible communication latency for relatively smaller message sizes (up-to 1 Megabyte) and that too for CPU memory buffers. With the advent of CUDA-Aware MPI runtimes, a lot of research has been conducted to improve performance of GPU buffer based communication. However, little exists in current state of the art that deals with very large message communication of GPU buffers. In this paper, we investigate these new challenges by analyzing the performance bottlenecks in existing CUDA-Aware MPI runtimes like MVAPICH2-GDR, and propose hierarchical collective designs to improve communication latency of the MPI_Bcast primitive by exploiting a new communication library called NCCL. To the best of our knowledge, this is the first work that addresses these new requirements where GPU buffers are used for communication with message sizes surpassing hundreds of megabytes. We highlight the design challenges for our work along with the details of design and implementation. In addition, we provide a comprehensive performance evaluation using a Micro-benchmark and a CUDA-Aware adaptation of Microsoft CNTK DL framework. We report up to 47% improvement in training time for CNTK using the proposed hierarchical MPI_Bcast design.

Question: How large is the message size?


Detecting readers with dyslexia using machine learning with eye tracking measures. Worldwide, around 10% of the population has dyslexia, a specific learning disorder. Most of previous eye tracking experiments with people with and without dyslexia have found differences between populations suggesting that eye movements reflect the difficulties of individuals with dyslexia. In this paper, we present the first statistical model to predict readers with and without dyslexia using eye tracking measures. The model is trained and evaluated in a 10-fold cross experiment with a dataset composed of 1,135 readings of people with and without dyslexia that were recorded with an eye tracker. Our model, based on a Support Vector Machine binary classifier, reaches 80.18% accuracy using the most informative features. To the best of our knowledge, this is the first time that eye tracking measures are used to predict automatically readers with dyslexia using machine learning.

Question: How can we detect readers with dyslexia using eye tracking measures?


A Corpus of Sentence-level Revisions in Academic Writing: A Step towards Understanding Statement Strength in Communication. The strength with which a statement is made can have a significant impact on the audience. For example, international relations can be strained by how the media in one country describes an event in another; and papers can be rejected because they overstate or understate their findings. It is thus important to understand the effects of statement strength. A first step is to be able to distinguish between strong and weak statements. However, even this problem is understudied, partly due to a lack of data. Since strength is inherently relative, revisions of texts that make claims are a natural source of data on strength differences. In this paper, we introduce a corpus of sentence-level revisions from academic writing. We also describe insights gained from our annotation efforts for this task.

Question: What is the difference between strong and weak statements?


SANE: System for Fine Grained Named Entity Typing on Textual Data. Assignment of fine-grained types to named entities is gaining popularity as one of the major Information Extraction tasks due to its applications in several areas of Natural Language Processing. Existing systems use huge knowledge bases to improve the accuracy of the fine-grained types. We designed and developed SANE, a system that uses Wikipedia categories to fine grain the type of the named entities recognized in the textual data. The main contribution of this work is building a named entity typing system without the use of knowledge bases. Through our experiments, 1) we establish the usefulness of Wikipedia categories to Named Entity Typing and 2) we show that SANE's performance is on par with the state-ofthe-art.

Question: What is the main contribution of this work?


Depth-Based Hand Pose Estimation: Methods, Data, and Challenges. Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and have released software and evaluation code. We summarize important conclusions here: (1) Coarse pose estimation appears viable for scenes with isolated hands. However, high precision pose estimation [required for immersive virtual reality and cluttered scenes (where hands may be interacting with nearby objects and surfaces) remain a challenge. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress.

Question: What is the nearest-neighbor baseline?


Identifying Generalization Properties in Neural Networks. While it has not yet been proven, empirical evidence suggests that model generalization is related to local properties of the optima which can be described via the Hessian. We connect model generalization with the local property of a solution under the PAC-Bayes paradigm. In particular, we prove that model generalization ability is related to the Hessian, the higher-order "smoothness" terms characterized by the Lipschitz constant of the Hessian, and the scales of the parameters. Guided by the proof, we propose a metric to score the generalization capability of the model, as well as an algorithm that optimizes the perturbed model accordingly.

Question: How to identify generalization properties in neural networks?


Single-Stage Single-Switch PFC Flyback Converter Using a Synchronous Rectifier. A single-stage single-switch power factor correction (PFC) flyback converter with a synchronous rectifier (SR) is proposed for improving power factor and efficiency. Using a variable switching-frequency controller, this converter is continuously operated with a reduced turn-on switching loss at the boundary of the continuous conduction mode and discontinuous conduction mode (DCM). The proposed PFC circuit provides relatively low dc-link voltage in the universal line voltage, and also complies with Standard IEC 61000-3-2 Class D limits. In addition, a new driving circuit as the voltage driven-synchronous rectifier is proposed to achieve high efficiency. In particular, since a driving signal is generated according to the voltage polarity, the SR driving circuit can easily be used in DCM applications. The proposed PFC circuit and SR driving circuit in the flyback converter with the reduced switching loss are analyzed in detail and optimized for high performance. Experimental results for a 19 V/90 W adapter at the variable switching-frequency of 30~70 kHz were obtained to show the performance of the proposed converter.

Question: How to improve the power factor of a flyback converter?


Offloading in Mobile Cloudlet Systems with Intermittent Connectivity. The emergence of mobile cloud computing enables mobile users to offload applications to nearby mobile resource-rich devices (i.e., cloudlets) to reduce energy consumption and improve performance. However, due to mobility and cloudlet capacity, the connections between a mobile user and mobile cloudlets can be intermittent. As a result, offloading actions taken by the mobile user may fail (e.g., the user moves out of communication range of cloudlets). In this paper, we develop an optimal offloading algorithm for the mobile user in such an intermittently connected cloudlet system, considering the users' local load and availability of cloudlets. We examine users' mobility patterns and cloudlets' admission control, and derive the probability of successful offloading actions analytically. We formulate and solve a Markov decision process (MDP) model to obtain an optimal policy for the mobile user with the objective to minimize the computation and offloading costs. Furthermore, we prove that the optimal policy of the MDP has a threshold structure. Subsequently, we introduce a fast algorithm for energy-constrained users to make offloading decisions. The numerical results show that the analytical form of the successful offloading probability is a good estimation in various mobility cases. Furthermore, the proposed MDP offloading algorithm for mobile users outperforms conventional baseline schemes.

Question: How to offload in mobile cloudlet systems with intermittent connectivity?


RoadGraph: High level sensor data fusion between objects and street network. The RoadGraph is a graph based environmental model for driver assistance systems. It integrates information from different sources like digital maps, onboard sensors and V2X communication into one single model about the vehicle's environment. At the moment of information aggregation some function independent situation analysis is done. In this paper we look at techniques for lane-precise map-matching even with moderate GPS reception using distinct information sources. We also analyze the concepts of aggregating objects from different sources with a-priori knowledge of the street-layout. Results of this novelty approach are shown.

Question: What is a graph based environmental model for driver assistance systems?


Innovating with Concept Mapping. Student-generated concept maps (Cmaps) have been the preferred choice to design assessment tasks, which are time-consuming in real classroom settings. We have proposed the use of Cmap with errors (elaborated by teachers) to develop assessment tasks, as a way to address the logistical practicality obstacles usually found in classrooms. This paper compared two different tasks, finding the errors and judging the selected propositions in Cmap with errors, exploring two topics with different levels of difficulty. Our results confirmed Cmap with errors as a straightforward approach to include concept mapping into the classroom routine to foster the pedagogic resonance (the bridge between teacher knowledge and student learning), which is critical for motivating students to learn meaningfully. Moreover, Cmaps with errors are also amenable for on-line learning platforms. Future works of our research group will develop an automated process for evaluation and feedback using the task formats presented in this paper.

Question: How to design assessment tasks using concept mapping?


Learning to aggregate vertical results into web search results. Aggregated search is the task of integrating results from potentially multiple specialized search services, or verticals, into the Web search results. The task requires predicting not only which verticals to present (the focus of most prior research), but also predicting where in the Web results to present them (i.e., above or below the Web results, or somewhere in between). Learning models to aggregate results from multiple verticals is associated with two major challenges. First, because verticals retrieve different types of results and address different search tasks, results from different verticals are associated with different types of predictive evidence (or features). Second, even when a feature is common across verticals, its predictiveness may be vertical-specific. Therefore, approaches to aggregating vertical results require handling an inconsistent feature representation across verticals, and, potentially, a vertical-specific relationship between features and relevance. We present 3 general approaches that address these challenges in different ways and compare their results across a set of 13 verticals and 1070 queries. We show that the best approaches are those that allow the learning algorithm to learn a vertical-specific relationship between features and relevance.

Question: How to aggregate vertical results into web search results?


RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments. RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. RGB-D cameras rely on either structured light patterns combined with stereo sensing [6,10] or time-of-flight laser sensing [1] to generate depth estimates that can be associated with RGB pixels. Very soon, small, high-quality RGB-D cameras developed for computer gaming and home entertainment applications will become available at cost below $100. In this paper we investigate how such cameras can be used in the context of robotics, specifically for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. The robotics and computer vision communities have developed a variety of techniques for 3D mapping based on laser range scans [8, 11], stereo cameras [7], monocular cameras [3], and unsorted collections of photos [4]. While RGB-D cameras provide the opportunity to build 3D maps of unprecedented richness, they have drawbacks that make their application to 3D mapping difficult: They provide depth only up to a limited distance (typically less than 5m), depth values are much noisier than those provided by laser scanners, and their field of view (∼ 60◦) is far more constrained than that of specialized cameras or laser scanners typically used for 3D mapping (∼ 180◦). In our work, we use a camera developed by PrimeSense [10]. The key insights of this investigation are: first, that existing frame matching techniques are not sufficient to provide robust visual odometry with these cameras; second, that a tight integration of depth and color information can yield robust frame matching and loop closure detection; third, that building on best practice techniques in SLAM and computer graphics makes it possible to build and visualize accurate and extremely rich 3D maps with such cameras; and, fourth, that it will

Question: What is the difference between RGB-D and RGB-D cameras?


NFC antenna research and a simple impedance matching method. Near Field Communication (NFC) technology evolved from a non-contact radio frequency identification (RFID) technology and intercommunication technology. Sensor reader, contactless card and peer to peer features integrate to a single chip. This chip could communicate with compatible devices by the identification and data exchange at a short distance. The technology was a simple merger of RFID technology and network technology at first, but it has evolved into a short-range wireless communications technology, which develops very quickly. This paper introduces the concept and characteristics of NFC technology first. Then, starting from the antenna coil used by NFC technology, this paper accomplished the theoretical analysis, Matlab simulation, HFSS and ADS2009 joint simulation respectively, and improved design an antenna impedance matching approach.

Question: What is NFC technology?


On label dependence and loss minimization in multi-label classification. Most of the multi-label classification (MLC) methods proposed in recent years intended to exploit, in one way or the other, dependencies between the class labels. Comparing to simple binary relevance learning as a baseline, any gain in performance is normally explained by the fact that this method is ignoring such dependencies. Without questioning the correctness of such studies, one has to admit that a blanket explanation of that kind is hiding many subtle details, and indeed, the underlying mechanisms and true reasons for the improvements reported in experimental studies are rarely laid bare. Rather than proposing yet another MLC algorithm, the aim of this paper is to elaborate more closely on the idea of exploiting label dependence, thereby contributing to a better understanding of MLC. Adopting a statistical perspective, we claim that two types of label dependence should be distinguished, namely conditional and marginal dependence. Subsequently, we present three scenarios in which the exploitation of one of these types of dependence may boost the predictive performance of a classifier. In this regard, a close connection with loss minimization is established, showing that the benefit of exploiting label dependence does also depend on the type of loss to be minimized. Concrete theoretical results are presented for two representative loss functions, namely the Hamming loss and the subset 0/1 loss. In addition, we give an overview of state-of-the-art decomposition algorithms for MLC and we try to reveal the reasons for their effectiveness. Our conclusions are supported by carefully designed experiments on synthetic and benchmark data.

Question: What is the difference between conditional and marginal dependence?


Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks. Five billion people in the world lack access to quality surgical care. Surgeon skill varies dramatically, and many surgical patients suffer complications and avoidable harm. Improving surgical training and feedback would help to reduce the rate of complications—half of which have been shown to be preventable. To do this, it is essential to assess operative skill, a process that currently requires experts and is manual, time consuming, and subjective. In this work, we introduce an approach to automatically assess surgeon performance by tracking and analyzing tool movements in surgical videos, leveraging region-based convolutional neural networks. In order to study this problem, we also introduce a new dataset, m2cai16-tool-locations, which extends the m2cai16-tool dataset with spatial bounds of tools. While previous methods have addressed tool presence detection, ours is the first to not only detect presence but also spatially localize surgical tools in real-world laparoscopic surgical videos. We show that our method both effectively detects the spatial bounds of tools as well as significantly outperforms existing methods on tool presence detection. We further demonstrate the ability of our method to assess surgical quality through analysis of tool usage patterns, movement range, and economy of motion.

Question: How to assess surgical skill?


Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions.. Nanopore sequencing technology has the potential to render other sequencing technologies obsolete with its ability to generate long reads and provide portability. However, high error rates of the technology pose a challenge while generating accurate genome assemblies. The tools used for nanopore sequence analysis are of critical importance, as they should overcome the high error rates of the technology. Our goal in this work is to comprehensively analyze current publicly available tools for nanopore sequence analysis to understand their advantages, disadvantages and performance bottlenecks. It is important to understand where the current tools do not perform well to develop better tools. To this end, we (1) analyze the multiple steps and the associated tools in the genome assembly pipeline using nanopore sequence data, and (2) provide guidelines for determining the appropriate tools for each step. Based on our analyses, we make four key observations: (1) the choice of the tool for basecalling plays a critical role in overcoming the high error rates of nanopore sequencing technology. (2) Read-to-read overlap finding tools, GraphMap and Minimap, perform similarly in terms of accuracy. However, Minimap has a lower memory usage, and it is faster than GraphMap. (3) There is a trade-off between accuracy and performance when deciding on the appropriate tool for the assembly step. The fast but less accurate assembler Miniasm can be used for quick initial assembly, and further polishing can be applied on top of it to increase the accuracy, which leads to faster overall assembly. (4) The state-of-the-art polishing tool, Racon, generates high-quality consensus sequences while providing a significant speedup over another polishing tool, Nanopolish. We analyze various combinations of different tools and expose the trade-offs between accur

Question: What is the difference between nanopore sequencing and other sequencing technologies?


Potential of Estimating Soil Moisture Under Vegetation Cover by Means of PolSAR. In this paper, the potential of using polarimetric SAR (PolSAR) acquisitions for the estimation of volumetric soil moisture under agricultural vegetation is investigated. Soil-moisture estimation by means of SAR is a topic that is intensively investigated but yet not solved satisfactorily. The key problem is the presence of vegetation cover which biases soil-moisture estimates. In this paper, we discuss the problem of soil-moisture estimation in the presence of agricultural vegetation by means of L-band PolSAR images. SAR polarimetry allows the decomposition of the scattering signature into canonical scattering components and their quantification. We discuss simple canonical models for surface, dihedral, and vegetation scattering and use them to model and interpret scattering processes. The performance and modifications of the individual scattering components are discussed. The obtained surface and dihedral components are then used to retrieve surface soil moisture. The investigations cover, for the first time, the whole vegetation-growing period for three crop types using SAR data and ground measurements acquired in the frame of the AgriSAR campaign.

Question: How to estimate soil moisture under vegetation cover?


ProbView: A Flexible Probabilistic Database System. Probability theory is mathematically the best understood paradigm for modeling and manipulating uncertain information. Probabilities of complex events can be computed from those of basic events on which they depend, using any of a number of strategies. Which strategy is appropriate depends very much on the known interdependencies among the events involved. Previous work on probabilistic databases has assumed a fixed and restrictivecombination strategy (e.g., assuming all events are pairwise independent). In this article, we characterize, using postulates, whole classes of strategies for conjunction, disjunction, and negation, meaningful from the viewpoint of probability theory. (1) We propose a probabilistic relational data model and a genericprobabilistic relational algebra that neatly captures various strategiessatisfying the postulates, within a single unified framework. (2) We show that as long as the chosen strategies can be computed in polynomial time, queries in the positive fragment of the probabilistic relational algebra have essentially the same data complexity as classical relational algebra. (3) We establish various containments and equivalences between algebraic expressions, similar in spirit to those in classical algebra. (4) We develop algorithms for maintaining materialized probabilistic views. (5) Based on these ideas, we have developed a prototype probabilistic database system called ProbView on top of Dbase V.0. We validate our complexity results with experiments and show that rewriting certain types of queries to other equivalent forms often yields substantial savings.

Question: What is the probability of a random person being a Canadian?


Digital Literacy: A Conceptual Framework for Survival Skills in the Digital Era. Digital literacy involves more than the mere ability to use software or operate a digital device; it includes a large variety of complex cognitive, motor, sociological, and emotional skills, which users need in order to function effectively in digital environments. The tasks required in this context include, for example, "reading" instructions from graphical displays in user interfaces; using digital reproduction to create new, meaningful materials from existing ones; constructing knowledge from a nonlinear, hypertextual navigation; evaluating the quality and validity of information; and have a mature and realistic understanding of the "rules" that prevail in the cyberspace. This newly emerging concept of digital literacy may be used as a measure of the quality of learners' work in digital environments, and provide scholars and developers with a more effective means of communication in designing better user-oriented environments. This article proposes a holistic, refined conceptual framework for digital literacy, which includes photo-visual literacy; reproduction literacy; branching literacy; information literacy; and socioemotional literacy.

Question: What is digital literacy?


Physics 101: Learning Physical Object Properties from Unlabeled Videos. We study the problem of learning physical properties of objects from unlabeled videos. Humans can learn basic physical laws when they are very young, which suggests that such tasks may be important goals for computational vision systems. We consider various scenarios: objects sliding down an inclined surface and colliding; objects attached to a spring; objects falling onto various surfaces, etc. Many physical properties like mass, density, and coefficient of restitution influence the outcome of these scenarios, and our goal is to recover them automatically. We have collected 17,408 video clips containing 101 objects of various materials and appearances (shapes, colors, and sizes). Together, they form a dataset, named Physics 101, for studying object-centered physical properties. We propose an unsupervised representation learning model, which explicitly encodes basic physical laws into the structure and use them, with automatically discovered observations from videos, as supervision. Experiments demonstrate that our model can learn physical properties of objects from video. We also illustrate how its generative nature enables solving other tasks such as outcome prediction.

Question: What is the name of the dataset?


Immersion, presence and performance in virtual environments: an experiment with tri-dimensional chess. This paper describes an experiment to assess the influence of immersion on performance in immersive virtual environments. The task involved Tri-Dimensional Chess, and required subjects to reproduce on a real chess board the state of the board learned from a sequence of moves witnessed in a virtual environment. Twenty four subjects were allocated to a factorial design consisting of two levels of immersion (exocentric screen based, and egocentric HMD based), and two kinds of environment (plain and realistic. The results suggest that egocentric subjects performed better than exocentric, and those in the more realistic environment performed better than those in the less realistic environment. Previous knowledge of chess, and amount of virtual practice were also significant, and may be considered as control variables to equalise these factors amongst the subjects. Other things being equal, males remembered the moves better than females, although female performance improved with higher spatial ability test score. The paper also attempts to clarify the relationship between immersion, presence and performance, and locates the experiment within such a theoretical framework.

Question: What is the difference between immersion, presence and performance?


Decision feedback equalization. As real world communication channels are stressed with higher data rates, intersymbol interference (ISI) becomes a dominant limiting factor. One way to combat this effect that has recently received considerable attention is the use of a decision feedback equalizer (DFE) in the receiver. The action of the DFE is to feed back a weighted sum of past decision to cancel the ISI they cause in the present signaling interval. This paper summarizes the work in this area beginning with the linear equalizer. Three performance criteria have been used to derive optimum systems; 1) minimize the noise variance under a "zero forcing" (ZF) constraint i.e., insist that all intersymbol interference is cancelled, 2) minimize the mean-square error (MMSE) between the true sample and the observed signal just prior to the decision threshold, and 3) minimize the probability of error (Min Pe). The transmitter can be fixed and the receiver optimized or one can obtain the joint optimum transmitter and receiver. The number of past decisions used in the feedback equalization can be finite or infinite. The infinite case is easier to handle analytically. In addition to reviewing the work done in the area, we show that the linear equalizer is in fact a portion of the DFE receiver and that the processing done by the DFE is exactly equivalent to the general problem of linear prediction. Other similarities in the various system structures are also shown. The effect of error propagation due to incorrect decisions is discussed, and the coaxial cable channel is used as an example to demonstrate the improvement available using DFE.

Question: What is decision feedback equalization?


weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming. Selective weed treatment is a critical step in autonomous crop management as related to crop health and yield. However, a key challenge is reliable and accurate weed detection to minimize damage to surrounding plants. In this letter, we present an approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV). We use the recently developed encoder–decoder cascaded convolutional neural network, SegNet, that infers dense semantic classes while allowing any number of input image channels and class balancing with our sugar beet and weed datasets. To obtain training datasets, we established an experimental field with varying herbicide levels resulting in field plots containing only either crop or weed, enabling us to use the normalized difference vegetation index as a distinguishable feature for automatic ground truth generation. We train six models with different numbers of input channels and condition (fine tune) it to achieve  $\sim$0.8 F1-score and 0.78 area under the curve classification metrics. For the model deployment, an embedded Graphics Processing Unit (GPU) system (Jetson TX2) is tested for MAV integration. Dataset used in this letter is released to support the community and future work.

Question: What is the difference between a MAV and a drone?


Robust Real-time Object Detection. This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the "Integral Image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers [6]. The third contribution is a method for combining classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performace comparable to the best previous systems [18, 13, 16, 12, 1]. Implemented on a conventional desktop, face detection proceeds at 15 frames per second.

Question: What is the name of the algorithm?


Malassezia Intra-Specific Diversity and Potentially New Species in the Skin Microbiota from Brazilian Healthy Subjects and Seborrheic Dermatitis Patients. Malassezia yeasts are part of the resident cutaneous microbiota, and are also associated with skin diseases such as seborrheic dermatitis (SD). The role these fungi play in skin diseases and why they are pathogenic for only some individuals remain unclear. This study aimed to characterize Malassezia microbiota from different body sites in healthy and SD subjects from Brazil. Scalp and forehead samples from healthy, mild SD and severe SD subjects were collected. Non-scalp lesions from severe SD patients were also sampled. 5.8S rDNA/ITS2 amplicons from Malassezia sp. were analyzed by RFLP and sequencing. Results indicate that Malassezia microbiota did not group according to health condition or body area. Phylogenetic analysis revealed that three groups of sequences did not cluster together with any formally described species, suggesting that they might belong to potential new species. One of them was found in high proportions in scalp samples. A large variety of Malassezia subtypes were detected, indicating intra-specific diversity. Higher M. globosa proportions were found in non-scalp lesions from severe SD subjects compared with other areas, suggesting closer association of this species with SD lesions from areas other than scalp. Our results show the first panorama of Malassezia microbiota in Brazilian subjects using molecular techniques and provide new perspectives for further studies to elucidate the association between Malassezia microbiota and skin diseases.

Question: What is the difference between scalp and non-scalp lesions?


Embodiment of Wearable Augmented Reality Technology in Tourism Experiences. The increasing use of wearable devices for tourism purposes sets the stage for a critical discussion on technological mediation in tourism experiences. This paper provides a theoretical reflection on the phenomenon of embodiment relation in technological mediation and then assesses the embodiment of wearable augmented reality technology in a tourism attraction. The findings suggest that technology embodiment is a multidimensional construct consisting of ownership, location, and agency. These support the concept of technology withdrawal, where technology disappears as it becomes part of human actions, and contest the interplay of subjectivity and intentionality between humans and technology in situated experiences such as tourism. It was also found that technology embodiment affects enjoyment and enhances experience with tourism attractions.

Question: What is the difference between technology withdrawal and technology embodiment?


Chapter 3 Eye Tracking and Eye-Based Human – Computer Interaction. Eye tracking has a long history in medical and psychological research as a tool for recording and studying human visual behavior. Real-time gaze-based text entry can also be a powerful means of communication and control for people with physical disabilities. Following recent technological advances and the advent of affordable eye trackers, there is a growing interest in pervasive attention-aware systems and interfaces that have the potential to revolutionize mainstream humantechnology interaction. In this chapter, we provide an introduction to the state-of-the art in eye tracking technology and gaze estimation. We discuss challenges involved in using a perceptual organ, the eye, as an input modality. Examples of real life applications are reviewed, together with design solutions derived from research results. We also discuss how to match the user requirements and key features of different eye tracking systems to find the best system for each task and application.

Question: What is the state of the art in eye tracking technology?


Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms. In this paper, we establish max-flow min-cut theorems for several important classes of multicommodity flow problems. In particular, we show that for any n-node multicommodity flow problem with uniform demands, the max-flow for the problem is within an O(log n) factor of the upper bound implied by the min-cut. The result (which is existentially optimal) establishes an important analogue of the famous 1-commodity max-flow min-cut theorem for problems with multiple commodities. The result also has substantial applications to the field of approximation algorithms. For example, we use the flow result to design the first polynomial-time (polylog n-times-optimal) approximation algorithms for well-known NP-hard optimization problems such as graph partitioning, min-cut linear arrangement, crossing number, VLSI layout, and minimum feedback arc set. Applications of the flow results to path routing problems, network reconfiguration, communication in distributed networks, scientific computing and rapidly mixing Markov chains are also described in the paper.

Question: What is the max-flow for the problem?


Consumption of ultra-processed foods and likely impact on human health. Evidence from Canada.. OBJECTIVE To investigate consumption of ultra-processed products in Canada and to assess their association with dietary quality.   DESIGN Application of a classification of foodstuffs based on the nature, extent and purpose of food processing to data from a national household food budget survey. Foods are classified as unprocessed/minimally processed foods (Group 1), processed culinary ingredients (Group 2) or ultra-processed products (Group 3).   SETTING All provinces and territories of Canada, 2001.   SUBJECTS Households (n 5643).   RESULTS Food purchases provided a mean per capita energy availability of 8908 (se 81) kJ/d (2129 (se 19) kcal/d). Over 61·7 % of dietary energy came from ultra-processed products (Group 3), 25·6 % from Group 1 and 12·7 % from Group 2. The overall diet exceeded WHO upper limits for fat, saturated fat, free sugars and Na density, with less fibre than recommended. It also exceeded the average energy density target of the World Cancer Research Fund/American Institute for Cancer Research. Group 3 products taken together are more fatty, sugary, salty and energy-dense than a combination of Group 1 and Group 2 items. Only the 20 % lowest consumers of ultra-processed products (who consumed 33·2 % of energy from these products) were anywhere near reaching all nutrient goals for the prevention of obesity and chronic non-communicable diseases.   CONCLUSIONS The 2001 Canadian diet was dominated by ultra-processed products. As a group, these products are unhealthy. The present analysis indicates that any substantial improvement of the diet would involve much lower consumption of ultra-processed products and much higher consumption of meals and dishes prepared from minimally processed foods and processed culinary ingredients.

Question: What is the Canadian diet?


Conscious intention and brain activity. The problem of free will lies at the heart of modern scientific studies of consciousness. An influential series of experiments by Libet has suggested that conscious intentions arise as a result of brain activity. This contrasts with traditional concepts of free will, in which the mind controls the body. A more recent study by Haggard and Eimer has further examined the relation between intention and brain processes, concluding that conscious awareness of intention is linked to the choice or selection of a specific action, and not to the earliest initiation of action processes. The exchange of views in this paper further explores the relation between conscious intention and brain activity.

Question: What is the relation between intention and brain activity?


A systematic review and meta-analysis of dressings used for wound healing: the efficiency of honey compared to silver on burns.. BACKGROUND Honey has the antibacterial effect of silver without the toxic effect of silver on the skin. Even so, silver is the dominant antibacterial dressing used in wound healing.   OBJECTIVES To evaluate the healing effects of honey dressings compared to silver dressings for acute or chronic wounds.   DESIGN A systematic review with meta-analysis.   METHOD The search, conducted in seven databases, resulted in six randomised controlled trial studies from South Asia focusing on antibacterial properties and healing times of honey and silver.   RESULT Honey was more efficacious for wound healing than silver, as measured in the number of days needed for wounds to heal (pooled risk difference -20, 95% CI -0.29 to -0.11, p < .001). Honey turned out to have more antibacterial qualities than silver.   CONCLUSION All the included studies based on burns showed the unequivocal result that honey had an even more positive effect than silver on wound healing.

Question: What is the difference between honey and silver?


SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging. Convolutional Neural Networks (CNNs) are typically trained in the RGB color space. However, in medical imaging, we believe that pixel stain quantities offer a fundamental view of the interaction between tissues and stain chemicals. Since the optical density (OD) colorspace allows to compute pixel stain quantities from pixel RGB intensities using the Beer-Lambert's law, we propose a stain deconvolutional layer, hereby named as SD-Layer, affixed at the front of CNN that performs two functions: (1) it transforms the input RGB microscopic images to Optical Density (OD) space and (2) this layer deconvolves OD image with the stain basis learned through backpropagation and provides tissue-specific stain absorption quantities as input to the following CNN layers. With the introduction of only nine additional learnable parameters in the proposed SD-Layer, we obtain a considerably improved performance on two standard CNN architectures: AlexNet and T-CNN. Using the T-CNN architecture prefixed with the proposed SD-Layer, we obtain 5-fold crossvalidation accuracy of 93.2% in the problem of differentiating malignant immature White Blood Cells (WBCs) from normal immature WBCs for cancer detection.

Question: What is the difference between a convolutional neural network and a deconvolutional neural network?


Designing the whyline: a debugging interface for asking questions about program behavior. Debugging is still among the most common and costly of programming activities. One reason is that current debugging tools do not directly support the inquisitive nature of the activity. Interrogative Debugging is a new debugging paradigm in which programmers can ask why did and even why didn't questions directly about their program's runtime failures. The Whyline is a prototype Interrogative Debugging interface for the Alice programming environment that visualizes answers in terms of runtime events directly relevant to a programmer's question. Comparisons of identical debugging scenarios from user tests with and without the Whyline showed that the Whyline reduced debugging time by nearly a factor of 8, and helped programmers complete 40% more tasks.

Question: What is a debugging interface?


Dynamic question generation system for web-based testing using particle swarm optimization. One aim of testing is to identify weaknesses in students' knowledge. Computerized tests are now one of the most important ways to judge learning, and, selecting tailored questions for each learner is a significant part of such tests. Therefore, one current trend is that computerized adaptive tests (CATs) not only assist teachers in estimating the learning performance of students, but also facilitate understanding of problems in their learning process. These tests, must effectively and efficiently select questions from a large-scale item bank, and to cope with this problem we propose a dynamic question generation system for web-based tests using the novel approach of particle swarm optimization (PSO). The dynamic question generation system is built to select tailored questions for each learner from the item bank to satisfy multiple assessment requirements. Furthermore, the proposed approach is able to efficiently generate near-optimal questions that satisfy multiple assessment criteria. With a series of experiments, we compare the efficiency and efficacy of the PSO approach with other approaches. The experimental results show that the PSO approach is suitable for the selection of near-optimal questions from large-scale item banks. 2007 Elsevier Ltd. All rights reserved.

Question: What is the difference between a dynamic question generation system and a dynamic question selection system?


Cyber and Physical Security Vulnerability Assessment for IoT-Based Smart Homes. The Internet of Things (IoT) is an emerging paradigm focusing on the connection of devices, objects, or "things" to each other, to the Internet, and to users. IoT technology is anticipated to become an essential requirement in the development of smart homes, as it offers convenience and efficiency to home residents so that they can achieve better quality of life. Application of the IoT model to smart homes, by connecting objects to the Internet, poses new security and privacy challenges in terms of the confidentiality, authenticity, and integrity of the data sensed, collected, and exchanged by the IoT objects. These challenges make smart homes extremely vulnerable to different types of security attacks, resulting in IoT-based smart homes being insecure. Therefore, it is necessary to identify the possible security risks to develop a complete picture of the security status of smart homes. This article applies the operationally critical threat, asset, and vulnerability evaluation (OCTAVE) methodology, known as OCTAVE Allegro, to assess the security risks of smart homes. The OCTAVE Allegro method focuses on information assets and considers different information containers such as databases, physical papers, and humans. The key goals of this study are to highlight the various security vulnerabilities of IoT-based smart homes, to present the risks on home inhabitants, and to propose approaches to mitigating the identified risks. The research findings can be used as a foundation for improving the security requirements of IoT-based smart homes.

Question: What is the security status of IoT-based smart homes?


Using Multiple Segmentations to Discover Objects and their Extent in Image Collections. Given a large dataset of images, we seek to automatically determine the visually similar object and scene classes together with their image segmentation. To achieve this we combine two ideas: (i) that a set of segmented objects can be partitioned into visual object classes using topic discovery models from statistical text analysis; and (ii) that visual object classes can be used to assess the accuracy of a segmentation. To tie these ideas together we compute multiple segmentations of each image and then: (i) learn the object classes; and (ii) choose the correct segmentations. We demonstrate that such an algorithm succeeds in automatically discovering many familiar objects in a variety of image datasets, including those from Caltech, MSRC and LabelMe.

Question: How to automatically discover objects in images?


A Case for Standard Non-blocking Collective Operations. In this paper we make the case for adding standard nonblocking collective operations to the MPI standard. The non-blocking point-to-point and blocking collective operations currently defined by MPI provide important performance and abstraction benefits. To allow these benefits to be simultaneously realized, we present an application programming interface for non-blocking collective operations in MPI. Microbenchmark and application-based performance results demonstrate that non-blocking collective operations offer not only improved convenience, but improved performance as well, when compared to manual use of threads with blocking collectives.

Question: What is the case for standard non-blocking collective operations?


Electronically Reconfigurable Microwave Bandpass Filter. A new class of reconfigurable bandpass filter based on parallel-coupled switched delay line approach is presented in this paper. The approach allows both center frequency and passband bandwidth to be reconfigured electronically. In contrast to conventional approach, the effects of the switch losses are relatively insignificant and can be treated as an auxiliary effect and therefore the filter has low passband loss. Despite the loss performance, the filter provides excellent linearity performance with large power handling capability. Theoretical analysis is presented and the feasibility of the approach has been experimentally verified with microstrip circuit prototypes. The first filter prototype provides narrowband quasi-elliptic filter response with constant bandwidth (50-MHz) tuning across 45% tuning range. The minimum stopband rejection level is 45 dB. The second filter possesses passband-width 3:1 ratio tunability. The third filter provides both functionalities which allow center frequency and passband bandwidth to be reconfigured. All filters have a < 4-dB passband loss with an OIP3 of >20 dBm and are immune from power saturation effect. The measured results demonstrate improved performance in terms of linear and nonlinear characteristic compared with prior works.

Question: What is the difference between a bandpass filter and a bandstop filter?


Differential Impairment of Remembering the Past and Imagining Novel Events after Thalamic Lesions. Vividly remembering the past and imagining the future (mental time travel) seem to rely on common neural substrates and mental time travel impairments in patients with brain lesions seem to encompass both temporal domains. However, because future thinking—or more generally imagining novel events—involves the recombination of stored elements into a new event, it requires additional resources that are not shared by episodic memory. We aimed to demonstrate this asymmetry in an event generation task administered to two patients with lesions in the medial dorsal thalamus. Because of the dense connection with pFC, this nucleus of the thalamus is implicated in executive aspects of memory (strategic retrieval), which are presumably more important for future thinking than for episodic memory. Compared with groups of healthy matched control participants, both patients could only produce novel events with extensive help of the experimenter (prompting) in the absence of episodic memory problems. Impairments were most pronounced for imagining personal fictitious and impersonal events. More precisely, the patients' descriptions of novel events lacked content and spatio-temporal relations. The observed impairment is unlikely to trace back to disturbances in self-projection, scene construction, or time concept and could be explained by a recombination deficit. Thus, although memory and the imagination of novel events are tightly linked, they also partly rely on different processes.

Question: What is the medial dorsal thalamus?


Personality and job performance: the Big Five revisited.. Prior meta-analyses investigating the relation between the Big 5 personality dimensions and job performance have all contained a threat to construct validity, in that much of the data included within these analyses was not derived from actual Big 5 measures. In addition, these reviews did not address the relations between the Big 5 and contextual performance. Therefore, the present study sought to provide a meta-analytic estimate of the criterion-related validity of explicit Big 5 measures for predicting job performance and contextual performance. The results for job performance closely paralleled 2 of the previous meta-analyses, whereas analyses with contextual performance showed more complex relations among the Big 5 and performance. A more critical interpretation of the Big 5-performance relationship is presented, and suggestions for future research aimed at enhancing the validity of personality predictors are provided.

Question: What is the big five personality traits?


Development and validation of blue ray, an optical modem for the MEDUSA class AUVs. Wireless optical communications are emerging as a viable solution for high-speed data transmission over short ranges in the ocean, complementing mainstream acoustic communication systems that operate over much longer ranges, but at lower data rates. The current drive to develop cooperative autonomous vehicular systems to carry out surveying and other complex missions in the ocean critically depends on the existence of such communication links to share sensory and coordination information. This paper presents a compact, low power consumption, cost-efficient, and lightweight optical modem designed for fast data transmission between MEDUSA underwater vehicles at ranges on the order of 10 m. The transmitter uses LEDs for ON-OFF keying, while the receiver front-end adopts a transimpedance architecture with a photodiode detector. This simplistic transmission and reception technique reduces the overall design and hardware complexities compared to, e.g., laser diodes and photomultiplier tubes. The LEDs are arranged in a circular array, with a photodiode at the centre to enable half-duplex operation. These modems are designed to achieve data rates of 20-200 kb/s over short-range, line-of-sight, configurations. A transparent casing is customised to fit the Medusa vehicles, with proper alignment for inter-vehicle communication when they move in formation.

Question: What is the development and validation of blue ray, an optical modem for the MEDUSA class AUVs?


Pixel Recurrent Neural Networks. Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel values and encodes the complete set of dependencies in the image. Architectural novelties include fast twodimensional recurrent layers and an effective use of residual connections in deep recurrent networks. We achieve log-likelihood scores on natural images that are considerably better than the previous state of the art. Our main results also provide benchmarks on the diverse ImageNet dataset. Samples generated from the model appear crisp, varied and globally coherent.

Question: What is a deep neural network?


Weakly supervised detection with decoupled attention-based deep representation. Training object detectors with only image-level annotations is an important problem with a variety of applications. However, due to the deformable nature of objects, a target object delineated by a bounding box always includes irrelevant context and occlusions, which causes large intra-class object variations and ambiguity in object-background distinction. For this reason, identifying the object of interest from a substantial amount of cluttered backgrounds is very challenging. In this paper, we propose a decoupled attention-based deep model to optimize region-based object representation. Different from existing approaches posing object representation in a single-tower model, our proposed network decouples object representation into two separate modules, i.e., image representation and attention localization. The image representation module captures content-based semantic representation, while the attention localization module regresses an attention map which simultaneously highlights the locations of the discriminative object parts and down weights the irrelevant backgrounds presented in the image. The combined representation alleviates the impact from the noisy context and occlusions inside an object bounding box. As a result, object-background ambiguity can be largely reduced and background regions can be suppressed effectively. In addition, the proposed object representation model can be seamlessly integrated into a state-of-the-art weakly supervised detection framework, and the entire model can be trained end-to-end. We extensively evaluate the detection performance on the PASCAL VOC 2007, VOC 2010 and VOC2012 datasets. Experimental results demonstrate that our approach effectively improves weakly supervised object detection.

Question: What is the difference between weakly supervised and supervised object detection?


The Situated Function - Behaviour - Structure Framework. This paper extends the Function-Behaviour-Structure (FBS) framework, which proposed eight fundamental processes involved in designing. This framework did not explicitly account for the dynamic character of the context in which designing takes place, described by the notion of situatedness. This paper describes this concept as a recursive interrelationship between different environments, which, together with a model of constructive memory, provides the foundation of a situated FBS framework. The eight fundamental processes are then reconstructed within this new framework to represent designing in a dynamic world.

Question: What is the situated function-behaviour-structure framework?


Eye localization from thermal infrared images. By using the knowledge of facial structure and temperature distribution, this paper proposes an automatic eye localization method from long wave infrared thermal images both with eyeglasses and without eyeglasses. First, with the help of support vector machine classifier, three gray-projection features are defined to determine whether a subject is with eyeglasses. For subjects with eyeglasses, the locations of valleys in the projection curve are used to perform eye localization. For subjects without eyeglasses, a facial structure consisting of 15 sub-regions is proposed to extract Haar-like features. Eight classifiers are learned from the features selected by Adaboost algorithm for left and right eye, respectively. A vote strategy is employed to find the most likely eyes. To evaluate the effectiveness of our approach, experiments are performed on NVIE and Equinox databases. The eyeglass detection results on NVIE database and Equinox database are 99.36% and 95%, respectively, which demonstrate the effectiveness and robustness of our eyeglass detection method. Eye localization results of withindatabase experiments and cross-database experiments on these two databases are very comparable with the previous results in this field, verifying the effectiveness and the generalization ability of our approach. & 2013 Elsevier Ltd. All rights reserved.

Question: How to localize eyes from thermal infrared images?


A Survey of Face Recognition Techniques. Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.

Question: What is the state of the art of face recognition technology?


Local sleep spindle modulations in relation to specific memory cues. Sleep spindles have been connected to memory processes in various ways. In addition, spindles appear to be modulated at the local cortical network level. We investigated whether cueing specific memories during sleep leads to localized spindle modulations in humans. During learning of word-location associations, words presented in the left and right visual hemifields were paired with different odors. By presenting a single odor during a subsequent nap, we aimed to selectively reactivate a subset of the studied material in sleeping subjects. During sleep, we observed topographically restricted spindle responses to memory cues, suggesting successful reactivation of specific memory traces. In particular, we found higher amplitude and greater incidence of fast spindles over posterior brain areas involved in visuospatial processing, contralateral to the visual field being cued. These results suggest that sleep spindles in different cortical areas reflect the reprocessing of specific memory traces.

Question: How does sleep spindles relate to memory?


Tuning of PID controller based on Fruit Fly Optimization Algorithm. The Proportional - Integral - Derivative (PID) controllers are one of the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. PID tuning is the key issue in the design of PID controllers and most of the tuning processes are implemented manually resulting in difficulty and time consuming. To enhance the capabilities of traditional PID parameters tuning techniques, modern heuristics approaches, such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are employed recent years. In this paper, a novel tuning method based on Fruit Fly Optimization Algorithm (FOA) is proposed to optimize PID controller parameters. Each fruit fly's position represents a candidate solution for PID parameters. When the fruit fly swarm flies towards one location, it is treated as the evolution of each iterative swarm. After hundreds of iteration, the tuning results - the best PID controller parameters can be obtained. The main advantages of the proposed method include ease of implementation, stable convergence characteristic, large searching range, ease of transformation of such concept into program code and ease of understanding. Simulation results demonstrate that the FOA-Based optimized PID (FOA - PID) controller is with the capability of providing satisfactory closed - loop performance.

Question: Tuning of PID controller based on Fruit Fly Optimization Algorithm


Broder and Mitzenmacher : Network Applications of Bloom Filters : A Survey 487. A Bloom filter is a simple space-efficient randomized data structure for representing a set in order to support membership queries. Bloom filters allow false positives but the space savings often outweigh this drawback when the probability of an error is controlled. Bloom filters have been used in database applications since the 1970s, but only in recent years have they become popular in the networking literature. The aim of this paper is to survey the ways in which Bloom filters have been used and modified in a variety of network problems, with the aim of providing a unified mathematical and practical framework for understanding them and stimulating their use in future applications.

Question: What is a bloom filter?


A model-free mapless navigation method for mobile robot using reinforcement learning. In this paper, a simple and efficient method is proposed using one of the basic reinforcement learning tool Q-learning. This method is to achieve the aim of mobile robot maples navigation with collision-free. Thus, an end-to-end navigation model is built, which uses lidar sensor massage as input and moving command of mobile robot as output, so as to simplify the process of environmental perception and decision making on action. Through simulation and experiments, its effectiveness is proved. After trained by this method, mobile robot can safely reach the navigation target in an unknown environment without any prior demonstration. In addition, an extensive quantitative and qualitative evaluation of this method is presented by the comparison with traditional path planning method based on foregone global environment map.

Question: What is reinforcement learning?


A restricted Boltzmann machine based two-lead electrocardiography classification. An restricted Boltzmann machine learning algorithm were proposed in the two-lead heart beat classification problem. ECG classification is a complex pattern recognition problem. The unsupervised learning algorithm of restricted Boltzmann machine is ideal in mining the massive unlabelled ECG wave beats collected in the heart healthcare monitoring applications. A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. In this paper a deep belief network was constructed and the RBM based algorithm was used in the classification problem. Under the recommended twelve classes by the ANSI/AAMI EC57: 1998/(R)2008 standard as the waveform labels, the algorithm was evaluated on the two-lead ECG dataset of MIT-BIH and gets the performance with accuracy of 98.829%. The proposed algorithm performed well in the two-lead ECG classification problem, which could be generalized to multi-lead unsupervised ECG classification or detection problems.

Question: What is the difference between a restricted Boltzmann machine and a deep belief network?


Implementation of a Practical Distributed Calculation System with Browsers and JavaScript, and Application to Distributed Deep Learning. Deep learning can achieve outstanding results in various fields. However, it requires so significant computational power that graphics processing units (GPUs) and/or numerous computers are often required for the practical application. We have developed a new distributed calculation framework called "Sashimi" that allows any computer to be used as a distribution node only by accessing a website. We have also developed a new JavaScript neural network framework called "Sukiyaki" that uses general purpose GPUs with web browsers. Sukiyaki performs 30 times faster than a conventional JavaScript library for deep convolutional neural networks (deep CNNs) learning. The combination of Sashimi and Sukiyaki, as well as new distribution algorithms, demonstrates the distributed deep learning of deep CNNs only with web browsers on various devices. The libraries that comprise the proposed methods are available under MIT license athttp://mil-tokyo.github.io/.

Question: How does deep learning work?


Smart Semantic Middleware for the Internet of Things. As ubiquitous systems become increasingly complex, traditional solutions to manage and control them reach their limits and pose a need for self-manageability. Also, heterogeneity of the ubiquitous components, standards, data formats, etc, creates significant obstacles for interoperability in such complex systems. The promising technologies to tackle these problems are the Semantic technologies, for interoperability, and the Agent technologies for management of complex systems. This paper describes our vision of a middleware for the Internet of Things, which will allow creation of self-managed complex systems, in particular industrial ones, consisting of distributed and heterogeneous components of different nature. We also present an analysis of issues to be resolved to realize such a middleware.

Question: What is the vision of a middleware for the Internet of Things?


Synthesis, properties, and applications of iron nanoparticles.. Iron, the most ubiquitous of the transition metals and the fourth most plentiful element in the Earth's crust, is the structural backbone of our modern infrastructure. It is therefore ironic that as a nanoparticle, iron has been somewhat neglected in favor of its own oxides, as well as other metals such as cobalt, nickel, gold, and platinum. This is unfortunate, but understandable. Iron's reactivity is important in macroscopic applications (particularly rusting), but is a dominant concern at the nanoscale. Finely divided iron has long been known to be pyrophoric, which is a major reason that iron nanoparticles have not been more fully studied to date. This extreme reactivity has traditionally made iron nanoparticles difficult to study and inconvenient for practical applications. Iron however has a great deal to offer at the nanoscale, including very potent magnetic and catalytic properties. Recent work has begun to take advantage of iron's potential, and work in this field appears to be blossoming.

Question: What is the most common use of iron?


A framework and graphical development environment for robust NLP tools and applications..

Question: What is a framework and graphical development environment for robust NLP tools and applications?


A Behavioral Model of Rational Choice. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Question: What is the difference between JSTOR and JSTOR archive?


Linearization Through Dithering: A 50 MHz Bandwidth, 10-b ENOB, 8.2 mW VCO-Based ADC. Non-linear voltage-to-frequency characteristic of a voltage-controlled oscillator (VCO) severely curtails the dynamic range of analog-to-digital converters (ADCs) built with VCOs. Typical approaches to enhance the dynamic range include embedding the VCO-based ADC in a ΔΣ loop or to post-process the digital data for calibration, both of which impose significant power constraints. In contrast, in this work the VCO-based ADC is linearized through a filtered dithering technique, wherein the VCO-based ADC is used as a fine stage that processes the residue from a coarse stage in a 0-1 MASH structure. The proposed filtered dithering technique conditions the signal to the VCO input to appear as white noise thereby eliminating spurious signal content arising out of the VCO nonlinearity. The work resorts to multiple other signal processing techniques to build a high-resolution, wideband prototype, in 65 nm complementary metal-oxide semiconductor (CMOS), that achieves 10 effective number of bits (ENOB) in digitizing signals with 50 MHz bandwidth consuming 8.2 mW at a figure of merit (FoM) of 90 fJ/conv.step.

Question: What is a 0-1 MASH structure?


Surprisingly Easy Hard-Attention for Sequence to Sequence Learning. In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy, accurate, and efficient attention mechanism for sequence to sequence learning. The method combines the advantage of sharp focus in hard attention and the implementation ease of soft attention. On five translation and two morphological inflection tasks we show effortless and consistent gains in BLEU compared to existing attention mechanisms.

Question: What is a beam approximation of the joint distribution between attention and output?


Effect of Control-Loops Interactions on Power Stability Limits of VSC Integrated to AC System. This paper investigates the effect of control-loops interactions on power stability limits of the voltage-source converter (VSC) as connected to an ac system. The focus is put on the physical mechanism of the control-loops interactions in the VSC, revealing that interactions among the control loops result in the production of an additional loop. The open-loop gain of the additional loop is employed to quantify the severity of the control-loop interactions. Furthermore, the power current sensitivity, closely related to control-loops interactions, is applied to estimate the maximum transferrable power of the VSC connected to an ac grid. On that basis, stability analysis results show that interactions between dc-link voltage control and phase-locked loop restrict the power angle to about 51° for stable operation with no dynamic reactive power supported. Conversely, the system is capable of approaching the ac-side maximum power transfer limits with alternating voltage control included. Simulations in MATLAB/Simulink are conducted to validate the stability analysis.

Question: What is the effect of control-loops interactions on power stability limits of the VSC?


Analyzing User Preference for Social Image Recommendation. With the incredibly growing amount of multimedia data shared on the social media platforms, recommender systems have become an important necessity to ease users' burden on the information overload. In such a scenario, extensive amount of heterogeneous information such as tags, image content, in addition to the user-to-item preferences, is extremely valuable for making effective recommendations. In this paper, we explore a novel hybrid algorithm termed STM, for image recommendation. STM jointly considers the problem of image content analysis with the users' preferences on the basis of sparse representation. STM is able to tackle the challenges of highly sparse user feedbacks and cold-start problmes in the social network scenario. In addition, our model is based on the classical probabilistic matrix factorization and can be easily extended to incorporate other useful information such as the social relationships. We evaluate our approach with a newly collected 0.3 million social image data set from Flickr. The experimental results demonstrate that sparse topic modeling of the image content leads to more effective recommendations, , with a significant performance gain over the state-of-the-art alternatives.

Question: What is the difference between the state-of-the-art alternatives and our approach?


THE FUNCTIONAL APPROACH TO THE STUDY OF ATTITUDES. At the psychological level the reasons for holding or for changing attitudes are found in the functions they perform for the individual, specifically the functions of adjustment, ego defense, value expression, and knowledge. The conditions necessary to arouse or modify an attitude vary according to the motivational basis of the attitude. Ego-defensive attitudes, for example, can be aroused by threats, appeals to hatred and repressed impulses, and authoritarian suggestion, and can be changed by removal of threat, catharsis, and self-insight. Expressive attitudes are aroused by cues associated with the individual's values and by the need to reassert his self-image and can be changed by showing the appropriateness of the new or modified beliefs to the self-concept Brain washing is primarily directed at the value-expressive function and operates by controlling all environmental supports of old values. Changing attitudes may involve generalization of change to related areas of belief and feeling. Minimal generalization seems to be the rule among adults; for example, in politics voting for an opposition candidate does not have much effect upon party identification. The author is Professor of Psychology at the University of Michigan, former president of the Society for the Psychological Study of Social Issues, and coeditor of Research Methods in the Behavioral Sciences and Public Opinion and Propaganda.

Question: What is the functional approach to the study of attitudes?


Learning Styles and Learning Spaces : A Review of the Multidisciplinary Application of Experiential Learning Theory in Higher Education. Using the concept of learning style and learning space to describe the interface between learners and the learning environment in higher education, in this chapter we review studies addressing how learning style information and the experiential learning model have been applied to improve teaching and learning in sixteen different academic fields and professions. Studies suggest that experiential learning affords educators a way to design and implement teaching and learning strategies conducive to creating a learning environment beneficial for both faculty and students with possibilities for institutional wide dissemination of its core principles and practices. Drawing from the findings generated from the studies, we propose guiding principles for creating growth promoting learning spaces throughout higher education.

Question: What is the difference between experiential learning and traditional learning?


Spatial-Aware Object-Level Saliency Prediction by Learning Graphlet Hierarchies. To fill the semantic gap between the predictive power of computational saliency models and human behavior, this paper proposes to predict where people look at using spatial-aware object-level cues. While object-level saliency has been recently suggested by psychophysics experiments and shown effective with a few computational models, the spatial relationship between the objects has not yet been explored in this context. We in this work for the first time explicitly model such spatial relationship, as well as leveraging semantic information of an image to enhance object-level saliency modeling. The core computational module is a graphlet-based (i.e., graphlets are moderate-sized connected subgraphs) deep architecture, which hierarchically learns a saliency map from raw image pixels to object-level graphlets (oGLs) and further to spatial-level graphlets (sGLs). Eye tracking data are also used to leverage human experience in saliency prediction. Experimental results demonstrate that the proposed oGLs and sGLs well capture object-level and spatial-level cues relating to saliency, and the resulting saliency model performs competitively compared with the state-of-the-art.

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


Psychological aspects of natural language. use: our words, our selves.. The words people use in their daily lives can reveal important aspects of their social and psychological worlds. With advances in computer technology, text analysis allows researchers to reliably and quickly assess features of what people say as well as subtleties in their linguistic styles. Following a brief review of several text analysis programs, we summarize some of the evidence that links natural word use to personality, social and situational fluctuations, and psychological interventions. Of particular interest are findings that point to the psychological value of studying particles-parts of speech that include pronouns, articles, prepositions, conjunctives, and auxiliary verbs. Particles, which serve as the glue that holds nouns and regular verbs together, can serve as markers of emotional state, social identity, and cognitive styles.

Question: What is the psychological value of studying particles?