title: Online Reputation Systems in Web 2.0 Era

abstract: Web 2.0 has transformed how reputation systems are designed and used by the Web. Based on a thorough review of the existing online reputation systems and their challenges in use, this paper studied a case of Amazon’s reputation system for the impacts of Web 2.0. Through our case study, several distinguished features of new generation reputation systems are noted including multimedia feedbacks, reviewer centered, folksonomy, community contribution, comprehensive reputation, dynamic and interactive system etc. These new developments move towards a relatively trustworthy and reliable online reputation system in the Web 2.0 era.


title: Temporal localization of audio events for conflict monitoring in social media

abstract: With the explosion in the availability of user-generated videos documenting any conflicts and human rights abuses around the world, analysts and researchers increasingly find themselves overwhelmed with massive amounts of video data to acquire and analyze useful information. In this paper, we develop a temporal localization framework for intense audio events in videos which addresses the problem. The proposed method utilizes Localized Self-Paced Reranking (LSPaR) to refine the localization results. LSPaR utilizes samples from easy to noisier ones so that it can overcome the noisiness of the initial retrieval results from user-generated videos. We show our framework's efficacy on localizing intense audio event like gunshot, and further experiments also indicate that our methods can be generalized to localizing other audio events in noisy videos.


title: The cerebellar microcircuit as an adaptive filter: experimental and computational evidence

abstract: Initial investigations of the cerebellar microcircuit inspired the Marr-Albus theoretical framework of cerebellar function. We review recent developments in the experimental understanding of cerebellar microcircuit characteristics and in the computational analysis of Marr-Albus models. We conclude that many Marr-Albus models are in effect adaptive filters, and that evidence for symmetrical long-term potentiation and long-term depression, interneuron plasticity, silent parallel fibre synapses and recurrent mossy fibre connectivity is strikingly congruent with predictions from adaptive-filter models of cerebellar function. This congruence suggests that insights from adaptive-filter theory might help to address outstanding issues of cerebellar function, including both microcircuit processing and extra-cerebellar connectivity.


Bottom-up Deep Learning using the Hebbian Principle Aseem


title: Super mario evolution

abstract: We introduce a new reinforcement learning benchmark based on the classic platform game Super Mario Bros. The benchmark has a high-dimensional input space, and achieving a good score requires sophisticated and varied strategies. However, it has tunable difficulty, and at the lowest difficulty setting decent score can be achieved using rudimentary strategies and a small fraction of the input space. To investigate the properties of the benchmark, we evolve neural network-based controllers using different network architectures and input spaces. We show that it is relatively easy to learn basic strategies capable of clearing individual levels of low difficulty, but that these controllers have problems with generalization to unseen levels and with taking larger parts of the input space into account. A number of directions worth exploring for learning betterperforming strategies are discussed.


title: Adaptive Sharing for Online Social Networks: A Trade-off Between Privacy Risk and Social Benefit

abstract: Online social networks such as Facebook allow users to control which friend sees what information, but it can be a laborious process for users to specify every receiver for each piece of information they share. Therefore, users usually group their friends into social circles, and select the most appropriate social circle to share particular information with. However, social circles are not formed for setting privacy policies, and even the most appropriate social circle still cannot adapt to the changes of users' privacy requirements influenced by the changes in context. This problem drives the need for better privacy control which can adaptively filter the members in a selected social circle to satisfy users' requirements while maintaining users' social needs. To enable such adaptive sharing, this paper proposes a utility-based trade-off framework that models users' concerns (i.e. Potential privacy risks) and incentives of sharing (i.e. Potential social benefits), and quantifies users' requirements as a trade-off between these two types of utilities. By balancing these two metrics, our framework suggests a subset of a selected circle that aims to maximise users' overall utility of sharing. Numerical simulation results compare the outcome of three sharing strategies in randomly changing contexts.


title: Schemaless nosql data stores - object-nosql mappers to the rescue?

abstract: NoSQL data stores are becoming increasingly popular in application development. These systems are attractive for developers due to their ability to handle large volumes of data, as well as data with a high degree of structural variety. Typically, NoSQL data stores are accessed programmatically. Due to the imminent lack of standardized query languages, building applications against the native interfaces of NoSQL data stores creates an unfortunate technical lock-in. To re-gain platform independence, developers turn to object mapper libraries as an additional level of abstraction when accessing NoSQL data stores. The current market for Java object mappers that support NoSQL data stores is still volatile, with commercial and open source products competing for adoption. In this paper, we give an overview on the state-of-the-art in Object-Relational Mappers that can handle also NoSQL data stores, as well as dedicated Object-NoSQL Mappers. We are able to show that choosing the right object mapper library is a strategic decision with far reaching consequences: Current mappers diverge in the NoSQL data stores that they support, in their features, their robustness, their truthfulness to the documentation and query standards, and ultimately, in the runtime overhead that they introduce. Especially in web development, runtime overhead is a crucial aspect contributing to the application latency, and ultimately, the user experience. By shedding light on the current market, we intend to provide software architects with the necessary information to make informed decisions.


title: Nonsinusoidal oscillations underlie pathological phase-amplitude coupling in the motor cortex in Parkinson’s disease

abstract: Parkinson’s disease (PD) is associated with abnormal beta oscillations (13-30 Hz) in the basal ganglia and motor cortex (M1). Recent reports show that M1 beta-high gamma (50-200 Hz) phase-amplitude coupling (PAC) is exaggerated in PD and is reduced following acute deep brain stimulation (DBS). Here we analyze invasive M1 electrocorticography recordings in PD patients on and off DBS, and in isolated cervical dystonia patients, and show that M1 beta oscillations are nonsinusoidal, having sharp and asymmetric features. These sharp oscillatory beta features underlie the previously reported PAC, providing an alternative to the standard interpretation of PAC as an interaction between two distinct frequency components. Specifically, the ratio between peak and trough sharpness is nearly perfectly correlated with beta-high gamma PAC (r = 0.96) and predicts PD-related motor deficit. Using a simulation of the local field potential, we demonstrate that sharp oscillatory waves can arise from synchronous synaptic activity. We propose that exaggerated beta-high gamma PAC may actually reflect such synchronous synaptic activity, manifesting as sharp beta oscillations that are “smoothed out” with DBS. These results support the “desynchronization” hypothesis of DBS wherein DBS counteracts pathological synchronization throughout the basal ganglia-thalamocortical loop. We argue that PAC can be influenced by more than one mechanism. In this case synaptic synchrony, rather than the often assumed spike-field coherence, may underlie exaggerated PAC. These often overlooked temporal features of the oscillatory waveform carry critical physiological information about neural processes and dynamics that may lead to better understanding of underlying neuropathology.


title: Applications of Graph Coloring in Modern Computer Science

abstract:  Abstract—Graph coloring is one of the most important concepts in graph theory and is used in many real time applications in computer science. The main aim of this paper is to present the importance of graph coloring ideas in various areas of compute applications for researches that they can use graph coloring concepts for the research. Graph coloring used in various research areas of computer science such data mining, image segmentation, clustering, image capturing, networking etc. This papers mainly focused on important applications such as Guarding an Art Gallery, Physical layout segmentation, Round-Robin Sports Scheduling, Aircraft scheduling, Biprocessor tasks, Frequency assignment, Final Exam Timetabling as a Grouping Problem, Map coloring and GSM mobile phone networks, and Student Time Table.


title: Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling

abstract: We study parameter inference in large-scale latent variable models. We first propose an unified treatment of online inference for latent variable models from a non-canonical exponential family, and draw explicit links between several previously proposed frequentist or Bayesian methods. We then propose a novel inference method for the frequentist estimation of parameters, that adapts MCMC methods to online inference of latent variable models with the proper use of local Gibbs sampling. Then, for latent Dirich-let allocation,we provide an extensive set of experiments and comparisons with existing work, where our new approach outperforms all previously proposed methods. In particular, using Gibbs sampling for latent variable inference is superior to variational inference in terms of test log-likelihoods. Moreover, Bayesian inference through variational methods perform poorly, sometimes leading to worse fits with latent variables of higher dimensionality.


title: Is Linux kernel oops useful or not

abstract: Linux kernel oops is invoked when the kernel detects an erroneous state inside itself. It kills an offending process and allows Linux to continue its operation under a compromised reliability. We investigate how reliable Linux is after a kernel oops in this paper. To investigate the reliability after a kernel oops, we analyze the scope of error propagation through an experimental campaign of fault injection in Linux 2.6.38. The error propagation scope is process-local if an error is confined in the process context that activated it, while the scope is kernel-global if an error propagates to other processes' contexts or global data structures. If the scope is process-local, Linux can be reliable even after a kernel oops. Our findings are twofold. First, the error propagation scope is mostly process-local. Thus, Linux remains consistent after a kernel oops in most cases. Second, Linux stops its execution before accessing inconsistent states when kernel-global errors occur because synchronization primitives prevent the inconsistent states from being accessed by other processes.


Compact wide stopband microstrip lowpass filter using polygon patches and meandered lines


title: LTE evolution — Latency reduction and reliability enhancements for wireless industrial automation

abstract: Industrial automation applications generally have stringent communication requirements in terms of low latency and high reliability. Existing wireless technologies operating in the unlicensed spectrum are unable to fulfill these requirements owing to the regulatory constraints for coexistence and spectrum sharing. In this paper, we (i) comprehensively analyze and classify the typical industrial automation applications based on their latency and reliability requirements, (ii) investigate the performance of LTE and its latency reduction and reliability enhancements, and (iii) identify which classes of industrial automation applications can be satisfied by using LTE and its enhancements. Our detailed system level simulation results indicate that LTE (release 14 and beyond) is able to satisfy the requirements for some of the identified industrial automation use-cases such as machine tools, production lines, and logistics.


title: Hybrid Directional Discontinuous Reception (HD-DRX) for 5G Communication

abstract: For mmWave directional air interface expected in 5G communications, current discontinuous reception (DRX) mechanisms would be inadequate. Beam searching, for alignment of beams at User Equipment (UE) and 5G base station (NR nodeB), cannot be avoided in directional communication. We propose to exploit dual connectivity of UE, to both LTE eNB and NR nodeB, for effective 5G DRX. We present a novel hybrid directional-DRX (HD-DRX) mechanism, where beam searching is performed only when necessary. Probabilistic estimate of power saving and delay is conducted by capturing various states of UE through a semi-Markov process. Our numerical analysis achieves 13% improvement in power saving for HD-DRX compared with directional-DRX. We validate our numerical analysis with simulation studies on real traffic trace.


title: Potential for discrimination in online targeted advertising

abstract: Recently, online targeted advertising platforms like Facebook have been criticized for allowing advertisers to discriminate against users belonging to sensitive groups, i.e., to exclude users belonging to a certain race or gender from receiving their ads. Such criticisms have led, for instance, Facebook to disallow the use of attributes such as ethnic affinity from being used by advertisers when targeting ads related to housing or employment or financial services. In this paper, we show that such measures are far from sufficient and that the problem of discrimination in targeted advertising is much more pernicious. We argue that discrimination measures should be based on the targeted population and not on the attributes used for targeting. We systematically investigate the different targeting methods offered by Facebook for their ability to enable discriminatory advertising. We show that a malicious advertiser can create highly discriminatory ads without using sensitive attributes. Our findings call for exploring fundamentally new methods for mitigating discrimination in online targeted advertising.


title: Determination/estimation of an optimal replacement interval under minimal repair

abstract: In this paper the problem of estimation of an optimal replacement interval for a system which is minimally repaired at failures is studied. The problem is investigated both under a parametric and a nonparametric form of the failure intensity of the system. It is assumed that observational data from n systems are available. Some asymptotic results are shown. A graphical procedure for determining/estimating an optimal replacement interval is presented. The procedure is particularly valuable for sensitivity analyses, for example with respect to the costs involved.


title: Review: Application of data mining techniques in customer relationship management: A literature review and classification

abstract: Despite the importance of data mining techniques to customer relationship management (CRM), there is a lack of a comprehensive literature review and a classification scheme for it. This is the first identifiable academic literature review of the application of data mining techniques to CRM. It provides an academic database of literature between the period of 2000-2006 covering 24 journals and proposes a classification scheme to classify the articles. Nine hundred articles were identified and reviewed for their direct relevance to applying data mining techniques to CRM. Eighty-seven articles were subsequently selected, reviewed and classified. Each of the 87 selected papers was categorized on four CRM dimensions (Customer Identification, Customer Attraction, Customer Retention and Customer Development) and seven data mining functions (Association, Classification, Clustering, Forecasting, Regression, Sequence Discovery and Visualization). Papers were further classified into nine sub-categories of CRM elements under different data mining techniques based on the major focus of each paper. The review and classification process was independently verified. Findings of this paper indicate that the research area of customer retention received most research attention. Of these, most are related to one-to-one marketing and loyalty programs respectively. On the other hand, classification and association models are the two commonly used models for data mining in CRM. Our analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of data mining techniques in CRM.


title: Interprofessional education: effects on professional practice and healthcare outcomes (update)

abstract: BACKGROUND ::: The delivery of effective, high-quality patient care is a complex activity. It demands health and social care professionals collaborate in an effective manner. Research continues to suggest that collaboration between these professionals can be problematic. Interprofessional education (IPE) offers a possible way to improve interprofessional collaboration and patient care. ::: ::: ::: OBJECTIVES ::: To assess the effectiveness of IPE interventions compared to separate, profession-specific education interventions; and to assess the effectiveness of IPE interventions compared to no education intervention. ::: ::: ::: SEARCH METHODS ::: For this update we searched the Cochrane Effective Practice and Organisation of Care Group specialised register, MEDLINE and CINAHL, for the years 2006 to 2011. We also handsearched the Journal of Interprofessional Care (2006 to 2011), reference lists of all included studies, the proceedings of leading IPE conferences, and websites of IPE organisations. ::: ::: ::: SELECTION CRITERIA ::: Randomised controlled trials (RCTs), controlled before and after (CBA) studies and interrupted time series (ITS) studies of IPE interventions that reported objectively measured or self reported (validated instrument) patient/client or healthcare process outcomes. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: At least two review authors independently assessed the eligibility of potentially relevant studies. For included studies, at least two review authors extracted data and assessed study quality. A meta-analysis of study outcomes was not possible due to heterogeneity in study designs and outcome measures. Consequently, the results are presented in a narrative format. ::: ::: ::: MAIN RESULTS ::: This update located nine new studies, which were added to the six studies from our last update in 2008. This review now includes 15 studies (eight RCTs, five CBA and two ITS studies). All of these studies measured the effectiveness of IPE interventions compared to no educational intervention. Seven studies indicated that IPE produced positive outcomes in the following areas: diabetes care, emergency department culture and patient satisfaction; collaborative team behaviour and reduction of clinical error rates for emergency department teams; collaborative team behaviour in operating rooms; management of care delivered in cases of domestic violence; and mental health practitioner competencies related to the delivery of patient care. In addition, four of the studies reported mixed outcomes (positive and neutral) and four studies reported that the IPE interventions had no impact on either professional practice or patient care. ::: ::: ::: AUTHORS' CONCLUSIONS ::: This updated review reports on 15 studies that met the inclusion criteria (nine studies from this update and six studies from the 2008 update). Although these studies reported some positive outcomes, due to the small number of studies and the heterogeneity of interventions and outcome measures, it is not possible to draw generalisable inferences about the key elements of IPE and its effectiveness. To improve the quality of evidence relating to IPE and patient outcomes or healthcare process outcomes, the following three gaps will need to be filled: first, studies that assess the effectiveness of IPE interventions compared to separate, profession-specific interventions; second, RCT, CBA or ITS studies with qualitative strands examining processes relating to the IPE and practice changes; third, cost-benefit analyses.


title: Photoplethysmogram (PPG) signal analysis and wavelet de-noising

abstract: The use of optical sensors is nowadays very common in the area of non-invasive diagnosis. This is mainly because of their salient features like, simple construction, low cost, easy to use, relatively inexpensive nature etc. Photoplethysmography (PPG) sensor is one among the wide variety of optical sensors available, and is used to measure the blood volumetric changes occurring in the various parts of the body. PPG signal contains rich source of information related to cardio-pulmonary system. But the major problem associated with the signal is the motion artifacts, causing corruption in the original PPG signal. The aim of this study is to know in deep about the different parameters of PPG signal. Peak and transition points of the pulse signal are determined as a part of pulse wave analysis. Wavelet transform is now commonly used in a wide variety of signal processing applications. In this work wavelet de-noising is used for removing AWGN from the PPG signal. Various mother wavelets are studied and the performance of them are evaluated based on the cross correlation with the original signal.


title: Autonomous shaping: knowledge transfer in reinforcement learning

abstract: We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses prior experience on a sequence of tasks to learn a portable predictor that estimates intermediate rewards, resulting in accelerated learning in later tasks that are related but distinct. Such agents can be trained on a sequence of relatively easy tasks in order to develop a more informative measure of reward that can be transferred to improve performance on more difficult tasks without requiring a hand coded shaping function. We use a rod positioning task to show that this significantly improves performance even after a very brief training period.


title: Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening Comprehension Test by Machine

abstract: Multimedia or spoken content presents more attractive information than plain text content, but it's more difficult to display on a screen and be selected by a user. As a result, accessing large collections of the former is much more difficult and time-consuming than the latter for humans. It's highly attractive to develop a machine which can automatically understand spoken content and summarize the key information for humans to browse over. In this endeavor, we propose a new task of machine comprehension of spoken content. We define the initial goal as the listening comprehension test of TOEFL, a challenging academic English examination for English learners whose native language is not English. We further propose an Attention-based Multi-hop Recurrent Neural Network (AMRNN) architecture for this task, achieving encouraging results in the initial tests. Initial results also have shown that word-level attention is probably more robust than sentence-level attention for this task with ASR errors.


title: Video Semantic Object Segmentation by Self-Adaptation of DCNN

abstract: This paper proposes a new framework for semantic segmentation of objects in videos. We address the label inconsistency problem of deep convolutional neural networks (DCNNs) by exploiting the fact that videos have multiple frames; in a few frames the object is confidently-estimated (CE) and we use the information in them to improve labels of the other frames. Given the semantic segmentation results of each frame obtained from DCNN, we sample several CE frames to adapt the DCNN model to the input video by focusing on specific instances in the video rather than general objects in various circumstances. We propose offline and online approaches under different supervision levels. In experiments our method achieved great improvement over the original model and previous state-of-the-art methods.


title: Fuzzy sliding mode control of unicycle robot

abstract: This paper proposes a single wheel balanced robot. In this paper, unicycle robot is implemented by the mobile inverted pendulum control method for pitch axis and the reaction wheel pendulum control method for roll axis. We assumed that both Roll dynamics and Pitch dynamics are decoupled. So, we obtained, respectively, the Roll dynamics and Pitch dynamics. And we designed the controller separately. Robot's angle data is obtained by a fusion of gyro sensor and accelerometer. Experiment results show the performance of the controller.


title: The simplex method of linear programming using LU decomposition

abstract: Standard computer implementations of Dantzig's simplex method for linear programming are based upon forming the inverse of the basic matrix and updating the inverse after every step of the method. These implementations have bad round-off error properties. This paper gives the theoretical background for an implementation which is based upon the LU decomposition, computed with row interchanges, of the basic matrix. The implementation is slow, but has good round-off error behavior. The implementation appears as CACM Algorithm 350.


title: A study on surveillance video abstraction techniques

abstract: The goal of surveillance video abstraction is to generate a video abstract that includes important events and object by eliminating the redundant frames, lacking from activity in original video. Although many research and progresses have been done in video abstraction, the developed approaches either fail to accurately and effectively cover the overall visual content of video or they are computationally expensive in term of time or process. In this paper, firstly we critically review the applicable video abstraction techniques in surveillance domain based on our hierarchical classification, and then briefly introduce a new approach for generating a static surveillance video abstraction, which mitigate the drawbacks of reviewed approaches.


title: Pipelined Compaction for the LSM-Tree

abstract: Write-optimized data structures like Log-Structured Merge-tree (LSM-tree) and its variants are widely used in key-value storage systems like Big Table and Cassandra. Due to deferral and batching, the LSM-tree based storage systems need background compactions to merge key-value entries and keep them sorted for future queries and scans. Background compactions play a key role on the performance of the LSM-tree based storage systems. Existing studies about the background compaction focus on decreasing the compaction frequency, reducing I/Os or confining compactions on hot data key-ranges. They do not pay much attention to the computation time in background compactions. However, the computation time is no longer negligible, and even the computation takes more than 60% of the total compaction time in storage systems using flash based SSDs. Therefore, an alternative method to speedup the compaction is to make good use of the parallelism of underlying hardware including CPUs and I/O devices. In this paper, we analyze the compaction procedure, recognize the performance bottleneck, and propose the Pipelined Compaction Procedure (PCP) to better utilize the parallelism of CPUs and I/O devices. Theoretical analysis proves that PCP can improve the compaction bandwidth. Furthermore, we implement PCP in real system and conduct extensive experiments. The experimental results show that the pipelined compaction procedure can increase the compaction bandwidth and storage system throughput by 77% and 62% respectively.


title: GraphPad: Optimized Graph Primitives for Parallel and Distributed Platforms

abstract: The duality between graphs and matrices means that many common graph analyses can be expressed with primitives such as generalized sparse matrix-vector multiplication (SpMSpV) and sparse matrix-matrix multiplication (SpGEMM). Achieving high performance on these primitives is challenging due to limited arithmetic intensity, irregular memory accesses, and significant network communication requirements in the distributed setting. In this paper we implement four graph applications using GraphPad, our optimized multinode implementations of generalized linear algebra primitives such as SpMSpV and SpGEMM. GraphPad is highly flexible to accommodate multiple data layouts, partitioning strategies, and incorporates communication optimizations. Our performance at scale can exceed that of CombBLAS by up to 40x. In addition to GraphPad's performance in a distributed setting, it is also within 2x the performance of GraphMat, a high performance graph framework on a single node for four out of five benchmarks. We also show our communication optimizations and flexibility are critical for good performance on both HPC clusters and commodity cloud platforms.


title: Gradient echo time dependence and quantitative parameter maps for somatosensory activation in rats at 7 T.

abstract: The dependence of functional magnetic resonance imaging (MRI) contrast on the gradient echo time TE in T*2-weighted blood oxygenation level-dependent (BOLD) fast low-angle shot (FLASH) imaging has been studied at 7 T for electrical forepaw stimulation in α-chloralose anesthetized rats. The observed variation of both the activation signal intensity and spatial pattern with echo time TE, resulting from the regional heterogeneity of T*2, was assessed by the calculation of quantitative T*2 and quantitative STE=0 maps, the latter representing the back-extrapolated signal intensity for TE = 0. The subsequently determined T*2 and STE=0 activation maps allowed a pixelwise separation of true BOLD from inflow contributions to forepaw stimulation-induced signal change in the somatosensory cortex of rat brain. For functional activation experiments performed with one single echo time the prior measurement of a quantitative T*2 map is recommended as minimum further information to judge the intensity and the regional pattern of the resulting activation maps. Magn Reson Med 42:118–126, 1999. © 1999 Wiley-Liss, Inc.


title: The Service Dominant Strategy Canvas: Towards Networked Business Models

abstract: Service orientation, customer focus and collaboration between firms are profoundly changing the way of doing business. Marketing scholars are the first academics to conceptualize these changes under a new mindset, known as the Service Dominant Logic. However, management constructs are needed to apply this mindset to the business environment. Therefore, we have developed a new conceptual model of a Service Dominant Strategy with a visual representation in the form of a canvas. Our model is constructed by integrating current definitions of a Service Dominant strategy and by confronting them with traditional strategies. The model facilitates the design of Service Dominant strategies by answering the questions associated with fifteen elements. Experimental application of our approach in several industry domains shows the importance of both strategic level design and Service Dominant thinking.


title: An active contour model based on fused texture features for image segmentation

abstract: Abstract Texture image segmentation plays an important role in various computer vision tasks. In this paper, a convex texture image segmentation model is proposed. First, the texture features of Gabor and GLCM (gray level co-occurrence matrix) are extracted for original image. Then, the two kinds of texture features are fused together to effectively construct a discriminative feature space by concatenating with each other. In the image segmentation step, a convex energy function is defined by taking the non-convex vector-valued model of Active Contour without Edges (ACWE) into a global minimization framework (GMAC). The proposed global minimization energy function with fused textures (GMFT) can avoid the existence of local minima in the minimization of the vector-valued ACWE model. In addition, a fast dual formulation is adopted to achieve the efficient contour evolution. The experimental results on synthetic and natural animal images demonstrate that the proposed GMFT model obtains more satisfactory segmentation results compared to two state-of-the-art methods in terms of segmentation accuracy and efficiency.


title: Controlling Home Appliances Remotely through Voice Command

abstract: the main concern in systems development is the ::: integration of technologies to increase customer satisfaction. ::: Research presented in this paper focuses mainly in three things ::: first to understand the speech or voice of user second is to control ::: the home appliances through voice call and third is to finds ::: intrusion in the house. The user can make a voice call in order to ::: perform certain actions such as switching lights on/off, getting the ::: status of any appliance etc. And when system finds intrusion it ::: sends an alert voice message to preconfigured cell when the user ::: is away from the place. The proposed system is implemented ::: using voice Global System for Mobile Communications (GSM) ::: and wireless technology based on .NET framework and Attention ::: (AT) commands. Microsoft speech reorganization engine, speech ::: SDK 5.1 is used to understand the voice command of user. As it is ::: wireless so more cost effective and easy to use. The GSM ::: technology used in system provide the everywhere access of the system for security. Experimental results show that the system is more secure and cost effective as compared to existing systems. We conclude that this system provides solution for the problems faced by home owner in daily life and make their life easy and comfortable by proposing cost effective and reliable solution.


Predict the Popularity of YouTube Videos Using Early View Data


title: A virtual reality keyboard with realistic haptic feedback in a fully immersive virtual environment

abstract: This study presents a 3D virtual reality (VR) keyboard system with realistic haptic feedback. The system uses two five-fingered data gloves to track finger positions and postures, uses micro-speakers to create simulated vibrations, and uses a head-mounted display (HMD) for 3D display. When users press a virtual key in the VR environment, the system can provide realistic simulated key click haptic feedback to users. The results of this study show that the advantages of the haptic VR keyboard are that users can use it when wearing HMDs (users do not need to remove HMDs to use the VR keyboard), the haptic VR keyboard can pop-up display at any location in the VR environments (users do not need to go to a specific location to use an actual physical keyboard), and the haptic VR keyboard can be used to provide realistic key click haptic feedback (which other studies have shown enhances user performance). The results also show that the haptic VR keyboard system can be used to create complex vibrations that simulate measured vibrations from a real keyboard and enhance keyboard interaction in a fully immersive VR environment.


title: Energy modeling of supercomputers and large-scale scientific applications

abstract: We derive models of time, power, and energy as a function of frequency and demonstrate that these models accurately reflect measurements of large, parallel, scientific applications. We then apply these models to explain how sensitive the various applications are to CPU frequency; to analyze the compute-boundedness (frequency sensitivity) of complete, production workloads running on three of the world's fastest supercomputers; to predict how changes in system power properties will impact applications; and to determine what CPU frequencies offer the best energy, EDP, and ED2P savings.


title: Inverse Synthetic Aperture Secondary Radar Concept for Precise Wireless Positioning

abstract: In this paper, the novel inverse synthetic aperture secondary radar wireless positioning technique is introduced. The proposed concept allows for a precise spatial localization of a backscatter transponder even in dense multipath environments. A novel secondary radar signal evaluation concept compensates for the unknown modulation phase of the returned signal and thus leads to radar signals comparable to common primary radar. With use of this concept, inverse synthetic aperture radar algorithms can be applied to the signals of backscatter transponder systems. In simulations and first experiments, we used a broadband holographic reconstruction principle to realize the inverse synthetic aperture approach. The movement of the transponder along a short arbitrary aperture path is determined with assisting relative sensors (dead reckoning or inertia sensors). A set of signals measured along the aperture is adaptively focused to the transponder position. By this focusing technique, multipath reflections can be suppressed impressively and a precise indoor positioning becomes feasible. With our technique, completely new and powerful options for integrated navigation and sensor fusion in RF identification systems and wireless local positioning systems are now possible.


title: Applying Behavioral Detection on Android-Based Devices

abstract: We present Andromaly - a behavioral-based detection framework for Android-powered mobile devices. The proposed framework realizes a Host-based Intrusion Detection System (HIDS) that continuously monitors various features and events obtained from the mobile device, and then applies Machine Learning methods to classify the collected data as normal (benign) or abnormal (malicious). Since no malicious applications are yet available for Android, we evaluated Andromaly’s ability to differentiate between game and tool applications. Successful differentiation between games and tools is expected to provide a positive indication about the ability of such methods to learn and model the behavior of an Android application and potentially detect malicious applications. Several combinations of classification algorithms, feature selections and the number of top features were evaluated. Empirical results suggest that the proposed detection framework is effective in detecting types of applications having similar behavior, which is an indication for the ability to detect unknown malware in the Android framework.


title: Microfluidics: Fluid physics at the nanoliter scale

abstract: Microfabricated integrated circuits revolutionized computation by vastly reducing the space, labor, and time required for calculations. Microfluidic systems hold similar promise for the large-scale automation of chemistry and biology, suggesting the possibility of numerous experiments performed rapidly and in parallel, while consuming little reagent. While it is too early to tell whether such a vision will be realized, significant progress has been achieved, and various applications of significant scientific and practical interest have been developed. Here a review of the physics of small volumes (nanoliters) of fluids is presented, as parametrized by a series of dimensionless numbers expressing the relative importance of various physical phenomena. Specifically, this review explores the Reynolds number Re, addressing inertial effects; the Peclet number Pe, which concerns convective and diffusive transport; the capillary number Ca expressing the importance of interfacial tension; the Deborah, Weissenberg, and elasticity numbers De, Wi, and El, describing elastic effects due to deformable microstructural elements like polymers; the Grashof and Rayleigh numbers Gr and Ra, describing density-driven flows; and the Knudsen number, describing the importance of noncontinuum molecular effects. Furthermore, the long-range nature of viscous flows and the small device dimensions inherent in microfluidics mean that the influence of boundaries is typically significant. A variety of strategies have been developed to manipulate fluids by exploiting boundary effects; among these are electrokinetic effects, acoustic streaming, and fluid-structure interactions. The goal is to describe the physics behind the rich variety of fluid phenomena occurring on the nanoliter scale using simple scaling arguments, with the hopes of developing an intuitive sense for this occasionally counterintuitive world.


title: Characterizing spatiotemporal dynamics of land cover with multi-temporal remotely sensed imagery in Beijing during 1978–2010

abstract: To facilitate urban planning and management in fast-growing metropolitan areas, it is highly necessary to detect the spatiotemporal changes of different land cover types. This study aimed at identifying Beijing’s land cover types and detecting the characteristics of their spatiotemporal changes using time series remote sensing and GIS techniques from 1978 to 2010. A total of 16 Landsat MSS/TM/ETM+ images were collected during the spring and late summer seasons. After preprocessing the dataset, artificial neural network was used to perform the land cover classification. Consequently, four maps were generated for 1978, 1992, 2000, and 2010, with six classes (agriculture, woodland, grassland, water, urban, and barren land) according to the level I classification scheme. Three transition matrices were constructed to represent all possible changes that occur in the landscape. The results showed that agriculture, barren land, and grassland had an increase in area, while urban, water, and woodland had a reduction within the study area. A total of 2,032.341 km2 agriculture was reduced and 2,359.146 km2 woodland was increased. In the three periods for 1978–1992, 1992–2000, 2000–2010, agriculture had the largest amount of transfer out primarily to urban class around central urban areas and woodland had the most transfer in mainly from barren land in mountainous areas. More importantly, the driving forces analysis including economic development, growth of population and construction areas, and institutional policies was conducted to find out the primary factors inducing the land cover change.


title: MODELLING THE HYDRAULIC RELATIONSHIP BETWEEN THE RIVER DERWENT AND THE CORALLIAN LIMESTONE AQUIFER

abstract: Abstract The River Derwent in North Yorkshire has been identified by the Environment Agency as a river with a high priority for flow alleviation due to low flows during summer months. The river flows across the Corallian Limestone aquifer and loses flow to this aquifer, via a set of solution hollows in the stream bed. This loss in flow has been gauged, as of the order of 15 to 35 Ml/day, and in dry years the entire river flow can be lost to the aquifer. Groundwater abstraction from the Corallian Limestone aquifer varies seasonally, and is currently between 25 and 35 Ml/day, with the higher rates of pumping occurring during the summer months. One of the main groundwater abstractions is for public water supply, and this source is located about 1.5 km from the river. Abstraction from this source is largely sustained by the leakage from the river. The Corallian Limestone aquifer and its hydraulic relationship with the River Derwent has been represented using the USGS modelling package MODFLOW, to improve the understanding of the flow regime and to provide a tool to assist in the evaluation of different water resource management options. This model has provided a good representation of the aquifer system, and in particular has shown that leakage from the River Derwent is a key factor in controlling the groundwater regime and in maintaining a water balance for the aquifer. The model has been used to examine different management options for the aquifer system, including the influence of reducing the volume of water available to leak through the river bed on the groundwater abstraction sources. This paper will describe how the model has been applied to this aquifer system, the benefits obtained and the problems encountered.


title: Monte Carlo Tree Search for Cross-Stratum Optimization of Survivable Inter-Data Center Elastic Optical Network

abstract: In last few years, cloud computing and other services based on data centers have evolved from an emerging technology to a recognized approach that is gaining broad acceptance and deployment. Therefore, there is a significant need to provide efficient and reliable operation of inter-data center networks based on optical technologies. In this article, we focus on cross stratum optimization of an inter-data center elastic optical network with additional survivability requirements. We propose a novel optimization approach that employs machine learning Monte Carlo Tree Search (MCTS) algorithm for simulation of future traffic to improve the performance of the network regarding blocking probability and operational cost. We evaluate the performance of the proposed method and other reference algorithms under various network scenarios, using representative topologies and real data provided by Amazon Web Services. The main conclusion is that the approach based on the MCTS algorithm enables better coordination of resource allocation in both strata, which results in lower blocking of requests and lower cost taking into account the survivability requirements, in comparison to other algorithms.


title: Using CNN To Classify Spectrograms of Seismic Events From Llaima Volcano (Chile)

abstract: The monitoring of volcanoes is mainly performed by sensors installed on their structures, aiming at recording seismic activities and reporting them to observatories to be later analyzed by specialists. However, due to the high volume of data continuously collected, the use of automatic techniques is an important requirement to support real time analyses. In this sense, a basic but challenging task is the classification of seismic activities to identify signals yielded by different sources as, for instance, the movement of magmatic fluids. Although there exists several approaches proposed to perform such task, they were mainly designed to deal with raw signals. In this paper, we present a 2D approach developed considering two main steps. Firstly, spectrograms for every collected signal are calculated by using Fourier Transform. Secondly, we set a deep neural network to discriminate seismic activities by analyzing the spectrogram shapes. As a consequence, our classifier provided outstanding results with accuracy rates greater than 95%.


title: A Generative Model of People in Clothing

abstract: We present the first image-based generative model of people in clothing for the full body. We sidestep the commonly used complex graphics rendering pipeline and the need for high-quality 3D scans of dressed people. Instead, we learn generative models from a large image database. The main challenge is to cope with the high variance in human pose, shape and appearance. For this reason, pure image-based approaches have not been considered so far. We show that this challenge can be overcome by splitting the generating process in two parts. First, we learn to generate a semantic segmentation of the body and clothing. Second, we learn a conditional model on the resulting segments that creates realistic images. The full model is differentiable and can be conditioned on pose, shape or color. The result are samples of people in different clothing items and styles. The proposed model can generate entirely new people with realistic clothing. In several experiments we present encouraging results that suggest an entirely data-driven approach to people generation is possible.


title: Individual Trust in Online Firms: Scale Development and Initial Test

abstract: The importance of trust as a key facilitator of electronic commerce is increasingly being recognized in academic and practitioner communities. However, empirical research in this area has been beset by conflicting conceptualizations of the trust construct, inadequate attention to its underlying dimensions, causes, and effects, and lack of a validated trust scale. This paper addresses these limitations in part by theoretically conceptualizing and empirically validating a scale to measure individual trust in online firms. The proposed scale taps into three key dimensions of trust: trustee's ability, benevolence, and integrity. An iterative testing and refinement procedure using two field surveys of online retailing and online banking users, leads to a final seven-item trust scale that exhibits adequate levels of reliability, convergent validity, discriminant validity, and nomological validity. It is expected that the scale presented in this paper will assist future empirical research on trust in online entities.


title: Emotion recognition from physiological signals using wireless sensors for presence technologies

abstract: In this article we describe a new approach to enhance presence technologies. First, we discuss the strong relationship between cognitive processes and emotions and how human physiology is uniquely affected when experiencing each emotion. Secondly, we introduce our prototype multimodal affective user interface. In the remainder of the paper we describe the emotion elicitation experiment we designed and conducted and the algorithms we implemented to analyse the physiological signals associated with emotions. These algorithms can then be used to recognise the affective states of users from physiological data collected via non-invasive technologies. The affective intelligent user interfaces we plan to create will adapt to user affect dynamically in the current context, thus providing enhanced social presence.


title: Performance analysis of FMCW-based X-band weather radar

abstract: In this paper, a performance analysis of weather radar system works on X-band frequency is approached using an EM & Circuit simulator. The radar which is designed using frequency modulated continuous wave (FMCW) technique is intended to operate at center frequency of 9GHz. The FMCW signal referred as chirp signal is generated using a direct digital synthesizer (DDS) with a bandwidth of 50MHz. By using double up-converter to reach the operating frequency, the signal is powered using 2Watt amplifier to be transmitted by the transmitter antenna. The reflected signal is then received by the receiver antenna to be further processed and analyzed. From the simulation results, it shows that the reflected signal has a delay time compared to the reference signal indicating the performance of FMCW radar weather to detect the object as far as 800 meters from the radar.


title: Patient-centredness: a conceptual framework and review of the empirical literature.

abstract: A 'patient-centred' approach is increasingly regarded as crucial for the delivery of high quality care by doctors. However, there is considerable ambiguity concerning the exact meaning of the term and the optimum method of measuring the process and outcomes of patient-centred care. This paper reviews the conceptual and empirical literature in order to develop a model of the various aspects of the doctor-patient relationship encompassed by the concept of 'patient-centredness' and to assess the advantages and disadvantages of alternative methods of measurement. Five conceptual dimensions are identified: biopsychosocial perspective; 'patient-as-person'; sharing power and responsibility; therapeutic alliance; and 'doctor-as-person'. Two main approaches to measurement are evaluated: self-report instruments and external observation methods. A number of recommendations concerning the measurement of patient-centredness are made.


title: Development of a Biomimetic Hand Exotendon Device (BiomHED) for Restoration of Functional Hand Movement Post-Stroke

abstract: Significant functional impairment of the hand is common among stroke survivors and restoration of hand function should be prioritized during post-stroke rehabilitation. The goal of this study was to develop a novel biomimetic device to assist patients in producing complex hand movements with a limited number of actuators. The Biomimetic Hand Exoskeleton Device (BiomHED) is actuated by exotendons that mimic the geometry of the major tendons of the hand. Ten unimpaired subjects and four chronic stroke survivors participated in experiments that tested the efficacy of the system. The exotendons reproduced distinct spatial joint coordination patterns similar to their target muscle-tendon units for both subject groups. In stroke survivors, the exotendon-produced joint angular displacements were smaller, but not significantly different, than those of unimpaired subjects [Formula: see text]. Even with limited use of the BiomHED, the kinematic workspace of the index finger increased by 63%-1014% in stroke survivors. The device improved the kinematics of the tip-pinch task in stroke survivors and resulted in a significant reduction in the fingertip-thumb tip distance ( 17.9 ±15.3 mm). This device is expected to enable effective "task-oriented" training of the hand post-stroke.


title: Dual buck inverter with series connected diodes and single inductor

abstract: In a DC-AC system, some problems may threaten the reliability of the whole system, such as the shoot through issue and the failure of reverse recovery. Some methods are proposed to improve the reliability of the converters. The dual buck inverters can solve the above problems without adding dead time but the dual buck topology has a main drawback of low magnetic utilization which increases the volume and weight of the system. This paper firstly summarizes the traditional dual buck topologies including a kind of single inductor dual buck inverter which can make full use of the inductance. Then a method to improve the reliability of the MOSFET inverter is proposed. A kind of novel dual buck inverter with series connected diodes and single inductor is introduced. The novel inverter retains the dual buck topologies' advantage of high reliability and can make full use of the inductance. Also, compared to the traditional single inductor dual buck topology, the controlling strategy of the proposed inverter is simpler. Finally, the simulation and experimental results verified the theoretical analysis.


title: Degenerative lumbar spinal stenosis: correlation with Oswestry Disability Index and MR Imaging

abstract: Because neither the degree of constriction of the spinal canal considered to be symptomatic for lumbar spinal stenosis nor the relationship between the clinical appearance and the degree of a radiologically verified constriction is clear, a correlation of patient's disability level and radiographic constriction of the lumbar spinal canal is of interest. The aim of this study was to establish a relationship between the degree of radiologically established anatomical stenosis and the severity of self-assessed Oswestry Disability Index in patients undergoing surgery for degenerative lumbar spinal stenosis. Sixty-three consecutive patients with degenerative lumbar spinal stenosis who were scheduled for elective surgery were enrolled in the study. All patients underwent preoperative magnetic resonance imaging and completed a self-assessment Oswestry Disability Index questionnaire. Quantitative image evaluation for lumbar spinal stenosis included the dural sac cross-sectional area, and qualitative evaluation of the lateral recess and foraminal stenosis were also performed. Every patient subsequently answered the national translation of the Oswestry Disability Index questionnaire and the percentage disability was calculated. Statistical analysis of the data was performed to seek a relationship between radiological stenosis and percentage disability recorded by the Oswestry Disability Index. Upon radiological assessment, 27 of the 63 patients evaluated had severe and 33 patients had moderate central dural sac stenosis; 11 had grade 3 and 27 had grade 2 nerve root compromise in the lateral recess; 22 had grade 3 and 37 had grade 2 foraminal stenosis. On the basis of the percentage disability score, of the 63 patients, 10 patients demonstrated mild disability, 13 patients moderate disability, 25 patients severe disability, 12 patients were crippled and three patients were bedridden. Radiologically, eight patients with severe central stenosis and nine patients with moderate lateral stenosis demonstrated only minimal disability on percentage Oswestry Disability Index scores. Statistical evaluation of central and lateral radiological stenosis versus Oswestry Disability Index percentage scores showed no significant correlation. In conclusion, lumbar spinal stenosis remains a clinico-radiological syndrome, and both the clinical picture and the magnetic resonance imaging findings are important when evaluating and discussing surgery with patients having this diagnosis. MR imaging has to be used to determine the levels to be decompressed.


Evaluating user participation and user influence in an enterprise system


title: Analysis of dynamically scheduled tile algorithms for dense linear algebra on multicore architectures. Concurrency Computat.: Pract

abstract: The objective of this paper is to analyze the dynamic scheduling of dense linear algebra algorithms on shared-memory, multicore architectures. Current numerical libraries (e.g., linear algebra package) show clear limitations on such emerging systems mainly because of their coarse granularity tasks. Thus, many numerical algorithms need to be redesigned to better fit the architectural design of the multicore platform. The parallel linear algebra for scalable multicore architectures library developed at the University of Tennessee tackles this challenge by using tile algorithms to achieve a finer task granularity. These tile algorithms can then be represented by directed acyclic graphs, where nodes are the tasks and edges are the dependencies between the tasks. The paramount key to achieve high performance is to implement a runtime environment to efficiently schedule the execution of the directed acyclic graph across the multicore platform. This paper studies the impact on the overall performance of some parameters, both at the level of the scheduler (e.g., window size and locality) and the algorithms (e.g., left-looking and right-looking variants). We conclude that some commonly accepted rules for dense linear algebra algorithms may need to be revisited. Copyright © 2011 John Wiley & Sons, Ltd.


title: The Intestinal Bacterial Community in the Food Waste-Reducing Larvae of Hermetia illucens

abstract: As it is known that food waste can be reduced by the larvae of Hermetia illucens (Black soldier fly, BSF), the scientific and commercial value of BSF larvae has increased recently. We hypothesised that the ability of catabolic degradation by BSF larvae might be due to intestinal microorganisms. Herein, we analysed the bacterial communities in the gut of BSF larvae by pyrosequencing of extracting intestinal metagenomic DNA from larvae that had been fed three different diets. The 16S rRNA sequencing results produced 9737, 9723 and 5985 PCR products from larval samples fed food waste, cooked rice and calf forage, respectively. A BLAST search using the EzTaxon program showed that the bacterial community in the gut of larvae fed three different diets was mainly composed of the four phyla with dissimilar proportions. Although the composition of the bacterial communities depended on the different nutrient sources, the identified bacterial strains in the gut of BSF larvae represented unique bacterial species that were unlike the intestinal microflora of other insects. Thus, our study analysed the structure of the bacterial communities in the gut of BSF larvae after three different feedings and assessed the application of particular bacteria for the efficient degradation of organic compounds.


title: Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture

abstract: In the current manufacturing world, the role of maintenance has been receiving increasingly more attention while companies understand that maintenance, when well performed, can be a strategic factor to achieve the corporate goals. The latest trends of maintenance leans towards the predictive approach, exemplified by the Prognosis and Health Management (PHM) and the Condition-based Maintenance (CBM) techniques. The implementation of such approaches demands a well structured architecture and can be boosted through the use of emergent ICT technologies, namely Internet of Things (IoT), cloud computing, advanced data analytics and augmented reality. Therefore, this paper describes the architecture of an intelligent and predictive maintenance system, aligned with Industry 4.0 principles, that considers advanced and online analysis of the collected data for the earlier detection of the occurrence of possible machine failures, and supports technicians during the maintenance interventions by providing a guided intelligent decision support.


title: Relief mapping of non-height-field surface details

abstract: The ability to represent non-height-field mesostructure details is of great importance for rendering complex surface patterns, such as weave and multilayer structures. Currently, such representations are based on the use of 3D textures or large datasets of sampled data. While some of the 3D-texture-based approaches can achieve interactive performance, all these approaches require large amounts of memory. We present a technique for mapping non-height-field structures onto arbitrary polygonal models in real time, which has low memory requirements. It generalizes the notion of relief mapping to support multiple layers. This technique can be used to render realistic impostors of 3D objects that can be viewed from close proximity and from a wide angular range. Contrary to traditional impostors, these new one-polygon representations can be observed from both sides, producing correct parallax and views that are consistent with the observation of the 3D geometry they represent.


title: Multiresistant acinetobacter in the UK: how big a threat?

abstract: For a decade, the threat from Gram-positive pathogens, especially methicillin-resistant Staphylococcus aureus (MRSA), has seemed dominant. Yet it is gradually becoming clear that we are far closer to the ‘end of antibiotics’ with Acinetobacter spp. and Pseudomonas aeruginosa than with any Grampositive pathogen. In the case of Acinetobacter spp., strains susceptible only to obscure or longdiscarded antibiotics are achieving local spread in the UK and elsewhere. How big a threat do these pose? Members of the genus Acinetobacter—principally A. baumannii—are opportunistic pathogens with a capacity for long-term survival in the hospital environment and with the ability to cause fatal infections in severely debilitated patients. Factors predisposing to infections include previous antibiotic therapy, major surgery, burns, immunosuppression, the presence of invasive devices and, especially, mechanical ventilation. During the past 30 years, isolates of Acinetobacter spp. have progressively accumulated resistance to successive penicillins, cephalosporins, quinolones and aminoglycosides. A UK survey in 2001 found resistance to cephalosporins in more than 85% of isolates, with only imipenem, meropenem, colistin and sulbactam remaining active against more than 90% of organisms. Consequently, in the UK, as in many other places, carbapenems have become the therapy of choice for serious acinetobacter infections, with few agents in reserve behind them. It is, therefore, worrying that the number of carbapenem-resistant acinetobacter isolates is increasing. The Health Protection Agency’s reference laboratories in England are consulted on growing numbers of individual resistant isolates and, more disturbingly, on resistant outbreak strains, several of which are now circulating in southeast England. In the USA, surveillance from the TSN database shows the proportion of imipenem resistance increasing from 1.8% in 1996 to 7.3% in 2002, by which time a further 6.2% showed intermediate resistance. Carbapenem resistance in Acinetobacter spp. can be due to acquired class B IMP and VIM metallob-lactamases, to class D carbapenemases of the OXA-23 and -24 clusters, to other uncharacterized OXA enzymes, or to b-lactamase-independent mechanisms. Reports of carbapenemase-mediated resistance dominate in the literature, but this may be because enzyme production is easy to define compared with non-b-lactamase mechanisms, such as low permeability of the outer membrane, reduced affinity of penicillin-binding proteins and/ or up-regulated efflux. Metallo-enzymes have been identified only twice in UK acinetobacters, with one report concerning a patient apparently infected in Spain. This contrasts


WIRELESS MESH NETWORKS : OPPORTUNITIES AND CHALLENGES


Keshif : Out-ofthe-Box Visual and Interactive Data Exploration Environment


title: Modeling the ownership of source code topics

abstract: Exploring linguistic topics in source code is a program comprehension activity that shows promise in helping a developer to become familiar with an unfamiliar software system. Examining ownership in source code can reveal complementary information, such as who to contact with questions regarding a source code entity, but the relationship between linguistic topics and ownership is an unexplored area. In this paper we combine software repository mining and topic modeling to measure the ownership of linguistic topics in source code. We conduct an exploratory study of the relationship between linguistic topics and ownership in source code using 10 open source Java systems. We find that classes that belong to the same linguistic topic tend to have similar ownership characteristics, which suggests that conceptually related classes often share the same owner(s). We also find that similar topics tend to share the same ownership characteristics, which suggests that the same developers own related topics.


Bayesian Persuasion


title: Graphical Tactile Displays for Visually-Impaired People

abstract: This paper presents an up-to-date survey of graphical tactile displays. These devices provide information through the sense of touch. At best, they should display both text and graphics (text may be considered a type of graphic). Graphs made with shapeable sheets result in bulky items awkward to store and transport; their production is expensive and time-consuming and they deteriorate quickly. Research is ongoing for a refreshable tactile display that acts as an output device for a computer or other information source and can present the information in text and graphics. The work in this field has branched into diverse areas, from physiological studies to technological aspects and challenges. Moreover, interest in these devices is now being shown by other fields such as virtual reality, minimally invasive surgery and teleoperation. It is attracting more and more people, research and money. Many proposals have been put forward, several of them succeeding in the task of presenting tactile information. However, most are research prototypes and very expensive to produce commercially. Thus the goal of an efficient low-cost tactile display for visually-impaired people has not yet been reached.


title: A 2.14-GHz GaN MMIC Doherty Power Amplifier for Small-Cell Base Stations

abstract: A novel 2.14-GHz Doherty power amplifier (PA) was designed and fabricated using a 0.25- μm GaN on SiC monolithic microwave integrated circuit (MMIC), to build small-cell base stations. To reduce the size and loss, lumped passive elements were employed in a manner of minimizing the device count. The core components of the PA were integrated on the MMIC die to reduce the area, and low-loss chip inductors were mounted around the die to enhance the efficiency. An unconventional uneven power splitting was also used to enhance the performance. For a continuous wave, a 2-dB-gain-compression power of 40.5 dBm was obtained with a drain efficiency (DE) of 60.4%. At 7.3-dB backed-off power, a DE of 52.2% was obtained with a power gain of 15.7 dB. When a 10-MHz-bandwidth long-term evolution signal with 7.1-dB peak-to-average power ratio was applied, an adjacent channel leakage ratio (ACLR) of -34.7 dBc with a DE of 51.8% was achieved at an average power of 33.2 dBm. After a digital pre-distortion process, the ACLR and DE were improved to -49.6 dBc and 52.7%, respectively.


title: Design and Comparison of a 10-kW Interleaved Boost Converter for PV Application Using Si and SiC Devices

abstract: Grid-connected photovoltaic (PV) inverters have a dc/dc converter connected to the PV for executing the maximum power point tracking. The design of an interleaved boost converter (IBC) with three switching legs for a 10-kW PV inverter is presented in this paper. This paper shows how the use of silicon carbide (SiC) switches and powdered iron core inductors enables the operation of the converter at a higher switching frequency and when increasing the converter power density. The IBC is designed using a 1.2-kV SiC MOSFET and Schottky diodes and Kool $\text{M}\mu $ powdered iron inductors. The design is compared with an IBC built with a silicon (Si) IGBT, fast recovery Si diodes, and ferrite cores. The use of SiC devices reduces the switching loses drastically and there are no reverse recovery losses, resulting in improved efficiency. The higher frequency and higher saturation flux density of the powdered iron core enable the reduction in core size by three times. A 10-kW prototype is built and tested for both the Si and SiC designs and compared with theoretical estimations.


title: Delayed Mesh Infection: A Rare Complication of Laparoscopic Inguinal Hernia Repair (TEP—Totally Extra-Peritoneal Repair)

abstract: Hernias ::: are amongst the oldest afflictions of mankind. The tension-free mesh repairs ::: revolutionized and radically changed the whole concept of anatomical tissue ::: repairs. The introduction of mesh, though beneficial, posed a new set of ::: postoperative problems with the mesh infection being the most morbid one. Laparoscopic surgery has been able to reduce the ::: incidence of mesh infection as opposed to the open hernia repairs. The ::: infection occurs mostly early but rarely does it present several years after ::: the surgery. Herein we report our case of delayed mesh infection developing 6 ::: years postoperatively. This is our first such case in a series of more than 1000 laparoscopic hernia repairs over a period of 6 ::: years (2010-2016). The ::: patient needed an open exploration which revealed a large preperitoneal cavity ::: containing 770 ml of pus with a mesh floating in it. The mesh was removed and ::: the thorough toileting of the wound was done.


title: A Procrustes Problem on the Stiefel Manifold

abstract: An orthogonal Procrustes problem on the Stiefel manifold is studied, where a matrix Q with orthonormal columns is to be found that minimizes \(\| AQ-B\|_{\rm F}\) for an \(l \times m\) matrix A and an \(l \times n\) matrix B with \(l \geq m\) and \(m > n\). Based on the normal and secular equations and the properties of the Stiefel manifold, necessary conditions for a global minimum, as well as necessary and sufficient conditions for a local minimum, are derived.


title: Empathic arousal and social understanding in individuals with autism: evidence from fMRI and ERP measurements

abstract: Lack of empathy is a hallmark of social impairments in individuals with autism spectrum disorder (ASD). However, the concept empathy encompasses several socio-emotional and behavioral components underpinned by interacting brain circuits. This study examined empathic arousal and social understanding in individuals with ASD and matched controls by combining pressure pain thresholds (PPT) with functional magnetic resonance imaging (study 1) and electroencephalography/event-related potentials and eye-tracking responses (study 2) to empathy-eliciting stimuli depicting physical bodily injuries. Results indicate that participants with ASD had lower PPT than controls. When viewing body parts being accidentally injured, increased hemodynamic responses in the somatosensory cortex (SI/SII) but decreased responses in the anterior mid-cingulate and anterior insula as well as heightened N2 but preserved late-positive potentials (LPP) were detected in ASD participants. When viewing a person intentionally hurting another, decreased hemodynamic responses in the medial prefrontal cortex and reduced LPP were observed in the ASD group. PPT was a mediator for the SI/SII


title: A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation

abstract: An ensemble Kalman filter may be considered for the 4D assimilation of atmospheric data. In this paper, an efficient implementation of the analysis step of the filter is proposed. It employs a Schur (elementwise) product of the covariances of the background error calculated from the ensemble and a correlation function having local support to filter the small (and noisy) background-error covariances associated with remote observations. To solve the Kalman filter equations, the observations are organized into batches that are assimilated sequentially. For each batch, a Cholesky decomposition method is used to solve the system of linear equations. The ensemble of background fields is updated at each step of the sequential algorithm and, as more and more batches of observations are assimilated, evolves to eventually become the ensemble of analysis fields. A prototype sequential filter has been developed. Experiments are performed with a simulated observational network consisting of 542 radiosonde and 615 satellite-thickness profiles. Experimental results indicate that the quality of the analysis is almost independent of the number of batches (except when the ensemble is very small). This supports the use of a sequential algorithm. A parallel version of the algorithm is described and used to assimilate over 100 000 observations into a pair of 50-member ensembles. Its operation count is proportional to the number of observations, the number of analysis grid points, and the number of ensemble members. In view of the flexibility of the sequential filter and its encouraging performance on a NEC SX-4 computer, an application with a primitive equations model can now be envisioned.


title: VAMS 2017: Workshop on Value-Aware and Multistakeholder Recommendation

abstract: In this paper, we summarize VAMS 2017 - a workshop on value-aware and multistakeholder recommendation co-located with RecSys 2017. The workshop encouraged forward-thinking papers in this new area of recommender systems research and obtained a diverse set of responses ranging from application results to research overviews.


title: Real-Time Scheduling via Reinforcement Learning

abstract: Cyber-physical systems, such as mobile robots, must respond adaptively to dynamic operating conditions. Effective operation of these systems requires that sensing and actuation tasks are performed in a timely manner. Additionally, execution of mission specific tasks such as imaging a room must be balanced against the need to perform more general tasks such as obstacle avoidance. This problem has been addressed by maintaining relative utilization of shared resources among tasks near a user-specified target level. Producing optimal scheduling strategies requires complete prior knowledge of task behavior, which is unlikely to be available in practice. Instead, suitable scheduling strategies must be learned online through interaction with the system. We consider the sample complexity of reinforcement learning in this domain, and demonstrate that while the problem state space is countably infinite, we may leverage the problem's structure to guarantee efficient learning.


title: Efficient Byzantine Fault-Tolerance

abstract: We present two asynchronous Byzantine fault-tolerant state machine replication (BFT) algorithms, which improve previous algorithms in terms of several metrics. First, they require only 2f+1 replicas, instead of the usual 3f+1. Second, the trusted service in which this reduction of replicas is based is quite simple, making a verified implementation straightforward (and even feasible using commercial trusted hardware). Third, in nice executions the two algorithms run in the minimum number of communication steps for nonspeculative and speculative algorithms, respectively, four and three steps. Besides the obvious benefits in terms of cost, resilience and management complexity-fewer replicas to tolerate a certain number of faults-our algorithms are simpler than previous ones, being closer to crash fault-tolerant replication algorithms. The performance evaluation shows that, even with the trusted component access overhead, they can have better throughput than Castro and Liskov's PBFT, and better latency in networks with nonnegligible communication delays.


title: A Complementary Resistive Switch-based Crossbar Array Adder

abstract: Redox-based resistive switching devices (ReRAM) are an emerging class of nonvolatile storage elements suited for nanoscale memory applications. In terms of logic operations, ReRAM devices were suggested to be used as programmable interconnects, large-scale look-up tables or for sequential logic operations. However, without additional selector devices these approaches are not suited for use in large scale nanocrossbar memory arrays, which is the preferred architecture for ReRAM devices due to the minimum area consumption. To overcome this issue for the sequential logic approach, we recently introduced a novel concept, which is suited for passive crossbar arrays using complementary resistive switches (CRSs). CRS cells offer two high resistive storage states, and thus, parasitic “sneak” currents are efficiently avoided. However, until now the CRS-based logic-in-memory approach was only shown to be able to perform basic Boolean logic operations using a single CRS cell. In this paper, we introduce two multi-bit adder schemes using the CRS-based logic-in-memory approach. We proof the concepts by means of SPICE simulations using a dynamical memristive device model of a ReRAM cell. Finally, we show the advantages of our novel adder concept in terms of step count and number of devices in comparison to a recently published adder approach, which applies the conventional ReRAM-based sequential logic concept introduced by Borghetti


title: 3D medial axis point approximation using nearest neighbors and the normal field

abstract: We present a novel method to approximate medial axis points given a set of points sampled from a surface and the normal vectors to the surface at those points. For each sample point, we find its maximal tangent ball containing no other sample points, by iteratively reducing its radius using nearest neighbor queries. We prove that the center of the ball constructed by our algorithm converges to a true medial axis point as the sampling density increases to infinity. We also propose a simple heuristic to handle noisy samples. By simple extensions, our method is applied to medial axis point simplification, local feature size estimation and feature-sensitive point decimation. Our algorithm is simple, easy to implement, and suitable for parallel computation using GPU because the iteration process for each sample point runs independently. Experimental results show that our method is efficient both in time and in space.


Coding of DWT Coefficients using Run-length coding and Huffman Coding for the purpose of Color Image Compression


title: Microbial biopesticides: opportunities and challenges.

abstract: Pesticides based on microorganisms and their products have proven to be highly effective, species specific and eco-friendly in nature, leading to their adoption in pest management strategies around the world. The microbial biopesticide market constitutes about 90% of total biopesticides and there is ample scope for further development in agriculture and public health, although there are challenges as well. This article reviews the various microbial biopesticides that are commercially available, the different approaches for their production and development, the recent technological advances and the challenges faced by the microbial biopesticide field in the future.


title: Handling pure camera rotation in keyframe-based SLAM

abstract: Handling degenerate rotation-only camera motion is a challenge for keyframe-based simultaneous localization and mapping with six degrees of freedom. Existing systems usually filter corresponding keyframe candidates, resulting in mapping starvation and tracking failure. We propose to employ these otherwise discarded keyframes to build up local panorama maps registered in the 3D map. Thus, the system is able to maintain tracking during rotational camera motions. Additionally, we seek to actively associate panoramic and 3D map data for improved 3D mapping through the triangulation of more new 3D map features. We demonstrate the efficacy of our approach in several evaluations that show how the combined system handles rotation only camera motion while creating larger and denser maps compared to a standard SLAM system.


title: SDxVPN: A software-defined solution for VPN service providers

abstract: BGP/MPLS IP VPN and VPLS services are considered to be widely used in IP/MPLS networks for connecting customers' remote sites. However, service providers struggle with many challenges to provide these services. Management complexity, equipment costs, and last but not least, scalability issues emerging as the customers increase in number, are just some of these problems. Software-defined networking (SDN) is an emerging paradigm that can solve aforementioned issues using a logically centralized controller for network devices. In this paper, we propose a SDN-based solution called SDxVPN which considerably lowers the complexity of VPN service definition and management. Our method eliminates complex and costly device interactions that used to be done through several control plane protocols and enables customers to determine their service specifications, define restriction policies and even interconnect with other customers automatically without operator's intervention. We describe our prototype implementation of SDxVPN and its scalability evaluations under several representative scenarios. The results indicate the effectiveness of the proposed solution for deployment to provide large scale VPN services.


title: A hierarchical neural-network model for control and learning of voluntary movement

abstract: In order to control voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: the determination of a desired trajectory in the visual coordinates, the transformation of its coordinates to the body coordinates and the generation of motor command. Based on physiological knowledge and previous models, we propose a hierarchical neural network model which accounts for the generation of motor command. In our model the association cortex provides the motor cortex with the desired trajectory in the body coordinates, where the motor command is then calculated by means of long-loop sensory feedback. Within the spinocerebellum--magnocellular red nucleus system, an internal neural model of the dynamics of the musculoskeletal system is acquired with practice, because of the heterosynaptic plasticity, while monitoring the motor command and the results of movement. Internal feedback control with this dynamical model updates the motor command by predicting a possible error of movement. Within the cerebrocerebellum--parvocellular red nucleus system, an internal neural model of the inverse-dynamics of the musculo-skeletal system is acquired while monitoring the desired trajectory and the motor command. The inverse-dynamics model substitutes for other brain regions in the complex computation of the motor command. The dynamics and the inverse-dynamics models are realized by a parallel distributed neural network, which comprises many sub-systems computing various nonlinear transformations of input signals and a neuron with heterosynaptic plasticity (that is, changes of synaptic weights are assumed proportional to a product of two kinds of synaptic inputs). Control and learning performance of the model was investigated by computer simulation, in which a robotic manipulator was used as a controlled system, with the following results: (1) Both the dynamics and the inverse-dynamics models were acquired during control of movements. (2) As motor learning proceeded, the inverse-dynamics model gradually took the place of external feedback as the main controller. Concomitantly, overall control performance became much better. (3) Once the neural network model learned to control some movement, it could control quite different and faster movements. (4) The neural network model worked well even when only very limited information about the fundamental dynamical structure of the controlled system was available.(ABSTRACT TRUNCATED AT 400 WORDS)


title: A control-oriented lithium-ion battery pack model for plug-in hybrid electric vehicle cycle-life studies and system design with consideration of health management

abstract: Abstract A crucial step towards the large-scale introduction of plug-in hybrid electric vehicles (PHEVs) in the market is to reduce the cost of its battery systems. Currently, battery cycle- and calendar-life represents one of the greatest uncertainties in the total life-cycle cost of battery systems. The field of battery aging modeling and prognosis has seen progress with respect to model-based and data-driven approaches to describe the aging of battery cells. However, in real world applications cells are interconnected and aging propagates. The propagation of aging from one cell to others exhibits itself in a reduced battery system life. This paper proposes a control-oriented battery pack model that describes the propagation of aging and its effect on the life span of battery systems. The modeling approach is such that it is able to predict pack aging, thermal, and electrical dynamics under actual PHEV operation, and includes consideration of random variability of the cells, electrical topology and thermal management. The modeling approach is based on the interaction between dynamic system models of the electrical and thermal dynamics, and dynamic models of cell aging. The system-level state-of-health (SOH) is assessed based on knowledge of individual cells SOH, pack electrical topology and voltage equalization approach.


title: Performance of High Strength POFA Concrete in Acidic Environment

abstract: Malaysia as the world’s largest exporter of palm oil has been facing problem in disposing palm oil fuel ash, a by-product of palm oil mill since many years ago. The discovery made by researchers of Universiti Teknologi Malaysia last century in revealing the potential of this finely ground waste as a partial cement replacement in normal concrete has stem efforts towards studying the possibility of using it in high strength concrete production. This paper illustrates the durability aspect of high strength concrete produced using POFA of different fineness when exposed to acidic environment. Two POFA concrete mixes with different fineness termed (POFA 45 and POFA 10) at 20% replacement level by weight of cement and an OPC concrete mix functioning as control specimen termed Po was considered in this study. All the specimens were subjected to water curing for 28 days before immersed in the hydrochloric solution having pH 2 for 1800 hours. The progressive deterioration was evaluated through mass changing of the specimens, visual inspection and relative compressive strength determinations. Conclusively, the study found that increase in the POFA fineness enhances the resistance of high strength POFA concrete towards acid attack.


title: Alar Contour Grafts in Rhinoplasty: A Safe and Reproducible Way to Refine Alar Contour Aesthetics.

abstract: BACKGROUND ::: Alar rim deformities such as retraction, notching, collapse, and asymmetry are common problems in rhinoplasty patients. Although alar rim deformities may be improved through rhinoplasty, this area is prone to late changes because of scarring of the soft triangles and a paucity of native structural support. The purpose of this study was to analyze the effect of alar contour grafts on primary rhinoplasty. ::: ::: ::: METHODS ::: Fifty consecutive primary rhinoplasty patients with preoperative and postoperative photographs who received alar contour grafts were evaluated for alar aesthetics; 50 consecutive primary rhinoplasty patients without such grafts served as controls. Differences among alar retraction, notching, collapse, and asymmetry from anterior, lateral, and basal views were evaluated. Follow-up ranged from 1 to 4 years and was graded on a four-point scale. ::: ::: ::: RESULTS ::: The average difference between the two groups' aggregate preoperative scores was 0.21 (p = 0.24). The average preoperative and postoperative scores in the nongraft group were significant for worsening retraction, notching, and collapse but insignificant for asymmetry. The preoperative and postoperative scores for the graft group were insignificant for retraction but improved significantly for notching, collapse, and asymmetry. Postoperatively, the aggregate average of the scores in the nongroup was 0.32 points worse (p < 0.01), whereas the graft group had a 0.33-point improvement (p < 0.01). ::: ::: ::: CONCLUSIONS ::: Alar contour grafts have a clear and important impact on cosmetic results of primary rhinoplasty. Use of alar contour grafts has been shown to improve aesthetics, whereas there is a worsening of the measured parameters postoperatively without use of these grafts. ::: ::: ::: CLINICAL QUESTION/LEVEL OF EVIDENCE ::: Therapeutic, III.


title: LEARNING THEORY: The Advantage of Abstract Examples in Learning Math

abstract: Undergraduate students may benefit more from learning mathematics through a single abstract, symbolic representation than from learning multiple concrete examples.


title: An Adaptive Nonlinear Current Observer for Boost PFC AC/DC Converters

abstract: Power factor correction (PFC) is an essential part of ac/dc converters in order to improve the quality of the current drawn from the utility grid. The PFC closed-loop control system requires a precise measurement of the boost inductor current in order to tightly shape the input current. Current sensors are widely used in the PFC closed-loop control system to measure the boost inductor current. Current sensors introduce delay and noise to the control circuitry. Also, they significantly contribute to the overall cost of the converter. Therefore, they make the implementation of the PFC converter complicated and costly. Current sensorless control techniques can offer a cost-effective solution for various applications. The unique structure of the boost PFC converter makes it challenging to robustly estimate the inductor current due to the nonlinear structure of the converter. Also, it is shown in this paper that the system loses observability at some singular operating points, which makes the observer design more challenging. In addition, the load value is unknown in most applications. Thus, the observer should be able to estimate the inductor current in the presence of uncertainties in the load and other parameters. This paper presents an adaptive nonlinear observer for the boost PFC, which is able to accurately estimate the inductor current. The adaptive structure of the converter allows the robust and reliable performance of the observer in the presence of parameter uncertainties, particularly load variations. Also, an auxiliary compensation is integrated into the observer to circumvent the singular operating points and provide a precise estimation for the entire range of operation. Experimental results are presented to verify the feasibility of the proposed sensorless control approach.


title: Serverless Computing: Current Trends and Open Problems

abstract: Serverless computing has emerged as a new compelling paradigm for the deployment of applications and services. It represents an evolution of cloud programming models, abstractions, and platforms, and is a testament to the maturity and wide adoption of cloud technologies. In this chapter, we survey existing serverless platforms from industry, academia, and open-source projects, identify key characteristics and use cases, and describe technical challenges and open problems.


title: HyFinBall: a two-handed, hybrid 2D/3D desktop VR interface for multi-dimensional visualization

abstract: This paper presents the concept, working prototype and design space of a two-handed, hybrid spatial user interface for minimally immersive desktop VR targeted at multi-dimensional visualizations. The user interface supports dual button balls (6DOF isotonic controllers with multiple buttons) which automatically switch between 6DOF mode (xyz + yaw,pitch,roll) and planar-3DOF mode (xy + yaw) upon contacting the desktop. The mode switch automatically switches a button ball’s visual representation between a 3D cursor and a mouse-like 2D cursor while also switching the available user interaction techniques (ITs) between 3D and 2D ITs. Further, the small form factor of the button ball allows the user to engage in 2D multi-touch or 3D gestures without releasing and re-acquiring the device. We call the device and hybrid interface the HyFinBall interface which is an abbreviation for ‘Hybrid Finger Ball.’ We describe the user interface (hardware and software), the design space, as well as preliminary results of a formal user study. This is done in the context of a rich, visual analytics interface containing coordinated views with 2D and 3D visualizations and interactions


title: We are family: Joint pose estimation of multiple persons

abstract: We present a novel multi-person pose estimation framework, which extends pictorial structures (PS) to explicitly model interactions between people and to estimate their poses jointly. Interactions are modeled as occlusions between people. First, we propose an occlusion probability predictor, based on the location of persons automatically detected in the image, and incorporate the predictions as occlusion priors into our multi-person PS model. Moreover, our model includes an inter-people exclusion penalty, preventing body parts from different people from occupying the same image region. Thanks to these elements, our model has a global view of the scene, resulting in better pose estimates in group photos, where several persons stand nearby and occlude each other. In a comprehensive evaluation on a new, challenging group photo datasets we demonstrate the benefits of our multi-person model over a state-of-the-art single-person pose estimator which treats each person independently.


title: PLTD: Patch-Based Low-Rank Tensor Decomposition for Hyperspectral Images

abstract: Recent years has witnessed growing interest in hyperspectral image (HSI) processing. In practice, however, HSIs always suffer from huge data size and mass of redundant information, which hinder their application in many cases. HSI compression is a straightforward way of relieving these problems. However, most of the conventional image encoding algorithms mainly focus on the spatial dimensions, and they need not consider the redundancy in the spectral dimension. In this paper, we propose a novel HSI compression and reconstruction algorithm via patch-based low-rank tensor decomposition (PLTD). Instead of processing the HSI separately by spectral channel or by pixel, we represent each local patch of the HSI as a third-order tensor. Then, the similar tensor patches are grouped by clustering to form a fourth-order tensor per cluster. Since the grouped tensor is assumed to be redundant, each cluster can be approximately decomposed to a coefficient tensor and three dictionary matrices, which leads to a low-rank tensor representation of both the spatial and spectral modes. The reconstructed HSI can then be simply obtained by the product of the coefficient tensor and dictionary matrices per cluster. In this way, the proposed PLTD algorithm simultaneously removes the redundancy in both the spatial and spectral domains in a unified framework. The extensive experimental results on various public HSI datasets demonstrate that the proposed method outperforms the traditional image compression approaches and other tensor-based methods.


title: signSGD with Majority Vote is Communication Efficient And Byzantine Fault Tolerant

abstract: Training neural networks on large datasets can be accelerated by distributing the workload over a network of machines. As datasets grow ever larger, networks of hundreds or thousands of machines become economically viable. The time cost of communicating gradients limits the effectiveness of using such large machine counts, as may the increased chance of network faults. We explore a particularly simple algorithm for robust, communication-efficient learning---signSGD. Workers transmit only the sign of their gradient vector to a server, and the overall update is decided by a majority vote. This algorithm uses $32\times$ less communication per iteration than full-precision, distributed SGD. Under natural conditions verified by experiment, we prove that signSGD converges in the large and mini-batch settings, establishing convergence for a parameter regime of Adam as a byproduct. We model adversaries as those workers who may compute a stochastic gradient estimate and manipulate it, but may not coordinate with other adversaries. Aggregating sign gradients by majority vote means that no individual worker has too much power. We prove that unlike SGD, majority vote is robust when up to 50% of workers behave adversarially. On the practical side, we built our distributed training system in Pytorch. Benchmarking against the state of the art collective communications library (NCCL), our framework---with the parameter server housed entirely on one machine---led to a 25% reduction in time for training resnet50 on Imagenet when using 15 AWS p3.2xlarge machines.


Viscous effects on the flow coefficient for a supersonic nozzle