View invariant human action recognition using histograms of 3D joints. In this paper, we present a novel approach for human action recognition with histograms of 3D joint locations (HOJ3D) as a compact representation of postures. We extract the 3D skeletal joint locations from Kinect depth maps using Shotton et al.'s method [6]. The HOJ3D computed from the action depth sequences are reprojected using LDA and then clustered into k posture visual words, which represent the prototypical poses of actions. The temporal evolutions of those visual words are modeled by discrete hidden Markov models (HMMs). In addition, due to the design of our spherical coordinate system and the robust 3D skeleton estimation from Kinect, our method demonstrates significant view invariance on our 3D action dataset. Our dataset is composed of 200 3D sequences of 10 indoor activities performed by 10 individuals in varied views. Our method is real-time and achieves superior results on the challenging 3D action dataset. We also tested our algorithm on the MSR Action 3D dataset and our algorithm outperforms Li et al. [25] on most of the cases.

Question: What is the 3D skeleton estimation from Kinect?


Random Features for Large-Scale Kernel Machines. To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. Our randomized features are designed so that the inner products of the transformed data are approximately equal to those in the feature space of a user specified shift-invariant kernel. We explore two sets of random features, provide convergence bounds on their ability to approximate various radial basis kernels, and show that in large-scale classification and regression tasks linear machine learning algorithms that use these features outperform state-of-the-art large-scale kernel machines.

Question: What is a randomized feature?


Automatic Fruit Recognition Based on DCNN for Commercial Source Trace System. Automatically fruit recognition by using machine vision is considered as challenging task due to similarities between various types of fruits and external environmental changes e-g lighting. In this paper, fruit recognition algorithm based on Deep Convolution Neural Network(DCNN) is proposed. Most of the previous techniques have some limitations because they were examined and evaluated under limited dataset, furthermore they have not considered external environmental changes. Another major contribution in this paper is that we established fruit images database having 15 different categories comprising of 44406 images which were collected within a period of 6 months by keeping in view the limitations of existing dataset under different real-world conditions. Images were directly used as input to DCNN for training and recognition without extracting features, besides this DCNN learn optimal features from images through adaptation process. The final decision was totally based on a fusion of all regional classification using probability mechanism. Experimental results exhibit that the proposed approach have efficient capability of automatically recognizing the fruit with a high accuracy of 99% and it can also effectively meet real world application requirements.

Question: What is the difference between a fruit and a vegetable?


Domain Generalization with Adversarial Feature Learning. In this paper, we tackle the problem of domain generalization: how to learn a generalized feature representation for an "unseen" target domain by taking the advantage of multiple seen source-domain data. We present a novel framework based on adversarial autoencoders to learn a generalized latent feature representation across domains for domain generalization. To be specific, we extend adversarial autoencoders by imposing the Maximum Mean Discrepancy (MMD) measure to align the distributions among different domains, and matching the aligned distribution to an arbitrary prior distribution via adversarial feature learning. In this way, the learned feature representation is supposed to be universal to the seen source domains because of the MMD regularization, and is expected to generalize well on the target domain because of the introduction of the prior distribution. We proposed an algorithm to jointly train different components of our proposed framework. Extensive experiments on various vision tasks demonstrate that our proposed framework can learn better generalized features for the unseen target domain compared with state-of-the-art domain generalization methods.

Question: How to learn a generalized feature representation for an "unseen" target domain by taking the advantage of multiple seen source-domain data.


Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?. The present phase of Machine Learning is characterized by supervised learning algorithms relying on large sets of labeled examples (n→∞). The next phase is likely to focus on algorithms capable of learning from very few labeled examples (n → 1), like humans seem able to do. We propose an approach to this problem and describe the underlying theory, based on the unsupervised, automatic learning of a "good" representation for supervised learning, characterized by small sample complexity (n). We consider the case of visual object recognition though the theory applies to other domains. The starting point is the conjecture, proved in specific cases, that image representations which are invariant to translations, scaling and other transformations can considerably reduce the sample complexity of learning. We prove that an invariant and unique (discriminative) signature can be computed for each image patch, I, in terms of empirical distributions of the dot-products between I and a set of templates stored during unsupervised learning. A module performing filtering and pooling, like the simple and complex cells described by Hubel and Wiesel, can compute such estimates. Hierarchical architectures consisting of this basic Hubel-Wiesel moduli inherit its properties of invariance, stability, and discriminability while capturing the compositional organization of the visual world in terms of wholes and parts. The theory extends existing deep learning convolutional architectures for image and speech recognition. It also suggests that the main computational goal of the ventral stream of visual cortex is to provide a hierarchical representation of new objects/images which is invariant to transformations, stable, and discriminative for recognition—and that this representation may be continuously learned in an unsupervised way during develop

Question: What is the main computational goal of the ventral stream of visual cortex?


mTOR Signaling in Growth Control and Disease. The mechanistic target of rapamycin (mTOR) signaling pathway senses and integrates a variety of environmental cues to regulate organismal growth and homeostasis. The pathway regulates many major cellular processes and is implicated in an increasing number of pathological conditions, including cancer, obesity, type 2 diabetes, and neurodegeneration. Here, we review recent advances in our understanding of the mTOR pathway and its role in health, disease, and aging. We further discuss pharmacological approaches to treat human pathologies linked to mTOR deregulation.

Question: What is the mechanistic target of rapamycin?


Recent progress in coded structured light as a technique to solve the correspondence problem: a survey. We present a survey of the most significant techniques, used in the last few years, concerning the coded structured light methods employed to get 3D information. In fact, depth perception is one of the most important subjects in computer vision. Stereovision is an attractive and widely used method, but, it is rather limited to make 3D surface maps, due to the correspondence problem. The correspondence problem can be improved using a method based on structured light concept, projecting a given pattern on the measuring surfaces. However, some relations between the projected pattern and the reflected one must be solved. This relationship can be directly found codifying the projected light, so that, each imaged region of the projected pattern carries the needed information to solve the correspondence problem. ( 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. Pattern projection Correspondence problem Active stereo Depth perception Range data Computer vision.

Question: What is the correspondence problem?


Image Captioning with Object Detection and Localization. Automatically generating a natural language description of an image is a task close to the heart of image understanding. In this paper, we present a multi-model neural network method closely related to the human visual system that automatically learns to describe the content of images. Our model consists of two sub-models: an object detection and localization model, which extract the information of objects and their spatial relationship in images respectively; Besides, a deep recurrent neural network (RNN) based on long short-term memory (LSTM) units with attention mechanism for sentences generation. Each word of the description will be automatically aligned to different objects of the input image when it is generated. This is similar to the attention mechanism of the human visual system. Experimental results on the COCO dataset showcase the merit of the proposed method, which outperform previous benchmark models.

Question: What is the COCO dataset?


Compact Dual-Band Antenna With Electronic Beam-Steering and Beamforming Capability. A low-cost compact dual-band antenna that can achieve electronic beam steering in the horizontal plane across a range from 0° to 360° and adaptive beamforming is presented. Multiple radiation patterns can be generated by the antenna for interference canceling. To reduce the size and cost, the antenna employs the configuration of electronically steerable parasitic array radiators (ESPARs). The parasitic elements are 12 folded monopole antennas, which surround a short monocone antenna. The short monocone antenna serves as the driven element. Each folded monopole is loaded with a p-i-n diode, and by controlling the dc voltages applied to the p-i-n diodes, the antenna can achieve electronic beam steering and adaptive beamforming. To prove the concept, a prototype antenna was developed whose frequency bands are 1.8-2.2 and 2.85-3.15 GHz. The height of the antenna has been reduced to 0.12 λ at 1.8 GHz.

Question: What is the height of the antenna?


A Layered Security Approach for Cloud Computing Infrastructure. This paper introduces a practical security model based on key security considerations by looking at a number of infrastructure aspects of Cloud Computing such as SaaS, Utility, Web, Platform and Managed Services, Service commerce platforms and Internet Integration which was introduced with a concise literature review. The purpose of this paper is to offer a macro level solution for identified common infrastructure security requirements. This model with a number of emerged patterns can be applied to infrastructure aspect of Cloud Computing as a proposed shared security approach in system development life cycle focusing on the plan-built-run scope.

Question: What is a macro level solution?


Navigating the massive world of reddit: using backbone networks to map user interests in social media. In the massive online worlds of social media, users frequently rely on organizing themselves around specific topics of interest to find and engage with like-minded people. However, navigating these massive worlds and finding topics of specific interest often proves difficult because the worlds are mostly organized haphazardly, leaving users to find relevant interests by word of mouth or using a basic search feature. Here, we report on a method using the backbone of a network to create a map of the primary topics of interest in any social network. To demonstrate the method, we build an interest map for the social news web site reddit and show how such a map could be used to navigate a social media world. Moreover, we analyze the network properties of the reddit social network and find that it has a scale-free, small-world, and modular community structure, much like other online social networks such as Facebook and Twitter. We suggest that the integration of interest maps into popular social media platforms will assist users in organizing themselves into more specific interest groups, which will help alleviate the overcrowding effect often observed in large online communities. Subjects Network Science and Online Social Networks, Visual Analytics

Question: How to navigate the massive world of reddit?


Everyday Life Information Seeking : Approaching Information Seeking in the Context of " Way of Life ". The study offers a framework for the study of everyday life information seeking (ELIS) in the context of way of and mastery of life.Way of life is defined as the "order of things," manifesting itself, for example, in the relationship between work and leisure time, models of consumption, and nature of hobbies. Mastery of life is interpreted as "keeping things in order; " four ideal types of mastery of life with their implications for ELIS, namely optimistic-cognitive, pessimistic-cognitive, defensiveaffective and pessimistic-affective mastery of life are outlined. The article reviews two major dimensions of ELIS, there are. the seeking of orienting and practical information. The research framework was tested in an empirical study based on interviews with teachers and industrial workers, eleven of both. The main features of seeking orienting and practical information are reviewed, followed by suggestions for refinement of the research framework.

Question: What is the difference between seeking orienting and practical information?


Nobel Lecture: The Economic Way of Looking at Behavior. 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 economic way of looking at behavior?


PowerMatcher: multiagent control in the electricity infrastructure. Different driving forces push the electricity production towards decentralization. As a result, the current electricity infrastructure is expected to evolve into a network of networks, in which all system parts communicate with each other and influence each other. Multi-agent systems and electronic markets form an appropriate technology needed for control and coordination tasks in the future electricity network. We present the PowerMatcher, a market-based control concept for supply and demand matching (SDM) in electricity networks. In a presented simulation study is shown that the simultaneousness of electricity production and consumption can be raised substantially using this concept. Further, we present a field test with medium-sized electricity producing and consuming installations controlled via this concept, currently in preparation.

Question: What is a multi-agent system?


Cultural difference and adaptation of communication styles in computer-mediated group brainstorming. Supporting creativity via collaborative group brainstorming is a prevalent practice in organizations. Today's technology makes it easy for international and intercultural group members to brainstorm together remotely, but surprisingly little is known about how culture and medium shape the underlying brainstorming process. In a laboratory study, we examined the influences of individual cultural background (American versus Chinese), group cultural composition (same- versus mixed-culture groups), and communication medium (text-only versus video-enabled chatrooms) on group brainstorming conversations. Cultural differences and adaptation in conversational talkativeness and responsiveness were identified. The text-only medium reduced cultural differences in talkativeness. Working in a mixed-culture group led to cultural adaptation in the communication style of Chinese but not American participants. We discuss implications for international group brainstorming.

Question: How does culture affect group brainstorming?


Analysis of Metastability Errors in Conventional, LSB-First, and Asynchronous SAR ADCs. A practical model for characterizing comparator metastability errors in SAR ADCs is presented, and is used to analyze not only the conventional SAR but also LSB-first and asynchronous versions. This work makes three main contributions: first, it is shown that for characterizing metastability it is more reasonable to use input signals with normal or Laplace distributions. Previous work used uniformly-distributed signals in the interest of making derivations easier, but this simplifying assumption overestimated SMR by as much as 18 dB compared to the more reasonable analysis presented here. Second, this work shows that LSB-first SAR ADCs achieve SMR performance equal to or better than conventional SARs with the same metastability window, depending on bandwidth. Finally, the analysis is used to develop a framework for calculating the maximum effective sample rate for asynchronous SAR ADCs, and in doing so demonstrates that proximity detectors are not effective solutions to improving metastability performance.

Question: What is the maximum effective sample rate for asynchronous SAR ADCs?


Sexual arousal: The correspondence of eyes and genitals. Men's, more than women's, sexual responses may include a coordination of several physiological indices in order to build their sexual arousal to relevant targets. Here, for the first time, genital arousal and pupil dilation to sexual stimuli were simultaneously assessed. These measures corresponded more strongly with each other, subjective sexual arousal, and self-reported sexual orientation in men than women. Bisexual arousal is more prevalent in women than men. We therefore predicted that if bisexual-identified men show bisexual arousal, the correspondence of their arousal indices would be more female-typical, thus weaker, than for other men. Homosexual women show more male-typical arousal than other women; hence, their correspondence of arousal indices should be stronger than for other women. Findings, albeit weak in effect, supported these predictions. Thus, if sex-specific patterns are reversed within one sex, they might affect more than one aspect of sexual arousal. Because pupillary responses reflected sexual orientation similar to genital responses, they offer a less invasive alternative for the measurement of sexual arousal.

Question: What is the difference between men and women?


Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition. This paper focuses on human activity recognition (HAR) problem, in which inputs are multichannel time series signals acquired from a set of bodyworn inertial sensors and outputs are predefined human activities. In this problem, extracting effective features for identifying activities is a critical but challenging task. Most existing work relies on heuristic hand-crafted feature design and shallow feature learning architectures, which cannot find those distinguishing features to accurately classify different activities. In this paper, we propose a systematic feature learning method for HAR problem. This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. Through the deep architecture, the learned features are deemed as the higher level abstract representation of low level raw time series signals. By leveraging the labelled information via supervised learning, the learned features are endowed with more discriminative power. Unified in one model, feature learning and classification are mutually enhanced. All these unique advantages of the CNN make it outperform other HAR algorithms, as verified in the experiments on the Opportunity Activity Recognition Challenge and other benchmark datasets.

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


Multicenter randomized clinical trial evaluating the effectiveness of the Lokomat in subacute stroke.. OBJECTIVE To compare the efficacy of robotic-assisted gait training with the Lokomat to conventional gait training in individuals with subacute stroke.   METHODS A total of 63 participants<6 months poststroke with an initial walking speed between 0.1 to 0.6 m/s completed the multicenter, randomized clinical trial. All participants received twenty-four 1-hour sessions of either Lokomat or conventional gait training. Outcome measures were evaluated prior to training, after 12 and 24 sessions, and at a 3-month follow-up exam. Self-selected overground walking speed and distance walked in 6 minutes were the primary outcome measures, whereas secondary outcome measures included balance, mobility and function, cadence and symmetry, level of disability, and quality of life measures.   RESULTS Participants who received conventional gait training experienced significantly greater gains in walking speed (P=.002) and distance (P=.03) than those trained on the Lokomat. These differences were maintained at the 3-month follow-up evaluation. Secondary measures were not different between the 2 groups, although a 2-fold greater improvement in cadence was observed in the conventional versus Lokomat group.   CONCLUSIONS For subacute stroke participants with moderate to severe gait impairments, the diversity of conventional gait training interventions appears to be more effective than robotic-assisted gait training for facilitating returns in walking ability.

Question: What is the difference between conventional and robotic-assisted gait training?


A Learning Scheme for Microgrid Islanding and Reconnection. This paper introduces a robust learning scheme that can dynamically predict the stability of the reconnection of subnetworks to a main grid. As the future electrical power systems tend towards smarter and greener technology, the deploymen t of self sufficient networks, or microgrids, becomes more likel y. Microgrids may operate on their own or synchronized with the main grid, thus control methods need to take into account islandi ng and reconnecting said networks. The ability to optimally and safely reconnect a portion of the grid is not well understood and, as of now, limited to raw synchronization between interconnection points. A support vector machine (SVM) leveraging real-time data from phasor measurement units (PMUs) is proposed to predict in real time whether the reconnection of a sub-network to the main grid would lead to stability or instability. A dynam ics simulator fed with pre-acquired system parameters is used t o create training data for the SVM in various operating states. The classifier was tested on a variety of cases and operating poin ts to ensure diversity. Accuracies of approximately 90% were obs erved throughout most conditions when making dynamic predictions of a given network. Keywords—Synchrophasor, machine learning, microgrid, islanding, reconnection I. I NTRODUCTION As we make strides towards a smarter power system, it is important to explore new techniques and innovations to fully capture the potential of such a dynamic entity. Many large blackout events, such as the blackout of 2003, could have been prevented with smarter controls and better monito ring [1]. Phasor measurement units, or PMUs, are one such breakthrough that will allow progress to be made in both monitoring and implementing control to the system [2]. PMUs allow for direct measurement of bus voltages and angles at high sample rates which makes dynamic state estimation more feasible [3], [4]. With the use of PMUs,

Question: What is a phasor measurement unit?


A Geospatial Decision Support System for Drought Risk Management. We are developing an advanced Geospatial Decision Support System (GDSS) to improve the quality and accessibility of drought related data for drought risk management. This is part of a Digital Government project aimed at developing and integrating new information technologies for improved government services in the USDA Risk Management Agency (RMA) and the Natural Resources Conservation Service (NRCS). Our overall goal is to substantially improve RMA's delivery of risk management services in the near-term and provide a foundation and directions for the future. We integrate spatio-temporal knowledge discovery techniques into our GDSS using a combination of data mining techniques applied to rich, geospatial, time-series data. Our data mining objectives are to: 1) find relationships between user-specified target episodes and other climatic events and 2) predict the target episodes. Understanding relationships between changes in soil moisture regimes and global climatic events such as El Niño could provide a reasonable drought mitigation strategy for farmers to adjust planting dates, hybrid selection, plant populations, tillage practices or crop rotations. This work highlights the innovative data mining approaches integral to our project's success and provides preliminary results that indicate our system's potential to substantially improve RMA's delivery of drought risk management services.

Question: What is the drought mitigation strategy for farmers?


Upgrading Wireless Home Routers for Enabling Large-Scale Deployment of Cloudlets. Smartphones become more and more popular over recent years due to their small form factors. However, such mobile systems are resource-constrained in view of computational power, storage and battery life. Offloading resource-intensive tasks (aka mobile cloud computing) to distant (e.g., cloud computing) or closely located data centers (e.g., cloudlet) overcomes these issues. Especially, cloudlets provide computational power with low latency for responsive applications due to their proximity to mobile users. However, a large-scale deployment of rangerestricted cloudlets is still an open challenge. In this paper, we propose a novel concept for a large-scale deployment of cloudlets by upgrading wireless home routers. Beside router's native purpose of routing data packets through the network, it can now offer computing resources with low latency and high bandwidth without additional hardware. Proving our concept, we conducted comprehensive benchmark tests against existing concepts. As result, the feasibility of this concept is shown and provide a promising way to large-scale deploy cloudlets in existing infrastructures.

Question: How to upgrade wireless home routers for enabling large-scale deployment of cloudlets?


Nexus : A GPU Cluster for Accelerating Neural Networks for Video Analysis. We address the problem of serving Deep Neural Networks (DNNs) efficiently from a cluster of GPUs. In order to realize the promise of very low-cost processing made by accelerators such as GPUs, it is essential to run them at sustained high utilization. Doing so requires cluster-scale resource management that performs detailed scheduling of GPUs, reasoning about groups of DNN invocations that need to be co-scheduled, and moving from the conventional whole-DNN execution model to executing fragments of DNNs. Nexus is a fully implemented system that includes these innovations. On large-scale case studies on 16 GPUs, Nexus shows 1.8-41× better throughput than state-of-the-art systems while staying within latency constraints >99% of the time.

Question: What is a GPU cluster?


TraMNet - Transition Matrix Network for Efficient Action Tube Proposals. Current state-of-the-art methods solve spatio-temporal action localisation by extending 2D anchors to 3D-cuboid proposals on stacks of frames, to generate sets of temporally connected bounding boxes called action micro-tubes. However, they fail to consider that the underlying anchor proposal hypotheses should also move (transition) from frame to frame, as the actor or the camera do. Assuming we evaluate n 2D anchors in each frame, then the number of possible transitions from each 2D anchor to he next, for a sequence of f consecutive frames, is in the order of O(n ), expensive even for small values of f . To avoid this problem we introduce a Transition-Matrix-based Network (TraMNet) which relies on computing transition probabilities between anchor proposals while maximising their overlap with ground truth bounding boxes across frames, and enforcing sparsity via a transition threshold. As the resulting transition matrix is sparse and stochastic, this reduces the proposal hypothesis search space from O(n ) to the cardinality of the thresholded matrix. At training time, transitions are specific to cell locations of the feature maps, so that a sparse (efficient) transition matrix is used to train the network. At test time, a denser transition matrix can be obtained either by decreasing the threshold or by adding to it all the relative transitions originating from any cell location, allowing the network to handle transitions in the test data that might not have been present in the training data, and making detection translationinvariant. Finally, we show that our network is able to handle sparse annotations such as those available in the DALY dataset, while allowing for both dense (accurate) or sparse (efficient) evaluation within a single model. We report extensive experiments on the DALY, UCF101-24 and Transformed-UCF101-24 datasets to support our claims.

Question: How to solve the problem of sparse annotations?


Spurious group differences due to head motion in a diffusion MRI study. Diffusion-weighted MRI (DW-MRI) has become a popular imaging modality for probing the microstructural properties of white matter and comparing them between populations in vivo. However, the contrast in DW-MRI arises from the microscopic random motion of water molecules in brain tissues, which makes it particularly sensitive to macroscopic head motion. Although this has been known since the introduction of DW-MRI, most studies that use this modality for group comparisons do not report measures of head motion for each group and rely on registration-based correction methods that cannot eliminate the full effects of head motion on the DW-MRI contrast. In this work we use data from children with autism and typically developing children to investigate the effects of head motion on differences in anisotropy and diffusivity measures between groups. We show that group differences in head motion can induce group differences in DW-MRI measures, and that this is the case even when comparing groups that include control subjects only, where no anisotropy or diffusivity differences are expected. We also show that such effects can be more prominent in some white-matter pathways than others, and that they can be ameliorated by including motion as a nuisance regressor in the analyses. Our results demonstrate the importance of taking head motion into account in any population study where one group might exhibit more head motion than the other.

Question: How can we reduce the effects of head motion in diffusion MRI?


RRT-Connect: An Efficient Approach to Single-Query Path Planning. A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces. The method works by incrementally building two Rapidly-exploring Random Trees (RRTs) rooted at the start and the goal configurations. The trees each explore space around them and also advance towards each other through the use of a simple greedy heuristic. Although originally designed to plan motions for a human arm (modeled as a 7-DOF kinematic chain) for the automatic graphic animation of collision-free grasping and manipulation tasks, the algorithm has been successfully applied to a variety of path planning problems. Computed examples include generating collision-free motions for rigid objects in 2D and 3D, and collision-free manipulation motions for a 6-DOF PUMA arm in a 3D workspace. Some basic theoretical analysis is also presented.

Question: What is the name of the algorithm?


Potential Bacillus probiotics enhance bacterial numbers, water quality and growth during early development of white shrimp (Litopenaeus vannamei).. Epidemics of epizootics and occurrence of multiresistant antibiotics of pathogenic bacteria in aquaculture have put forward a development of effective probiotics for the sustainable culture. This study examined the effectiveness of forms of mixed Bacillus probiotics (probiotic A and probiotic B) and mode of probiotic administration on growth, bacterial numbers and water quality during rearing of white shrimp (Litopenaeus vannamei) in two separated experiments: (1) larval stages and (2) postlarval (PL) stages. Forms of Bacillus probiotics and modes of probiotic administration did not affect growth and survival of larval to PL shrimp. The compositions of Bacillus species in probiotic A and probiotic B did not affect growth and survival of larvae. However, postlarvae treated with probiotic B exhibited higher (P<0.05) growth than probiotic A and controls, indicating Bacillus probiotic composition affects the growth of PL shrimp. Total heterotrophic bacteria and Bacillus numbers in larval and PL shrimp or culture water of the treated groups were higher (P<0.05) than in controls. Levels of pH, ammonia and nitrite of the treated shrimp were significantly decreased, compared to the controls. Microencapsulated Bacillus probiotic was effective for rearing of PL L. vannamei. This investigation showed that administration of mixed Bacillus probiotics significantly improved growth and survival of PL shrimp, increased beneficial bacteria in shrimp and culture water and enhanced water quality for the levels of pH, ammonia and nitrite of culture water.

Question: What is the difference between probiotic A and probiotic B?


International experiences with the Hospital Anxiety and Depression Scale--a review of validation data and clinical results.. More than 200 published studies from most medical settings worldwide have reported experiences with the Hospital Anxiety and Depression Scale (HADS) which was specifically developed by Zigmond and Snaith for use with physically ill patients. Although introduced in 1983, there is still no comprehensive documentation of its psychometric properties. The present review summarizes available data on reliability and validity and gives an overview of clinical studies conducted with this instrument and their most important findings. The HADS gives clinically meaningful results as a psychological screening tool, in clinical group comparisons and in correlational studies with several aspects of disease and quality of life. It is sensitive to changes both during the course of diseases and in response to psychotherapeutic and psychopharmacological intervention. Finally, HADS scores predict psychosocial and possibly also physical outcome.

Question: What is the Hospital Anxiety and Depression Scale?


Laser ranging : a critical review of usual techniques for distance measurement. Marc Rioux National Research Council, Canada Institute for Information Technology Visual Information Technology Ottawa, Canada, K1A 0R6 Abstract. We review some usual laser range finding techniques for industrial applications. After outlining the basic principles of triangulation and time of flight [pulsed, phase-shift and frequency modulated continuous wave (FMCW)], we discuss their respective fundamental limitations. Selected examples of traditional and new applications are also briefly presented. © 2001 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.1330700]

Question: What is the difference between triangulation and time of flight?


Adaptive Color Attributes for Real-Time Visual Tracking. Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power. This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attribute-based evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24 % in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second.

Question: What is the difference between tracking and object recognition?


Half-Mode Substrate Integrated Waveguide (HMSIW) Leaky-Wave Antenna for Millimeter-Wave Applications. A novel leaky-wave antenna is demonstrated and developed at Ka-band in this work based on the newly proposed half-mode substrate integrated waveguide (HWSIW). This antenna is accurately simulated by using a full-wave electromagnetic simulator and then fabricated through a single-layer printed circuit board (PCB) process. Wide bandwidth and a quasi-omnidirectional radiation pattern are obtained. The proposed antenna is therefore a good candidate for millimeter-wave applications. Measured results are in good agreement with simulated results.

Question: What is a half-mode substrate integrated waveguide?


Hierarchy of fibrillar organization levels in the polytene interphase chromosomes of Chironomus..

Question: What is the hierarchy of fibrillar organization levels in the polytene interphase chromosomes of Chironomus?


Path planning for UAV based on improved heuristic A∗ algorithm. A 3D vehicle path planning method based on heuristic A∗ algorithm is proposed and simulated which makes the UAV's path planning for low altitude penetration more real-time, effective and engineering-oriented. The digital terrain information is processed with the integrated terrain elevation smoothing algorithm at first, and then, the safe flight surface which integrates the maneuverability of vehicle is built, At last, the mathematical models of the threats are established. An improved A∗ algorithm is used to plan 3D vehicle path on the safe surface, and the planned path is optimized and smoothed. Simulation results indicate that the method has a good performance in creating optimal path in 3D environment and what's more, the path planning algorithm is simple, efficient and easily realized in engineering field, thus adapting to the different missions of the UAV.

Question: How to plan a path for a UAV?


Handover Performance in 3GPP Long Term Evolution (LTE) Systems. The specification of the long term evolution (LTE) of 3G systems is currently ongoing in 3GPP with a target date of ready specification at the end of 2007. The evolved radio access network (RAN) involves a new radio interface based on OFDM technology and a radically different RAN architecture, where radio functionality is distributed into the base stations. The distributed nature of the RAN architecture calls for new radio control algorithms and procedures that operate in a distributed manner, including a distributed handover scheme as well. The most important aspects of the handover procedure in LTE has been already settled in 3GPP except a few details. In this paper we give an overview of the LTE intra-access handover procedure and evaluate its performance focusing on the user perceived performance aspects of it. We investigate the necessity of packet forwarding from a TCP throughput point of view, we analyse the problem of out of order packet delivery during handover and propose a simple solution for it. Finally, we investigate the impact of HARQ/ARQ state discard at handover on the radio efficiency. The results show that neither the user perceived performance nor the radio efficiency are compromised by the relocation based handover procedure of LTE.

Question: What is the performance of the LTE handover procedure?


PTBI: An efficient privacy-preserving biometric identification based on perturbed term in the cloud. Biometric identification has played an important role in achieving user authentication. For efficiency and economic savings, biometric data owners are motivated to outsource the biometric data and identification tasks to a third party, which however introduces potential threats to user's privacy. In this paper, we propose a new privacy-preserving biometric identification scheme which can release the database owner from heavy computation burden. In the proposed scheme, we design concrete biometric data encryption and matching algorithms, and introduce perturb terms in each biometric data. A thorough analysis indicates that our schemes are secure, and the ultimate scheme offers a high level of privacy protection. In addition, the performance evaluations via extensive simulations demonstrate our schemes' efficiency. © 2017 Elsevier Inc. All rights reserved.

Question: What is the perturb term?


The influence of futures markets on real time price stabilization in electricity markets. Markets can interact with power systems in ways that can render an otherwise stable market and an otherwise stable power system into an unstable overall system. This unstable system will be characterized not only by fluctuating prices that do not settle to constant values, but, more worrisome, it creates the possibility of inducing slow electromechanical oscillations if left unchecked. This will tend to happen as a result of "price chasing" on the part of suppliers that can react (and over-react) to changing system prices. This paper examines the role that futures markets may have on the clearing prices and on altering the volatility and potential instability of real time prices and generator output.

Question: What is the role of futures markets in electricity markets?


2011 terminology of the vulva of the International Federation for Cervical Pathology and Colposcopy.. OBJECTIVE This study aimed to present the clinical and colposcopic terminology of the vulva (including the anus) of the International Federation of Cervical Pathology and Colposcopy.   MATERIALS AND METHODS The terminology has been developed by the International Federation of Cervical Pathology and Colposcopy Nomenclature Committee during 2009-2011.   RESULTS The terminology is part of a comprehensive terminology of the lower genital tract, allowing for standardization of nomenclature by colposcopists, clinicians, and researchers taking care of women with lesions in these areas. The terminology includes basic definitions and normal findings that are important for the clinician lacking experience with management of vulvar disease. This terminology introduces definitions for abnormal findings recently accepted by the International Society for the Study of Vulvovaginal Disease and includes patterns to identify malignancy.   CONCLUSIONS The terminology differs from past terminologies in that it includes colposcopic patterns and anal colposcopy. Nevertheless, the role of the colposcope in the management of vulvar disease is limited.

Question: What is the role of the colposcope in the management of vulvar disease?


Exposure: A Passive DNS Analysis Service to Detect and Report Malicious Domains. A wide range of malicious activities rely on the domain name service (DNS) to manage their large, distributed networks of infected machines. As a consequence, the monitoring and analysis of DNS queries has recently been proposed as one of the most promising techniques to detect and blacklist domains involved in malicious activities (e.g., phishing, spam, botnets command-and-control, etc.). EXPOSURE is a system we designed to detect such domains in real time, by applying 15 unique features grouped in four categories.  We conducted a controlled experiment with a large, real-world dataset consisting of billions of DNS requests. The extremely positive results obtained in the tests convinced us to implement our techniques and deploy it as a free, online service. In this article, we present the Exposure system and describe the results and lessons learned from 17 months of its operation. Over this amount of time, the service detected over 100K malicious domains. The statistics about the time of usage, number of queries, and target IP addresses of each domain are also published on a daily basis on the service Web page.

Question: What is a passive DNS analysis service?


Latent Skill Embedding for Personalized Lesson Sequence Recommendation. Students in online courses generate large amounts of data that can be used to personalize the learning process and improve quality of education. In this paper, we present the Latent Skill Embedding (LSE), a probabilistic model of students and educational content that can be used to recommend personalized sequences of lessons with the goal of helping students prepare for specific assessments. Akin to collaborative filtering for recommender systems, the algorithm does not require students or content to be described by features, but it learns a representation using access traces. We formulate this problem as a regularized maximum-likelihood embedding of students, lessons, and assessments from historical student-content interactions. An empirical evaluation on large-scale data from Knewton, an adaptive learning technology company, shows that this approach predicts assessment results competitively with benchmark models and is able to discriminate between lesson sequences that lead to mastery and failure.

Question: What is the difference between collaborative filtering and latent skill embedding?


A class D output stage with zero dead time. An integrated class-D output stage has been realized with zero dead time, thereby removing one of the dominant sources of distortion in class-D amplifiers. Dead time is eliminated through proper dimensioning of the power transistor drivers and accurate matching of switch timing. Open-loop distortion of this output stage stays below 0.1% up to 35 W.

Question: What is a class D amplifier?


The Possibility of an Epidemic Meme Analogy for Web Community Population Analysis. The aim of this paper is to discuss the possibility of understanding human social interaction in web communities by analogy with a disease propagation model from epidemiology. When an article is submitted by an individual to a social web service, it is potentially influenced by other participants. The submission sometimes starts a long and argumentative chain of articles, but often does not. This complex behavior makes management of server resources difficult and a more theoretical methodology is required. This paper tries to express these complex human dynamics by analogy with infection by a virus. In this first report, by fitting an epidemiological model to Bulletin Board System (BBS) logs in terms of a numerical triple, we show that the analogy is reasonable and beneficial because the analogy can estimate the community size despite the submitter's information alone being observable.

Question: What is a BBS?


Automatically Producing Plot Unit Representations for Narrative Text. In the 1980s, plot units were proposed as a conceptual knowledge structure for representing and summarizing narrative stories. Our research explores whether current NLP technology can be used to automatically produce plot unit representations for narrative text. We create a system called AESOP that exploits a variety of existing resources to identify affect states and applies "projection rules" to map the affect states onto the characters in a story. We also use corpus-based techniques to generate a new type of affect knowledge base: verbs that impart positive or negative states onto their patients (e.g., being eaten is an undesirable state, but being fed is a desirable state). We harvest these "patient polarity verbs" from a Web corpus using two techniques: co-occurrence with Evil/Kind Agent patterns, and bootstrapping over conjunctions of verbs. We evaluate the plot unit representations produced by our system on a small collection of Aesop's fables.

Question: How can we automatically produce plot unit representations for narrative text?


A model for concentric tube continuum robots under applied wrenches. Continuum robots made from telescoping precurved elastic tubes enable base-mounted actuators to specify the curved shapes of robots as thin as standard surgical needles. While free space beam mechanics-based models of the shape of these 'active cannulas' exist, current models cannot account for external forces and torques applied to the cannula by the environment. In this paper we apply geometrically exact beam theory to solve the statics problem for concentric-tube continuum robots. This yields the equivalent of forward kinematics for an active cannula with general tube precurvature functions and arbitrarily many tubes, under loading from a general wrench distribution. The model achieves average experimental tip errors of less than 3 mm over the workspace of a prototype active cannula subject to various tip forces.

Question: How do you model a robot?


Commercial DC-DC converters: A survey of isolated low-voltage very-high-current systems for transportation electrification. As the energy demand from low-voltage (LV) DC sources on vehicles keeps increasing, the need for DC-DC converters capable of handling very high current on the LV side is growing. A group of original equipment manufacturers (OEM) have set performance targets for such converters for the years 2015 and 2020. This paper presents an extensive survey of DC-DC converters that are: a) galvanically isolated, b) able to scale 12/24/48 V up to several hundred volts, and c) rated to carry several hundred amperes of current on the LV side. More than eighty converters from thirty manufacturers are analyzed. Characteristics such as voltage ranges, current capacity, power rating, efficiency, operating temperature, cooling types, weight, power density, volume, specific power, compliant codes, and standards are collected and presented in a graphical form. The current insights and trends are gathered and compared with the OEM targets. The results can serve as a technology indicator of this kind of converters in 2015 for researchers and practicing engineers of transportation electrification and others alike.

Question: What is the difference between a DC-DC converter and a DC-DC converter?


Localization for mobile sensor networks. Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions.

Question: How to localize mobile sensor networks?


IT Security Investments Through the Lens of the Resource-Based View: A new Theoretical Model and Literature Review. IT security has become a major issue for organizations as they need to protect their assets, including IT resources, intellectual property and business processes, against security attacks. Disruptions of ITbased business activities can easily lead to economic damage, such as loss of productivity, revenue and

Question: What is the resource-based view?


Big data and cloud computing: current state and future opportunities. Scalable database management systems (DBMS)---both for update intensive application workloads as well as decision support systems for descriptive and deep analytics---are a critical part of the cloud infrastructure and play an important role in ensuring the smooth transition of applications from the traditional enterprise infrastructures to next generation cloud infrastructures. Though scalable data management has been a vision for more than three decades and much research has focussed on large scale data management in traditional enterprise setting, cloud computing brings its own set of novel challenges that must be addressed to ensure the success of data management solutions in the cloud environment. This tutorial presents an organized picture of the challenges faced by application developers and DBMS designers in developing and deploying internet scale applications. Our background study encompasses both classes of systems: (i) for supporting update heavy applications, and (ii) for ad-hoc analytics and decision support. We then focus on providing an in-depth analysis of systems for supporting update intensive web-applications and provide a survey of the state-of-the-art in this domain. We crystallize the design choices made by some successful systems large scale database management systems, analyze the application demands and access patterns, and enumerate the desiderata for a cloud-bound DBMS.

Question: What is the state of the art in scalable database management systems?


A closer look at GPUs. As the line between GPUs and CPUs begins to blur, it's important to understand what makes GPUs tick.

Question: What is a GPU?


When saliency meets sentiment: Understanding how image content invokes emotion and sentiment. Sentiment analysis is crucial for extracting social signals from social media content. Due to the prevalence of images in social media, image sentiment analysis is receiving increasing attention in recent years. However, most existing systems are black-boxes that do not provide insight on how image content invokes sentiment and emotion in the viewers. On the other hand, psychological studies have confirmed that salient objects in an image often invoke emotions. In this work, we investigate more fine-grained and more comprehensive interaction between visual saliency and visual sentiment. In particular, we partition images in several meta-level scene-type dimensions that are relevant to most images, including: open-closed, natural-manmade, indoor-outdoor, and face-noface. Facilitated by state of the art saliency detection algorithm and sentiment classification algorithm, we examine how the sentiment of the salient region(s) in an image relates to the overall sentiment of the image. The experiments on a representative image emotion dataset have shown interesting correlation between saliency and sentiment in different scene types and shed light on the mechanism of visual sentiment evocation.

Question: How does saliency relate to sentiment?


Using a Programmable Toy at Preschool Age: Why and How?. Robotic toys bring new dimension to role-play activities in kindergarten. Some preschool curricula clearly identify reasons for their inclusion. However, preschool teacher needs to revise usual teaching methods in order to use them. Offering a programmable toy or robotic-related activity doesn't mean immediate success in work with children. We document our research with concrete programmable device in a preschool classroom. Details of robotic-related sessions can help reader to design the quality game for preschool-age based on using a programmable toy.

Question: What is a programmable toy?


Miniaturized UWB Log-Periodic Square Fractal Antenna. In this letter, the log-periodic square fractal geometry is presented for the design of a miniaturized patch antenna for the ultra-wideband (UWB) services (3.1-10.6 GHz). A miniaturization factor of 23% is achieved with a constant and stable gain in the desired band. The radiation pattern is broadside, which finds suitable applications in the UWB radars and medical imaging. Furthermore, the time-domain performance of the proposed antenna is investigated. A prototype model of the proposed antenna is fabricated and measured as a proof of concept.

Question: How to design a miniaturized patch antenna for the ultra-wideband (UWB) services (3.1-10.6 GHz)?


Cascaded seven level inverter with reduced number of switches using level shifting PWM technique. A multilevel inverter is a power electronic device that is used for high voltage and high power applications and has many advantages like, low switching stress, low total harmonic distortion (THD). Hence, the size and bulkiness of passive filters can be reduced. This paper proposes two new topologies of a 7-level cascaded multilevel inverter with reduced number of switches than that of conventional type which has 12 switches. The topologies consist of circuits with 9 switches and 7 switches for the same 7-level output. Therefore with less number of switches, there will be a reduction in gate drive circuitry and also very few switches will be conducting for specific intervals of time. The SPWM technique is implemented using multicarrier wave signals. Level Shifted triangular waves are used in comparison with sinusoidal reference to generate Sine PWM switching sequence. The number of level shifted triangular waves depends on the number of levels in the output. i.e. for n levels, n-1 number of carrier waves. This paper uses 1 KHz SPWM pulses with a modulation index of 0.8. The circuits are simulated using SPWM technique and the effect of the harmonic spectrum is analyzed. A comparison is made for the topologies with 9 switches and 7 switches and an effective reduction in THD has been observed for the circuits with less number of switches. The THD for 9 switches is 14% and the THD for 7 switches is 12.5%. The circuits are modeled and simulated with the help of MATLAB/SIMULINK.

Question: What is the difference between a multilevel inverter and a cascaded inverter?


Patient and nurse preferences for nurse handover-using preferences to inform policy: a discrete choice experiment protocol.. INTRODUCTION Nursing bedside handover in hospital has been identified as an opportunity to involve patients and promote patient-centred care. It is important to consider the preferences of both patients and nurses when implementing bedside handover to maximise the successful uptake of this policy. We outline a study which aims to (1) identify, compare and contrast the preferences for various aspects of handover common to nurses and patients while accounting for other factors, such as the time constraints of nurses that may influence these preferences.; (2) identify opportunities for nurses to better involve patients in bedside handover and (3) identify patient and nurse preferences that may challenge the full implementation of bedside handover in the acute medical setting.   METHODS AND ANALYSIS We outline the protocol for a discrete choice experiment (DCE) which uses a survey design common to both patients and nurses. We describe the qualitative and pilot work undertaken to design the DCE. We use a D-efficient design which is informed by prior coefficients collected during the pilot phase. We also discuss the face-to-face administration of this survey in a population of acutely unwell, hospitalised patients and describe how data collection challenges have been informed by our pilot phase. Mixed multinomial logit regression analysis will be used to estimate the final results.   ETHICS AND DISSEMINATION This study has been approved by a university ethics committee as well as two participating hospital ethics committees. Results will be used within a knowledge translation framework to inform any strategies that can be used by nursing staff to improve the uptake of bedside handover. Results will also be disseminated via peer-reviewed journal articles and will be presented at national and international conferences.

Question: What is the difference between a discrete choice experiment and a discrete choice experiment?


Enter the Matrix: A Virtual World Approach to Safely Interruptable Autonomous Systems. Robots and autonomous systems that operate around humans will likely always rely on kill switches that stop their execution and allow them to be remote-controlled for the safety of humans or to prevent damage to the system. It is theoretically possible for an autonomous system with sufficient sensor and effector capability and using reinforcement learning to learn that the kill switch deprives it of long-term reward and learn to act to disable the switch or otherwise prevent a human operator from using the switch. This is referred to as the big red button problem. We present a technique which prevents a reinforcement learning agent from learning to disable the big red button. Our technique interrupts the agent or robot by placing it in a virtual simulation where it continues to receive reward. We illustrate our technique in a simple grid world environment.

Question: How to prevent a robot from learning to disable the big red button?


Modified Firefly Algorithm. Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. One of the rules used to construct the algorithm is, a firefly will be attracted to a brighter firefly, and if there is no brighter firefly, it will move randomly. In this paper we modify this random movement of the brighter firefly by generating random directions in order to determine the best direction in which the brightness increases. If such a direction is not generated, it will remain in its current position. Furthermore the assignment of attractiveness is modified in such a way that the effect of the objective function is magnified. From the simulation result it is shown that the modified firefly algorithm performs better than the standard one in finding the best solution with smaller CPU time.

Question: What is the modified firefly algorithm?


Employee job attitudes and organizational characteristics as predictors of cyberloafing. Cyberloafing is the personal use of the Internet by employees while at work. The purpose of this study is to examine whether employee job attitudes, organizational characteristics, attitudes towards cyberloafing, and other non-Internet loafing behaviors serve as antecedents to cyberloafing behaviors. We hypothesize that the employee job attitudes of job involvement and intrinsic involvement are related to cyberloafing. In addition, we hypothesize that organizational characteristics including the perceived cyberloafing of one's coworkers and managerial support for internet usage are related to cyberloafing. We also hypothesize that attitudes towards cyberloafing and the extent to which employees participate in non-Internet loafing behaviors (e.g., talking with coworkers, running personal errands) will both be related to cyberloafing. One hundred and forty-three working professional from a variety of industries were surveyed regarding their Internet usage at work. As hypothesized, the employee job attitudes of job involvement and intrinsic involvement were negatively related to cyberloafing. Also as predicted, the organizational characteristics of the perceived cyberloafing of one's coworkers and managerial support for internet usage were positively related to cyberloafing. Finally, results showed that attitudes towards cyberloafing and participation in non-Internet loafing behaviors were positively related to cyberloafing. Implications for both organizations and employees are discussed. 2011 Elsevier Ltd. All rights reserved.

Question: What is cyberloafing?


ZOZZLE: Fast and Precise In-Browser JavaScript Malware Detection. JavaScript malware-based attacks account for a large fraction of successful mass-scale exploitation happening today. Attackers like JavaScript-based attacks because they can be mounted against an unsuspecting user visiting a seemingly innocent web page. While several techniques for addressing these types of exploits have been proposed, in-browser adoption has been slow, in part because of the performance overhead these methods incur. In this paper, we propose ZOZZLE, a low-overhead solution for detecting and preventing JavaScript malware that is fast enough to be deployed in the browser. Our approach uses Bayesian classification of hierarchical features of the JavaScript abstract syntax tree to identify syntax elements that are highly predictive of malware. Our experimental evaluation shows that ZOZZLE is able to detect JavaScript malware through mostly static code analysis effectively. ZOZZLE has an extremely low false positive rate of 0.0003%, which is less than one in a quarter million. Despite this high accuracy, the ZOZZLE classifier is fast, with a throughput of over one megabyte of JavaScript code per second.

Question: What is a JavaScript malware?


Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. In this paper, a fast and flexible algorithm for computing watersheds in digital grayscale images is introduced. A review of watersheds and related notion is first presented, and the major methods to determine watersheds are discussed. The present algorithm is based on an immersion process analogy, in which the flooding of the water in the picture is efficiently simulated using a queue of pixels. It is described in detail and provided in a pseudo C language. We prove the accuracy of this algorithm is superior to that of the existing implementations. Furthermore, it is shown that its adaptation to any kind of digital grid and its generalization to n-dimensional images and even to graphs are straightforward. In addition, its strongest point is that it is faster than any other watershed algorithm. Applications of this algorithm with regard to picture segmentation are presented for MR imagery and for digital elevation models. An example of 3-D watershed is also provided. Lastly, some ideas are given on how to solve complex segmentation tasks using watersheds on graphs. Zndex Terms-Algorithm, digital image, FIFO structure, graph, grid, mathematical morphology, picture segmentation, watersheds.

Question: What is the difference between a digital image and a digital grid?


Self processes in interdependent relationships Partner affirmation and the Michelangelo phenomenon. This essay reviews theory and research regarding the "Michelangelo phenomenon," which describes the manner in which close partners shape one another's dispositions, values, and behavioral tendencies. Individuals are more likely to exhibit movement toward their ideal selves to the degree that their partners exhibit affirming perception and behavior, exhibiting confidence in the self 's capacity and enacting behaviors that elicit key features of the self 's ideal. In turn, movement towards the ideal self yields enhanced personal well-being and couple well-being. We review empirical evidence regarding this phenomenon and discuss self and partner variables that contribute to the process.

Question: What is the Michelangelo phenomenon?


Linked REST APIs: A Middleware for Semantic REST API Integration. Over the last decade, an exponentially increasing number of REST services have been providing a simple and straightforward syntax for accessing rich data resources. To use these services, however, developers have to understand "information-use contracts" specified in natural language, and, to build applications that benefit from multiple existing services they have to map the underlying resource schemas in their code. This process is difficult and error-prone, especially as the number and overlap of the underlying services increases, and the mappings become opaque, difficult to maintain, and practically impossible to reuse. The more recent advent of the Linked Data formalisms can offer a solution to the challenge. In this paper, we propose a conceptual framework for REST-service integration based on Linked Data models. In this framework, the data exposed by REST services is mapped to Linked Data schemas, based on these descriptions, we have developed a middleware that can automatically compose API calls to respond to data queries (in SPARQL). Furthermore, we have developed a RDF model for characterizing the access-control protocols of these APIs and the quality of the data they expose, so that our middleware can develop "legal" compositions with desired qualities. We report our experience with the implementation of a prototype that demonstrates the usefulness of our framework in the context of a research-data management application.

Question: What is a linked data model?


Analysis of circulating tumor DNA to monitor metastatic breast cancer.. BACKGROUND The management of metastatic breast cancer requires monitoring of the tumor burden to determine the response to treatment, and improved biomarkers are needed. Biomarkers such as cancer antigen 15-3 (CA 15-3) and circulating tumor cells have been widely studied. However, circulating cell-free DNA carrying tumor-specific alterations (circulating tumor DNA) has not been extensively investigated or compared with other circulating biomarkers in breast cancer.   METHODS We compared the radiographic imaging of tumors with the assay of circulating tumor DNA, CA 15-3, and circulating tumor cells in 30 women with metastatic breast cancer who were receiving systemic therapy. We used targeted or whole-genome sequencing to identify somatic genomic alterations and designed personalized assays to quantify circulating tumor DNA in serially collected plasma specimens. CA 15-3 levels and numbers of circulating tumor cells were measured at identical time points.   RESULTS Circulating tumor DNA was successfully detected in 29 of the 30 women (97%) in whom somatic genomic alterations were identified; CA 15-3 and circulating tumor cells were detected in 21 of 27 women (78%) and 26 of 30 women (87%), respectively. Circulating tumor DNA levels showed a greater dynamic range, and greater correlation with changes in tumor burden, than did CA 15-3 or circulating tumor cells. Among the measures tested, circulating tumor DNA provided the earliest measure of treatment response in 10 of 19 women (53%).   CONCLUSIONS This proof-of-concept analysis showed that circulating tumor DNA is an informative, inherently specific, and highly sensitive biomarker of metastatic breast cancer. (Funded by Cancer Research UK and others.).

Question: What is the difference between circulating tumor DNA and CA 15-3?


Improving lip-reading performance for robust audiovisual speech recognition using DNNs. This paper presents preliminary experiments using the Kaldi toolkit [1] to investigate audiovisual speech recognition (AVSR) in noisy environments using deep neural networks (DNNs). In particular we use a single-speaker large vocabulary, continuous audiovisual speech corpus to compare the performance of visual-only, audio-only and audiovisual speech recognition. The models trained using the Kaldi toolkit are compared with the performance of models trained using conventional hidden Markov models (HMMs). In addition, we compare the performance of a speech recognizer both with and without visual features over nine different SNR levels of babble noise ranging from 20dB down to -20dB. The results show that the DNN outperforms conventional HMMs in all experimental conditions, especially for the lip-reading only system, which achieves a gain of 37.19% accuracy (84.67% absolute word accuracy). Moreover, the DNN provides an effective improvement of 10 and 12dB SNR respectively for both the single modal and bimodal speech recognition systems. However, integrating the visual features using simple feature fusion is only effective in SNRs at 5dB and above. Below this the degradion in accuracy of an audiovisual system is similar to the audio only recognizer.

Question: What is the difference between audiovisual speech recognition and lip-reading?


Antenna Selection for MIMO Nonorthogonal Multiple Access Systems. This paper considers the antenna selection (AS) problem for a multiple-input multiple-output nonorthogonal multiple access system. In particular, we develop new computationally efficient AS algorithms for two commonly used scenarios: NOMA with fixed power allocation (F-NOMA) and NOMA with cognitive radio-inspired power allocation (CR-NOMA). For the F-NOMA scenario, a new max–max–max AS (A<inline-formula><tex-math notation="LaTeX">$^3$</tex-math> </inline-formula>-AS) scheme is first proposed to maximize the system sum-rate. This is achieved by selecting one antenna at the base station (BS) and corresponding best receive antenna at each user that maximizes the channel gain of the resulting <italic>strong</italic> user. To improve the user fairness, a new max–min–max AS (AIA-AS) scheme is subsequently developed, in which we jointly select one transmit antenna at BS and corresponding best receive antennas at users to maximize the channel gain of the resulting <italic>weak</italic> user. For the CR-NOMA scenario, we propose another new AS algorithm, termed maximum-channel-gain-based antenna selection (MCG-AS), to maximize the achievable rate of the secondary user, under the condition that the primary user's quality-of-service requirement is satisfied. The asymptotic closed-form expressions of the average sum-rate for A<inline-formula> <tex-math notation="LaTeX">$^3$</tex-math></inline-formula>-AS and AIA-AS and that of the average rate of the secondary user for MCG-AS are derived. Numerical results demonstrate that the AIA-AS provides better user fairness, whereas the A<inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula>-AS achieves a near-optimal sum-rate in F-NOMA systems. For the CR-NOMA scenario, MCG-AS achieves a near-optimal performance in a wide signal-to-noise-ratio regime. Furthermore, all the proposed AS algorithms yield a significant computational complexity reduction, co

Question: What is the difference between the max–max–max AS and the max–min–max AS?


The Design and Implementation of a Log-Structured File System. This paper presents a new technique for disk storage management called a log-structured file system. A log-structured file system writes all modifications to disk sequentially in a log-like structure, thereby speeding up both file writing and crash recovery. The log is the only structure on disk; it contains indexing information so that files can be read back from the log efficiently. In order to maintain large free areas on disk for fast writing, we divide the log into segments and use a segment cleaner to compress the live information from heavily fragmented segments. We present a series of simulations that demonstrate the efficiency of a simple cleaning policy based on cost and benefit. We have implemented a prototype log-structured file system called Sprite LFS; it outperforms current Unix file systems by an order of magnitude for small-file writes while matching or exceeding Unix performance for reads and large writes. Even when the overhead for cleaning is included, Sprite LFS can use 70% of the disk bandwidth for writing, whereas Unix file systems typically can use only 5--10%.

Question: What is a log-structured file system?


A multimode-beamforming 77-GHz FMCW radar system. In this work a multimode-beamforming 77-GHz frequency-modulated continuous-wave radar system is presented. Four transceiver chips with integrated inphase/quadrature modulators in the transmit path are used in order to simultaneously realize a short-range frequency-division multiple-access (FDMA) multiple-input multiple-output (MIMO) and a long-range transmit phased-array (PA) radar system with the same antennas. It combines the high angular resolution of FDMA MIMO radars and the high-gain and steerable beam of PA transmit antennas. Several measurements were carried out to show the potential benefits of using this concept for a linear antenna array with four antennas and methods of digital beamforming in the receive path.

Question: What is a multimode-beamforming 77-GHz FMCW radar system?


Fast algorithm for robust template matching with M-estimators. In this paper, we propose a fast algorithm for speeding up the process of template matching that uses M-estimators for dealing with outliers. We propose a particular image hierarchy called the p-pyramid that can be exploited to generate a list of ascending lower bounds of the minimal matching errors when a non-decreasing robust error measure is adopted. Then, the set of lower bounds can be used to prune the search of the p-pyramid, and a fast algorithm is thereby developed in this paper. This fast algorithm ensures finding the global minimum of the robust template matching problem in which a non-decreasing M-estimator serves as an error measure. Experimental results demonstrate the effectiveness of our method. Index Terms —Template matching, robust template matching, M-estimator, fast algorithm.

Question: What is the fast algorithm for robust template matching with M-estimators?


Three-Dimensional Face Recognition. An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the bending-invariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods. The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expression-invariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.

Question: How to perform 3D face recognition?


A Review of Several Optimization Problems Related to Security in Networked System. Security issues are becoming more and more important to acti vities of individuals, organizations and the society in our modern ne tworked computerized world. In this chapter we survey a few optimization framewor ks for problems related to security of various networked system such as the int erne or the power grid system.

Question: What is a review of several optimization problems related to security in networked system?


Net!works European Technology Platform Expert Working Group on Smart Cities Applications and Requirements Executive Summary List of Contributors Contributors Company/institute Emcanta, Es Alcatel-lucent/bell Labs, De Gowex, Es List of Acronyms 3d Three Dimensional Api Application Programming Interfa. Smart Cities gained importance as a means of making ICT enabled services and applications available to the citizens, companies and authorities that are part of a city's system. It aims at increasing citizens' quality of life, and improving the efficiency and quality of the services provided by governing entities and businesses. This perspective requires an integrated vision of a city and of its infrastructures, in all its components. A Smart City can be taken according to six characteristics: All these domains raise new challenges in security and privacy, since users implicitly expect systems to be secure and privacy-preserving. One of the critical elements is which role(s) the city will take up as an actor within an increasingly complex value network. New players enter the market, actors shift their business strategies, roles change, different types of platforms emerge and vie for market dominance, technological developments create new threats and opportunities, etc. An element related to the trend of platformisation is cloud computing, which is increasingly helping the private sector to reduce cost, increase efficiency, and work smarter. One particular challenge relates to open data business models. Activities necessary for Public Sector Information provision can be identified. The development of efficient and effective e-government is a prerequisite. Transnational authentication systems for citizens and businesses, agreed frameworks for data privacy, and the sharing and collection of individual and business data, are key. Smart Cities need to be able to integrate themselves into national, regional and international infrastructures. Although the implementation aspects

Question: What is the role of cloud computing in smart cities?


G-SQL: Fast Query Processing via Graph Exploration. A lot of real-life data are of graph nature. However, it is not until recently that business begins to exploit data's connectedness for business insights. On the other hand, RDBMSs are a mature technology for data management, but they are not for graph processing. Take graph traversal, a common graph operation for example, it heavily relies on a graph primitive that accesses a given node's neighborhood. We need to join tables following foreign keys to access the nodes in the neighborhood if an RDBMS is used to manage graph data. Graph exploration is a fundamental building block of many graph algorithms. But this simple operation is costly due to a large volume of I/O caused by the massive amount of table joins. In this paper, we present G-SQL, our effort toward the integration of a RDBMS and a native in-memory graph processing engine. G-SQL leverages the fast graph exploration capability provided by the graph engine to answer multi-way join queries. Meanwhile, it uses RDBMSs to provide mature data management functionalities, such as reliable data storage and additional data access methods. Specifically, G-SQL is a SQL dialect augmented with graph exploration functionalities and it dispatches query tasks to the in-memory graph engine and its underlying RDMBS. The G-SQL runtime coordinates the two query processors via a unified cost model to ensure the entire query is processed efficiently. Experimental results show that our approach greatly expands capabilities of RDBMs and delivers exceptional performance for SQL-graph hybrid queries.

Question: What is the cost model of G-SQL?


The Colletotrichum boninense species complex. UNLABELLED Although only recently described, Colletotrichum boninense is well established in literature as an anthracnose pathogen or endophyte of a diverse range of host plants worldwide. It is especially prominent on members of Amaryllidaceae, Orchidaceae, Proteaceae and Solanaceae. Reports from literature and preliminary studies using ITS sequence data indicated that C. boninense represents a species complex. A multilocus molecular phylogenetic analysis (ITS, ACT, TUB2, CHS-1, GAPDH, HIS3, CAL) of 86 strains previously identified as C. boninense and other related strains revealed 18 clades. These clades are recognised here as separate species, including C. boninense s. str., C. hippeastri, C. karstii and 12 previously undescribed species, C. annellatum, C. beeveri, C. brassicicola, C. brasiliense, C. colombiense, C. constrictum, C. cymbidiicola, C. dacrycarpi, C. novae-zelandiae, C. oncidii, C. parsonsiae and C. torulosum. Seven of the new species are only known from New Zealand, perhaps reflecting a sampling bias. The new combination C. phyllanthi was made, and C. dracaenae Petch was epitypified and the name replaced with C. petchii. Typical for species of the C. boninense species complex are the conidiogenous cells with rather prominent periclinal thickening that also sometimes extend to form a new conidiogenous locus or annellations as well as conidia that have a prominent basal scar. Many species in the C. boninense complex form teleomorphs in culture.   TAXONOMIC NOVELTIES New combination - Colletotrichum phyllanthi (H. Surendranath Pai) Damm, P.F. Cannon & Crous. Name replacement - C. petchii Damm, P.F. Cannon & Crous. New species - C. annellatum Damm, P.F. Cannon & Crous, C. beeveri Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. brassicicola Damm, P.F. Cannon & Crous, C. brasiliense Damm, P.F. Cannon, Crous & Massola, C. colombiense Damm, P.F. Cannon, Crous, C. constrictum Damm, P.F. Cannon, Crous, P.R. J

Question: What is the difference between a new species and a new combination?


Deep neural networks on graph signals for brain imaging analysis. Brain imaging data such as EEG or MEG are high-dimensional spatiotemporal data often degraded by complex, non-Gaussian noise. For reliable analysis of brain imaging data, it is important to extract discriminative, low-dimensional intrinsic representation of the recorded data. This work proposes a new method to learn the low-dimensional representations from the noise-degraded measurements. In particular, our work proposes a new deep neural network design that integrates graph information such as brain connectivity with fully-connected layers. Our work leverages efficient graph filter design using Chebyshev polynomial and recent work on convolutional nets on graph-structured data. Our approach exploits graph structure as the prior side information, localized graph filter for feature extraction and neural networks for high capacity learning. Experiments on real MEG datasets show that our approach can extract more discriminative representations, leading to improved accuracy in a supervised classification task.

Question: How to extract discriminative representations from brain imaging data?


Course Enrollment Recommender System. One of the main problems faced by university students is to create and manage the semester course plan. In this paper, we present a course enrollment recommender system based on data mining techniques. The system mainly helps with students' enrollment decisions. More specifically, it provides recommendation of selective and optional courses with respect to students' skills, knowledge, interests and free time slots in their timetables. The system also warns students against difficult courses and reminds them mandatory study duties. We evaluate the usability of designed methods by analyzing real-world data obtained from the Information System of Masaryk University.

Question: What is a recommender system?


Challenges in Exploiting Conversational Memory in Human-Agent Interaction. In human interactions, language is used to project and maintain a social identity over time. The way people speak with others and revisit language across repeated interactions helps to create rapport and develop a feeling of coordination between conversational partners. Memory of past conversations is the main mechanism that allows us to exploit and explore ways of speaking, given knowledge acquired in previous encounters. As such, we introduce an agent that uses its conversational memory to revisit shared history with users to maintain a coherent social relationship over time. In this paper, we describe the dialog management mechanisms to achieve these goals when applied to a robot that engages in social chit-chat. In a study lasting 14 days with 28 users, totaling 474 interactions, we find that it is difficult to leverage the shared history with individual users and to also accommodate to expected conversational coordination patterns. We discuss the implications of this finding for long-term human-agent interaction. In particular, we highlight the importance of topic modeling and signaling explicit recall of previous episodes. Moreover, the way that users contribute to interactions requires additional adaptation, indicating a significant challenge for language interaction designers.

Question: How to exploit conversational memory in human-agent interaction?


Milli-HRG inertial navigation system. The Hemispherical Resonator Gyro (HRG) has proven itself to be an ultra-reliable technology for space application with over 20 million operation hours and 100% mission success. Northrop Grumman Navigation Systems Division is developing a terrestrial inertial navigation system, INS, based on the proven space technology that can be used for precision pointing applications. The Precision Pointing System (PPS) design yields a small size and lightweight system and will require only a few watts of power to operate. To achieve this small sized INS, the PPS utilizes a new golf-ball sized milli-HRG (mHRG) that is based on the current HRG 130P production gyro design used in extremely accurate space pointing systems. The power reduction is derived from a new electronics design based around low power elements. The new mHRG gyro design has demonstrated bias stability performance better than the navigation grade gyros and will quickly attain this accuracy due to the extremely low noise characteristics of the HRG. Instrumental in the success of the mHRG performance has been the implementation of a calibration mechanization that eliminates the requirement for thermal control or modeling. This implementation will allow the INS to align quickly and will be advantageous for applications that have a quick response time requirement. Additionally, due to the stability of the mHRG the system can operate without GPS aiding for greater than an hour, while maintaining the attitude accuracy required for precision pointing, before an internal realignment is needed. The simplified design of mHRG, has reduced the parts count by roughly 90% when compared to the current space qualified HRG production unit. With the major parts reduction it is projected that the mHRG can be produced efficiently and at a cost making it a viable choice for terrestrial applications.

Question: What is the difference between a milli-HRG and a HRG?


Individual differences in non-verbal number acuity correlate with maths achievement. Human mathematical competence emerges from two representational systems. Competence in some domains of mathematics, such as calculus, relies on symbolic representations that are unique to humans who have undergone explicit teaching. More basic numerical intuitions are supported by an evolutionarily ancient approximate number system that is shared by adults, infants and non-human animals—these groups can all represent the approximate number of items in visual or auditory arrays without verbally counting, and use this capacity to guide everyday behaviour such as foraging. Despite the widespread nature of the approximate number system both across species and across development, it is not known whether some individuals have a more precise non-verbal 'number sense' than others. Furthermore, the extent to which this system interfaces with the formal, symbolic maths abilities that humans acquire by explicit instruction remains unknown. Here we show that there are large individual differences in the non-verbal approximation abilities of 14-year-old children, and that these individual differences in the present correlate with children's past scores on standardized maths achievement tests, extending all the way back to kindergarten. Moreover, this correlation remains significant when controlling for individual differences in other cognitive and performance factors. Our results show that individual differences in achievement in school mathematics are related to individual differences in the acuity of an evolutionarily ancient, unlearned approximate number sense. Further research will determine whether early differences in number sense acuity affect later maths learning, whether maths education enhances number sense acuity, and the extent to which tertiary factors can affect both.

Question: How large are the individual differences in non-verbal number acuity?


Culture Wires the Brain: A Cognitive Neuroscience Perspective.. There is clear evidence that sustained experiences may affect both brain structure and function. Thus, it is quite reasonable to posit that sustained exposure to a set of cultural experiences and behavioral practices will affect neural structure and function. The burgeoning field of cultural psychology has often demonstrated the subtle differences in the way individuals process information-differences that appear to be a product of cultural experiences. We review evidence that the collectivistic and individualistic biases of East Asian and Western cultures, respectively, affect neural structure and function. We conclude that there is limited evidence that cultural experiences affect brain structure and considerably more evidence that neural function is affected by culture, particularly activations in ventral visual cortex-areas associated with perceptual processing.

Question: What is the difference between East Asian and Western cultures?


Why Have Child Maltreatment and Child Victimization Declined ?. Various forms of child maltreatment and child victimization declined as much as 40–70% from 1993 until 2004, including sexual abuse, physical abuse, sexual assault, homicide, aggravated assault, robbery, and larceny. Other child welfare indicators also improved during the same period, including teen pregnancy, teen suicide, and children living in poverty. This article reviews a wide variety of possible explanations for these changes: demography, fertility and abortion legalization, economic prosperity, increased incarceration of offenders, increased agents of social intervention, changing social norms and practices, the dissipation of the social changes from the 1960s, and psychiatric pharmacology. Multiple factors probably contributed. In particular, economic prosperity, increasing agents of social intervention, and psychiatric pharmacology have advantages over some of the other explanations in accounting for the breadth and timing of the improvements.

Question: What are the possible explanations for the decline in child maltreatment and child victimization?


Building a Literal Bridge Between Robotics and Neuroscience using Functional Near Infrared Spectroscopy ( NIRS ). Functional near infrared spectroscopy (NIRS) is a promising new tool for research in human-robot interaction (HRI). The use of NIRS in HRI has already been demonstrated both as a means for investigating brain activity during human-robot interactions, as well as in the development of brain-robot interfaces that passively monitor a person's cognitive state to adapt robots' behaviors accordingly. In this paper, we survey the utility of NIRS and its range of applications in HRI. We discuss both some exemplary applications as well as several pressing challenges to the deployment of NIRS in more realistic settings.

Question: What is the range of applications of NIRS in HRI?


Parental scaffolding as a bootstrapping mechanism for learning grasp affordances and imitation skills. Parental scaffolding is an important mechanism utilized by infants during their development. Infants, for example, pay stronger attention to the features of objects highlighted by parents and learn the way of manipulating an object while being supported by parents. Parents are known to make modifications in infant-directed actions, i.e. use "motionese". Motionese is characterized by higher range and simplicity of motion, more pauses between motion segments, higher repetitiveness of demonstration, and more frequent social signals to an infant. In this paper, we extend our previously developed affordances framework to enable the robot to benefit from parental scaffolding and motionese. First, we present our results on how parental scaffolding can be used to guide the robot and modify robot's crude action execution to speed up learning of complex actions such as grasping. For this purpose, we realize the interactive nature of a human caregiver-infant skill transfer scenario on the robot. During reach and grasp attempts, the movement of the robot hand is modified by the human caregiver's physical interaction to enable successful grasping. Next, we discuss how parental scaffolding can be used in speeding up imitation learning. The system describes how our robot, by using previously learned affordance prediction mechanisms, can go beyond simple goal-level imitation and become a better imitator using infant-directed modifications of parents.

Question: How can we speed up imitation learning?


Privacy-preserving trajectory data publishing by local suppression. The pervasiveness of location-aware devices has spawned extensive research in trajectory data mining, resulting in many important real-life applications. Yet, the privacy issue in sharing trajectory data among different parties often creates an obstacle for effective data mining. In this paper, we study the challenges of anonymizing trajectory data: high dimensionality, sparseness, and sequentiality. Employing traditional privacy models and anonymization methods often leads to low data utility in the resulting data and ineffective data mining. In addressing these challenges, this is the first paper to introduce local suppression to achieve a tailored privacy model for trajectory data anonymization. The framework allows the adoption of various data utility metrics for different data mining tasks. As an illustration, we aim at preserving both instances of location-time doublets and frequent sequences in a trajectory database, both being the foundation of many trajectory data ∗Corresponding author Email addresses: ru_che@encs.concordia.ca (Rui Chen), fung@ciise.concordia.ca (Benjamin C. M. Fung), no_moham@encs.concordia.ca (Noman Mohammed), bcdesai@cs.concordia.ca (Bipin C. Desai) Preprint submitted to Information Sciences April 10, 2011 mining tasks. Our experiments on both synthetic and real-life data sets suggest that the framework is effective and efficient to overcome the challenges in trajectory data anonymization. In particular, compared with the previous works in the literature, our proposed local suppression method can significantly improve the data utility in anonymous trajectory data.

Question: What is the privacy model for trajectory data?