Maternal depression and early childhood growth in developing countries: systematic review and meta-analysis.. OBJECTIVE To investigate the relationship between maternal depression and child growth in developing countries through a systematic literature review and meta-analysis.   METHODS Six databases were searched for studies from developing countries on maternal depression and child growth published up until 2010. Standard meta-analytical methods were followed and pooled odds ratios (ORs) for underweight and stunting in the children of depressed mothers were calculated using random effects models for all studies and for subsets of studies that met strict criteria on study design, exposure to maternal depression and outcome variables. The population attributable risk (PAR) was estimated for selected studies.   FINDINGS Seventeen studies including a total of 13,923 mother and child pairs from 11 countries met inclusion criteria. The children of mothers with depression or depressive symptoms were more likely to be underweight (OR: 1.5; 95% confidence interval, CI: 1.2-1.8) or stunted (OR: 1.4; 95% CI: 1.2-1.7). Subanalysis of three longitudinal studies showed a stronger effect: the OR for underweight was 2.2 (95% CI: 1.5-3.2) and for stunting, 2.0 (95% CI: 1.0-3.9). The PAR for selected studies indicated that if the infant population were entirely unexposed to maternal depressive symptoms 23% to 29% fewer children would be underweight or stunted.   CONCLUSION Maternal depression was associated with early childhood underweight and stunting. Rigorous prospective studies are needed to identify mechanisms and causes. Early identification, treatment and prevention of maternal depression may help reduce child stunting and underweight in developing countries.

Question: What is the relationship between maternal depression and child growth in developing countries?


SPARSKIT: a basic tool kit for sparse matrix computations - Version 2. This paper presents the main features of a tool package for manipulating and working with sparse matrices. One of the goals of the package is to provide basic tools to facilitate exchange of software and data between researchers in sparse matrix computations. Our starting point is the Harwell/Boeing collection of matrices for which we provide a number of tools. Among other things the package provides programs for converting data structures, printing simple statistics on a matrix, plotting a matrix profile, performing basic linear algebra operations with sparse matrices and so on. *RIACS, Mail Stop 230-5, NASA Ames Research Center, Moffett Field, CA 94035. This work was supported in part by the NAS Systems Division, via Cooperative Agreement NCC 2-387 between NASA and the University Space Research Association (USRA) and in part by the Department of Energy under grant DE-FG02-85ER25001.

Question: What is a sparse matrix?


Sign Language Recognition using Sequential Pattern Trees. This paper presents a novel, discriminative, multi-class classifier based on Sequential Pattern Trees. It is efficient to learn, compared to other Sequential Pattern methods, and scalable for use with large classifier banks. For these reasons it is well suited to Sign Language Recognition. Using deterministic robust features based on hand trajectories, sign level classifiers are built from sub-units. Results are presented both on a large lexicon single signer data set and a multi-signer Kinect™ data set. In both cases it is shown to out perform the non-discriminative Markov model approach and be equivalent to previous, more costly, Sequential Pattern (SP) techniques.

Question: What is a sequential pattern tree?


The compressive response of carbon fiber composite pyramidal truss sandwich cores Dedicated to Professor. Pyramidal truss sandwich cores with relative densities 4q in the range 1 – 10 % have been made from carbon fiber reinforced polymer laminates using a snap-fitting method. The measured quasi-static uniaxial compressive strength increased with increasing 4q from 1 to 11 MPa over the relative density range investigated here. A robust face-sheet/ truss joint design was developed to suppress truss– face sheet node fracture. Core failure then occurred by either (i) Euler buckling (4q < 2%) or (ii) delamination failure (4q > 2%) of the struts. Micro-buckling failure of the struts was not observed in the experiments reported here. Analytical models for the collapse of the composite cores by Euler bucking, delamination failure and micro-buckling of the struts have been developed. Good agreement between the measurements and predictions based on the Euler buckling and delamination failure of the struts is obtained. The micro-buckling analysis indicates this mechanism of failure is not activated until delamination is suppressed. The measurements and predictions reported here indicate that composite cellular materials with a pyramidal micro-structure reside in a gap in the strength versus density material property space, providing new opportunities for lightweight, high strength structural design.

Question: What is the relative density of carbon fiber composite pyramidal truss sandwich cores?


A 96-GHz Ortho-Mode Transducer for the Polatron. We describe the design, simulation, fabrication, and performance of a 96-GHz ortho-mode transducer (OMT) to be used for the Polatron—a bolometric receiver with polarization capability. The OMT has low loss, good isolation, and moderately broad bandwidth, and its performance closely resembles simulation results.

Question: What is a bolometric receiver?


Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning. End-to-end neural models show great promise towards building conversational agents that are trained from data and on-line experience using supervised and reinforcement learning. However, these models require a large corpus of dialogues to learn effectively. For goal-oriented dialogues, such datasets are expensive to collect and annotate, since each task involves a separate schema and database of entities. Further, the Wizard-of-Oz approach commonly used for dialogue collection does not provide sufficient coverage of salient dialogue flows, which is critical for guaranteeing an acceptable task completion rate in consumer-facing conversational agents. In this paper, we study a recently proposed approach for building an agent for arbitrary tasks by combining dialogue self-play and crowd-sourcing to generate fully-annotated dialogues with diverse and natural utterances. We discuss the advantages of this approach for industry applications of conversational agents, wherein an agent can be rapidly bootstrapped to deploy in front of users and further optimized via interactive learning from actual users of the system.

Question: How to build a neural conversational agent?


Impact of probing procedure on flip chip reliability. Probe-after-bump is the primary probing procedure for flip chip technology, since it does not directly contact the bump pad, and involves a preferred under bump metallurgy (UBM) step coverage on the bump pads. However, the probe-after-bump procedure suffers from low throughputs and high cost. It also delays the yield feedback to the fab, and makes difficult clarification of the accountability of the low yield bumped wafer between the fab and the bumping house. The probe-before-bump procedure can solve these problems, but the probing tips may over-probe or penetrate the bump pads, leading to poor UBM step coverage, due to inadequate probing conditions or poor probing cards. This work examines the impact of probing procedure on flip chip reliability, using printing and electroplating bumpings on aluminum and copper pads. Bump height, bump shear strength, die shear force, UBM step coverage, and reliability testing are used to determine the influence of probing procedure on flip chip reliability. The experimental results reveal that bump quality and reliability test in the probe-before-bump procedure, under adequate probing conditions, differ slightly from the corresponding items in the probe-after-bump procedure. UBM gives superior step coverage of probe marks in both probe-before-bump and probe-after-bump procedures, implying that UBM achieves greater adhesion and barrier function between the solder bump and the bump pad. Both printing and electroplating bump processes slightly influence all evaluated items. The heights of probe marks on the copper pads are 40–60% lower than those on the aluminum pads, indicating that the copper pad enhances UBM step coverage. This finding reveals that adequate probing conditions of the probe-before-bump procedure are suited to sort flip chip wafers and do not significantly affect bump height, bump shear strength, die shear force, or flip chip reliability. 2002 Elsevier Science Ltd

Question: What is the difference between the probe-before-bump and the probe-after-bump procedure?


Optimized Broadcast for Deep Learning Workloads on Dense-GPU InfiniBand Clusters: MPI or NCCL?. Traditionally, MPI runtimes have been designed for clusters with a large number of nodes. However, with the advent of MPI+CUDA applications and dense multi-GPU systems, it has become important to design efficient communication schemes. This coupled with new application workloads brought forward by Deep Learning frameworks like Caffe and Microsoft CNTK pose additional design constraints due to very large message communication of GPU buffers during the training phase. In this context, special-purpose libraries like NCCL have been proposed. In this paper, we propose a pipelined chain (ring) design for the MPI_Bcast collective operation along with an enhanced collective tuning framework in MVAPICH2-GDR that enables efficient intra-/internode multi-GPU communication. We present an in-depth performance landscape for the proposed MPI_Bcast schemes along with a comparative analysis of NCCL Broadcast and NCCL-based MPI_Bcast. The proposed designs for MVAPICH2-GDR enable up to 14X and 16.6X improvement, compared to NCCL-based solutions, for intra- and internode broadcast latency, respectively. In addition, the proposed designs provide up to 7% improvement over NCCL-based solutions for data parallel training of the VGG network on 128 GPUs using Microsoft CNTK. The proposed solutions outperform the recently introduced NCCL2 library for small and medium message sizes and offer comparable/better performance for very large message sizes.

Question: What is the difference between MPI and NCCL?


GIS METHODS IN TIME-GEOGRAPHIC RESEARCH: GEOCOMPUTATION AND GEOVISUALIZATION OF HUMAN ACTIVITY PATTERNS. Over the past 40 years or so, human activities and movements in space-time have attracted considerable research interest in geography. One of the earliest analytical perspectives for the analysis of human activity patterns and movements in spacetime is time geography. Despite the usefulness of time geography in many areas of geographical research, there are very few studies that actually implemented its constructs as analytical methods up to the mid-1990s. With increasing availability of geo-referenced individual-level data and improvement in the geo-computational capabilities of Geographical Information Systems (GIS), it is now more feasible than ever before to operationalize and implement time-geographic constructs. This paper discusses recent applications of GIS-based geo-computation and three-dimensional (3-D) geo-visualization methods in time-geographic research. The usefulness of these methods is illustrated through examples drawn from the author's recent studies. The paper attempts to show that GIS provides an effective environment for implementing timegeographic constructs and for the future development of operational methods in time-geographic research.

Question: What is the difference between time geography and time geography?


Combining Confocal Imaging and Descattering. In translucent objects, light paths are affected by multiple scattering, which is polluting any observation. Confocal imaging reduces the influence of such global illumination effects by carefully focusing illumination and viewing rays from a large aperture to a specific location within the object volume. The selected light paths still contain some global scattering contributions, though. Descattering based on high frequency illumination serves the same purpose. It removes the global component from observed light paths. We demonstrate that confocal imaging and descattering are orthogonal and propose a novel descattering protocol that analyzes the light transport in a neighborhood of light transport paths. In combination with confocal imaging, our descattering method achieves optical sectioning in translucent media with higher contrast and better resolution.

Question: How does confocal imaging work?


ENHANCEMENT STRATEGIES FOR FRAME-TO-FRAME UAS STEREO VISUAL ODOMETRY. Autonomous navigation of indoor unmanned aircraft systems (UAS) requires accurate pose estimations usually obtained from indirect measurements. Navigation based on inertial measurement units (IMU) is known to be affected by high drift rates. The incorporation of cameras provides complementary information due to the different underlying measurement principle. The scale ambiguity problem for monocular cameras is avoided when a light-weight stereo camera setup is used. However, also frame-to-frame stereo visual odometry (VO) approaches are known to accumulate pose estimation errors over time. Several valuable real-time capable techniques for outlier detection and drift reduction in frame-to-frame VO, for example robust relative orientation estimation using random sample consensus (RANSAC) and bundle adjustment, are available. This study addresses the problem of choosing appropriate VO components. We propose a frame-to-frame stereo VO method based on carefully selected components and parameters. This method is evaluated regarding the impact and value of different outlier detection and drift-reduction strategies, for example keyframe selection and sparse bundle adjustment (SBA), using reference benchmark data as well as own real stereo data. The experimental results demonstrate that our VO method is able to estimate quite accurate trajectories. Feature bucketing and keyframe selection are simple but effective strategies which further improve the VO results. Furthermore, introducing the stereo baseline constraint in pose graph optimization (PGO) leads to significant improvements. * Corresponding author

Question: How to choose the best stereo camera setup for frame-to-frame stereo visual odometry?


MICROCONTROLLER BASED POWER INVERTER. 1 CHAPTER ONE: BACKGROUND STUDY 2 1.

Question: What is a microcontroller based power inverter?


Control structure design for complete chemical plants. Control structure design deals with the structural decisions of the control system, including what to control and how to pair the variables to form control loops. Although these are very important issues, these decisions are in most cases made in an ad hoc fashion, based on experience and engineering insight, without considering the details of each problem. In the paper, a systematic procedure for control structure design for complete chemical plants (plantwide control) is presented. It starts with carefully defining the operational and economic objectives, and the degrees of freedom available to fulfill them. Other issues, discussed in the paper, include inventory and production rate control, decentralized versus multivariable control, loss in performance by bottom-up design, and a definition of a the ''complexity number'' for the control system. # 2003 Elsevier Ltd. All rights reserved.

Question: What is the control structure design for complete chemical plants?


From Visual Data Exploration to Visual Data Mining: A Survey. We survey work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data. Basic terminology related to data mining, data sets, and visualization is introduced. Previous work on information visualization is reviewed in light of different categorizations of techniques and systems. The role of interaction techniques is discussed, in addition to work addressing the question of selecting and evaluating visualization techniques. We review some representative work on the use of information visualization techniques in the context of mining data. This includes both visual data exploration and visually expressing the outcome of specific mining algorithms. We also review recent innovative approaches that attempt to integrate visualization into the DM/KDD process, using it to enhance user interaction and comprehension.

Question: What is visual data mining?


Compact Single- and Dual-Band Filtering Patch Antenna Arrays Using Novel Feeding Scheme. This paper presents two compact patch antenna arrays using a novel feeding scheme to integrate bandpass responses. The two arrays have no filtering circuits but reveal high filtering performance. For each array, only a microstrip feed line is coupled to radiating patches and specific coupling regions are employed to obtain the desired current distribution on the radiating patches. In the operating band, the induced currents on the patches are in phase, resulting in high boresight gain. At the out-of-band frequencies, the patches are not excited or the induced currents on them are 180° out of phase, resulting in radiation suppression. Using this method, out-of-band radiation can be suppressed without degrading the in-band performance. For demonstration, a four-element planar patch array is first implemented. Single-band bandpass response is observed in the experiment. Measured in-band gains are 12.6 dBi, whereas out-of-band suppression is more than 20 dB. Then the feeding scheme is employed to design two three-element subarrays to form a planar dual-band patch array. Also, dual-band bandpass responses are obtained with high frequency selectivity.

Question: What is the purpose of the paper?


Smart cities in Europe. Urban performance currently depends not only on the city's endowment of hard infrastructure ('physical capital'), but also, and increasingly so, on the availability and quality of knowledge communication and social infrastructure ('human and social capital'). The latter form of capital is decisive for urban competitiveness. Against this background, the concept of the 'smart city' has recently been introduced as a strategic device to encompass modern urban production factors in a common framework and, in particular, to highlight the importance of Information and Communication Technologies (ICTs) in the last 20 years for enhancing the competitive profile of a city.

Question: What is the concept of the smart city?


A Simplification-Translation-Restoration Framework for Cross-Domain SMT Applications. Integration of domain specific knowledge into a general purpose statistical machine translation (SMT) system poses challenges due to insufficient bilingual corpora. In this paper we propose a simplification-translation-restoration (STR) framework for domain adaptation in SMT by simplifying domain specific segments of a text. For an in-domain text, we identify the critical segments and modify them to alleviate the data sparseness problem in the out-domain SMT system. After we receive the translation result, these critical segments are then restored according to the provided in-domain knowledge. We conduct experiments on an English-toChinese translation task in the medical domain and evaluate each step of the STR framework. The translation results show significant improvement of our approach over the out-domain and the naïve in-domain SMT systems. 用於跨領域統計式機器翻譯系統之簡化-翻譯-還原架構

Question: What is a simplification-translation-restoration framework for cross-domain SMT applications?


Ultrasound for fetal assessment in early pregnancy.. BACKGROUND Advantages of early pregnancy ultrasound screening are thought to be more accurate calculation of gestational age, earlier identification of multiple pregnancies, and diagnosis of non-viable pregnancies and certain fetal malformations.   OBJECTIVES The objective of this review was to assess the use of routine (screening) ultrasound compared with the selective use of ultrasound in early pregnancy (ie before 24 weeks).   SEARCH STRATEGY The Cochrane Pregnancy and Childbirth Group trials register and the Cochrane Controlled Trials Register (up to July 1998) were searched.   SELECTION CRITERIA Adequately controlled trials of routine ultrasound imaging in early pregnancy.   DATA COLLECTION AND ANALYSIS One reviewer assessed trial quality and extracted data. Study authors were contacted for additional information.   MAIN RESULTS Nine trials were included. The quality of the trials was generally good. Routine ultrasound examination was associated with earlier detection of multiple pregnancies (twins undiagnosed at 26 weeks, odds ratio 0.08, 95% confidence interval 0.04 to 0.16) and reduced rates of induction of labour for post-term pregnancy (odds ratio 0. 61, 95% confidence interval 0.52 to 0.72). There were no differences detected for substantive clinical outcomes such as perinatal mortality (odds ratio 0.86, 95% confidence interval 0.67 to 1.12). Where detection of fetal abnormality was a specific aim of the examination, the number of terminations of pregnancy for fetal anomaly increased.   REVIEWER'S CONCLUSIONS Routine ultrasound in early pregnancy appears to enable better gestational age assessment, earlier detection of multiple pregnancies and earlier detection of clinically unsuspected fetal malformation at a time when termination of pregnancy is possible. However the benefits for other substantive outcomes are less clear.

Question: What is the best way to detect multiple pregnancies?


Minimum Inhibitory Concentration ( MIC ) Of various synthetic and natural antimicrobial agents using E coli screened from VIT sewage treatment plant *. The Minimum Inhibitory Concentration (MIC) Test is an important evaluating tool in the field of microbiology. It gives us an idea of the microbial activity assessment in any source sample containing microbes. Validating the effectiveness of disinfection and decontamination is a vital and often challenging task. In clinical and laboratory environments, presence of sterile conditions is a compulsion and various disinfectants are used to sterilize the surroundings from bacteria, fungi and other microorganisms. The broth dilution and agar diffusion methods of MIC Test tell us about the efficiency of the disinfectant under use. By subjecting a given disinfectant sample to the MIC Test, we can find out its efficiency in lysing the microorganisms present in the inoculums. Thus two or more disinfectants can be compared also at the same time to test their lysing capacity under normal conditions. The MIC Test also helps to choose the more economic disinfectant from a given batch of disinfectants. The results obtained from the MIC Test can also be used to prepare a new anti microbial agent which has greater efficiency in lysing the microorganisms and is economic financially. Minimum Inhibitory Concentration (MIC) of various synthetic like chloramphenical, chloroxylenol and 10% hydrochloric acid with butyl oleylamine in aqueous solution), and natural antimicrobial agents like Sample C (Grass extract) and Sample D (Grass+Neem extract) using E coli screened from VIT sewage treatment plant will be discussed.

Question: What is the minimum inhibitory concentration of a disinfectant?


Advances in Spectral-Spatial Classification of Hyperspectral Images. Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

Question: What is the minimum spanning forest algorithm?


Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging. Environmental audio tagging aims to predict only the presence or absence of certain acoustic events in the interested acoustic scene. In this paper, we make contributions to audio tagging in two parts, respectively, acoustic modeling and feature learning. We propose to use a shrinking deep neural network DNN framework incorporating unsupervised feature learning to handle the multilabel classification task. For the acoustic modeling, a large set of contextual frames of the chunk are fed into the DNN to perform a multilabel classification for the expected tags, considering that only chunk or utterance level rather than frame-level labels are available. Dropout and background noise aware training are also adopted to improve the generalization capability of the DNNs. For the unsupervised feature learning, we propose to use a symmetric or asymmetric deep denoising auto-encoder syDAE or asyDAE to generate new data-driven features from the logarithmic Mel-filter banks features. The new features, which are smoothed against background noise and more compact with contextual information, can further improve the performance of the DNN baseline. Compared with the standard Gaussian mixture model baseline of the DCASE 2016 audio tagging challenge, our proposed method obtains a significant equal error rate EER reduction from 0.21 to 0.13 on the development set. The proposed asyDAE system can get a relative 6.7% EER reduction compared with the strong DNN baseline on the development set. Finally, the results also show that our approach obtains the state-of-the-art performance with 0.15 EER on the evaluation set of the DCASE 2016 audio tagging task while EER of the first prize of this challenge is 0.17.

Question: What is the state-of-the-art performance on the development set of the DCASE 2016 audio tagging task?


Dynamic Memory Networks for Visual and Textual Question Answering. Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering. One such architecture, the dynamic memory network (DMN), obtained high accuracy on a variety of language tasks. However, it was not shown whether the architecture achieves strong results for question answering when supporting facts are not marked during training or whether it could be applied to other modalities such as images. Based on an analysis of the DMN, we propose several improvements to its memory and input modules. Together with these changes we introduce a novel input module for images in order to be able to answer visual questions. Our new DMN+ model improves the state of the art on both the Visual Question Answering dataset and the bAbI-10k text question-answering dataset without supporting fact supervision.

Question: What is the state of the art for visual question answering?


Automatic recognition of unified parkinson's disease rating from speech with acoustic, i-vector and phonotactic features. Parkinson's Disease is a neurodegenerative disease affecting millions of people globally, most of whom present difficulties producing speech sounds. In this paper, we describe a system to identify the degree to which a person suffers from the disease. We use a number of automatic phone recognition-based features and we augment these with i-vector features and utterancelevel acoustic aggregations. On the Interspeech 2015 ComParE challenge corpus, we find that these features allow for prediction well above the challenge baseline, particularly under crossvalidation evaluation.

Question: How to recognize Parkinson's disease from speech?


Wireless Industrial Monitoring and Control Networks: The Journey So Far and the Road Ahead. While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today's industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks.

Question: What is the difference between wired and wireless industrial monitoring and control networks?


Exploratory Innovation, Exploitative Innovation, and Performance: Effects of Organizational Antecedents and Environmental Moderators. AND

Question: What is the difference between exploratory innovation and exploitative innovation?


Fast Anomaly Detection for Streaming Data. This paper introduces Streaming Half-Space-Trees (HS-Trees), a fast one-class anomaly detector for evolving data streams. It requires only normal data for training and works well when anomalous data are rare. The model features an ensemble of random HS-Trees, and the tree structure is constructed without any data. This makes the method highly efficient because it requires no model restructuring when adapting to evolving data streams. Our analysis shows that Streaming HS-Trees has constant amortised time complexity and constant memory requirement. When compared with a state-of-theart method, our method performs favourably in terms of detection accuracy and runtime performance. Our experimental results also show that the detection performance of Streaming HS-Trees is not sensitive to its parameter settings.

Question: What is a streaming half-space-tree?


Cytokine removal with high cut-off membrane: review of literature.. During the last decade, blood purification therapies have been proposed as an effective therapy to control the cytokines dysregulation in systemic inflammatory syndromes. Among them, the treatment with high cut-off membranes is characterized by larger pore size and more effective clearance for middle molecular weight molecules (cytokines). In this paper, we performed a thoughtful review of the literature on HCO being used for blood purification indications in all systemic inflammation syndromes. Clinical and experimental studies show that the use of high effluent flows in a pure diffusive treatment effectively removes serum cytokines with a safe profile in albumin clearance. In clinical studies, the removal of these inflammatory mediators is associated with a significant improvement in hemodynamic condition, oxygenation indices, and organ dysfunction.

Question: What is the difference between a diffusive and a convective treatment?


Guided Image Filtering. In this paper, we propose a novel explicit image filter called guided filter. Derived from a local linear model, the guided filter computes the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter [1], but it has better behaviors near edges. The guided filter is also a more generic concept beyond smoothing: It can transfer the structures of the guidance image to the filtering output, enabling new filtering applications like dehazing and guided feathering. Moreover, the guided filter naturally has a fast and nonapproximate linear time algorithm, regardless of the kernel size and the intensity range. Currently, it is one of the fastest edge-preserving filters. Experiments show that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications, including edge-aware smoothing, detail enhancement, HDR compression, image matting/feathering, dehazing, joint upsampling, etc.

Question: What is the guided filter?


A Privacy-Aware Authentication Scheme for Distributed Mobile Cloud Computing Services. In modern societies, the number of mobile users has dramatically risen in recent years. In this paper, an efficient authentication scheme for distributed mobile cloud computing services is proposed. The proposed scheme provides security and convenience for mobile users to access multiple mobile cloud computing services from multiple service providers using only a single private key. The security strength of the proposed scheme is based on bilinear pairing cryptosystem and dynamic nonce generation. In addition, the scheme supports mutual authentication, key exchange, user anonymity, and user untraceability. From system implementation point of view, verification tables are not required for the trusted smart card generator (SCG) service and cloud computing service providers when adopting the proposed scheme. In consequence, this scheme reduces the usage of memory spaces on these corresponding service providers. In one mobile user authentication session, only the targeted cloud service provider needs to interact with the service requestor (user). The trusted SCG serves as the secure key distributor for distributed cloud service providers and mobile clients. In the proposed scheme, the trusted SCG service is not involved in individual user authentication process. With this design, our scheme reduces authentication processing time required by communication and computation between cloud service providers and traditional trusted third party service. Formal security proof and performance analyses are conducted to show that the scheme is both secure and efficient.

Question: What is the difference between a privacy-aware authentication scheme and a privacy-aware authentication scheme?


Integral Channel Features – Addendum. This document is meant to serve as an addendum to [1], published at BMVC 2009. The purpose of this addendum is twofold: (1) to respond to feedback we've received since publication and (2) to describe a number of changes, especially to the non-maximal suppression, that further improve performance. The performance of our updated detection increases 5% to over 91% detection rate at 1 false positive per image on the INRIA dataset, and similarly on the Caltech Pedestrian Dataset, while overall system runtime for multiscale detection decreases by 1/3 to just under 1.5s per 640×480 image. We begin by rectifying an important omission to the related work. Levi and Weiss had an innovative application of integral images to multiple image channels quite early on, demonstrating good results on face detection from few training examples [4]. This work appears to be the earliest such use of integral images, indeed the authors even describe a precursor to integral histograms. Many thanks to Mark Everingham for sending us this reference.

Question: What is the purpose of this document?


Topic Chronicle Forest for Topic Discovery and Tracking. To ease comprehension of given time-stamped corpora, we extend topic models to handle both the specificity and temporality of topics; this is a significant advance over previous models which fail to provide both views simultaneously. Our proposed model consists of the Topic Chronicle Forest(TCF) and Thematic Dirichlet Processes(TDP). TCF is a set of Topic Chronicle Trees, where each tree is a hierarchy of topics that becomes more specialized toward the leaves. Only one tree is defined in each time interval, a region, and is used for TDP to generate a document. The advantage of our approach lies in providing more compact topic organization, while preserving both the semantic of a given corpus and the thematic of each document. Experiments show that TCF is a useful extension for longitudinal topic discovery and tracking, and helps us to organize and digest data sets.

Question: What is the advantage of topic models?


A Framework for Validating Models of Evasion Attacks on Machine Learning, with Application to PDF Malware Detection. Machine learning (ML) techniques are increasingly common in security applications, such as malware and intrusion detection. However, there is increasing evidence that machine learning models are susceptible to evasion attacks, in which an adversary makes small changes to the input (such as malware) in order to cause erroneous predictions (for example, to avoid being detected). Evasion attacks on ML fall into two broad categories: 1) those which generate actual malicious instances and demonstrate both evasion of ML and efficacy of attack (we call these problem space attacks), and 2) attacks which directly manipulate features used by ML, abstracting efficacy of attack into a mathematical cost function (we call these feature space attacks). Central to our inquiry is the following fundamental question: are feature space models of attacks useful proxies for real attacks? In the process of answering this question, we make two major contributions: 1) a general methodology for evaluating validity of mathematical models of ML evasion attacks, and 2) an application of this methodology as a systematic hypothesis-driven evaluation of feature space evasion attacks on ML-based PDF malware detectors. Specific to our case study, we find that a) feature space evasion models are in general not adequate in representing real attacks, b) such models can be significantly improved by identifying conserved features (features that are invariant in real attacks) whenever these exist, and c) ML hardened using the improved feature space models remains robust to alternative attacks, in contrast to ML hardened using a very powerful class of problem space attacks, which does not.

Question: What is the difference between a feature space attack and a problem space attack?


ObliviStore: High Performance Oblivious Cloud Storage. We design and build ObliviStore, a high performance, distributed ORAM-based cloud data store secure in the malicious model. To the best of our knowledge, ObliviStore is the fastest ORAM implementation known to date, and is faster by 10X or more in comparison with the best known ORAM implementation. ObliviStore achieves high throughput by making I/O operations asynchronous. Asynchrony introduces security challenges, i.e., we must prevent information leakage not only through access patterns, but also through timing of I/O events. We propose various practical optimizations which are key to achieving high performance, as well as techniques for a data center to dynamically scale up a distributed ORAM. We show that with 11 trusted machines (each with a modern CPU), and 20 Solid State Drives, ObliviStore achieves a throughput of 31.5MB/s with a block size of 4KB.

Question: What is the fastest oblivious cloud storage?


Analysis and comparison of MIMO radar waveforms. Choosing a proper waveform is a critical task for the implementation of multiple-input multiple-output (MIMO) radars. In addition to the general requirements for radar waveforms such as good resolution, low sidelobes, etc, MIMO radar waveforms also should possess good orthogonality. In this paper we give a brief overview of MIMO radar waveforms, which are classified into four categories: (1) time division multiple access (TDMA), (2) frequency division multiple access (FDMA), (3) Doppler division multiple access (DDMA), and (4) code division multiple access (CDMA). A special circulating MIMO waveform is also addressed The properties as well as application limitations of different waveforms are analyzed and compared. Some simulations results are also presented to illustrate the respective performance of different waveforms.

Question: What is the difference between TDMA, FDMA, DDMA, and CDMA?


Nerve entrapment syndromes as a cause of pain in the hip, groin and buttock.. In sports medicine, chronic hip, groin and buttock pain is a common diagnostic problem. Because of the complex anatomy of this region and the many potential neurological causes for pain, few sports clinicians have a detailed understanding of this problem. This paper discusses the clinical aspects of nerve entrapment syndromes related to sport and takes a regional approach in order to provide a diagnostic framework for the general sports physician. The various neurological syndromes are discussed and the surgical management elaborated in detail. For some specific conditions, such as the so-called 'piriformis syndrome', the pathophysiological understanding has changed since the early descriptions and now this particular diagnosis is often ascribed to almost any cause of buttock and/or hamstring symptoms. We discuss the nature of the origin of local symptoms and note that the often described symptoms are more likely due to compression of structures other then the sciatic nerve. Furthermore, the role of piriformis hypertrophy or anatomical nerve variations in the genesis of this syndrome must be questioned. We suggest renaming this the 'deep gluteal syndrome' to account for all of the observed phenomena. As sports medicine continues to develop a scientific basis, the role of nerve entrapments as the basis for chronic symptomatology is undergoing a new understanding and clinicians need to be aware of the diagnostic possibilities and be able to advise patients accordingly on the basis of scientific fact not anecdotal fiction.

Question: What is the cause of chronic hip, groin and buttock pain?


Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks. Automatic localization of the standard plane containing complicated anatomical structures in ultrasound (US) videos remains a challenging problem. In this paper, we present a learning-based approach to locate the fetal abdominal standard plane (FASP) in US videos by constructing a domain transferred deep convolutional neural network (CNN). Compared with previous works based on low-level features, our approach is able to represent the complicated appearance of the FASP and hence achieve better classification performance. More importantly, in order to reduce the overfitting problem caused by the small amount of training samples, we propose a transfer learning strategy, which transfers the knowledge in the low layers of a base CNN trained from a large database of natural images to our task-specific CNN. Extensive experiments demonstrate that our approach outperforms the state-of-the-art method for the FASP localization as well as the CNN only trained on the limited US training samples. The proposed approach can be easily extended to other similar medical image computing problems, which often suffer from the insufficient training samples when exploiting the deep CNN to represent high-level features.

Question: How to localize the standard plane in fetal ultrasound?


Fascia lliaca block for analgesia after hip arthroplasty: a randomized double-blind, placebo-controlled trial.. BACKGROUND AND OBJECTIVES Fascia iliaca block (FIB) is often used to treat pain after total hip arthroplasty (THA), despite a lack of randomized trials to evaluate its efficacy for this indication. The objective of this study was to assess the analgesic benefit of FIB after THA. Our primary hypothesis was administration of FIB decreases the intensity of postoperative pain (numeric rating scale [NRS-11] score) compared with sham block (SB) in patients after THA.   METHODS After institutional review board approval and informed consent, 32 eligible patients having THA were recruited. In the postoperative care unit, although all patients received intravenous morphine sulfate patient-controlled analgesia, patients reporting pain of 3 or greater on the NRS-11 scale were randomized to receive ultrasound-guided fascia iliaca (30 mL 0.5% ropivacaine) or SB (30 mL 0.9% NaCl) using identical technique, below fascia iliaca. The primary outcome was pain intensity (NRS-11) after FIB.   RESULTS Thirty-two patients (16 in each group) completed the study; all patients received an FIB. There was no difference in pain intensity (NRS-11 = 5.0 ± 0.6 vs 4.7 ± 0.6, respectively) after FIB versus SB or in opioid consumption (8.97 ± 1.6 vs 5.7 ± 1.6 mg morphine, respectively) between the groups at 1 hour. The morphine consumption after 24 hours was similar in both groups (49.0 ± 29.9 vs 50.4 ± 34.5 mg, P = 0.88, respectively).   CONCLUSIONS The evidence in these data suggests that the difference in average pain intensity after FIB versus SB was not significant (95% confidence interval, -2.2-1.4 NRS units).

Question: What is the difference between fascia iliaca block and fascia iliaca block?


Mitigation of Continuous and Pulsed Radio Interference with GNSS Antenna Arrays. Dr. Andriy Konovaltsev received his engineer diploma and the Ph.D. degree in electrical engineering from Kharkov State Technical University of Radio Electronics, Ukraine in 1993 and 1996, correspondingly. He joined the Institute of Communications and Navigation of DLR in 2001. His research interests are in array processing for satellite navigation systems, signal processing algorithms for navigation receivers including synchronisation, multipath and radio interference mitigation.

Question: What is the institute of communications and navigation?


Protein folding and misfolding. The manner in which a newly synthesized chain of amino acids transforms itself into a perfectly folded protein depends both on the intrinsic properties of the amino-acid sequence and on multiple contributing influences from the crowded cellular milieu. Folding and unfolding are crucial ways of regulating biological activity and targeting proteins to different cellular locations. Aggregation of misfolded proteins that escape the cellular quality-control mechanisms is a common feature of a wide range of highly debilitating and increasingly prevalent diseases.

Question: What is the difference between folding and unfolding?


FC^4: Fully Convolutional Color Constancy with Confidence-Weighted Pooling. Improvements in color constancy have arisen from the use of convolutional neural networks (CNNs). However, the patch-based CNNs that exist for this problem are faced with the issue of estimation ambiguity, where a patch may contain insufficient information to establish a unique or even a limited possible range of illumination colors. Image patches with estimation ambiguity not only appear with great frequency in photographs, but also significantly degrade the quality of network training and inference. To overcome this problem, we present a fully convolutional network architecture in which patches throughout an image can carry different confidence weights according to the value they provide for color constancy estimation. These confidence weights are learned and applied within a novel pooling layer where the local estimates are merged into a global solution. With this formulation, the network is able to determine what to learn and how to pool automatically from color constancy datasets without additional supervision. The proposed network also allows for end-to-end training, and achieves higher efficiency and accuracy. On standard benchmarks, our network outperforms the previous state-of-the-art while achieving 120x greater efficiency.

Question: What is a fully convolutional network?


Question: What is a blockdag?


NEAT-o-Games: blending physical activity and fun in the daily routine. This article describes research that aims to encourage physical activity through a novel pervasive gaming paradigm. Data from a wearable accelerometer are logged wirelessly to a cell phone and control the animation of an avatar that represents the player in a virtual race game with other players over the cellular network. Winners are declared every day and players with an excess of activity points can spend some to get hints in mental games of the suite, like Sudoku. The racing game runs in the background throughout the day and every little move counts. As the gaming platform is embedded in the daily routine of players, it may act as a strong behavioral modifier and increase everyday physical activity other than volitional sporting exercise. Such physical activity (e.g., taking the stairs), is termed NEAT and was shown to play a major role in obesity prevention and intervention. A pilot experiment demonstrates that players are engaged in NEAT-o-Games and become more physically active while having a good dosage of fun.

Question: What is NEAT?


3D Mesh Compression: Survey, Comparisons, and Emerging Trends. 3D meshes are commonly used to represent virtual surface and volumes. However, their raw data representations take a large amount of space. Hence, 3D mesh compression has been an active research topic since the mid 1990s. In 2005, two very good review articles describing the pioneering works were published. Yet, new technologies have emerged since then. In this article, we summarize the early works and put the focus on these novel approaches. We classify and describe the algorithms, evaluate their performance, and provide synthetic comparisons. We also outline the emerging trends for future research.

Question: What is the earliest work on mesh compression?


Schema Theory for Semantic Link Network. Semantic link network (SLN) is a loosely coupled semantic data model for managing Web resources. Its nodes can be any types of resources. Its edges can be any semantic relations. Potential semantic links can be derived out according to reasoning rules on semantic relations. This paper proposes the schema theory for SLN including the concepts, rule-constraint normal forms and relevant algorithms. The theory provides the basis for normalized management of SLN and its applications. A case study demonstrates the proposed theory.

Question: What is the schema theory for semantic link network?


Terra: a virtual machine-based platform for trusted computing. We present a flexible architecture for trusted computing, called Terra, that allows applications with a wide range of security requirements to run simultaneously on commodity hardware. Applications on Terra enjoy the semantics of running on a separate, dedicated, tamper-resistant hardware platform, while retaining the ability to run side-by-side with normal applications on a general-purpose computing platform. Terra achieves this synthesis by use of a trusted virtual machine monitor (TVMM) that partitions a tamper-resistant hardware platform into multiple, isolated virtual machines (VM), providing the appearance of multiple boxes on a single, general-purpose platform. To each VM, the TVMM provides the semantics of either an "open box," i.e. a general-purpose hardware platform like today's PCs and workstations, or a "closed box," an opaque special-purpose platform that protects the privacy and integrity of its contents like today's game consoles and cellular phones. The software stack in each VM can be tailored from the hardware interface up to meet the security requirements of its application(s). The hardware and TVMM can act as a trusted party to allow closed-box VMs to cryptographically identify the software they run, i.e. what is in the box, to remote parties. We explore the strengths and limitations of this architecture by describing our prototype implementation and several applications that we developed for it.

Question: What is a virtual machine?


Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. This paper proposes a two-phase scheme for removing salt-and-pepper impulse noise. In the first phase, an adaptive median filter is used to identify pixels which are likely to be contaminated by noise (noise candidates). In the second phase, the image is restored using a specialized regularization method that applies only to those selected noise candidates. In terms of edge preservation and noise suppression, our restored images show a significant improvement compared to those restored by using just nonlinear filters or regularization methods only. Our scheme can remove salt-and-pepper-noise with a noise level as high as 90%.

Question: How to remove salt-and-pepper noise?


DeepTLR: A single deep convolutional network for detection and classification of traffic lights. Reliable real-time detection of traffic lights is a major concern for the task of autonomous driving. As deep convolutional networks have proven to be a powerful tool in visual object detection, we propose DeepTLR, a camera-based system for real-time detection and classification of traffic lights. Detection and state classification are realized using a single deep convolutional network. DeepTLR does not use any prior knowledge about traffic light locations. Also the detection is executed frame by frame without using temporal information. It is able to detect traffic lights on the whole camera image without any presegmentation. This is achieved by classifying each fine-grained pixel region of the input image and performing a bounding box regression on regions of each class. We show that our algorithm is able to run on frame-rates required for real-time applications while reaching notable results.

Question: What is a deep convolutional network?


0.18um BCD technology with best-in-class LDMOS from 6 V to 45 V. We propose a novel nLDMOS structure and a design concept in BCD technology with the best-in-class performance. The drift profile is optimized and the multi-oxide in the drift region is adopted to approach the RESURF limit of on-resistance vs. BVdss characteristic (i.e., 36V DMOS has a Ron_sp of 20mohm-mm2 with a BVdss of 50V; 45V DMOS has a Ron_sp of 28mohm-mm2 with a BVdss of 65V). Moreover, this modification requires merely three extra masks in the Non-Epitaxy LV process to achieve this improvement. Therefore it is not only a high performance but also a low cost solution.

Question: What is the best-in-class performance of the nLDMOS?


Challenges Facing the Industrial Implementation of Fog Computing. Recently, the industries converge to the integration of the industry 4.0 paradigm to keep responding to the variable market demands. This integration is realized by the adoption of several components of the industry 4.0 such as IoT, Big Data and Cloud Computing, etc. Several difficulties concerning the integration of data management were encountered during first level of Industry 4.0 integration because of the unexpected quantity of data generated by IoT devices. The Fog computing can be considered as a new component of Industry 4.0 to resolve this kind of problem. However its implementation in the industrial field faces several challenges from different natures. This paper explains the role of Fog Computing solution to enhance the Cloud layer (distribution, low latency, real-time,. . . ) and studies its ability to be implemented in manufacturing systems. The Fog Manufacturing is introduced as the new industrial Fog vision. The challenges preventing the Fog Manufacturing implementation are studied and the links between each other are justified. A future use case is described to carry out the solutions given to satisfy the Fog Manufacturing challenges.

Question: What is the role of fog computing in the industrial field?


Think-aloud protocols: Analyzing three different think-aloud protocols with counts of verbalized frustrations in a usability study of an information-rich Web site. We describe an empirical, between-subjects study on the use of think-aloud protocols in usability testing of an information-rich Web site. This double-blind study used three different types of think-aloud protocols: a traditional protocol, a speech-communication protocol, and a coaching protocol. A silent condition served as the control. Eighty participants were recruited and randomly pre-assigned to one of four conditions. With the goal of keeping unintended bias to a minimum, data analysis did not count the number of identified usability problems by condition, which was considered too subjective. Rather, the study collected the number of verbalized and non-verbalized counts of frustration by condition that users experienced. The study also did a count of the number of verbalized and non-verbalized instances of positive comments by condition that users expressed. Results show that there were no statistical differences in the number of counts by condition with respect to the traditional, speech communication, or coaching condition. The study concludes that simply counting the verbalizations of users by condition does not give enough information to determine whether any of the conditions would lead to a better understanding of the usability problems associated with the Web site.

Question: What is a think-aloud protocol?


Anatomy of GPU Memory System for Multi-Application Execution. As GPUs make headway in the computing landscape spanning mobile platforms, supercomputers, cloud and virtual desktop platforms, supporting concurrent execution of multiple applications in GPUs becomes essential for unlocking their full potential. However, unlike CPUs, multi-application execution in GPUs is little explored. In this paper, we study the memory system of GPUs in a concurrently executing multi-application environment. We first present an analytical performance model for many-threaded architectures and show that the common use of misses-per-kilo-instruction (MPKI) as a proxy for performance is not accurate without considering the bandwidth usage of applications. We characterize the memory interference of applications and discuss the limitations of existing memory schedulers in mitigating this interference. We extend the analytical model to multiple applications and identify the key metrics to control various performance metrics. We conduct extensive simulations using an enhanced version of GPGPU-Sim targeted for concurrently executing multiple applications, and show that memory scheduling decisions based on MPKI and bandwidth information are more effective in enhancing throughput compared to the traditional FR-FCFS and the recently proposed RR FR-FCFS policies.

Question: What is the memory system of a GPU?


How Popular is Your Paper ? An Empirical Study of the Citation Distribution. Numerical data for the distribution of citations are examined for: (i) papers published in 1981 in journals which are catalogued by the Institute for Scientific Information (783,339 papers) and (ii) 20 years of publications in Physical Review D, vols. 11-50 (24,296 papers). A Zipf plot of the number of citations to a given paper versus its citation rank appears to be consistent with a power-law dependence for leading rank papers, with exponent close to −1/2. This, in turn, suggests that the number of papers with x citations, N(x), has a large-x power law decay N(x) ∼ x, with α ≈ 3. PACS. 02.50.-r Probability theory, stochastic processes, and statistics – 01.75.+m Science and society – 89.90.+n Other areas of general interest to physicists In this article, I consider a question which is of relevance to those for whom scientific publication is a primary means of scholarly communication. Namely, how often is a paper cited? While the average or total number of citations are often quoted anecdotally and tabulations of highly-cited papers exist [1,2], the focus of this work is on the more fundamental distribution of citations, namely, the number of papers which have been cited a total of x times,N(x). In spite of the fact that many academics are obliged to document their citations for merit-based considerations, there have been only a few scientific investigations on quantifying citations or related measures of scientific productivity. In a 1957 study based on the publication record of the scientific research staff at Brookhaven National Laboratory, Shockley [3] claimed that the scientific publication rate is described by a log-normal distribution. Much more recently, Laherrere and Sornette [4] have presented numerical evidence, based on data of the 1120 most-cited physicists from 1981 through June 1997, that the citation distribution of individual authors has a stretched exponential form, N(x) ∝

Question: How many citations does a paper get?


Extensions of compressed sensing. We study the notion of Compressed Sensing (CS) as put forward in [14] and related work [20, 3, 4]. The basic idea behind CS is that a signal or image, unknown but supposed to be compressible by a known transform, (eg. wavelet or Fourier), can be subjected to fewer measurements than the nominal number of pixels, and yet be accurately reconstructed. The samples are nonadaptive and measure 'random' linear combinations of the transform coefficients. Approximate reconstruction is obtained by solving for the transform coefficients consistent with measured data and having the smallest possible ` norm. We perform a series of numerical experiments which validate in general terms the basic idea proposed in [14, 3, 5], in the favorable case where the transform coefficients are sparse in the strong sense that the vast majority are zero. We then consider a range of less-favorable cases, in which the object has all coefficients nonzero, but the coefficients obey an ` bound, for some p ∈ (0, 1]. These experiments show that the basic inequalities behind the CS method seem to involve reasonable constants. We next consider synthetic examples modelling problems in spectroscopy and image processing, and note that reconstructions from CS are often visually "noisy" . We post-process using translation-invariant de-noising, and find the visual appearance considerably improved. We also consider a multiscale deployment of compressed sensing, in which various scales are segregated and CS applied separately to each; this gives much better quality reconstructions than a literal deployment of the CS methodology. We also report that several workable families of 'random' linear combinations all behave equivalently, including random spherical, random signs, partial Fourier and partial Hadamard. These results show that, when appropriately deployed in a favorable setting, the CS framework is able to save significantly over traditional sampling, and there are many use

Question: What is the difference between compressed sensing and traditional sampling?


The Semantic Web" in Scientific American.

Question: What is the semantic web?


The paradox of received social support: the importance of responsiveness.. Although the perception of available support is associated with positive outcomes, the receipt of actual support from close others is often associated with negative outcomes. In fact, support that is "invisible" (not perceived by the support recipient) is associated with better outcomes than "visible" support. To investigate this paradox, we proposed that received support (both visible and invisible) would be beneficial when it was responsive to the recipient's needs. Sixty-seven cohabiting couples participated in a daily-experience study in which they reported on the support they provided and received each day. Results indicated that both visible and invisible support were beneficial (i.e., associated with less sadness and anxiety and with greater relationship quality) only when the support was responsive. These findings suggest that the nature of support is an important determinant of when received support will be beneficial.

Question: What is the paradox of received social support?


A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification. In this study, a multi-task deep neural network is proposed for skin lesion analysis. The proposed multi-task learning model solves different tasks (e.g., lesion segmentation and two independent binary lesion classifications) at the same time by exploiting commonalities and differences across tasks. This results in improved learning efficiency and potential prediction accuracy for the task-specific models, when compared to training the individual models separately. The proposed multi-task deep learning model is trained and evaluated on the dermoscopic image sets from the International Skin Imaging Collaboration (ISIC) 2017 Challenge "Skin Lesion Analysis towards Melanoma Detection", which consists of 2000 training samples and 150 evaluation samples. The experimental results show that the proposed multi-task deep learning model achieves promising performances on skin lesion segmentation and classification. The average value of Jaccard index for lesion segmentation is 0.724, while the average values of area under the receiver operating characteristic curve (AUC) on two individual lesion classifications are 0.880 and 0.972, respectively.

Question: What is the average value of Jaccard index for lesion segmentation?


Evolving Personalized Content for Super Mario Bros Using Grammatical Evolution. Adapting game content to a particular player's needs and expertise constitutes an important aspect in game design. Most research in this direction has focused on adapting game difficulty to keep the player engaged in the game. Dynamic difficulty adjustment, however, focuses on one aspect of the gameplay experience by adjusting the content to increase or decrease perceived challenge. In this paper, we introduce a method for automatic level generation for the platform game Super Mario Bros using grammatical evolution. The grammatical evolution-based level generator is used to generate player-adapted content by employing an adaptation mechanism as a fitness function in grammatical evolution to optimize the player experience of three emotional states: engagement, frustration and challenge. The fitness functions used are models of player experience constructed in our previous work from crowd-sourced gameplay data collected from over 1500 game sessions.

Question: How to generate content for a game?


Cryolipolysis for Targeted Fat Reduction and Improved Appearance of the Enlarged Male Breast.. BACKGROUND Pseudogynecomastia refers to benign male breast enlargement due to excess subareolar fat. Standard treatment is surgical excision under general anesthesia, liposuction, or a combination of both.   OBJECTIVE The safety and efficacy of cryolipolysis was investigated for nonsurgical treatment of pseudogynecomastia.   METHODS AND MATERIALS Enrollment consisted of 21 males with pseudogynecomastia. Subjects received a first treatment consisting of a 60-minute cryolipolysis cycle, followed by a two-minute massage, and a second 60-minute cycle with 50% treatment area overlap. At 60 days of follow-up, subjects received a second 60-minute treatment. Safety was evaluated by monitoring side effects and adverse events. Efficacy was assessed by ultrasound, clinical photographs, and subject surveys.   RESULTS Surveys revealed that 95% of subjects reported improved visual appearance and 89% reported reduced embarrassment associated with pseudogynecomastia. Ultrasound showed mean fat layer reduction of 1.6 ± 1.2 mm. Blinded reviewers correctly identified 82% of baseline photographs. Side effects included mild discomfort during treatment and transient paresthesia and tenderness. One case of paradoxical hyperplasia (PH) occurred but likelihood of PH in the male breast is not believed to be greater than in any other treatment area.   CONCLUSION This study demonstrated feasibility of cryolipolysis for safe, effective, and well-tolerated nonsurgical treatment of pseudogynecomastia.

Question: What is the difference between cryolipolysis and liposuction?


Is dark silicon useful? Harnessing the four horsemen of the coming dark silicon apocalypse. Due to the breakdown of Dennardian scaling, the percentage of a silicon chip that can switch at full frequency is dropping exponentially with each process generation. This utilization wall forces designers to ensure that, at any point in time, large fractions of their chips are effectively dark or dim silicon, i.e., either idle or significantly underclocked.  As exponentially larger fractions of a chip's transistors become dark, silicon area becomes an exponentially cheaper resource relative to power and energy consumption. This shift is driving a new class of architectural techniques that "spend" area to "buy" energy efficiency. All of these techniques seek to introduce new forms of heterogeneity into the computational stack. We envision that ultimately we will see widespread use of specialized architectures that leverage these techniques in order to attain orders-of-magnitude improvements in energy efficiency.  However, many of these approaches also suffer from massive increases in complexity. As a result, we will need to look towards developing pervasively specialized architectures that insulate the hardware designer and the programmer from the underlying complexity of such systems. In this paper, I discuss four key approaches--the four horsemen--that have emerged as top contenders for thriving in the dark silicon age. Each class carries with its virtues deep-seated restrictions that requires a careful understanding of the underlying tradeoffs and benefits.

Question: What are the four horsemen of the dark silicon apocalypse?


Correlated discrete data generation using adversarial training. Generative Adversarial Networks (GAN) have shown great promise in tasks like synthetic image generation, image inpainting, style transfer, and anomaly detection. However, generating discrete data is a challenge. This work presents an adversarial training based correlated discrete data (CDD) generation model. It also details an approach for conditional CDD generation. The results of our approach are presented over two datasets; job-seeking candidates skill set (private dataset) and MNIST (public dataset). From quantitative and qualitative analysis of these results, we show that our model performs better as it leverages inherent correlation in the data, than an existing model that overlooks correlation.

Question: How to generate correlated discrete data?


Automatic Text Classification: A Technical Review. Automatic Text Classification is a semi-supervised machine learning task that automatically assigns a given document to a set of pre-defined categories based on its textual content and extracted features. Automatic Text Classification has important applications in content management, contextual search, opinion mining, product review analysis, spam filtering and text sentiment mining. This paper explains the generic strategy for automatic text classification and surveys existing solutions to major issues such as dealing with unstructured text, handling large number of attributes and selecting a machine learning technique appropriate to the text-classification application.

Question: What is the generic strategy for automatic text classification?


LKIM : The Linux Kernel Integrity. 509 he Linux Kernel Integrity Measurer (LKIM) is a next-generation technology for the detection of malicious modifications to a running piece of software. Unlike traditional antivirus systems, LKIM does not rely on a database of known malware signatures; instead, LKIM uses a precise model of expected program behavior to verify the consistency of critical data structures at runtime. APL and the Research Directorate of the National Security Agency (NSA) developed the LKIM prototype and are now working to transition the technology to a variety of critical government applications. LKIM: The Linux Kernel Integrity Measurer

Question: What is the research directorate of the national security agency?


Trust and mobile commerce in North America. Mobile Commerce (mCommerce) activities include the act of shopping and buying on mobile devices, along with the more recent emergence of mobile payment systems. Within North America, mCommerce activities have become increasingly popular and will likely continue on this upwards trend as mobile devices further proliferate markets. Historically, one common issue with the adoption and use of eCommerce systems (e.g., commerce activities on personal computers) is trust. Yet we know little of how trust and other social factors may affect mCommerce usageda new genre of commerce activities that explicitly occur on mobile devices. To help address this problem, we have conducted two studies that explore users' mCommerce activities. The first is a diary and interview study of mCommerce shoppers who have already adopted the technology and shop on their mobile devices regularly. Our study explores typical mCommerce routines and behaviors along with issues of trust, given its long-term concern for eCommerce. The second is a diary and interview study of new and existing users of mobile payment services in North America. Participants used a variety of services, including Google Wallet, Amazon Payments, LevelUp, Square and company apps geared towards payments (e.g., Starbucks). Our results show that when it comes to shopping on mobile devices, people have few trust concerns. Yet when mobile devices are used for payments within physical stores, trust issues emerge along with prepurchase anxiety and mental model challenges. We discuss these results and show the value in adapting and developing new trust mechanisms for mCommerce. © 2016 Elsevier Ltd. All rights reserved.

Question: What is the difference between mobile commerce and ecommerce?


Using WordNet as a Knowledge Base for Measuring Semantic Similarity between Words. In this paper we propose the use of WordNet as a knowledge base in an information retrieval task. The application areas range from information filtering and document retrieval to multimedia retrieval and data sharing in large scale distributed database systems. The WordNet derived knowledge base makes semantic knowledge available which can be used in overcoming many problems associated with the richness of natural language. A semantic similarity measure is also proposed which can be used as an alternative to pattern matching in the comparison process.

Question: What is the use of wordnet in information retrieval?


A Simulink-based robotic toolkit for simulation and control of the PUMA 560 robot manipulator. In this paper, a Simulink Robotic Toolkit (SRTK) for the Puma 560 robot manipulator is developed on the MATLAB/SIMULINK-based platform. Through the use of the Real-Time Linux Target and the Real-Time Windows Target, the SRTK can be executed on the Linux or Win32-based operating systems (e.g., Windows 95/98/NT) in real-time. Moreover, the graphical user-friendly nature of Simulink allows the SRTK to be a flexible tool that can easily be customized to fit the specific needs of the user. That is, based on the layered approach of the SRTK, the user can perform operations such as calibration, joint control, Cartesian control, Cartesian PD control, impedance control, some trajectory generation tasks, and real-time simulation of the Puma 560 through a user-friendly MATLAB-based graphical user interface (GUI) without writing any code. One of the key features of the toolkit is that users can easily incorporate additional functionality and hardware through the simple block diagram interface that Simulink provides. Moreover, the MATLABbased GUI can also easily be modified to allow the user to exploit the added functionality with the GUI by using straightforward MATLAB script files. Hence, the SRTK allows a researcher to use the Puma 560 without the burden of the external issues related to the control, interface, and software issues, while providing for the flexibility for easily modifying components for increased functionality.

Question: What is a Simulink-based robotic toolkit?


NE-Rank: A Novel Graph-Based Keyphrase Extraction in Twitter. The massive growth of the micro-blogging service Twitter has shed the light on the challenging problem of summarizing a collection of large number of tweets. This paper attempts to extract topical key phrases that would represent topics in tweets. Due to the informality, noise, and short length of tweets, such research is nontrivial. We tackle such challenges with extensive preprocessing approach. Followed by, introduction of new features that improve topical key phrase extraction in Twitter. We start by proposing a novel unsupervised graph-based keyword ranking method, called NE-Rank, that considers word weights in addition to edge weights when calculating the ranking. Then we introduce a new approach of leveraging hash tags when extracting key phrases. We have conducted a set of experiments showing the potential of both approaches with 16% to 39% improvement for NE-Rank and 20% improvement for hash tag enhanced extraction.

Question: What is the size of the canadian military?


Classifying streaming of Twitter data based on sentiment analysis using hybridization. Twitter is a social media that developed rapidly in today's modern world. As millions of Twitter messages are sent day by day, the value and importance of developing a new technique for detecting spammers become significant. Moreover, legitimate users are affected by means of spams in the form of unwanted URLs, irrelevant messages, etc. Another hot topic of research is sentiment analysis that is based on each tweet sent by the user and opinion mining of the customer reviews. Most commonly natural language processing is used for sentiment analysis. The text is collected from user's tweets by opinion mining and automatic sentiment analysis that are oriented with ternary classifications, such as "positive," "neutral," and "negative." Due to limited size, unstructured nature, misspells, slangs, and abbreviations, it is more challenging for researchers to find sentiments for Twitter data. In this paper, we collected 600 million public tweets using URL-based security tool and feature generation is applied for sentiment analysis. The ternary classification is processed based on preprocessing technique, and the results of tweets sent by the users are obtained. We use a hybridization technique using two optimization algorithms and one machine learning classifier, namely particle swarm optimization and genetic algorithm and decision tree for classification accuracy by sentiment analysis. The results are compared with previous works, and our proposed method shows a better analysis than that of other classifiers.

Question: How to classify tweets based on sentiment analysis?


2 A Physically Unclonable Function with BER < 10-8 for Robust Chip Authentication Using Oscillator Collapse in 40 nm CMOS. Security is a key concern in today's mobile devices and a number of hardware implementations of security primitives have been proposed, including true random number generators, differential power attack avoidance, and chip-ID generators [1-4]. Recently, physically unclonable functions (PUFs) were proposed as a secure method for chip authentication in unsecure environments [5-7]. A PUF is a function that maps an input code ("challenge") to an output code ("response") in a manner that is unique for every chip. PUFs are increasingly used for IC authentication to offer protection against identity theft, cloning, and counterfeit components [2-4].

Question: What is a physically unclonable function?


Aerial-guided navigation of a ground robot among movable obstacles. We demonstrate the fully autonomous collaboration of an aerial and a ground robot in a mock-up disaster scenario. Within this collaboration, we make use of the individual capabilities and strengths of both robots. The aerial robot first maps an area of interest, then it computes the fastest mission for the ground robot to reach a spotted victim and deliver a first-aid kit. Such a mission includes driving and removing obstacles in the way while being constantly monitored and commanded by the aerial robot. Our mission-planning algorithm distinguishes between movable and fixed obstacles and considers both the time for driving and removing obstacles. The entire mission is executed without any human interaction once the aerial robot is launched and requires a minimal amount of communication between the robots. We describe both the hardware and software of our system and detail our mission-planning algorithm. We present exhaustive results of both simulation and real experiments. Our system was successfully demonstrated more than 20 times at a trade fair.

Question: How to make a robot that can drive and remove obstacles?


Superfamilies of evolved and designed networks.. Complex biological, technological, and sociological networks can be of very different sizes and connectivities, making it difficult to compare their structures. Here we present an approach to systematically study similarity in the local structure of networks, based on the significance profile (SP) of small subgraphs in the network compared to randomized networks. We find several superfamilies of previously unrelated networks with very similar SPs. One superfamily, including transcription networks of microorganisms, represents "rate-limited" information-processing networks strongly constrained by the response time of their components. A distinct superfamily includes protein signaling, developmental genetic networks, and neuronal wiring. Additional superfamilies include power grids, protein-structure networks and geometric networks, World Wide Web links and social networks, and word-adjacency networks from different languages.

Question: What is the difference between a transcription network and a protein signaling network?


Constructing popular routes from uncertain trajectories. The advances in location-acquisition technologies have led to a myriad of spatial trajectories. These trajectories are usually generated at a low or an irregular frequency due to applications' characteristics or energy saving, leaving the routes between two consecutive points of a single trajectory uncertain (called an uncertain trajectory). In this paper, we present a Route Inference framework based on Collective Knowledge (abbreviated as RICK) to construct the popular routes from uncertain trajectories. Explicitly, given a location sequence and a time span, the RICK is able to construct the top-k routes which sequentially pass through the locations within the specified time span, by aggregating such uncertain trajectories in a mutual reinforcement way (i.e., uncertain + uncertain → certain). Our work can benefit trip planning, traffic management, and animal movement studies. The RICK comprises two components: routable graph construction and route inference. First, we explore the spatial and temporal characteristics of uncertain trajectories and construct a routable graph by collaborative learning among the uncertain trajectories. Second, in light of the routable graph, we propose a routing algorithm to construct the top-k routes according to a user-specified query. We have conducted extensive experiments on two real datasets, consisting of Foursquare check-in datasets and taxi trajectories. The results show that RICK is both effective and efficient.

Question: How to construct the top-k routes from uncertain trajectories?


A cross-cultural test of the Maluma-Takete phenomenon..

Question: What is the Maluma-Takete phenomenon?


Two-factor authentication: too little, too late. T wo-factor authentication isn't our savior. It won't defend against phishing. It's not going to prevent identity theft. It's not going to secure online accounts from fraudulent transactions. It solves the security problems we had 10 years ago, not the security problems we have today. The problem with passwords is that it is too easy to lose control of them. People give their passwords to other people. People write them down, and other people read them. People send them in email, and that email is intercepted. People use them to log into remote servers, and their communications are eavesdropped on. Passwords are also easy to guess. And once any of that happens, the password no longer works as an authentication token because you can never be sure who is typing in that password. Two-factor authentication mitigates this problem. If your password includes a number that changes every minute, or a unique reply to a random challenge, then it's difficult for someone else to intercept. You can't write down the ever-changing part. An intercepted password won't be usable the next time it's needed. And a two-factor password is more difficult to guess. Sure, someone can always give his password and token to his secretary, but no solution is foolproof. These tokens have been around for at least two decades, but it's only recently that they have received mass-market attention. AOL is rolling them out. Some banks are issuing them to customers , and even more are talking about doing it. It seems that corporations are finally recognizing the fact that passwords don't provide adequate security , and are hoping that two-factor authentication will fix their problems. Unfortunately, the nature of attacks has changed over those two decades. Back then, the threats were all passive: eavesdropping and offline password guessing. Today, the threats are more active: phishing and Trojan horses. Two new active attacks we're starting to see include:

Question: What are the threats to passwords?


Security in Container-Based Virtualization through vTPM. Cloud computing is a wide-spread technology that enables the enterprises to provide services to their customers with a lower cost, higher performance, better availability and scalability. However, privacy and security in cloud computing has always been a major challenge to service providers and a concern to its users. Trusted computing has led its way in securing the cloud computing and virtualized environment, during the past decades.  In this paper, first we study virtualized trusted platform modules and integration of vTPM in hypervisor-based virtualization. Then we propose two architectural solutions for integrating the vTPM in container-based virtualization model.

Question: What is the difference between hypervisor-based virtualization and container-based virtualization?


An Attribute-aware Neural Attentive Model for Next Basket Recommendation. Next basket recommendation is a new type of recommendation, which recommends a set of items, or a basket, to the user. Purchase in basket is a common behavior of consumers. Recently, deep neural networks have been applied to model sequential transactions of baskets in next basket recommendation. However, current methods do not track the user's evolving appetite for items explicitly, and they ignore important item attributes such as product category. In this paper, we propose a novel Attribute-aware Neural Attentive Model (ANAM) to address these problems. ANAM adopts an attention mechanism to explicitly model user's evolving appetite for items, and utilizes a hierarchical architecture to incorporate the attribute information. In specific, ANAM utilizes a recurrent neural network to model the user's sequential behavior over time, and relays the user's appetite toward items and their attributes to next basket through attention weights shared across baskets on the two different hierarchies. Experiment results on two public datasets (ıe Ta-Feng and JingDong) demonstrate the effectiveness of our ANAM model for next basket recommendation.

Question: What is the next basket recommendation?


Bitcoin Block Withholding Attack: Analysis and Mitigation. We address two problems: first, we study a variant of block withholding (BWH) attack in Bitcoins and second, we propose solutions to prevent all existing types of BWH attacks in Bitcoins. We analyze the strategies of a selfish Bitcoin miner who in connivance with one pool attacks another pool and receives reward from the former mining pool for attacking the latter. We name this attack as "sponsored block withholding attack." We present detailed quantitative analysis of the monetary incentive that a selfish miner can earn by adopting this strategy under different scenarios. We prove that under certain conditions, the attacker can maximize her revenue by adopting some strategies and by utilizing her computing power wisely. We also show that an attacker may use this strategy for attacking both the pools for earning higher amount of incentives. More importantly, we present a strategy that can effectively counter block withholding attack in any mining pool. First, we propose a generic scheme that uses cryptographic commitment schemes to counter BWH attack. Then, we suggest an alternative implementation of the same scheme using hash function. Our scheme protects a pool from rogue miners as well as rogue pool administrators. The scheme and its variant defend against BWH attack by making it impossible for the miners to distinguish between a partial proof of work and a complete proof of work. The scheme is so designed that the administrator cannot cheat on the entire pool. The scheme can be implemented by making minor changes to existing Bitcoin protocol. We also analyze the security of the scheme.

Question: What is a block withholding attack?


Normative Social Influence in Persuasive Technology: Intensity versus Effectiveness. Persuasion is a form of social influence that implies intentional but voluntary change of a person's behaviours, feelings or thoughts about an issue, object or action. Successful persuasion by means of persuasive technology relies on a number of factors and motivators. Recently, the so called social acceptance or rejection motivator has formed the backbone of a new type of persuasion, called Mass Interpersonal Persuasion, or MIP for short. This type of persuasion uses the social influence that is generated on online social networks to persuade people. Though it has been established that normative social influence can be used effectively in persuasive technology, it is unknown if the application of more social pressure also makes it more effective. In order to test the hypothesis that the effectiveness of persuasion increases when the persuasion becomes more intense, a quantitative experiment was conducted on the online social network of Facebook. Although evidence to support the hypothesis was found, it cannot be concluded from this experiment that when utilizing normative social influence in persuasive technology more intense persuasion is also more effective.

Question: Normative social influence in persuasive technology


Artificial Vision in Road Vehicles. The last few decades witnessed the birth and growth of a new sensibility to transportation efficiency. In particular, the need for efficient and improved people and goods mobility pushed researchers to address the problem of intelligent transportation systems. This paper surveys the most advanced approaches to the (partial) customization of road following task, using on-board systems based on artificial vision. The functionalities of lane detection, obstacle detection and pedestrian detection are described and classified, and their possible application on future road vehicles is discussed.

Question: What is the most advanced approach to the (partial) customization of road following task?


Designing augmented reality interfaces. Most interactive computer graphics appear on a screen separate from the real world and the user's surroundings. However this does not always have to be the case. In augmented reality (AR) interfaces, three-dimensional virtual images appear superimposed over real objects. AR applications typically use head-mounted or handheld displays to make computer graphics appear in the user's environment.

Question: How do you design an augmented reality interface?


Does High Self-Esteem Cause Better Performance, Interpersonal Success, Happiness, or Healthier Lifestyles?. Self-esteem has become a household word. Teachers, parents, therapists, and others have focused efforts on boosting self-esteem, on the assumption that high self-esteem will cause many positive outcomes and benefits-an assumption that is critically evaluated in this review. Appraisal of the effects of self-esteem is complicated by several factors. Because many people with high self-esteem exaggerate their successes and good traits, we emphasize objective measures of outcomes. High self-esteem is also a heterogeneous category, encompassing people who frankly accept their good qualities along with narcissistic, defensive, and conceited individuals. The modest correlations between self-esteem and school performance do not indicate that high self-esteem leads to good performance. Instead, high self-esteem is partly the result of good school performance. Efforts to boost the self-esteem of pupils have not been shown to improve academic performance and may sometimes be counterproductive. Job performance in adults is sometimes related to self-esteem, although the correlations vary widely, and the direction of causality has not been established. Occupational success may boost self-esteem rather than the reverse. Alternatively, self-esteem may be helpful only in some job contexts. Laboratory studies have generally failed to find that self-esteem causes good task performance, with the important exception that high self-esteem facilitates persistence after failure. People high in self-esteem claim to be more likable and attractive, to have better relationships, and to make better impressions on others than people with low self-esteem, but objective measures disconfirm most of these beliefs. Narcissists are charming at first but tend to alienate others eventually. Self-esteem has not been shown to predict the quality or duration of relationships. High self-esteem makes pe

Question: What is the difference between self-esteem and self-confidence?


Detection and Recognition of Malaysian Special License Plate Based On SIFT Features. Automated car license plate recognition systems are developed and applied for purpose of facilitating the surveillance, law enforcement, access control and intelligent transportation monitoring with least human intervention. In this paper, an algorithm based on SIFT feature points clustering and matching is proposed to address the issue of recognizing Malaysian special plates. These special plates do not follow the normal standard car plates' format as they may contain italic, cursive, connected and small letters. The algorithm is tested with 150 Malaysian special plate images under different environment and the promising experimental results demonstrate that the proposed algorithm is relatively robust. Keywords-license plate recognition, scale invariant feature transform (SIFT), feature extraction, homography, special license plate.

Question: What is the purpose of the algorithm?


Finding new terminology in very large corpora. Most technical and scientific terms are comprised of complex, multi-word noun phrases but certainly not all noun phrases are technical or scientific terms. The distinction of specific terminology from common non-specific noun phrases can be based on the observation that terms reveal a much lesser degree of distributional variation than non-specific noun phrases. We formalize the limited paradigmatic modifiability of terms and, subsequently, test the corresponding algorithm on bigram, trigram and quadgram noun phrases extracted from a 104-million-word biomedical text corpus. Using an already existing and community-wide curated biomedical terminology as an evaluation gold standard, we show that our algorithm significantly outperforms standard term identification measures and, therefore, qualifies as a high-performant building block for any terminology identification system. We also provide empirical evidence that the superiority of our approach, beyond a 10-million-word threshold, is essentially domain- and corpus-size-independent.

Question: What is the difference between a term and a noun phrase?


Low-Dimensional Representation of Spectral Envelope Without Deterioration for Full-Band Speech Analysis/Synthesis System. A speech coding for a full-band speech analysis/synthesis system is described. In this work, full-band speech is defined as speech with a sampling frequency above 40 kHz, whose Nyquist frequency covers the audible frequency range. In prior works, speech coding has generally focused on the narrowband speech with a sampling frequency below 16 kHz. On the other hand, statistical parametric speech synthesis currently uses the full-band speech, and low-dimensional representation of speech parameters is being used. The purpose of this study is to achieve speech coding without deterioration for full-band speech. We focus on a high-quality speech analysis/synthesis system and mel-cepstral analysis using frequency warping. In the frequency warping function, we directly use three auditory scales. We carried out a subjective evaluation using the WORLD vocoder and found that the optimum number of dimensions was around 50. The kind of frequency warping did not significantly affect the sound quality in the dimensions.

Question: How to represent the spectral envelope without deterioration for full-band speech?