title: A study of phase noise in CMOS oscillators

abstract: This paper presents a study of phase noise in two inductorless CMOS oscillators. First-order analysis of a linear oscillatory system leads to a noise shaping function and a new definition of Q. A linear model of CMOS ring oscillators is used to calculate their phase noise, and three phase noise phenomena, namely, additive noise, high-frequency multiplicative noise, and low-frequency multiplicative noise, are identified and formulated. Based on the same concepts, a CMOS relaxation oscillator is also analyzed. Issues and techniques related to simulation of noise in the time domain are described, and two prototypes fabricated in a 0.5-/spl mu/m CMOS technology are used to investigate the accuracy of the theoretical predictions. Compared with the measured results, the calculated phase noise values of a 2-GHz ring oscillator and a 900-MHz relaxation oscillator at 5 MHz offset have an error of approximately 4 dB.


title: Effective Techniques in Healthy Eating and Physical Activity Interventions: A Meta-Regression

abstract: Objective: Meta-analyses of behavior change (BC) interventions typically find large heterogeneity in effectiveness and small effects. This study aimed to assess the effectiveness of active BC interventions designed to promote physical activity and healthy eating and investigate whether theoretically specified BC techniques improve outcome. Design: Interventions, evaluated in experimental or quasi-experimental studies, using behavioral and/or cognitive techniques to increase physical activity and healthy eating in adults, were systematically reviewed. Intervention content was reliably classified into 26 BC techniques and the effects of individual techniques, and of a theoretically derived combination of self-regulation techniques, were assessed using meta-regression. Main Outcome Measures: Valid outcomes of physical activity and healthy eating. Results: The 122 evaluations (N 44,747) produced an overall pooled effect size of 0.31 (95% confidence interval 0.26 to 0.36, I 2 69%). The technique, “self-monitoring,” explained the greatest amount of among-study heterogeneity (13%). Interventions that combined selfmonitoring with at least one other technique derived from control theory were significantly more effective than the other interventions (0.42 vs. 0.26). Conclusion: Classifying interventions according to component techniques and theoretically derived technique combinations and conducting meta-regression enabled identification of effective components of interventions designed to increase physical activity and healthy eating.


title: DXTK: Enabling Resource-efficient Deep Learning on Mobile and Embedded Devices with the DeepX Toolkit

abstract: Deep learning is having a transformative effect on how sensor data are processed and interpreted. As a result, it is becoming increasingly feasible to build sensor-based computational models that are much more robust to real-world noise and complexity than previously possible. It is paramount that these innovations reach mobile and embedded devices that often rely on understanding and reacting to sensor data. However, deep models conventionally demand a level of system resources (e.g., memory and computation) that makes them problematic to run directly on constrained devices. ::: ::: In this work, we present the DeepX toolkit (DXTK); an open-source collection of software components for simplifying the execution of deep models on embedded and mobile platforms. DXTK contains a number of pre-trained low-resource deep models that users can quickly adopt and integrate for their particular application needs. Similarly, it offers a range of runtime options for executing deep models on devices ranging from Android platforms to Linux-based embedded platforms. But the heart of DXTK is a series of optimization techniques (viz. weight/sparse factorization, convolution separation, precision scaling, and parameter cleaning). These optimizers offer different methods for shaping the system resource needs and are compatible with a wide variety of different forms of deep neural networks. We hope that DXTK accelerates the study of resource-constrained deep learning in the community.


Slow Cortical Potential Neurofeedback in Chronic Tinnitus Therapy: A Case Report


title: Interactive Metro Map Editing

abstract: Manual editing of a metro map is essential because many aesthetic and readability demands in map generation cannot be achieved by using a fully automatic method. In addition, a metro map should be updated when new metro lines are developed in a city. Considering that manually designing a metro map is time-consuming and requires expert skills, we present an interactive editing system that considers human knowledge and adjusts the layout to make it consistent with user expectations. In other words, only a few stations are controlled and the remaining stations are relocated by our system. Our system supports both curvilinear and octilinear layouts when creating metro maps. It solves an optimization problem, in which even spaces, route straightness, and maximum included angles at junctions are considered to obtain a curvilinear result. The system then rotates each edge to extend either vertically, horizontally, or diagonally while approximating the station positions provided by users to generate an octilinear layout. Experimental results, quantitative and qualitative evaluations, and user studies show that our editing system is easy to use and allows even non-professionals to design a metro map.


title: A four-frames differencing technique for moving objects detection in wide area surveillance

abstract: Moving objects detection or change detection in video sequences, is a fundamental task in video surveillance applications. Although, existing methods perform well on videos filmed by stationary cameras, these methods fail dramatically in videos filmed by non-stationary cameras. In particular, in very low frame rate and sudden illumination change scenarios, like Wide-Area Motion Imagery (WAMI). In this paper, we show the limitation of the widely used three-frames differencing technique in such scenarios. Then, we propose a pixel-level algorithm based on four-frames differencing rather than three-frames differencing, which contains the temporal information needed to separate the foreground (moving objects) from the background robustly. We compare our method to the state-of-the-art methods in detection quality. Experimental results on the WPAFB2009 dataset show that our method outperforms existing methods.


title: FALCON: A Fourier Transform Based Approach for Fast and Secure Convolutional Neural Network Predictions

abstract: Machine learning as a service has been widely deployed to utilize deep neural network models to provide prediction services. However, this raises privacy concerns since clients need to send sensitive information to servers. In this paper, we focus on the scenario where clients want to classify private images with a convolutional neural network model hosted in the server, while both parties keep their data private. We present FALCON, a fast and secure approach for CNN predictions based on Fourier Transform. Our solution enables linear layers of a CNN model to be evaluated simply and efficiently with fully homomorphic encryption. We also introduce the first efficient and privacy-preserving protocol for softmax function, which is an indispensable component in CNNs and has not yet been evaluated in previous works due to its high complexity. We implemented the FALCON and evaluated the performance on real-world CNN models. The experimental results show that FALCON outperforms the best known works in both computation and communication cost.


title: Recent Literature on Gender Identity Disorder: A Compilation of Reviews I. Gender Identity Disorder Overview

abstract: While debates continue as to whether or not a diagnosis of gender identity disorder (GID) is wanted or needed by today's adult transsexual, there is increasing concern both in academic and lay literature regarding the diagnosis of children and adolescents with GID. This paper critically evaluates the diagnosis, assessment, and treatment of GID in children and adolescents in light of published controversies, evidence, and arguments in psychological, psychiatric, and lay discourse since the release of the DSM-IV. In the years since GID's first inclusion in the DSM-III, growing criticisms weigh heavily against the diagnosis of GID in children and adolescents. This analysis urges a re-evaluation of the GID diagnosis for children and, to a lesser extent, adolescents. Overall, there is deepening discomfort with pathologizing children and youth for extreme gender variance. In the very least, since this is a highly contentious diagnosis, with little established reliability and validity, and problematic a...


title: Achieving an Accurate Surface Profile of a Photonic Crystal for Near-Unity Solar Absorption in a Super Thin-Film Architecture

abstract: In this work, a teepee-like photonic crystal (PC) structure on crystalline silicon (c-Si) is experimentally demonstrated, which fulfills two critical criteria in solar energy harvesting by (i) its Gaussian-type gradient-index profile for excellent antireflection and (ii) near-orthogonal energy flow and vortex-like field concentration via the parallel-to-interface refraction effect inside the structure for enhanced light trapping. For the PC structure on 500-μm-thick c-Si, the average reflection is only ∼0.7% for λ = 400–1000 nm. For the same structure on a much thinner c-Si ( t = 10 μm), the absorption is near unity (A ∼ 99%) for visible wavelengths, while the absorption in the weakly absorbing range (λ ∼ 1000 nm) is significantly increased to 79%, comparing to only 6% absorption for a 10-μm-thick planar c-Si. In addition, the average absorption (∼94.7%) of the PC structure on 10 μm c-Si for λ = 400–1000 nm is only ∼3.8% less than the average absorption (∼98.5%) of the PC structure on 500 μm c-Si, while t...


ESC Guidelines on the diagnosis and management of acute pulmonary embolism


title: Robot control design using virtual path from android smartphone

abstract: Progress robot development very varied almost all fields seize the attention of the world's scientists and researchers. For some cases, robots are generally used to help humans in relieving job. The robot can replace human mobilization for move so that the efficiency of wasted time will be less. Many things need to be note what kind of robot control reliable to replace human work in mobilization. Virtual world as in android smartphone greatly benefit humans to creating control tools easy to use anywhere and could represent a desire humans. By making control robot using virtual paths on the Android smartphone which have an accuracy of more than 70% to a scale of 1:50 with the actual track in an environment that is not wide enough, the advantage can be used to control the robot move easily and efficiently compared to the usual manual control robot that will take more time to control The robot moves its place as desired.


title: Spirality and Optimal Orthogonal Drawings

abstract: We deal with the problem of constructing the orthogonal drawing of a graph with the minimum number of bends along the edges. The problem has been recently shown to be NP-complete in the general case. In this paper we introduce and study the new concept of spirality, which is a measure of how an orthogonal drawing is "rolled up," and develop a theory on the interplay between spirality and number of bends of orthogonal drawings. We exploit this theory to present polynomial time algorithms for two significant classes of graphs: series-parallel graphs and 3-planar graphs. Series-parallel graphs arise in a variety of problems such as scheduling, electrical networks, data-flow analysis, database logic programs, and circuit layout. Also, they play a central role in planarity problems. Furthermore, drawings of 3-planar graphs are a classical field of investigation.


title: Smart mobility: A survey

abstract: Internet of Things (IoT) describes a world where everyday objects are always connected to the Internet, allowing them to communicate and interact with each other. By connecting these everyday objects to the Internet and making them available everywhere at any time, IoT allows to remotely monitor, manage, and gather status information about them and their surrounding environment. IoT is a revolutionary concept that brought new experiences to everyday life and enabled Smart City initiatives all over the world. These initiatives are using a combination of technology paired with physical infrastructure and services, to improve people's quality of life. One of the high-priority domain to support the Smart City's vision is the field of Smart Mobility. This paper reviews the current IoT approaches and concepts related to Smart Cities and Smart Mobility. In addition, it analyzes distinct features and numerous applications covering both Intelligent Transportation and Real Time Traffic Management Systems.


title: A Compact Ultra-Wideband Bandpass Filter Based on Split-Mode Resonator

abstract: A compact ultra-wideband (UWB) bandpass filter is proposed based on the coplanar-waveguide (CPW) split-mode resonator. By suitably introducing a short-circuited stub to implement the shunt inductance between two quarter wavelength CPW stepped-impedance resonators, a strong magnetic coupling may be realized so that a CPW split-mode resonator may be constructed. Moreover, by properly designing the dual-metal-plane structure, one may accomplish a microstrip-to-CPW feeding mechanism to provide strong enough capacitive coupling for bandwidth enhancement and also introduce an extra electric coupling between input and output ports so that two transmission zeros may be created for selectivity improvement. The implemented UWB filter shows a fractional bandwidth of 116% and two transmission zeros at 1.705 and 11.39 GHz. Good agreement between simulated and measured responses is observed.


title: Solving the Multiple Traveling Salesman Problem by a Novel Meta-heuristic Algorithm

abstract: The multiple traveling salesman problem (MTSP) is a generalization of the famous traveling salesman problem (TSP), where more than one salesman is used in the solution. Although the MTSP is a typical kind of computationally complex combinatorial optimization problem, it can be extended to a wide variety of routing problems. This paper presents an efficient and evolutionary optimization algorithm which has been developed through combining Modified Imperialist Competitive Algorithm and Lin-Kernigan Algorithm (MICA) in order to solve the MTSP. In the proposed algorithm, an absorption function and several local search algorithms as a revolution operator are used. The performance of our algorithm was tested on several MTSP benchmark problems and the results confirmed that the MICA performs


title: NEW UPPER BOUNDS FOR SOME SPHERICAL CODES

abstract: The maximal cardinality of a code W on the unit sphere in n dimen- sions with (x,y) ≤ s whenever x,y ∈ W, x 6 y, is denoted by A(n,s). We use two methods for obtaining new upper bounds on A(n,s) for some values of n and s. We find new linear programming bounds by suitable polynomials of degrees which are higher than the degrees of the previously known good polynomials due to Lev- enshtein (11, 12). Also we investigate the possibilities for attaining the Levenshtein bounds (11, 12). In such cases we find the distance distributions of the correspond- ing feasible maximal spherical codes. Usually this leads to a contradiction showing that such codes do not exist.


title: Adaptive MIMO-OFDM based on partial channel state information

abstract: Relative to designs assuming no channel knowledge at the transmitter, considerably improved communications become possible when adapting the transmitter to the intended propagation channel. As perfect knowledge is rarely available, transmitter designs based on partial (statistical) channel state information (CSI) are of paramount importance not only because they are more practical but also because they encompass the perfect- and no-knowledge paradigms. In this paper, we first provide a partial CSI model for orthogonal frequency division multiplexed (OFDM) transmissions over multi-input multi-output (MIMO) frequency-selective fading channels. We then develop an adaptive MIMO-OFDM transmitter by applying an adaptive two-dimensional (2-D) coder-beamformer we derived recently on each OFDM subcarrier, along with an adaptive power and bit loading scheme across OFDM subcarriers. Relying on the available partial CSI at the transmitter, our objective is to maximize the transmission rate, while guaranteeing a prescribed error performance, under the constraint of fixed transmit-power. Numerical results confirm that the adaptive 2-D space-time coder-beamformer (with two basis beams as the two "strongest" eigenvectors of the channel's correlation matrix perceived at the transmitter) combined with adaptive OFDM (power and bit loaded with M-ary quadrature amplitude modulated (QAM) constellations) improves the transmission rate considerably.


title: Detecting and counting people in surveillance applications

abstract: A number of surveillance scenarios require the detection and tracking of people. Although person detection and counting systems are commercially available today, there is need for further research to address the challenges of real world scenarios. The focus of this work is the segmentation of groups of people into individuals. One relevant application of this algorithm is people counting. Experiments document that the presented approach leads to robust people counts.


title: Cellphone identification using noise estimates from recorded audio

abstract: Rapid developments in technologies related to cell phones have resulted in their much broader usage than mere talking devices used for making and receiving phone calls. User-generated audio recordings from cell phones can be very helpful in a number of forensic applications. This paper proposes a novel system for cellphone identification from speech samples recorded using the cellphone. The proposed system uses features based on estimates of noise associated with recordings and classifies them using sequential minimal optimization (SMO) based Support vector machine (SVM). The performance of the proposed system is tested on a custom database of twenty-six cell phones of five different manufacturers. The proposed system shows promising results with average classification accuracy around 90% for classifying cell phones belonging to five different manufacturers. The average classification accuracy reduces when all the cell phones belong to the same manufacturer.


title: When Does Frustration Not Reduce Continuance Intention of Online Gamers? the Expectancy Disconfirmation Perspective

abstract: 1. IntroductionOnline games have become one of the most important activities in cyberspace due to their popularity [China Internet Network Information Center 2014; State of Online Gaming Report 2013] and increasing profitability [Mozur 2013]. Moreover, a single online game vendor can generate revenue of 1.9 billion dollars in a quarter [Osawa 2015]. These figures confirm the value of online games to managers, warranting further research. Online games offer challenges to engage customers and attempt to strengthen their intention to play [ Wang & Wang 2008]. However, if the game proves too difficult for gamers to obtain achievements, they likely feel frustrated during gameplay [e.g., Blumberg et al. 2008; Chumbley & Griffiths 2006].Frustration has been a recent topic in the literature [e.g., Sun & Spears 2012; Totok & Karamcheti 2010; Vincent et al. 2010] as it reduces positive attitudes (toward search engine effectiveness), purchase intention [Sun & Spears 2012], service usage, and thus revenues [Totok & Karamcheti 2010]. The importance of frustration has also motivated scholars to examine its antecedents, such as slow response time and request rejection [Totok & Karamcheti 2010; Vincent et al. 2010]. However, frustration might not have the same effect on gamers. For example, frustration does not influence computer gamers' willingness to play [Chumbley & Griffiths 2006]. Moreover, games become so much part of the gamers' identity that even frustration does not inhibit their persistence in playing [Neys et al. 2014]. Such findings indicate the uniqueness of gaming contexts and the relevance of further research into the frustration issues in gaming contexts.To the best of our knowledge, no study has yet examined the mechanism underlying the relationship between frustration and continuance intention in an online gaming context, indicating a research gap. Research filling this gap can provide insights to online game providers, e.g., assisting them to make decisions on how challenging their games should be. Moreover, online game providers should be curious about the answer to the question: when does frustration not reduce continuance intention? Research answering this question helps managers to not only retain users, but also to stabilize their revenues, and thus build competitive advantage.Therefore, the purpose of this study is to establish when frustration does not reduce continuance intention. Since frustration is related to unmet needs or the discrepancy between the expected product/service performance and the actual product/service performance, we consulted the expectancy disconfirmation theory [Oliver 1981]. This theory posits that when actual product/service performance exceeds expected performance, customers perceive positive disconfirmation while when actual performance falls below expected performance, customers perceive negative disconfirmation [Churchill & Surprenant 1982]. This theory has a core element/construct, expectancy disconfirmation, which we use to develop hypotheses on the relations among frustration, expectancy disconfirmation, and continuance intention.As previous experiences shape expectation [Gebhard 1948], they should change the way in which users interpret frustration, i.e., the moderating effect of previous experiences. Hence, this study developed hypotheses on the moderator role of gamer experience in the relation between frustration and expectancy disconfirmation.2. Literature Review and Hypotheses2.1. Online GamesOnline games are computer games played through the Internet. Gamers can interact with the gaming systems and other gamers [Choi & Kim 2004]. Online games have various types, including social games, board games, sports games, and massively multiplayer online role-playing games (MMORPGs) [Entertainment Software Association 2014]. Among them, MMORPGs have attracted numerous gamers and scholars' attention [Kirby et al. …


title: Characterizing and quantifying human movement patterns using GPS data loggers in an area approaching malaria elimination in rural southern Zambia

abstract: In areas approaching malaria elimination, human mobility patterns are important in determining the proportion of malaria cases that are imported or the result of low-level, endemic transmission. A ...


title: Learning to Classify Text from Labeled and Unlabeled Documents

abstract: In many important text classification problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap. This paper shows that the accuracy of text classifiers trained with a small number of labeled documents can be improved by augmenting this small training set with a large pool of unlabeled documents. We present a theoretical argument showing that, under common assumptions, unlabeled data contain information about the target function. We then introduce an algorithm for learning from labeled and unlabeled text based on the combination of Expectation-Maximization with a naive Bayes classifier. The algorithm first trains a classifier using the available labeled documents, and probabilistically labels the unlabeled documents; it then trains a new classifier using the labels for all the documents, and iterates to convergence. Experimental results, obtained using text from three different realworld tasks, show that the use of unlabeled data reduces classification error by up to 33%.


title: New concepts for traffic, resource and mobility management in software-defined mobile networks

abstract: The evolution of mobile telecommunication networks is accompanied by new demands for the performance, portability, elasticity, and energy efficiency of network functions. Network Function Virtualization (NFV), Software Defined Networking (SDN), and cloud service technologies are claimed to be able to provide most of the capabilities. However, great leap forward will only be achieved if resource, traffic, and mobility management methods of mobile network services can efficiently utilize these technologies. This paper conceptualizes the future requirements of mobile networks and proposes new concepts and solutions in the form of Software-Defined Mobile Networks (SDMN) leveraging SDN, NFV and cloud technologies. We evaluate the proposed solutions through testbed implementations and simulations. The results reveal that our proposed SDMN enhancements supports heterogeneity in wireless networks with performance improvements through programmable interfaces and centralized control.


LVCSR System on a Hybrid GPU-CPU Embedded Platform for Real-Time Dialog Applications


title: Just in time, total quality management, and supply chain management: understanding their linkages and impact on business performance

abstract: In recent years, numerous approaches have been proposed to improve operations performance. Three in particular, just in time, supply chain management, and quality management, have received considerable attention. While the three are sometimes viewed and implemented as if they were independent and distinct, they can also be used as three prongs of an integrated operations strategy. This study empirically examines the extent to which just in time, supply chain management, and quality management are correlated, and how they impact business performance. Results demonstrate that at both strategic and operational levels, linkages exist between how just in time, total quality management, and supply chain management are viewed by organizations as part of their operations strategy. Results also indicate that a commitment to quality and an understanding of supply chain dynamics have the greatest effect on performance.


The Modulatory Effects of Mesenchymal Stem Cells on Osteoclastogenesis


Recent Progress in Electronic Skin


title: Resilience: An Indicator of Recovery Capability in Intermodal Freight Transport

abstract: In this paper, an indicator of network resilience is defined that quantifies the ability of an intermodal freight transport network to recover from disruptions due to natural or human-caused disaster. The indicator considers the network's inherent ability to cope with the negative consequences of disruptions as a result of its topological and operational attributes. Furthermore, the indicator explicitly accounts for the impact of potential recovery activities that might be taken in the immediate aftermath of the disruption to meet target operational service levels while adhering to a fixed budget. A stochastic mixed-integer program is proposed for quantifying network resilience and identifying an optimal postevent course of action (i.e., set of activities) to take. To solve this mathematical program, a technique that accounts for dependencies in random link attributes based on concepts of Benders decomposition, column generation, and Monte Carlo simulation is proposed. Experiments were conducted to illustrate the resilience concept and procedure for its measurement, and to assess the role of network topology in its magnitude.


title: Interpretable Convolutional Neural Networks

abstract: This paper proposes a method to modify a traditional convolutional neural network (CNN) into an interpretable CNN, in order to clarify knowledge representations in high conv-layers of the CNN. In an interpretable CNN, each filter in a high conv-layer represents a specific object part. Our interpretable CNNs use the same training data as ordinary CNNs without a need for any annotations of object parts or textures for supervision. The interpretable CNN automatically assigns each filter in a high conv-layer with an object part during the learning process. We can apply our method to different types of CNNs with various structures. The explicit knowledge representation in an interpretable CNN can help people understand the logic inside a CNN, i.e. what patterns are memorized by the CNN for prediction. Experiments have shown that filters in an interpretable CNN are more semantically meaningful than those in a traditional CNN. The code is available at https://github.com/zqs1022/interpretableCNN.


title: A Comprehensive Cybersecurity Audit Model to Improve Cybersecurity Assurance: The CyberSecurity Audit Model (CSAM)

abstract: Nowadays, private corporations and public institutions are dealing with constant and sophisticated cyberthreats and cyberattacks. As a general warning, organizations must build and develop a cybersecurity culture and awareness in order to defend against cybercriminals. Information Technology (IT) and Information Security (InfoSec) audits that were efficient in the past, are trying to converge into cybersecurity audits to address cyber threats, cyber risks and cyberattacks that evolve in an aggressive cyber landscape. However, the increase in number and complexity of cyberattacks and the convoluted cyberthreat landscape is challenging the running cybersecurity audit models and putting in evidence the critical need for a new cybersecurity audit model. This article reviews the best practices and methodologies of global leaders in the cybersecurity assurance and audit arena. By means of the analysis of the current approaches and theoretical background, their real scope, strengths and weaknesses are highlighted looking forward a most efficient and cohesive synthesis. As a resut, this article presents an original and comprehensive cybersecurity audit model as a proposal to be utilized for conducting cybersecurity audits in organizations and Nation States. The CyberSecurity Audit Model (CSAM) evaluates and validates audit, preventive, forensic and detective controls for all organizational functional areas. CSAM has been tested, implemented and validated along with the Cybersecurity Awareness TRAining Model (CATRAM) in a Canadian higher education institution. A research case study is being conducted to validate both models and the findings will be published accordingly.


title: Iterative Learning with Open-set Noisy Labels

abstract: Large-scale datasets possessing clean label annotations are crucial for training Convolutional Neural Networks (CNNs). However, labeling large-scale data can be very costly and error-prone, and even high-quality datasets are likely to contain noisy (incorrect) labels. Existing works usually employ a closed-set assumption, whereby the samples associated with noisy labels possess a true class contained within the set of known classes in the training data. However, such an assumption is too restrictive for many applications, since samples associated with noisy labels might in fact possess a true class that is not present in the training data. We refer to this more complex scenario as the \textbf{open-set noisy label} problem and show that it is nontrivial in order to make accurate predictions. To address this problem, we propose a novel iterative learning framework for training CNNs on datasets with open-set noisy labels. Our approach detects noisy labels and learns deep discriminative features in an iterative fashion. To benefit from the noisy label detection, we design a Siamese network to encourage clean labels and noisy labels to be dissimilar. A reweighting module is also applied to simultaneously emphasize the learning from clean labels and reduce the effect caused by noisy labels. Experiments on CIFAR-10, ImageNet and real-world noisy (web-search) datasets demonstrate that our proposed model can robustly train CNNs in the presence of a high proportion of open-set as well as closed-set noisy labels.


title: Online dictionary learning on symmetric positive definite manifolds with vision applications

abstract: Symmetric Positive Definite (SPD) matrices in the form of region covariances are considered rich descriptors for images and videos. Recent studies suggest that exploiting the Rie-mannian geometry of the SPD manifolds could lead to improved performances for vision applications. For tasks involving processing large-scale and dynamic data in computer vision, the underlying model is required to progressively and efficiently adapt itself to the new and unseen observations. Motivated by these requirements, this paper studies the problem of online dictionary learning on the SPD manifolds. We make use of the Stein divergence to recast the problem of online dictionary learning on the manifolds to a problem in Reproducing Kernel Hilbert Spaces, for which, we develop efficient algorithms by taking into account the geometric structure of the SPD manifolds. To our best knowledge, our work is the first study that provides a solution for online dictionary learning on the SPD manifolds. Empirical results on both large-scale image classification task and dynamic video processing tasks validate the superior performance of our approach as compared to several state-of-the-art algorithms.


ACCOUNTABLE ALGORITHMS


title: Global consensus on ADHD/HKD

abstract: A Global ADHD Working Group of experienced clinicians and researchers was gathered to review the latest evidence, discuss current best practice in the treatment of attention-deficit/hyperactivity disorder (ADHD), and make a statement based on consensus. The statement aims to re-affirm ADHD as a valid disorder that exists across different cultures, has a significant global impact, and should be diagnosed and effectively treated wherever it occurs. ADHD is one of the most common neurobehavioural disorders of childhood and impacts on many aspects of development, including social, emotional and cognitive functioning, in the home and school environment. Although these findings are from developed countries, the impact in developing countries is likely to be similar. There is strong supportive evidence for the validity of ADHD as a syndrome with neurobiological aspects, and complex genetic factors are primarily implicated in the aetiology. Accurate diagnosis and measurement of impairment is important to enable appropriate and successful management of symptoms. ADHD is a persistent condition that needs to be treated and monitored over time. The evidence supporting medication-based interventions (such as methylphenidate) is strong and consensus treatment algorithms to guide the multimodal treatment of ADHD, alone and in combination with common comorbidities, are suggested.


title: A framework for the competitive evaluation of model inference techniques

abstract: This paper describes the STAMINA competition1, which is designed to drive the evaluation and improvement of software model-inference approaches. To this end, the target models have certain characteristics that tend to appear in software-models; they have large alphabets, and states are not evenly connected by transitions (as has been the case in previous similar competitions). The paper describes the set-up of the competition that extends previous similar competitions in the field of regular grammar inference. However, this competition focusses on target models that are characteristic of software systems, and features a suitably adapted protocol for the generation of training and testing samples. Besides providing details of the competition itself, it also discusses how outcomes from the competition will be used to gain broader insights into the relative accuracy and efficiency of competing techniques.


title: AHP-Based Quantitative Approach for Assessing and Comparing Cloud Security

abstract: While Cloud usage increasingly involves security considerations, there is still a conspicuous lack of techniques for users to assess/ensure that the security level advertised by the Cloud Service Provider (CSP) is actually delivered. Recent efforts have proposed extending existing Cloud Service Level Agreements (SLAs) to the security domain, by creating Security SLAs (SecLAs) along with attempts to quantify and reason about the security assurance provided by CSPs. However, both technical and usability issues limit their adoption in practice. In this paper we introduce a new technique for conducting quantitative and qualitative analysis of the security level provided by CSPs. Our methodology significantly improves upon contemporary security assessment approaches by creating a novel decision making technique based on the Analytic Hierarchy Process (AHP) that allows the comparison and benchmarking of the security provided by a CSP based on its SecLA. Furthermore, our technique improves security requirements specifications by introducing a flexible and simple methodology that allows users to identify their specific security needs. The proposed technique is demonstrated with real-world CSP data obtained from the Cloud Security Alliance's Security, Trust and Assurance Registry.


title: Time Limits in Reinforcement Learning

abstract: In reinforcement learning, it is common to let an agent interact with its environment for a fixed amount of time before resetting the environment and repeating the process in a series of episodes. The task that the agent has to learn can either be to maximize its performance over (i) that fixed period, or (ii) an indefinite period where time limits are only used during training to diversify experience. In this paper, we investigate theoretically how time limits could effectively be handled in each of the two cases. In the first one, we argue that the terminations due to time limits are in fact part of the environment, and propose to include a notion of the remaining time as part of the agent's input. In the second case, the time limits are not part of the environment and are only used to facilitate learning. We argue that such terminations should not be treated as environmental ones and propose a method, specific to value-based algorithms, that incorporates this insight by continuing to bootstrap at the end of each partial episode. To illustrate the significance of our proposals, we perform several experiments on a range of environments from simple few-state transition graphs to complex control tasks, including novel and standard benchmark domains. Our results show that the proposed methods improve the performance and stability of existing reinforcement learning algorithms.


title: Ground Moving Targets Imaging Algorithm for Synthetic Aperture Radar

abstract: It is well known that the motion of a target induces range migration, especially for high-resolution synthetic aperture radar (SAR) systems. Ground moving target imaging necessitates the correction of the unknown range migration. To finely refocus a moving target, one must accurately obtain the motion parameters for compensating the target trajectory. However, in practice, these parameters usually cannot be precisely estimated. This paper proposes a new imaging approach for ground moving targets without a priori knowledge of their motion parameters. In the devised method, the azimuth compression function is constructed in range frequency domain, which can eliminate the coupling effect between range and azimuth. Theoretical analysis confirms that the methodology can precisely focus targets without interpolation procedure. The effectiveness of the proposed imaging technique is demonstrated by both simulated and real airborne SAR data.


title: Atmospheric Sampling on Ascension Island Using Multirotor UAVs

abstract: As part of an NERC-funded project investigating the southern methane anomaly, a team drawn from the Universities of Bristol, Birmingham and Royal Holloway flew small unmanned multirotors from Ascension Island for the purposes of atmospheric sampling. The objective of these flights was to collect air samples from below, within and above a persistent atmospheric feature, the Trade Wind Inversion, in order to characterise methane concentrations and their isotopic composition. These parameters allow the methane in the different air masses to be tied to different source locations, which can be further analysed using back trajectory atmospheric computer modelling. This paper describes the campaigns as a whole including the design of the bespoke eight rotor aircraft and the operational requirements that were needed in order to collect targeted multiple air samples up to 2.5 km above the ground level in under 20 min of flight time. Key features of the system described include real-time feedback of temperature and humidity, as well as system health data. This enabled detailed targeting of the air sampling design to be realised and planned during the flight mission on the downward leg, a capability that is invaluable in the presence of uncertainty in the pre-flight meteorological data. Environmental considerations are also outlined together with the flight plans that were created in order to rapidly fly vertical transects of the atmosphere whilst encountering changing wind conditions. Two sampling campaigns were carried out in September 2014 and July 2015 with over one hundred high altitude sampling missions. Lessons learned are given throughout, including those associated with operating in the testing environment encountered on Ascension Island.


title: MagNet: A Two-Pronged Defense against Adversarial Examples

abstract: Deep learning has shown impressive performance on hard perceptual problems. However, researchers found deep learning systems to be vulnerable to small, specially crafted perturbations that are imperceptible to humans. Such perturbations cause deep learning systems to mis-classify adversarial examples, with potentially disastrous consequences where safety or security is crucial. Prior defenses against adversarial examples either targeted specific attacks or were shown to be ineffective. We propose MagNet, a framework for defending neural network classifiers against adversarial examples. MagNet neither modifies the protected classifier nor requires knowledge of the process for generating adversarial examples. MagNet includes one or more separate detector networks and a reformer network. The detector networks learn to differentiate between normal and adversarial examples by approximating the manifold of normal examples. Since they assume no specific process for generating adversarial examples, they generalize well. The reformer network moves adversarial examples towards the manifold of normal examples, which is effective for correctly classifying adversarial examples with small perturbation. We discuss the intrinsic difficulties in defending against whitebox attack and propose a mechanism to defend against graybox attack. Inspired by the use of randomness in cryptography, we use diversity to strengthen MagNet. We show empirically that MagNet is effective against the most advanced state-of-the-art attacks in blackbox and graybox scenarios without sacrificing false positive rate on normal examples.


title: Who's afraid of Local Agenda 21? Top-down and bottom-up perspectives on local sustainability

abstract: Local Agenda 21 is essentially a process of democratic practicing, insofar as it involves sharing political competencies in decision making by the local authorities, and the mobilisation of all citizens and civil society organisations in the process. It is, thus, a course of action in which the willingness and openness of local political leaders is as important as the ability of citizens to take the initiative of learning about and getting involved in local public life. Unfortunately, there are no more than twenty LA21 processes running in Portugal, and most of them do not fulfil all the parameters required. This paper discusses some hypotheses on the lack of success of LA21 in Portugal, which are related to structural political conditions for local governance and public participation. Resorting to some surveys on environmental policy issues (applied to both the municipal leaders and the population), the aim is to characterise the trends of mobilisation on local sustainability in Portuguese society, particularly with regard to the citizenry and local administrations.


title: Constructive Consumer Choice Processes

abstract: Consumer decision making has been a focal interest in consumer research, and consideration of current marketplace trends ( e.g., technological change, an information explosion) indicates that this topic will continue to be critically important. We argue that consumer choice is inherently constructive. Due to limited processing capacity, consumers often do not have well-defined existing preferences, but construct them using a variety of strategies contingent on task demands. After describing constructive choice, consumer decision tasks, and decision strategies, we provide an integrative framework for understanding constructive choice, review evidence for constructive consumer choice in light of that framework, and identify knowledge gaps that suggest opportunities for additional research. Copyright 1998 by the University of Chicago.


title: Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification

abstract: Computer codes simulating physical systems usually have responses that consist of a set of distinct outputs (e.g., velocity and pressure) that evolve also in space and time and depend on many unknown input parameters (e.g., physical constants, initial/boundary conditions, etc.). Furthermore, essential engineering procedures such as uncertainty quantification, inverse problems or design are notoriously difficult to carry out mostly due to the limited simulations available. The aim of this work is to introduce a fully Bayesian approach for treating these problems which accounts for the uncertainty induced by the finite number of observations. Our model is built on a multi-dimensional Gaussian process that explicitly treats correlations between distinct output variables as well as space and/or time. The proper use of a separable covariance function enables us to describe the huge covariance matrix as a Kronecker product of smaller matrices leading to efficient algorithms for carrying out inference and predictions. The novelty of this work, is the recognition that the Gaussian process model defines a posterior probability measure on the function space of possible surrogates for the computer code and the derivation of an algorithmic procedure that allows us to sample it efficiently. We demonstrate how the scheme can be used in uncertainty quantification tasks in order to obtain error bars for the statistics of interest that account for the finite number of observations.


title: Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems

abstract: Open-domain human-computer conversation has attracted much attention in the field of NLP. Contrary to rule- or template-based domain-specific dialog systems, open-domain conversation usually requires data-driven approaches, which can be roughly divided into two categories: retrieval-based and generation-based systems. Retrieval systems search a user-issued utterance (called a query) in a large database, and return a reply that best matches the query. Generative approaches, typically based on recurrent neural networks (RNNs), can synthesize new replies, but they suffer from the problem of generating short, meaningless utterances. In this paper, we propose a novel ensemble of retrieval-based and generation-based dialog systems in the open domain. In our approach, the retrieved candidate, in addition to the original query, is fed to an RNN-based reply generator, so that the neural model is aware of more information. The generated reply is then fed back as a new candidate for post-reranking. Experimental results show that such ensemble outperforms each single part of it by a large margin.


title: FPGA-based Switched Reluctance Motor Drive and DC-DC converter models for high-bandwidth HIL real-time simulator

abstract: In this paper, we describe an FPGA implementation of a Switched Reluctance Motor Drive (SRM) and an H-bridge Buck-Boost converter targeted for Hardware-In-the Loop (HIL) testing of modern SRM controllers. These FPGA models allow the HIL simulation of SRM drive and boost converter with switching frequencies in the 50-100 kHz range because of the very high sampling rate of the FPGA. The models are also integrated into the RT-LAB realtime environment and directly linked with the simulator I/Os, providing ultra-low HIL gate-in-to-current-out latency, suitable for testing modern motor controllers.


title: Convolutional neural tensor network architecture for community-based question answering

abstract: Retrieving similar questions is very important in community-based question answering. A major challenge is the lexical gap in sentence matching. In this paper, we propose a convolutional neural tensor network architecture to encode the sentences in semantic space and model their interactions with a tensor layer. Our model integrates sentence modeling and semantic matching into a single model, which can not only capture the useful information with convolutional and pooling layers, but also learn the matching metrics between the question and its answer. Besides, our model is a general architecture, with no need for the other knowledge such as lexical or syntactic analysis. The experimental results shows that our method outperforms the other methods on two matching tasks.


title: High-performance flat-panel solar thermoelectric generators with high thermal concentration

abstract: The conversion of sunlight into electricity has been dominated by photovoltaic and solar thermal power generation. A highly efficient solar to electric energy conversion device based on nanostructured thermoelectric materials and high solar concentration is now demonstrated. The results show potential for cost effective solar thermoelectric generation.


title: Giftedness and evidence for reproducibly superior performance: an account based on the expert performance framework

abstract: Giftedness researchers have long debated whether there is empirical evidence to support a distinction between giftedness and attained level of achievement. In this paper we propose a general theoretical framework that establishes scientific criteria for acceptable evidence of superior reproducible performance, which any theory of exceptional performance must explain. We review evidence for superior reproducible performance, generally emerging only after extended periods of deliberate practice that result in subsequent physiological adaptations and complex cognitive mechanisms. We also apply this framework to examine proposed evidence for innate talents. With the exception of fixed genetic factors determining body size and height, we were unable to find evidence for innate constraints to the attainment of elite achievement for healthy individuals.


title: Rey auditory-verbal learning test: Structure analysis

abstract: One of the major advantages of the Rey Auditory-Verbal Learning Test (AVLT) is its multiple measures of learning and memory. This study evaluated empirically whether the different scores are, in fact, not merely different expressions of a single factor, but, rather, measures of different memory domains. The Rey AVLT was administered to 146 normal subjects. Factor analyses produced one, two, or three factors depending on the combination of scores included in the analysis and on the criteria used to determine the number of factors. The basic factors identified were acquisition and retention. The latter can be subdivided further into storage and retrieval, thus yielding a total of three factors.


title: A novel approach to extract colon lumen from CT images for virtual colonoscopy

abstract: An automatic method has been developed for segmentation of abdominal computed tomography (CT) images for virtual colonoscopy obtained after a bowel preparation of a low-residue diet with ingested contrast solutions to enhance the image intensities of residual colonic materials. Removal of the enhanced materials was performed electronically by a computer algorithm. The method is a multistage approach that employs a modified self-adaptive on-line vector quantization technique for a low-level image classification and utilizes a region-growing strategy for a high-level feature extraction. The low-level classification labels each voxel based on statistical analysis of its three-dimensional intensity vectors consisting of nearby voxels. The high-level processing extracts the labeled stool, fluid and air voxels within the colon, and eliminates bone and lung voxels which have similar image intensities as the enhanced materials and air, but are physically separated from the colon. This method was evaluated by volunteer studies based on both objective and subjective criteria. The validation demonstrated that the method has a high reproducibility and repeatability and a small error due to partial volume effect. As a result of this electronic colon cleansing, routine physical bowel cleansing prior to virtual colonoscopy may not be necessary.


title: A two-step supervised learning artificial neural network for imbalanced dataset problems

abstract: In this paper, a two-step supervised learning algorithm of a single layer feedforward Articial Neural Network (ANN) is proposed for solving imbalanced dataset problems. Levenberg Marquart backpropagation learning algorithm is utilized in therst step learning, while the second step learning mechanism is introduced by optimizing the decision threshold of the step function at the output layer of ANN using particle swarm optimization (PSO). After all the steps learning are accomplished, the best weights and decision threshold value are obtained to be used for testing process. Several imbalanced datasets, which are available in UCI Machine Learning Repository, are chosen as case study. The prediction performance is assessed by Geometric Mean (G-mean), which is a standard measure to indicate the efficiency of classier for imbalanced datasets. Based on the experimental results, the proposed method is able to provide good G-mean value compared with the conventional ANN approaches.


title: Self-certified Signatures

abstract: A digital signature provides the authenticity of a signed message with respect to a public key and a certificate provides the authorization of a signer for a public key. Digital signature and certificate are generated independently by different parties, but they are verified by the same verifier who wants to verify the signature. In the point of a verifier, verifying two independent digital signatures (a digital signature and the corresponding certificate) is a burden.In this paper we propose a new digital signature scheme called self-certified signature. In this scheme a signer computes a temporary signing key with his long-term signing key and its certification information together, and generates a signature on a message and certification information using the temporary signing key in a highly combined and unforgeable manner. Then, a verifier verifies both signer's signature on the message and related certification information together. This approach is very advantageous in efficiency. We extend the proposed self-certified signature scheme to multi-certification signature in which multiple certification information are verified. We apply it to public key infrastructure (PKI) and privilege management infrastructure (PMI) environments.


title: Probabilistic collaborative filtering with negative cross entropy

abstract: Relevance-Based Language Models are an effective IR approach which explicitly introduces the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models have shown to achieve state-of-the-art retrieval performance in the pseudo relevance feedback task. In this paper we propose a novel adaptation of this language modeling approach to rating-based Collaborative Filtering. In a memory-based approach, we apply the model to the formation of user neighbourhoods, and the generation of recommendations based on such neighbourhoods. We report experimental results where our method outperforms other standard memory-based algorithms in terms of ranking precision.


Blockchain for Configuration Management in IoT


title: Detection and Classification of Pests in Greenhouse Using Image Processing

abstract: The techniques of machine vision and digital image Processing are extensively applied to agricultural science and it have great perspective especially in the plant protection field, which ultimately leads to crops management. The paper proposes a software prototype system for early pest detection on the infected crops in greenhouse. Images of the infected leaf are captured by a camera with pan tilt and zoom and processed using image processing techniques to detect presence of pests. SVM classifier is used for classification. SVM classifier helps to detect the pests and in the classification of pest based on their features. Results show more precision in identifying the presence of pest at early stage.


title: Vocabulary Learning Strategies in an ESP Context

abstract: The paper focuses on vocabulary learning strategies as a subcategory of language learning strategies and their instruction within the ESP context at the Faculty of Maritime Studies and Transport in Portorož. Vocabulary strategy instruction will be implemented at our faculty as part of a broader PhD research into the effect of language learning strategy instruction on strategy use and subject-specific and general language acquisition. Additional variables that will be taken into consideration are language proficiency, motivation and learning styles of the students. The introductory section in which the situation that triggered my PhD research is presented is followed by a theoretical introduction to the concept of language and vocabulary learning strategies. The aspects that the paper focuses on are the central role of lexis within ESP, vocabulary learning strategy taxonomies, and the presentation of research studies made in the examined field to date. The final section presents the explicit vocabulary learning strategy instruction model. In the conclusion, some implications for teaching can be found.


title: Dynamic relationship and event discovery

abstract: This paper studies the problem of dynamic relationship and event discovery. A large body of previous work on relation extraction focuses on discovering predefined and static relationships between entities. In contrast, we aim to identify temporally defined (e.g., co-bursting) relationships that are not predefined by an existing schema, and we identify the underlying time constrained events that lead to these relationships. The key challenges in identifying such events include discovering and verifying dynamic connections among entities, and consolidating binary dynamic connections into events consisting of a set of entities that are connected at a given time period. We formalize this problem and introduce an efficient end-to-end pipeline as a solution. In particular, we introduce two formal notions, global temporal constraint cluster and local temporal constraint cluster, for detecting dynamic events. We further design efficient algorithms for discovering such events from a large graph of dynamic relationships. Finally, detailed experiments on real data show the effectiveness of our proposed solution.


title: Improvement and application of GM(1,1) model

abstract: The paper puts forward a new conversion method that convert original sequence using cosine function, which proves discrete data sequence can improve smoothness by this conversion in theory. And this conversion is used in GM(1,1) model, which is used in example prediction at the end of this paper, which proves the effectiveness of the method of this paper.


title: Bidirectional A Search with Additive Approximation Bounds

abstract: In this paper, we present new theoretical and experimental results for bidirectional A search. Unlike most previous research on this topic, our results do not require assumptions of either consistent or balanced heuristic functions for the search. Our theoretical work examines new results on the worst-case number of node expansions for inconsistent heuristic functions with bounded estimation errors. Additionally, we consider several alternative termination criteria in order to more quickly terminate the bidirectional search, and we provide worst-case approximation bounds for our suggested criteria. We prove that our approximation bounds are purely additive in nature (a general improvement over previous multiplicative approximations). Experimental evidence on large-scale road networks suggests that the errors introduced are truly quite negligible in practice, while the performance gains are significant.


title: Instance-sensitive Fully Convolutional Networks

abstract: Fully convolutional networks (FCNs) have been proven very successful for semantic segmentation, but the FCN outputs are unaware of object instances. In this paper, we develop FCNs that are capable of proposing instance-level segment candidates. In contrast to the previous FCN that generates one score map, our FCN is designed to compute a small set of instance-sensitive score maps, each of which is the outcome of a pixel-wise classifier of a relative position to instances. On top of these instance-sensitive score maps, a simple assembling module is able to output instance candidate at each position. In contrast to the recent DeepMask method for segmenting instances, our method does not have any high-dimensional layer related to the mask resolution, but instead exploits image local coherence for estimating instances. We present competitive results of instance segment proposal on both PASCAL VOC and MS COCO.


title: Workplace mental health: developing an integrated intervention approach

abstract: Mental health problems are prevalent and costly in working populations. Workplace interventions to address common mental health problems have evolved relatively independently along three main threads or disciplinary traditions: medicine, public health, and psychology. In this Debate piece, we argue that these three threads need to be integrated to optimise the prevention of mental health problems in working populations. To realise the greatest population mental health benefits, workplace mental health intervention needs to comprehensively 1) protect mental health by reducing work–related risk factors for mental health problems; 2) promote mental health by developing the positive aspects of work as well as worker strengths and positive capacities; and 3) address mental health problems among working people regardless of cause. We outline the evidence supporting such an integrated intervention approach and consider the research agenda and policy developments needed to move towards this goal, and propose the notion of integrated workplace mental health literacy. An integrated approach to workplace mental health combines the strengths of medicine, public health, and psychology, and has the potential to optimise both the prevention and management of mental health problems in the workplace.


title: The flipped classroom: a course redesign to foster learning and engagement in a health professions school.

abstract: Recent calls for educational reform highlight ongoing concerns about the ability of current curricula to equip aspiring health care professionals with the skills for success. Whereas a wide range of proposed solutions attempt to address apparent deficiencies in current educational models, a growing body of literature consistently points to the need to rethink the traditional in-class, lecture-based course model. One such proposal is the flipped classroom, in which content is offloaded for students to learn on their own, and class time is dedicated to engaging students in student-centered learning activities, like problem-based learning and inquiry-oriented strategies. In 2012, the authors flipped a required first-year pharmaceutics course at the University of North Carolina Eshelman School of Pharmacy. They offloaded all lectures to self-paced online videos and used class time to engage students in active learning exercises. In this article, the authors describe the philosophy and methodology used to redesign the Basic Pharmaceutics II course and outline the research they conducted to investigate the resulting outcomes. This article is intended to serve as a guide to instructors and educational programs seeking to develop, implement, and evaluate innovative and practical strategies to transform students' learning experience. As class attendance, students' learning, and the perceived value of this model all increased following participation in the flipped classroom, the authors conclude that this approach warrants careful consideration as educators aim to enhance learning, improve outcomes, and fully equip students to address 21st-century health care needs.


title: Effect of spironolactone on cytosolic free sodium concentration in platelets from hypertensive patients with primary aldosteronism

abstract: Cytosolic free sodium concentration ([Na+]i) was investigated in intact platelets from 5 hypertensive patients with primary aldosteronism (unilateral adenoma in 3 patients, and adrenal hyperplasia in 2 patients) and 21 normotensive control subjects. [Na+]i was measured using a novel sodium-sensitive fluorescent dye technique.


title: Twister: a runtime for iterative MapReduce

abstract: MapReduce programming model has simplified the implementation of many data parallel applications. The simplicity of the programming model and the quality of services provided by many implementations of MapReduce attract a lot of enthusiasm among distributed computing communities. From the years of experience in applying MapReduce to various scientific applications we identified a set of extensions to the programming model and improvements to its architecture that will expand the applicability of MapReduce to more classes of applications. In this paper, we present the programming model and the architecture of Twister an enhanced MapReduce runtime that supports iterative MapReduce computations efficiently. We also show performance comparisons of Twister with other similar runtimes such as Hadoop and DryadLINQ for large scale data parallel applications.


title: Single Open-phase Fault Detection with Fault-Tolerant Control of an Inverter-fed Permanent Magnet Synchronous Machine

abstract: In this paper first a current predictive method for single open-phase fault detection in a three phase drive with a permanent magnet synchronous machine is presented. The proposed method is based on a predictive stator current calculation. For each sampling interval the difference between the actual stator current and its predicted value in a previous sampling interval is calculated. To identify the location of a single open-phase fault, an identification method is presented which is based on the analysis of the stator current vector angle. After the single open-phase fault is detected and identified it is desirable for the electrical machine to continue operating with a reduced number of phases. For this purpose, a modified direct torque control algorithm for the fault-tolerant control is implemented. In order to improve the performance of the drive, a pre-firing angle is additionally introduced. All proposed methods have been simulated in Matlab/Simulink and verified on an experimental model.


title: Megahertz OCT for ultrawide-field retinal imaging with a 1050nm Fourier domain mode-locked laser

abstract: We demonstrate ultrahigh speed swept source retinal OCT imaging using a Fourier domain mode locked (FDML) laser. The laser uses a combination of a semiconductor optical amplifier and an ytterbium doped fiber amplifier to provide more than 50mW output power. The 1050nm FDML laser uses standard telecom fiber for the km long delay line instead of two orders of magnitude more expensive real single mode fiber. We investigate the influence of this “oligo-mode” fiber on the FDML laser performance. Two design configurations with 684,400 and 1,368,700 axial scans per second are investigated, 25x and 50x faster than current commercial instruments and more than 4x faster than previous single spot ophthalmic results. These high speeds enable the acquisition of densely sampled ultrawide-field data sets of the retina within a few seconds. Ultrawide-field data consisting of 1900 x 1900 A-scans with ~70° degrees angle of view are acquired within only 3 and 6 seconds using the different setups. Such OCT data sets, more than double as large as previously reported, are collapsed to a 4 megapixel high definition fundus image. We achieve good penetration into the choroid by hardware spectral shaping of the laser output. The axial resolution in tissue is 12µm (684kHz) and 19µm (1.37MHz). A series of new data processing and imaging extraction protocols, enabled by the ultrawide-field isotropic data sets, are presented. Dense isotropic sampling enables both, cross-sectional images along arbitrary coordinates and depth-resolved en-face fundus images. Additionally, we investigate how isotropic averaging compares to the averaging of cross-sections along the slow axis.


title: Design and Implementation of a Wireless In-Ovo EEG/EMG Recorder

abstract: The developmental origins of sleep and brain activity rhythms in higher vertebrate animals (birds and mammals) are currently unknown. In order to create an experimental system in which these could be better elucidated, we designed, built and tested a system for recording EEG and EMG signals in-ovo from chicken embryos incubated for 16-21 days. This system can remain attached to the individual subject through the process of hatching and continue to be worn post-natally. Electrode wires surgically implanted on the head of the embryo are connected to a battery-operated ultraportable transmitter which can either be attached to the eggshell or worn on the back. The transmitter processes up to 6 channels of data with a maximum sampling frequency of 500 Hz and a resolution of 12 bits. The radio link uses a carrier frequency of 4 MHz, and has a maximum transfer rate of 500 kbit/s; receiving antennas compatible with both in-egg recordings and post-natal recordings from freely-moving birds were produced. A receiver connected with one USB port of a PC transmits the data for digital storage. This system is based on discrete, off-the-shelf components, can provide a few days of continuous operation with a single lithium coin battery, and has a noise floor level of 0.35 μV. The transmitter dimensions are 16 × 13 × 1.5 mm and the weight without the battery is 0.7 g. The microprocessor allows flexible operation modes not usually made available in other small multichannel acquisition systems implemented by means of ad hoc mixed signal chips.


title: A Qualitative Study of Coping in Mothers of Children with an Autism Spectrum Disorder

abstract: ABSTRACTA significant body of research exists that explores the stressors of raising a child with an autism spectrum disorder (ASD). There are fewer studies, however, that examine specific effective coping strategies of mothers of children with an ASD. This qualitative study explored mothers’ perceptions of effective coping strategies for their parenting stressors. In-depth interviews were conducted with 11 mothers to inquire about their personal coping methods. Interviews were coded and emergent themes identified that included coping strategies such as “me time,” planning, knowledge is power, sharing the load, lifting the restraints of labels, and recognizing the joys. The information from this study may benefit mothers of children with ASD and inform pediatric therapists providing services to children with ASD and their families.


title: Bandwidth Enhancement of the Small-Size Internal Laptop Computer Antenna Using a Parasitic Open Slot for Penta-Band WWAN Operation

abstract: By embedding a parasitic open slot in the antenna ground of a small-size internal laptop computer antenna, enhanced bandwidth for the antenna's lower band to cover the GSM850/900 operation (824-960 MHz) can be achieved. The internal laptop computer antenna in this study is a small-size coupled-fed shorted T-monopole, whose length along the top edge of the display ground of the laptop computer is 43 mm only. The antenna can also provide a wide upper band to cover the GSM1800/1900/UMTS operation (1710-2170 MHz). With the inclusion of the parasitic open slot, the total antenna length along the top edge of the display ground is 48 mm only, and the occupied antenna volume is 10 × 3.5 × 48 mm3 above the top edge of the display ground, which is the smallest for the internal penta-band WWAN laptop computer antenna that have been reported for the present. Detailed results of the proposed parasitic open slot on bandwidth enhancement of the internal WWAN laptop computer antenna are presented.


Fully Porous 3 D Printed Titanium Femoral Stem to Reduce Stress-Shielding Following Total Hip Arthroplasty


title: Dropout Inference in Bayesian Neural Networks with Alpha-divergences

abstract: To obtain uncertainty estimates with real-world Bayesian deep learning models, practical inference approximations are needed. Dropout variational inference (VI) for example has been used for machine vision and medical applications, but VI can severely underestimates model uncertainty. Alpha-divergences are alternative divergences to VI's KL objective, which are able to avoid VI's uncertainty underestimation. But these are hard to use in practice: existing techniques can only use Gaussian approximating distributions, and require existing models to be changed radically, thus are of limited use for practitioners. We propose a reparametrisation of the alpha-divergence objectives, deriving a simple inference technique which, together with dropout, can be easily implemented with existing models by simply changing the loss of the model. We demonstrate improved uncertainty estimates and accuracy compared to VI in dropout networks. We study our model's epistemic uncertainty far away from the data using adversarial images, showing that these can be distinguished from non-adversarial images by examining our model's uncertainty.


title: How do Developers Test Android Applications?

abstract: Enabling fully automated testing of mobile applications has recently become an important topic of study for both researchers and practitioners. A plethora of tools and approaches have been proposed to aid mobile developers both by augmenting manual testing practices and by automating various parts of the testing process. However, current approaches for automated testing fall short in convincing developers about their benefits, leading to a majority of mobile testing being performed manually. With the goal of helping researchers and practitioners - who design approaches supporting mobile testing - to understand developer's needs, we analyzed survey responses from 102 open source contributors to Android projects about their practices when performing testing. The survey focused on questions regarding practices and preferences of developers/testers in-the-wild for (i) designing and generating test cases, (ii) automated testing practices, and (iii) perceptions of quality metrics such as code coverage for determining test quality. Analyzing the information gleaned from this survey, we compile a body of knowledge to help guide researchers and professionals toward tailoring new automated testing approaches to the need of a diverse set of open source developers.


title: Whole-system persistence

abstract: Today's databases and key-value stores commonly keep all their data in main memory. A single server can have over 100 GB of memory, and a cluster of such servers can have 10s to 100s of TB. However, a storage back end is still required for recovery from failures. Recovery can last for minutes for a single server or hours for a whole cluster, causing heavy load on the back end. Non-volatile main memory (NVRAM) technologies can help by allowing near-instantaneous recovery of in-memory state. However, today's software does not support this well. Block-based approaches such as persistent buffer caches suffer from data duplication and block transfer overheads. Recently, user-level persistent heaps have been shown to have much better performance than these. However they require substantial application modification and still have significant runtime overheads. This paper proposes whole-system persistence (WSP) as an alternative. WSP is aimed at systems where all memory is non-volatile. It transparently recovers an application's entire state, making a failure appear as a suspend/resume event. Runtime overheads are eliminated by using "flush on fail": transient state in processor registers and caches is flushed to NVRAM only on failure, using the residual energy from the system power supply. Our evaluation shows that this approach has 1.6--13 times better runtime performance than a persistent heap, and that flush-on-fail can complete safely within 2--35\% of the residual energy window provided by standard power supplies.


title: HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms with Concept Drift

abstract: Histogram-based similarity has been widely adopted in many machine learning tasks. However, measuring histogram similarity is a challenging task for streaming data, where the elements of a histogram are observed in a streaming manner. First, the ever-growing cardinality of histogram elements makes any similarity computation inefficient. Second, the concept-drift issue in the data streams also impairs the accurate assessment of the similarity. In this paper, we propose to overcome the above challenges with HistoSketch, a fast similarity-preserving sketching method for streaming histograms with concept drift. Specifically, HistoSketch is designed to incrementally maintain a set of compact and fixed-size sketches of streaming histograms to approximate similarity between the histograms, with the special consideration of gradually forgetting the outdated histogram elements. We evaluate HistoSketch on multiple classification tasks using both synthetic and real-world datasets. The results show that our method is able to efficiently approximate similarity for streaming histograms and quickly adapt to concept drift. Compared to full streaming histograms gradually forgetting the outdated histogram elements, HistoSketch is able to dramatically reduce the classification time (with a 7500x speedup) with only a modest loss in accuracy (about 3.5%).


title: The Subprime Mortgage Reforms: The Case of Us Market

abstract: The downfall of the mortgage industry in US has seen fingers being pointed to the government sponsored entities (Fannie Mae and Freddie Mac), while the roots cause of the formation of these entities were never closely followed. Originally the creation of the GSE was to create liquidity in the mortgage industry as part of public policy in providing housing for the middle and lower income group. Over time, resulting from a weak regulated environment saw the GSE suffering and now this left questioning the effectiveness of the initial policy.


title: Monocytes in atherosclerosis: subsets and functions

abstract: Chronic inflammation drives atherosclerosis, the leading cause of cardiovascular disease. Over the past two decades, data have emerged showing that immune cells are involved in the pathogenesis of atherosclerotic plaques. The accumulation and continued recruitment of leukocytes are associated with the development of 'vulnerable' plaques. These plaques are prone to rupture, leading to thrombosis, myocardial infarction or stroke, all of which are frequent causes of death. Plaque macrophages account for the majority of leukocytes in plaques, and are believed to differentiate from monocytes recruited from circulating blood. However, monocytes represent a heterogenous circulating population of cells. Experiments are needed to address whether monocyte recruitment to plaques and effector functions, such as the formation of foam cells, the production of nitric oxide and reactive oxygen species, and proteolysis are critical for the development and rupture of plaques, and thus for the pathophysiology of atherosclerosis, as well as elucidate the precise mechanisms involved.


title: Palm oil and health: a case of manipulated perception and misuse of science.

abstract: In the 1980s, a combination of forces came together to convince the public that food products containing tropical oils contributed to their risk of coronary heart disease. Tropical oils were competing with the U.S. soy bean oil market as an alternative vegetable oil, yet they were higher in saturated fat, which had become the target of the health promotion community for its theoretical association with coronary heart disease risk. Successful national campaigns were undertaken to force food manufacturers to remove tropical oils, including palm oil, from their products and to replace them with hydrogenated vegetable oils, resulting in increased intakes of trans-fatty acids, which later became the target of the same advocacy groups. Today palm oil is being touted as a suitable replacement for hydrogenated vegetable oils.


title: Offset-corrected 5GHz CMOS dynamic comparator using bulk voltage trimming: Design and analysis

abstract: This paper presents an improved two-stage dynamic comparator using a bulk voltage trimming technique for offset calibration. The comparator requires only a one-phase clock while exerting no extra load on the first stage, leading to higher operating speed. The calibration does not require any extra power supply and does not consume any quiescent current, while increasing the offset calibrating range by a factor of 2 over previous techniques. Detailed analysis of the method of calibrating both stages of the dynamic comparator is provided. Simulation results in a 65nm digital CMOS process show that the comparator is capable of working at a speed of 5GHz with 90uW of power consumption from a 1V power supply, achieving an input-referred offset calibrating range of ±35mV at ∼±2.3mV/step at the typical-typical process corner.


title: Social LSTM: Human Trajectory Prediction in Crowded Spaces

abstract: Pedestrians follow different trajectories to avoid obstacles and accommodate fellow pedestrians. Any autonomous vehicle navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collisions. This problem of trajectory prediction can be viewed as a sequence generation task, where we are interested in predicting the future trajectory of people based on their past positions. Following the recent success of Recurrent Neural Network (RNN) models for sequence prediction tasks, we propose an LSTM model which can learn general human movement and predict their future trajectories. This is in contrast to traditional approaches which use hand-crafted functions such as Social forces. We demonstrate the performance of our method on several public datasets. Our model outperforms state-of-the-art methods on some of these datasets. We also analyze the trajectories predicted by our model to demonstrate the motion behaviour learned by our model.


The Soft Robotics Toolkit : Shared Resources for Research and Design


title: Genteel Wearables: Bystander-Centered Design

abstract: Bystanders are often overlooked in the debate over wearable use in public spaces. Including bystander concerns in the design process will make wearables more "genteel," protecting bystanders' privacy while making users more comfortable operating wearable devices in public.


title: Nasal Alar Necrosis Following Hyaluronic Acid Injection into Nasolabial Folds: A Case Report

abstract: Injection of synthetic fillers for soft tissue augmentation is increasing over the last decade. One of the most common materials used is hyaluronic acid (HA) that is safe and temporary filler for soft tissue augmentation. We present a case of 54-year-old female who experienced vascular occlusion and nasal alar necrosis following HA injection to the nasolabial folds. She suffered from pain, necrosis, infection, and alar loss that finally required a reconstructive surgery for cosmetic appearance of the nose. The case highlights the importance of proper injection technique by an anesthesiologist, as well as the need for immediate recognition and treatment of vascular occlusion.


title: A multi-agent architecture for adaptive E-learning systems using a blackboard agent

abstract: This paper describes a multi-agent architecture for adaptive E-learning system. The proposed architecture is composed of several multi-agent levels and intelligent blackboard as an agent. The blackboard agent provides an easy way for agents to communicate and facilitates the cooperation and coordination among them.


title: Parsing Algebraic Word Problems into Equations

abstract: This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees. We use integer linear programming to generate equation trees and score their likelihood by learning local and global discriminative models. These models are trained on a small set of word problems and their answers, without any manual annotation, in order to choose the equation that best matches the problem text. We refer to the overall system as ALGES. We compare ALGES with previous work and show that it covers the full gamut of arithmetic operations whereas Hosseini et al. (2014) only handle addition and subtraction. In addition, ALGES overcomes the brittleness of the Kushman et al. (2014) approach on single-equation problems, yielding a 15% to 50% reduction in error.


title: Internet Addiction in Chinese Adolescents in Hong Kong: Assessment, Profiles, and Psychosocial Correlates

abstract: Internet addiction behavior was examined in 6,121 Chinese primary and secondary school students in Hong Kong based on the assessment frameworks of Ivan Goldberg and Kimberly Young. Results showed that scales derived from both frameworks (CIA-Goldberg Scale and CIA-Young Scale) were internally consistent and evidence supporting their validity was found. Descriptive statistical analyses revealed that roughly one-fifth of the respondents could be classified as Internet addicted based on either scale. Further analyses showed that Internet-addicted and -nonaddicted respondents differed in their Internet use and related behavior. Logistic regression analyses showed that engagement in certain on-line activities (such as playing on-line games and downloading software) and replacement of pastimes activities (such as watching TV and going out with friends) with Internet activities predicted a higher probability of Internet addiction.


title: Bayesian k-Means as a Maximization-Expectation Algorithm

abstract: We introduce a new class of "maximization-expectation" (ME) algorithms where we maximize over hidden variables but marginalize over random parameters. This reverses the roles of expectation and maximization in the classical expectation-maximization algorithm. In the context of clustering, we argue that these hard assignments open the door to very fast implementations based on data structures such as kd-trees and conga lines. The marginalization over parameters ensures that we retain the ability to infer model structure (i.e., number of clusters). As an important example, we discuss a top-down Bayesian k-means algorithm and a bottom-up agglomerative clustering algorithm. In experiments, we compare these algorithms against a number of alternative algorithms that have recently appeared in the literature.


title: MIMO systems with antenna selection

abstract: Multiple-input-multiple-output (MIMO) wireless systems are those that have multiple antenna elements at both the transmitter and receiver. They were first investigated by computer simulations in the 1980s. Since that time, interest in MIMO systems has exploded. They are now being used for third-generation cellular systems (W-CDMA) and are discussed for future high-performance modes of the highly successful IEEE 802.11 standard for wireless local area networks. MIMO-related topics also occupy a considerable part of today's academic communications research. The multiple antennas in MIMO systems can be exploited in two different ways. One is the creation of a highly effective antenna diversity system; the other is the use of the multiple antennas for the transmission of several parallel data streams to increase the capacity of the system. This article presented an overview of MIMO systems with antenna selection. The transmitter, the receiver, or both use only the signals from a subset of the available antennas. This allows considerable reductions in the hardware expense.


title: Does the technology acceptance model predict actual use? A systematic literature review

abstract: Context: The technology acceptance model (TAM) was proposed in 1989 as a means of predicting technology usage. However, it is usually validated by using a measure of behavioural intention to use (BI) rather than actual usage. Objective: This review examines the evidence that the TAM predicts actual usage using both subjective and objective measures of actual usage. Method: We performed a systematic literature review based on a search of six digital libraries, along with vote-counting meta-analysis to analyse the overall results. Results: The search identified 79 relevant empirical studies in 73 articles. The results show that BI is likely to be correlated with actual usage. However, the TAM variables perceived ease of use (PEU) and perceived usefulness (PU) are less likely to be correlated with actual usage. Conclusion: Care should be taken using the TAM outside the context in which it has been validated.


title: Development of metal plating on FRP exterior body panels

abstract: Abstract Application of Sheet Molding Compound (SMC) to exterior body panels of automobiles is intended to meet the demand for weight reduction. There has, on the other hand, always been a strong demand for metal plating exterior body panels, which is especially true for the domestic truck market. However, metal plating on SMC with good adhesion and surface quality has not been possible with conventional technology. In order to meet these needs, the technique of metal plating on SMC by changing in In-Mold Coating was developed.


title: Lignin as a cross-linker of acrylic acid-grafted carboxymethyl lignocellulose

abstract: Cunninghamia lanceolata wood meal samples with different lignin contents after delignification with an acidic NaClO2 system were carboxymethylated, and the degree of substitution (DS) and the distribution of the carboxymethyl (CM) groups were investigated by proton nuclear magnetic resonance (1H NMR) spectroscopy. Cellulose samples prepared from bleached kraft softwood pulp, food-grade konjac mannan, and commercial oat xylan (containing 10% arabinosyl and 15% glucosyl residues) were also investigated. The chemical shift of methylene protons in 1H NMR spectra of CM groups of carboxymethyl konjac mannan and commercial oat xylan appeared in the same region as those of carboxymethylcellulose. The DS of carboxymethyl lignocellulose (CMLC) increased slightly from 1.36 to 1.48 with decreasing lignin content, but the water solubility of CMLC clearly increased with decreasing lignin content. It was suggested that the covalent linkages between lignin and cell-wall polysaccharides play the role of cross-linker in CMLC. Water absorbents were synthesized by graft-copolymerization of acrylic acid onto CMLC samples with different lignin contents. The highest level of water absorbency was obtained from CMLC containing 14% of lignin, suggesting the importance of lignin as the cross-linker.


title: Partial molar volumes of air-component gases in binary liquid mixtures with n-alkanes and 1-alkanols at 298.15 K

abstract: Abstract An apparatus for the determination of differences between the densities of degassed and gas-saturated liquids at atmospheric pressure is described. Differences in density between prepared liquid samples were measured by means of a vibrating-tube densitometer (Anton Paar DMA 602). Density differences at 298.15 K were measured for (liquid-gas) pairs. The liquids included members of the homologous series of n -alkanes ( n -hexane, n -heptane, n -octane, n -decane, and n -hexadecane) and 1-alkanols (methanol, ethanol, 1-propanol, 1-butanol, 1-hexanol, 1-octanol, and 1-decanol). The selected gases were nitrogen, oxygen, argon, carbon dioxide and ordinary air. The density differences for those pairs whose solubility data were available from the literature partial molar volumes of gases in liquids were calculated, and correlated with the internal pressures of liquid solvents.


title: Patterns and Profiles of Response to Incivility in the Workplace

abstract: The authors draw on stress and coping theory to understand patterns of individual response to workplace incivility. According to data from 3 employee samples, incivility tended to trigger mildly negative appraisals, which could theoretically differentiate incivility from other categories of antisocial work behavior. Employees experiencing frequent and varied incivility from powerful instigators generally appraised their uncivil encounters more negatively. They responded to this stressor using a multifaceted array of coping strategies, which entailed support seeking, detachment, minimization, prosocial conflict avoidance, and assertive conflict avoidance. These coping reactions depended on the target’s appraisal of the situation, the situation’s duration, and the organizational position and power of both target and instigator. Implications for organizational science and practice are discussed.


title: CPL: a core language for cloud computing

abstract: Running distributed applications in the cloud involves deployment. That is, distribution and configuration of application services and middleware infrastructure. The considerable complexity of these tasks resulted in the emergence of declarative JSON-based domain-specific deployment languages to develop deployment programs. However, existing deployment programs unsafely compose artifacts written in different languages, leading to bugs that are hard to detect before run time. Furthermore, deployment languages do not provide extension points for custom implementations of existing cloud services such as application-specific load balancing policies. To address these shortcomings, we propose CPL (Cloud Platform Language), a statically-typed core language for programming both distributed applications as well as their deployment on a cloud platform. In CPL, application services and deployment programs interact through statically typed, extensible interfaces, and an application can trigger further deployment at run time. We provide a formal semantics of CPL and demonstrate that it enables type-safe, composable and extensible libraries of service combinators, such as load balancing and fault tolerance.


title: Aging effect in pattern, motion and cognitive visual evoked potentials

abstract: Abstract An electrophysiological study on the effect of aging on the visual pathway and various levels of visual information processing (primary cortex, associate visual motion processing cortex and cognitive cortical areas) was performed. We examined visual evoked potentials (VEPs) to pattern-reversal, motion-onset (translation and radial motion) and visual stimuli with a cognitive task (cognitive VEPs – P300 wave) at luminance of 17 cd/m 2 . The most significant age-related change in a group of 150 healthy volunteers (15–85 years of age) was the increase in the P300 wave latency (2 ms per 1 year of age). Delays of the motion-onset VEPs (0.47 ms/year in translation and 0.46 ms/year in radial motion) and the pattern-reversal VEPs (0.26 ms/year) and the reductions of their amplitudes with increasing subject age (primarily in P300) were also found to be significant. The amplitude of the motion-onset VEPs to radial motion remained the most constant parameter with increasing age. Age-related changes were stronger in males. Our results indicate that cognitive VEPs, despite larger variability of their parameters, could be a useful criterion for an objective evaluation of the aging processes within the CNS. Possible differences in aging between the motion-processing system and the form-processing system within the visual pathway might be indicated by the more pronounced delay in the motion-onset VEPs and by their preserved size for radial motion (a biologically significant variant of motion) compared to the changes in pattern-reversal VEPs.


title: A −80dBm BLE-compliant, FSK wake-up receiver with system and within-bit dutycycling for scalable power and latency

abstract: This paper presents an FSK wake-up receiver with a −80 dBm sensitivity using a packet structure and a duty cycling scheme compliant with the Bluetooth Low Energy (BLE) protocol trading off power with latency. Event-driven applications achieve power lower than 240nW from a 0.75V supply while latency-critical systems wake up in almost 200μ« at a 230μW consumption. A within-bit LC oscillator duty-cycling scheme is proposed to provide an extra 24% power reduction. Additionally, a custom FSK transmitter can trigger wake-up at 17nW only for an average latency of 5 seconds.