title: L.: A Mobile Indoor Navigation System Interface Adapted to Vision-Based Localization

abstract: Vision-based approaches for mobile indoor localization do not rely on the infrastructure and are therefore scalable and cheap. The particular requirements to a navigation user interface for a vision-based system, however, have not been investigated so far. Such mobile interfaces should adapt to localization accuracy, which strongly relies on distinctive reference images, and other factors, such as the phone's pose. If necessary, the system should motivate the user to point at distinctive regions with the smartphone to improve localization quality. We present a combined interface of Virtual Reality (VR) and Augmented Reality (AR) elements with indicators that help to communicate and ensure localization accuracy. In an evaluation with 81 participants, we found that AR was preferred in case of reliable localization, but with VR, navigation instructions were perceived more accurate in case of localization and orientation errors. The additional indicators showed a potential for making users choose distinctive reference images for reliable localization.


title: Modified Antipodal Fermi antenna with piecewise-linear approximation

abstract: The authors propose the dual-tapered structure consisting of piecewise-linear inner taper and outer shaped-comb corrugation and the antipodal feeding section to present a modified antipodal Fermi antenna. This antenna exhibits the performance with wider band (3.6-15 GHz) in return loss responses, moderately high gain (5.1-14.5 dBi), high front-to-back ratio (24.8-38.9 dB) and available end-fire (29.8° beamwidth and -18.7 dB side lobe level) in radiation patterns. The current distributions demonstrate the characteristics of leakage, transverse waves among dual-taper and transition on balun. The proposed antenna presents the longitudinal conformation for mounting application and evaluate the pulse responses with linearity test for ultra-wideband (UWB) ranging and imaging applications.


Continuous-Time Limit of Stochastic Gradient Descent Revisited


title: Abstract A quasi-Monte Carlo method for computing areas of point-sampled surfaces

abstract: A novel and efficient quasi-Monte Carlo method for computing the area of a point-sampled surface with associated surface normal for each point is presented. Our method operates directly on the point cloud without any surface reconstruction procedure. Using the Cauchy-Crofton formula, the area of the point-sampled surface is calculated by counting the number of intersection points between the point cloud and a set of uniformly distributed lines generated with low-discrepancy sequences. Based on a clustering technique, we also propose an effective algorithm for computing the intersection points of a line with the point-sampled surface. By testing on a number of point-based models, experiments suggest that our method is more robust and more efficient than those conventional approaches based on surface reconstruction.


title: Implicit theories about willpower predict self-regulation and grades in everyday life.

abstract: Laboratory research shows that when people believe that willpower is an abundant (rather than highly limited) resource they exhibit better self-control after demanding tasks. However, some have questioned whether this "nonlimited" theory leads to squandering of resources and worse outcomes in everyday life when demands on self-regulation are high. To examine this, we conducted a longitudinal study, assessing students' theories about willpower and tracking their self-regulation and academic performance. As hypothesized, a nonlimited theory predicted better self-regulation (better time management and less procrastination, unhealthy eating, and impulsive spending) for students who faced high self-regulatory demands. Moreover, among students taking a heavy course load, those with a nonlimited theory earned higher grades, which was mediated by less procrastination. These findings contradict the idea that a limited theory helps people allocate their resources more effectively; instead, it is people with the nonlimited theory who self-regulate well in the face of high demands.


title: Policy Optimization as Wasserstein Gradient Flows

abstract: Policy optimization is a core component of reinforcement learning (RL), and most existing RL methods directly optimize parameters of a policy based on maximizing the expected total reward, or its surrogate. Though often achieving encouraging empirical success, its underlying mathematical principle on {\em policy-distribution} optimization is unclear. We place policy optimization into the space of probability measures, and interpret it as Wasserstein gradient flows. On the probability-measure space, under specified circumstances, policy optimization becomes a convex problem in terms of distribution optimization. To make optimization feasible, we develop efficient algorithms by numerically solving the corresponding discrete gradient flows. Our technique is applicable to several RL settings, and is related to many state-of-the-art policy-optimization algorithms. Empirical results verify the effectiveness of our framework, often obtaining better performance compared to related algorithms.


title: Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis

abstract: We describe a neural network-based system for text-to-speech (TTS) synthesis that is able to generate speech audio in the voice of many different speakers, including those unseen during training. Our system consists of three independently trained components: (1) a speaker encoder network, trained on a speaker verification task using an independent dataset of noisy speech from thousands of speakers without transcripts, to generate a fixed-dimensional embedding vector from seconds of reference speech from a target speaker; (2) a sequence-to-sequence synthesis network based on Tacotron 2, which generates a mel spectrogram from text, conditioned on the speaker embedding; (3) an auto-regressive WaveNet-based vocoder that converts the mel spectrogram into a sequence of time domain waveform samples. We demonstrate that the proposed model is able to transfer the knowledge of speaker variability learned by the discriminatively-trained speaker encoder to the new task, and is able to synthesize natural speech from speakers that were not seen during training. We quantify the importance of training the speaker encoder on a large and diverse speaker set in order to obtain the best generalization performance. Finally, we show that randomly sampled speaker embeddings can be used to synthesize speech in the voice of novel speakers dissimilar from those used in training, indicating that the model has learned a high quality speaker representation.


Information Encoding by Deep Neural Networks: What Can We Learn?


title: Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN

abstract: The cycleGAN is becoming an influential method in medical image synthesis. However, due to a lack of direct constraints between input and synthetic images, the cycleGAN cannot guarantee structural consistency between these two images, and such consistency is of extreme importance in medical imaging. To overcome this, we propose a structure-constrained cycleGAN for brain MR-to-CT synthesis using unpaired data that defines an extra structure-consistency loss based on the modality independent neighborhood descriptor to constrain structural consistency. Additionally, we use a position-based selection strategy for selecting training images instead of a completely random selection scheme. Experimental results on synthesizing CT images from brain MR images demonstrate that our method is better than the conventional cycleGAN and approximates the cycleGAN trained with paired data.


title: Features and classifiers for replay spoofing attack detection

abstract: Automatic speaker verification (ASV) systems are known to be highly vulnerable against spoofing attacks. Various successful countermeasures have recently been proposed to detect spoofing attacks originating from speech synthesis (SS) and voice conversion (VC). However, detecting replay attacks, the most easily implementable spoofing attacks against ASV systems, has gained less attention. Thus, in this paper we present an experimental comparison of various feature extraction techniques and classifiers for replay attack detection. In total, six magnitude spectrum and three phase spectrum based features are used for feature extraction. For classification in turn, four different techniques are utilized. Experiments are conducted on recently released ASVspoof 2017 replay attack detection challenge. Experimental results reveals that magnitude spectrum features considerably outperform phase based features independent of the classifier. Comparative results using four different classifiers indicate that i-vector cosine scoring yields lower equal error rates (EERs) than other methods.


title: A Quantitative Assessment of Performance in Mobile App Development Tools

abstract: The popularity and maturity of mobile cross-platform tools increased considerably during the last years. They can be used to increase the efficiency during the development cycle of mobile applications. Although it is common knowledge that cross-platform tools come with a performance penalty, the size of the overhead is unclear. Our study targets the assessment of two representative cross-platform tools. This paper presents a quantitative performance analysis that is applied to a non-trivial, multi-screen app. Relevant performance properties on both low-end and high-end devices are evaluated and compared to a native Android and iOS implementation. We further reflect about the impact of the results on the user acceptance and experience, and define guidelines for the selection of a particular tool.


Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition


title: Transformational Leadership and Psychological Well-Being: The Mediating Role of Meaningful Work

abstract: Two studies investigated the relationship between transformational leadership, the meaning that individuals ascribe to their work, and their psychological well-being. In Study 1, the perceptions of meaningful work partially mediated the relationship between transformational leadership and positive affective well-being in a sample of Canadian health care workers (N 319). In Study 2, the meaning that a separate sample of service workers (N 146) ascribed to their work fully mediated the relationship between transformational leadership and psychological well-being, after controlling for humanistic work beliefs. Overall, these results support and add to the range of positive mental health effects associated with transformational leadership and are suggestive of interventions that organizations can make to improve well-being of workers.


title: A method for extracting major workflow composed of ingredients, tools, and actions from cooking procedural text

abstract: In this paper, we propose a method for extracting a major workflow of cooking procedure from a Japanese recipe on the Web. It is utilized for various applications including recipe search, summarization, and visualization. This major workflow is represented as a tree in which each leaf corresponds to a food or a cooking tool, each intermediate node corresponds to a cooking action, and the root node corresponds to the final cooking action. We had already developed a method for converting a procedural text to a workflow as a directed acyclic graph, and the proposed method is based on the previous one. The results are evaluated by comparing with manually extracted major workflows as a ground truth.


title: An introduction to security api analysis

abstract: A security API is an Application Program Interface that allows untrusted code to access sensitive resources in a secure way. Examples of security APIs include the interface between the tamper-resistant chip on a smartcard (trusted) and the card reader (untrusted), the interface between a cryptographic Hardware Security Module, or HSM (trusted) and the client machine (untrusted), and the Google maps API (an interface between a server, trusted by Google, and the rest of the Internet).


title: Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access

abstract: We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into $K$ orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the spectrum access problem is computationally expensive, in general, due to the large-state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to the spectrum access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. The experimental results demonstrate the strong performance of the algorithm.


title: Simulation Comparisons and Implementation of Induction Generator Wind Power Systems

abstract: This paper describes the performance comparison of a wind power systems based on two different induction generators as well as the experimental demonstration of a wind turbine simulator for the maximum power extraction. The two induction machines studied for the comparison are the squirrel-cage induction generator (SCIG) and the doubly fed induction generator (DFIG). The techniques of direct grid integration, independent power control, and the droop phenomenon of distribution line are studied and compared between the SCIG and DFIG systems. Both systems are modeled in Matlab/Simulink environment, and the operation is tested for the wind turbine maximum power extraction algorithm results. Based on the simulated wind turbine parameters, a commercial induction motor drive was programmed to emulate the wind turbine and is coupled to the experimental generator systems. The turbine experimental results matched well with the theoretical turbine operation.


title: Integrated gate driver circuit solutions

abstract: Power electronics systems are commonly used in motor drive, power supply and power conversion applications. They cover a wide output power spectrum: from several hundred watts in small drives up to megawatts in wind-power installations or large drive systems. Inside the system the gate driver circuit with its extensive control and monitoring functions forms the interface between the microcontroller and the power switches (IGBT). This paper will provide an overview of different gate driver topologies for different power ranges and will show numerous examples for monolithic integration of the driver functionality. Chipsets for high power drivers with a real potential separation (galvanic insulation) are the starting point, and a new and innovative concept for a digital gate driver and its integration is presented. In the second part, fully integrated gate driver solutions for the low power range, their technologies, circuit aspects and specific designs are shown and discussed.


title: The association between psychological distance and construal level: Evidence from an implicit association test

abstract: According to construal level theory (N. Liberman, Y. Trope, & E. Stephan, in press; Y. Trope & N.Liberman, 2003), people use a more abstract, high construal level when judging, perceiving, andpredicting more psychologically distal targets, and they judge more abstract targets as being morepsychologically distal. The present research demonstrated that associations between more distance andhigher level of construal also exist on a pure conceptual level. Eight experiments used the ImplicitAssociation Test (IAT; A. G. Greenwald, D. E. McGhee, & J. L. K. Schwartz, 1998) to demonstrate anassociation between words related to construal level (low vs. high) and words related to four dimensionsof distance (proximal vs. distal): temporal distance, spatial distance, social distance, and hypotheticality.In addition to demonstrating an association between level of construal and psychological distance, thesefindings also corroborate the assumption that all 4 dimensions of psychological distance are related tolevelofconstrualinasimilarwayandsupportthenotionthattheyallareformsofpsychologicaldistance.Keywords: psychological distance, construal level theory, abstraction, implicit association test, temporaldistance


title: Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN

abstract: Fine-grained image labels are desirable for many computer vision applications, such as visual search or mobile AI assistant. These applications rely on image classification models that can produce hundreds of thousands (e.g. 100K) of diversified fine-grained image labels on input images. However, training a network at this vocabulary scale is challenging, and suffers from intolerable large model size and slow training speed, which leads to unsatisfying classification performance. A straightforward solution would be training separate expert networks (specialists), with each specialist focusing on learning one specific vertical (e.g. cars, birds...). However, deploying dozens of expert networks in a practical system would significantly increase system complexity and inference latency, and consumes large amounts of computational resources. To address these challenges, we propose a Knowledge Concentration method, which effectively transfers the knowledge from dozens of specialists (multiple teacher networks) into one single model (one student network) to classify 100K object categories. There are three salient aspects in our method: (1) a multi-teacher single-student knowledge distillation framework; (2) a self-paced learning mechanism to allow the student to learn from different teachers at various paces; (3) structurally connected layers to expand the student network capacity with limited extra parameters. We validate our method on OpenImage and a newly collected dataset, Entity-Foto-Tree (EFT), with 100K categories, and show that the proposed model performs significantly better than the baseline generalist model.


title: An efficient crack detection method using percolation-based image processing

abstract: Crack detection on concrete surfaces is the most popular subject in the inspection of the concrete structures. The conventional method of crack detection is performed by experienced human inspectors by sketching the crack patterns manually. Some automated crack detection techniques utilizing image processing have been proposed. Although most of the image-based approaches pay attention to the accuracy of the crack detection results, the computation time is also important for practical use, because the size of the digital image reaches 10-mega pixels. In this paper, we introduce an efficient and high-speed method for crack detection employing percolation-based image processing. To reduce the computation time, we consult the ideas of the sequential similarity detection algorithm and active search (SSDA). According to the concept of SSDA, the percolation process is terminated by calculating the circularity midway through the processing. Moreover, percolation processing can be skipped for the next pixel depending on the circularity of neighboring pixels. The experimental result shows that the proposed approach is efficient in reducing the computation cost while preserving the accuracy of crack detection result.


title: Meta-Analysis: Recent Developments in Quantitative Methods for Literature Reviews

abstract: ▪ Abstract We describe the history and current status of the meta-analytic enterprise. The advantages and historical criticisms of meta-analysis are described, as are the basic steps in a meta-anal...


title: Physical-Layer Secret Key Agreement in Two-Way Wireless Relaying Systems

abstract: We consider secret key agreement based on radio propagation characteristics in a two-way relaying system where two legitimate parties named Alice and Bob communicate with each other via a trusted relay. In this system, Alice and Bob share secret keys generated from measured radio propagation characteristics with the help of the relay in the presence of an eavesdropper. We present four secret key agreement schemes: an amplify-and-forward (AF) scheme, a signal-combining amplify-and-forward (SC-AF) scheme, a multiple-access amplify-and-forward (MA-AF) scheme, and an amplify-and-forward with artificial noise (AF with AN) scheme. In these schemes, the basic idea is to share the effective fading coefficients between Alice and Bob and use them as the source of the secret keys. The AF scheme is based on a conventional amplify-and-forward two-way relaying method, whereas in the SC-AF scheme and the MA-AF scheme, we apply the idea of physical-layer network coding to the secret key agreement. In the AF with AN scheme, the relay transmits artificially generated noise, as well as channel information signal, in order to conceal the latter. Simulation results show that the MA-AF scheme outperforms the other schemes in Rayleigh fading channels, whereas the AF with AN scheme is suitable for Rician fading channels.


title: On the Design of Fine Grained Access Control With User Authentication Scheme for Telecare Medicine Information Systems

abstract: A telecare medicine information system (TMIS) for health-care delivery service requires information exchange among multiple IT systems, where different types of users with different access privileges are involved. In TMIS, users generally communicate via public channels. Hence, authentication is essential to provide access to the genuine users. However, access rights for the correct information and resources for different services to the genuine users can be provided with the help of efficient user access control mechanism. The existing user authentication protocols designed for TMIS only provide authentication, but for this kind of application, it is required that the authorized users should also have unique access privilege to access specific data. This paper puts forwards a new fine grained access control with user authentication scheme for TMIS. We present the formal security analysis using both the widely accepted real-or-random model and Burrows-Abadi–Needham logic. The proposed scheme supports user anonymity, forward secrecy, and efficient password change without contacting the remote server. In addition, the proposed scheme is comparable with respect to communication and computation costs as compared with other related schemes proposed in TMIS. Moreover, better tradeoff among security and functionality features, and communication and computation costs makes the proposed scheme suitable and practical for telecare medicine environments as compared with other existing related schemes.


title: Read disturb fault detection in STT-MRAM

abstract: Spin Transfer Torque Magnetic Random Access Memory (STT-MRAM) has potential to become a universal memory technology because of its various advantageous features such as high density, non-volatility, scalability, high endurance and CMOS compatibility. However, read disturb is a major reliability issue in which a read operation can lead to a bitflip, because read and write current share the same path. This major reliability challenge is growing with technology scaling as read to write current ratio decreases. In this paper, we propose a circuit-level technique to detect read disturb by sensing the current during the read operation. Experimental results show that the proposed technique can effectively detect read disturb at the cost of negligible power and area overhead.


The big hole in HCI research


Automatic liver tumor segmentation in follow-up CT studies using Convolutional Neural Networks


title: Natural language and natural selection

abstract: Many people have argued that the evolution of the human language faculty cannot be explained by Darwinian natural selection. Chomsky and Gould have suggested that language may have evolved as the by-product of selection for other abilities or as a consequence of as-yet unknown laws of growth and form. Others have argued that a biological specialization for grammar is incompatible with every tenet of Darwinian theory – that it shows no genetic variation, could not exist in any intermediate forms, confers no selective advantage, and would require more evolutionary time and genomic space than is available. We examine these arguments and show that they depend on inaccurate assumptions about biology or language or both. Evolutionary theory offers clear criteria for when a trait should be attributed to natural selection: complex design for some function, and the absence of alternative processes capable of explaining such complexity. Human language meets these criteria: Grammar is a complex mechanism tailored to the transmission of propositional structures through a serial interface. Autonomous and arbitrary grammatical phenomena have been offered as counterexamples to the position that language is an adaptation, but this reasoning is unsound: Communication protocols depend on arbitrary conventions that are adaptive as long as they are shared. Consequently, language acquisition in the child should systematically differ from language evolution in the species, and attempts to analogize them are misleading. Reviewing other arguments and data, we conclude that there is every reason to believe that a specialization for grammar evolved by a conventional neo-Darwinian process.


title: What features of online review affects readers' intention to travel?

abstract: Online review is becoming more popular among the public when considering the purchase of a product or service. This also applies in tourism industry when potential tourists were making decisions about travel destination. This research aims to identify online review elements/features that will affect readers' intention to travel. Hence, the model was designed by categorized online review characteristics namely system, information and informant characteristic. These characteristics then influence perceived informativeness, destination image and informant expertise and trustworthiness viewed in reader viewpoint and eventually affect reader intention to travel. Analysis of the model was conducted by using PLS SEM with 189 valid questionnaires. The result of this study proved that some elements/features of online review influence reader intention to travel. They are quality and quantity of the reviews and destination indicator which are available on the website. However, this study suggest that current features are not sufficient to explain reviewer credibility, thus other features need to be added to the website to serve this purpose.


title: Audio event and scene recognition: A unified approach using strongly and weakly labeled data

abstract: In this paper we propose a novel learning framework called Supervised and Weakly Supervised Learning where the goal is to learn simultaneously from weakly and strongly labeled data. Strongly labeled data can be simply understood as fully supervised data where all labeled instances are available. In weakly supervised learning only data is weakly labeled which prevents one from directly applying supervised learning methods. Our proposed framework is motivated by the fact that a small amount of strongly labeled data can give considerable improvement over only weakly supervised learning. The primary problem domain focus of this paper is acoustic event and scene detection in audio recordings. We first propose a naive formulation for leveraging labeled data in both forms. We then propose a more general framework for Supervised and Weakly Supervised Learning (SWSL). Based on this general framework, we propose a graph based approach for SWSL. Our main method is based on manifold regularization on graphs in which we show that the unified learning can be formulated as a constraint optimization problem which can be solved by iterative concave-convex procedure (CCCP). Our experiments show that our proposed framework can address several concerns of audio content analysis using weakly labeled data.


title: SPICE model development for SiC power MOSFET

abstract: SiC Power MOSFETs show a tremendous potential for high voltage, high temperature, high-power and high-frequency power electronic applications. A simplified SPICE model is proposed for the SiC Power MOSFET, CMF20120D, based on the understanding of the power MOSFET discrete devices terminal behavior. The aim of the model development is to reuse the available built-in MOSFET models of the regular lateral MOS devices in the commercial SPICE simulators and propose a new model to depict the same behavior as expected by SiC device. The advantage of such model is the limited number of required parameters, which can readily be extracted from simple external terminal measurements or from standard datasheets. The results of simulation and comparison with real device measurement show appreciable closeness with the datasheet.


title: Dempster-Shafer Theory Approach to FMEA

abstract: Failure Mode and Effects Analysis (FMEA) technique is a group based activity to prioritize risk. Often times there are issues with aggregating team member’s responses or ratings. Literature suggests several means to aggregate these responses $ computing average values, using g roup consensus, assigning weights to team member’s responses, and using fuzzy logic / inference systems, to name a few. However, each of these methods has certain shortcomings. Furthermore, ranking the possible failure modes in face of uncertainty or incomplete information presents its own challenges. The aforementioned aggregation techniques have been used with very limited success in such uncertain instances. This paper will propose a method that utilizes Dempster$Shafer Theory (DST) to overcome the se issues. It will be shown that DST will allow unbiased aggregation of team member’s ratings using rules of combination of evidence. Also this method will aid the difficulty in precisely rating a factor in the presence of incomplete information. To the best of our knowledge a DST based technique has not been applied to FMEA in the literature. A secondary contribution of this paper is introducing the concept of DST, which has many potential applications in the field of Industrial and System Engineering.


title: A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells

abstract: The problem of predicting the position of a freely foraging rat based on the ensemble firing patterns of place cells recorded from the CA1 region of its hippocampus is used to develop a two-stage statistical paradigm for neural spike train decoding. In the first, or encoding stage, place cell spiking activity is modeled as an inhomogeneous Poisson process whose instantaneous rate is a function of the animal’s position in space and phase of its theta rhythm. The animal’s path is modeled as a Gaussian random walk. In the second, or decoding stage, a Bayesian statistical paradigm is used to derive a nonlinear recursive causal filter algorithm for predicting the position of the animal from the place cell ensemble firing patterns. The algebra of the decoding algorithm defines an explicit map of the discrete spike trains into the position prediction. The confidence regions for the position predictions quantify spike train information in terms of the most probable locations of the animal given the ensemble firing pattern. Under our inhomogeneous Poisson model position was a three to five times stronger modulator of the place cell spiking activity than theta phase in an open circular environment. For animal 1 (2) the median decoding error based on 34 (33) place cells recorded during 10 min of foraging was 8.0 (7.7) cm. Our statistical paradigm provides a reliable approach for quantifying the spatial information in the ensemble place cell firing patterns and defines a generally applicable framework for studying information encoding in neural systems.


SOC and SOH Estimation for Multicell Lithium-ion Batteries Based on an Adaptive Hybrid Battery Model and Sliding-Mode Observer


Software Defect Prediction Models for Quality Improvement: A Literature Study


title: Active Stiffness Tuning of a Spring-Based Continuum Robot for MRI-Guided Neurosurgery

abstract: Deep intracranial tumor removal can be achieved if the neurosurgical robot has sufficient flexibility and stability. Toward achieving this goal, we have developed a spring-based continuum robot, namely a minimally invasive neurosurgical intracranial robot (MINIR-II) with novel tendon routing and tunable stiffness for use in a magnetic resonance imaging (MRI) environment. The robot consists of a pair of springs in parallel, i.e., an inner interconnected spring that promotes flexibility with decoupled segment motion and an outer spring that maintains its smooth curved shape during its interaction with the tissue. We propose a shape memory alloy (SMA) spring backbone that provides local stiffness control and a tendon routing configuration that enables independent segment locking. In this paper, we also present a detailed local stiffness analysis of the SMA backbone and model the relationship between the resistive force at the robot tip and the tension in the tendon. We also demonstrate through experiments, the validity of our local stiffness model of the SMA backbone and the correlation between the tendon tension and the resistive force. We also performed MRI compatibility studies of the three-segment MINIR-II robot by attaching it to a robotic platform that consists of SMA spring actuators with integrated water cooling modules.


title: What’s Different: Distributed, Continuous Monitoring of Duplicate-Resilient Aggregates on Data Streams

abstract: Emerging applications in sensor systems and network-wide IP traffic analysis present many technical challenges. They need distributed monitoring and continuous tracking of events. They have severe resource constraints not only at each site in terms of per-update processing time and archival space for highspeed streams of observations, but also crucially, communication constraints for collaborating on the monitoring task. These elements have been addressed in a series of recent works. A fundamental issue that arises is that one cannot make the "uniqueness" assumption on observed events which is present in previous works, since widescale monitoring invariably encounters the same events at different points. For example, within the network of an Internet Service Provider packets of the same flow will be observed in different routers; similarly, the same individual will be observed by multiple mobile sensors in monitoring wild animals. Aggregates of interest on such distributed environments must be resilient to duplicate observations. We study such duplicate-resilient aggregates that measure the extent of the duplication―how many unique observations are there, how many observations are unique―as well as standard holistic aggregates such as quantiles and heavy hitters over the unique items. We present accuracy guaranteed, highly communication-efficient algorithms for these aggregates that work within the time and space constraints of high speed streams. We also present results of a detailed experimental study on both real-life and synthetic data.


title: Figure Metadata Extraction from Digital Documents

abstract: Academic papers contain multiple figures (information graphics) representing important findings and experimental results. Automatic data extraction from such figures and classification of information graphics is not straightforward and a well studied problem in document analysis cite{4275059}. Also, very few digital library search engines index figures and/or associated metadata (figure caption) from PDF documents. We describe the very first step in indexing, classification and data extraction from figures in PDF documents - accurate automatic extraction of figures and associated metadata, a nontrivial task. Document layout, font information, lexical and linguistic features for figure caption extraction from PDF documents is considered for both rule based and machine learning based approaches. We also describe a digital library search engine that indexes figure captions and mentions from 150K documents, extracted by our custom built extractor.


title: Real-Time Global Localization of Robotic Cars in Lane Level via Lane Marking Detection and Shape Registration

abstract: In this paper, we propose an accurate and real-time positioning method for robotic cars in urban environments. The proposed method uses a robust lane marking detection algorithm, as well as an efficient shape registration algorithm between the detected lane markings and a GPS-based road shape prior, to improve the robustness and accuracy of the global localization of a robotic car. We show that, by formulating the positioning problem in a relative sense, we can estimate the global localization of a car in real time and bound its absolute error in the centimeter level by a cross-validation scheme. The cross-validation scheme integrates the vision-based lane marking detection with the shape registration, and it improves the accuracy and robustness of the overall localization system. The GPS localization can be refined by using lane marking detection when the GPS suffers from frequent satellite signal masking or blockage, whereas lane marking detection is validated and completed by the GPS-based road shape prior when it does not work well in adverse weather conditions or with poor lane signatures. We extensively evaluate the proposed method with a single forward-looking camera mounted on an autonomous vehicle that travels at 60 km/h through several urban street scenes.


title: Tones of Reduced T1-T4 Mandarin Disyllables

abstract: The lexical meaning of Chinese words is determined by syllables and lexical tones. Phonologically, there are four full tones. Empirically, however, it remains a puzzle how tones are recognized when they are reduced in natural speech. This article presents three studies on tones of reduced disyllables: (1) a corpus study on disyllabic reduction, (2) two tone categorical identification experiments on fully pronounced and reduced disyllables, and (3) an analysis of word identification responses of two disyllables. Utilizing a segment-aligned corpus, disyllables were classified by ear into four degrees of contraction (from none to full), i.e., where a disyllable is gradiently reduced towards one syllable. The results suggested that the onset of the second syllable was most likely to be shortened or deleted. For studying the lexical effect of tones, a Ganong-style word bias experiment was conducted on T1-T4 continua of three T1-T4 disyllables. Results of the fully pronounced stimuli confirmed that the lexical status of the disyllables affected the tone classification of F0 contours along a continuum from T1 to T4, showing distinct differences of tone identification in real words and nonwords. Then, this effect disappeared when the onset of the second syllable was removed to simulate a partly reduced disyllable. Insufficient segmental information seemed to deactivate the word-nonword contrast, i.e. lexical status seemed to override any acoustic information available. Tones tended to be recognized as those from a real word throughout the continua. Finally, responses to two T1-T4 disyllables from the identification experiment done by Tseng & Lee (2010) were re-analyzed. The results suggested that reduction degree, F0 shapes, word unit type, and exposure frequency seemed to play a role in the recognition of words and tones.


title: The relationship of nightmare frequency and nightmare distress to well-being.

abstract: Nightmares can be defined as very disturbing dreams, the events or emotions of which cause the dreamer to wake up. In contrast, unpleasant dreams can be defined in terms of a negative emotional rating of a dream, irrespective of whether or not the emotions or events of the dream woke the dreamer. This study addresses whether frequency of unpleasant dreams is a better index of low well-being than is frequency of nightmares. A total of 147 participants reported their nightmare frequency retrospectively and then kept a log of all dreams, including nightmares, for 2 weeks, and rated each dream for pleasantness/unpleasantness. Anxiety, depression, neuroticism, and acute stress were found to be associated with nightmare distress (ND) (the trait-like general level of distress in waking-life caused by having nightmares) and prospective frequency of unpleasant dreams, and less so with the mean emotional tone of all dreams, or retrospective or prospective nightmare frequency. Correlations between low well-being and retrospective nightmare frequency became insignificant when trait ND was controlled for, but correlations with prospective unpleasant dream frequency were maintained. The reporting of nightmares may thus be confounded and modulated by trait ND: such confounding does not occur for the reporting of unpleasant dreams in general. Thus there may be attributional components to deciding that one has been awoken by a dream, which can affect estimated nightmare frequency and its relationship with well-being. Underestimation of nightmare frequency by the retrospective questionnaire compared with logs was found to be a function of mean dream unpleasantness and ND.


title: Re-examining the influence of trust on online repeat purchase intention: The moderating role of habit and its antecedents

abstract: Customer loyalty or repeat purchasing is critical for the survival and success of any store. By focusing on online stores, this study investigates the moderating role of habit on the relationship between trust and repeat purchase intention. Prior research on online behavior continuance models perceives usefulness, trust, satisfaction, and perceived value as the major determinants of continued usage or loyalty, overlooking the important role of habit. We define habit in the context of online shopping as the extent to which buyers tend to shop online automatically without thinking. Building on recent research on the continued usage of IS and repeat purchasing, we develop a model suggesting that habit acts as a moderator between trust and repeat purchase intention, while familiarity, value and satisfaction are the three antecedents of habit. Data collected from 454 customers of the Yahoo!Kimo shopping mall provide strong support for the research model. The results indicate that a higher level of habit reduces the effect of trust on repeat purchase intention. The data also show that value, satisfaction, and familiarity are important to habit formation and thus relevant within the context of online repeat purchasing. The implications for theory and practice and suggestions for future research are also discussed.


title: FontCode: Embedding Information in Text Documents Using Glyph Perturbation

abstract: This paper presents a new method of embedding hidden information in a text document printed on paper or stored in digital formats. The hidden information can be robustly extracted later.


title: Etiology and Pharmacology of Neuropathic Pain.

abstract: Injury to or disease of the nervous system can invoke chronic and sometimes intractable neuropathic pain. Many parallel, interdependent, and time-dependent processes, including neuroimmune interactions at the peripheral, supraspinal, and spinal levels, contribute to the etiology of this "disease of pain." Recent work emphasizes the roles of colony-stimulating factor 1, ATP, and brain-derived neurotrophic factor. Excitatory processes are enhanced, and inhibitory processes are attenuated in the spinal dorsal horn and throughout the somatosensory system. This leads to central sensitization and aberrant processing such that tactile and innocuous thermal information is perceived as pain (allodynia). Processes involved in the onset of neuropathic pain differ from those involved in its long-term maintenance. Opioids display limited effectiveness, and less than 35% of patients derive meaningful benefit from other therapeutic approaches. We thus review promising therapeutic targets that have emerged over the last 20 years, including Na+, K+, Ca2+, hyperpolarization-activated cyclic nucleotide-gated channels, transient receptor potential channel type V1 channels, and adenosine A3 receptors. Despite this progress, the gabapentinoids retain their status as first-line treatments, yet their mechanism of action is poorly understood. We outline recent progress in understanding the etiology of neuropathic pain and show how this has provided insights into the cellular actions of pregabalin and gabapentin. Interactions of gabapentinoids with the α2δ-1 subunit of voltage-gated Ca2+ channels produce multiple and neuron type-specific actions in spinal cord and higher centers. We suggest that drugs that affect multiple processes, rather than a single specific target, show the greatest promise for future therapeutic development.


title: A Nonparametric Approach to Modeling Choice with Limited Data

abstract: Choice models today are ubiquitous across a range of applications in operations and marketing. Real-world implementations of many of these models face the formidable stumbling block of simply identifying the “right” model of choice to use. Because models of choice are inherently high-dimensional objects, the typical approach to dealing with this problem is positing, a priori, a parametric model that one believes adequately captures choice behavior. This approach can be substantially suboptimal in scenarios where one cares about using the choice model learned to make fine-grained predictions; one must contend with the risks of mis-specification and overfitting/underfitting. Thus motivated, we visit the following problem: For a “generic” model of consumer choice namely, distributions over preference lists and a limited amount of data on how consumers actually make decisions such as marginal information about these distributions, how may one predict revenues from offering a particular assortment of choices? An outcome of our investigation is a nonparametric approach in which the data automatically select the right choice model for revenue predictions. The approach is practical. Using a data set consisting of automobile sales transaction data from a major U.S. automaker, our method demonstrates a 20% improvement in prediction accuracy over state-of-the-art benchmark models; this improvement can translate into a 10% increase in revenues from optimizing the offer set. We also address a number of theoretical issues, among them a qualitative examination of the choice models implicitly learned by the approach. We believe that this paper takes a step toward “automating” the crucial task of choice model selection. ::: ::: This paper was accepted by Yossi Aviv, operations management.


title: How ( and where ) does moral judgment work ?

abstract: Moral psychology has long focused on reasoning, but recent evidence suggests that moral judgment is more a matter of emotion and affective intuition than deliberate reasoning. Here we discuss recent findings in psychology and cognitive neuroscience, including several studies that specifically investigate moral judgment. These findings indicate the importance of affect, although they allow that reasoning can play a restricted but significant role in moral judgment. They also point towards a preliminary account of the functional neuroanatomy of moral judgment, according to which many brain areas make important contributions to moral judgment although none is devoted specifically to it.


title: The Modified Abbreviated Math Anxiety Scale: A Valid and Reliable Instrument for Use with Children

abstract: Mathematics anxiety (MA) can be observed in children from primary school age into the teenage years and adulthood, but many MA rating scales are only suitable for use with adults or older adolescents. We have adapted one such rating scale, the Abbreviated Math Anxiety Scale (AMAS), to be used with British children aged 8-13. In this study we assess the scale’s reliability, factor structure and divergent validity. The modified AMAS (mAMAS) was administered to a very large (n=1746) cohort of British children and adolescents. This large sample size meant that as well as conducting confirmatory factor analysis on the scale itself, we were also able to split the sample to conduct exploratory and confirmatory factor analysis of items from the mAMAS alongside items from child test anxiety and general anxiety rating scales. Factor analysis of the mAMAS confirmed that it has the same underlying factor structure as the original AMAS, with subscales measuring anxiety about Learning and Evaluation in math. Furthermore, both exploratory and confirmatory factor analysis of the mAMAS alongside scales measuring test anxiety and general anxiety showed that mAMAS items cluster onto one factor (perceived to represent MA). The mAMAS provides a valid and reliable scale for measuring MA in children and adolescents, from a younger age than is possible with the original AMAS. Results from this study also suggest that MA is truly a unique construct, separate from both test anxiety and general anxiety, even in childhood.


title: Stochastic WaveNet: A Generative Latent Variable Model for Sequential Data

abstract: How to model distribution of sequential data, including but not limited to speech and human motions, is an important ongoing research problem. It has been demonstrated that model capacity can be significantly enhanced by introducing stochastic latent variables in the hidden states of recurrent neural networks. Simultaneously, WaveNet, equipped with dilated convolutions, achieves astonishing empirical performance in natural speech generation task. In this paper, we combine the ideas from both stochastic latent variables and dilated convolutions, and propose a new architecture to model sequential data, termed as Stochastic WaveNet, where stochastic latent variables are injected into the WaveNet structure. We argue that Stochastic WaveNet enjoys powerful distribution modeling capacity and the advantage of parallel training from dilated convolutions. In order to efficiently infer the posterior distribution of the latent variables, a novel inference network structure is designed based on the characteristics of WaveNet architecture. State-of-the-art performances on benchmark datasets are obtained by Stochastic WaveNet on natural speech modeling and high quality human handwriting samples can be generated as well.


title: The Spatial Dimension of Online Echo Chambers

abstract: This study explores the geographic dependencies of echo-chamber communication on Twitter during the Brexit referendum campaign. We review the literature on filter bubbles, echo chambers, and polarization to test five hypotheses positing that echo-chamber communication is associated with homophily in the physical world, chiefly the geographic proximity between users advocating sides of the campaign. The results support the hypothesis that echo chambers in the Leave campaign are associated with geographic propinquity, whereas in the Remain campaign the reverse relationship was found. This study presents evidence that geographically proximate social enclaves interact with polarized political discussion where echo-chamber communication is observed. The article concludes with a discussion of these findings and the contribution to research on filter bubbles and echo chambers.


title: The ‘popular’ culture of internet activism:

abstract: How does the internet contribute to changes in civic engagement in the USA? To answer this question we must examine the institutional context of US marketizing civil society and the cultures of good citizenship constructed online. Drawing upon the findings from a case study of ONE, a campaign targeting extreme poverty and the spread of AIDS, I demonstrate how the internet may function as a space of new divisions of labor between civil society organizational actors and lay activists. While organizational actors use Web 2.0 to make activism convenient and standardized, the public is asked to participate in what I term ‘visual labor’, creating and representing images of community online that legitimize the organization’s claims. At the same time, volunteer action is understood largely as performative. Ultimately, the article confronts the understanding of the internet as a post-bureaucratic democracy and emphasizes its cultural role in communicative capitalism.


title: Semi-Supervised Classification for Extracting Protein Interaction Sentences using Dependency Parsing

abstract: We introduce a relation extraction method to identify the sentences in biomedical text that indicate an interaction among the protein names mentioned. Our approach is based on the analysis of the paths between two protein names in the dependency parse trees of the sentences. Given two dependency trees, we define two separate similarity functions (kernels) based on cosine similarity and edit distance among the paths between the protein names. Using these similarity functions, we investigate the performances of two classes of learning algorithms, Support Vector Machines and k-nearest-neighbor, and the semisupervised counterparts of these algorithms, transductive SVMs and harmonic functions, respectively. Significant improvement over the previous results in the literature is reported as well as a new benchmark dataset is introduced. Semi-supervised algorithms perform better than their supervised version by a wide margin especially when the amount of labeled data is limited.


title: A review of voltage and frequency control strategies for islanded microgrid

abstract: Nowadays, a large part of the world population lives without access to electricity, principally due to the location, too far from the main grid. Renewable power sources connected in an autonomous local grid are a real opportunity for these communities. These weak electrical grids composed by several microsources, energy storage systems and loads can be considered as islanded microgrids. As it is disconnected from the main grid, the frequency and the voltage of the microgrid have to be controlled. The frequency is generally controlled maintaining the power balance between generation and consumption, using energy storage or dump loads. The voltage regulation is approached in different ways, according with the controller design objectives and the nature of the loads connected to the grid. This paper proposes a review of several voltage and frequency control strategies in islanded microgrids.


title: Learning taxonomic relations from heterogeneous evidence

abstract: Abstract. We present a novel approach to learning taxonomic relations betweenterms by considering multiple and heterogeneous sources of evidence. In order toderive an optimal combination of these sources, we exploit a machine-learning ap-proach, representing all the sources of evidence as ﬁrst-order features and trainingstandard classiﬁers. We consider in particular different f eatures derived from Word-Net, an approach matching Hearst-style patterns in a corpusand on the Web as wellas further methods mentioned in the literature. In particular, we explore differentclassiﬁers as well as various strategies for dealing with un balanced datasets. Weevaluate our approach by comparing the results with a reference taxonomy for thetourism domain.Keywords. ontology learning, taxonomies, text and web mining, machine learning 1. IntroductionTaxonomies, thesauri or concept hierarchies are a crucial component of many applica-tions within the Semantic Web [2], Knowledge Management [11], Information Retrieval[1,26], Text Clustering [17], Natural Language Processing and Information Systems ingeneral. In fact, there has been a long tradition in Artiﬁcia l Intelligence and related ﬁeldssuch as Natural Language Processing or Information Retrieval to automatically learntaxonomies from data. As text documents are massively available, most researchers haveattempted to learn taxonomies on the basis of textual input, whereby mainly three differ-ent learning paradigms have been exploited. First, several researchers have attempted toﬁnd taxonomic relations expressed more or less explicitly i n texts by matching certainpatterns which we will refer to as Hearst-patterns in line with Hearst’s seminal work [15]and follow-up work in [1], [6], [18] and [22]. Some researchers have even went furtherand searched for these patterns on the Web [7,9,20]. Other researchers have used theinternal structure of noun phrases to ﬁnd taxonomic relatio ns [4,29]. Second, many re-searchers follow Harris’ distributional hypothesis basically claiming that words or termsare semantically similar to the extent to which they share similar syntactic contexts [14].The most prominent examples of this approach are probably [3], [5], [10], [12], [16],[21], and more recently also [8] and [24]. Third and ﬁnally, t here are also approachesrelying on a document-based notion of term subsumption suchas the one found in [26].


title: A rapid hierarchical rendering technique for translucent materials

abstract: This paper introduces an efficient two-pass rendering technique for translucent materials. We decouple the computation of irradianceat the surface from the evaluation of scattering inside the material.This is done by splitting the evaluation into two passes, where the first pass consists of computing the irradiance at selected points on the surface. The second pass uses a rapid hierarchical integration technique to evaluate a diffusion approximation based on the irradiance samples. This approach is substantially faster than previous methods for rendering translucent materials, and it has the advantage that it integrates seamlessly with both scanline rendering and global illumination methods. We show several images and animations from our implementation that demonstrate that the approach is both fast and robust, making it suitable for rendering translucent materials in production.


title: WAIS-IV visual puzzles in a mixed clinical sample.

abstract: Little is known about which cognitive functions underlie the new Visual Puzzles subtest of the Wechsler Adult Intelligence Scale – Fourth Edition (WAIS-IV). The purpose of this study was to investigate relationships between Visual Puzzles and common neuropsychological measures in a mixed clinical sample. A total of 44 veterans (75% men) were administered the WAIS-IV as part of a neuropsychological evaluation. Average age was 47.4 years (SD = 11.8), and average education was 13.8 years (SD = 2.3). Correlations were conducted to examine relationships between Visual Puzzles, demographic variables, and neuropsychological measures. Hierarchical regression analysis was used to determine which measures contributed the most variance to Visual Puzzles. Visual Puzzles correlated significantly with measures of visuospatial reasoning, verbal learning and recall, mental flexibility, processing speed, and naming, which accounted for 50% of the variance in Visual Puzzles performance. The results indicate that Visual Puz...


title: Non-parametric Jensen-Shannon divergence

abstract: Quantifying the difference between two distributions is a common problem in many machine learning and data mining tasks. What is also common in many tasks is that we only have empirical data. That is, we do not know the true distributions nor their form, and hence, before we can measure their divergence we first need to assume a distribution or perform estimation. For exploratory purposes this is unsatisfactory, as we want to explore the data, not our expectations. In this paper we study how to non-parametrically measure the divergence between two distributions. More in particular, we formalise the well-known Jensen-Shannon divergence using cumulative distribution functions. This allows us to calculate divergences directly and efficiently from data without the need for estimation. Moreover, empirical evaluation shows that our method performs very well in detecting differences between distributions, outperforming the state of the art in both statistical power and efficiency for a wide range of tasks.


title: DéjàVu: a map of code duplicates on GitHub

abstract: Previous studies have shown that there is a non-trivial amount of duplication in source code. This paper analyzes a corpus of 4.5 million non-fork projects hosted on GitHub representing over 428 million files written in Java, C++, Python, and JavaScript. We found that this corpus has a mere 85 million unique files. In other words, 70% of the code on GitHub consists of clones of previously created files. There is considerable variation between language ecosystems. JavaScript has the highest rate of file duplication, only 6% of the files are distinct. Java, on the other hand, has the least duplication, 60% of files are distinct. Lastly, a project-level analysis shows that between 9% and 31% of the projects contain at least 80% of files that can be found elsewhere. These rates of duplication have implications for systems built on open source software as well as for researchers interested in analyzing large code bases. As a concrete artifact of this study, we have created DejaVu, a publicly available map of code duplicates in GitHub repositories.


1 Branch Flow Model: Relaxations and


title: A Continuous Toroidal Winding SRM With 6- or 12-Switch DC Converter

abstract: A new toroidally wound switched reluctance machine (TSRM) with a single continuous multitapped winding is presented. This machine may be driven by a 6-switch asymmetric converter that supplies unipolar coil currents or by a 12-switch voltage source converter that supplies bipolar coil currents. The TSRM magnetic design and power electronic control is described, and its performance is compared with a conventional switched reluctance motor (CSRM) and with a previously reported toroidal format switched reluctance machine that utilizes six discrete winding coils in a three-phase wye connection (WSRM) driven by a voltage source inverter. A fixed 6–4 CSRM magnetic circuit design is selected for the comparative analysis of the three machine topologies by static and dynamic finite-element simulations.


title: Socially-Aware Networking: A Survey

abstract: The widespread proliferation of handheld devices enables mobile carriers to be connected at anytime and anywhere. Meanwhile, the mobility patterns of mobile devices strongly depend on the users' movements, which are closely related to their social relationships and behaviors. Consequently, today's mobile networks are becoming increasingly human centric. This leads to the emergence of a new field which we call socially-aware networking (SAN). One of the major features of SAN is that social awareness becomes indispensable information for the design of networking solutions. This emerging paradigm is applicable to various types of networks (e.g. opportunistic networks, mobile social networks, delay tolerant networks, ad hoc networks, etc) where the users have social relationships and interactions. By exploiting social properties of nodes, SAN can provide better networking support to innovative applications and services. In addition, it facilitates the convergence of human society and cyber physical systems. In this paper, for the first time, to the best of our knowledge, we present a survey of this emerging field. Basic concepts of SAN are introduced. We intend to generalize the widely-used social properties in this regard. The state-of-the-art research on SAN is reviewed with focus on three aspects: routing and forwarding, incentive mechanisms and data dissemination. Some important open issues with respect to mobile social sensing and learning, privacy, node selfishness and scalability are discussed.


title: Bootstrapped switch in low-voltage digital 90nm CMOS technology

abstract: The design of a bootstrapped switch in digital 90 nm CMOS technology, with 1.0 V supply voltage and 1 GHz sampling frequency for a 300 fF capacitive load is presented. Simulation results indicate that the switch has 10 bit linearity up to an input signal of 1.0 V peak-to-peak and frequency of 100MHz. The switch is intended for use in state-of-the-art data converters.


title: One Gate Diode-Connected Dual-Gate a-IGZO TFT Driven Pixel Circuit for Active Matrix Organic Light-Emitting Diode Displays

abstract: A dual-gate (DG) amorphous indium–gallium–zinc-oxide thin-film transistor (TFT)-driven pixel circuit for active-matrix organic light-emitting diode displays is presented. One gate of the DGs serves as a primary gate (PG) and the other as an auxiliary gate (AG). The threshold voltage ( $V_{\mathrm {{TH}}}$ ) of the DG TFT under the PG operation is modulated by the AG bias voltage. The $V_{\mathrm {{TH}}}$ variation ( $\Delta V_{\mathrm {{TH}}}$ ) is compensated with the AG and the drain diode-connected structure. The validity of the presented pixel circuit is experimentally verified. The measured current error rates are less than 3.2% at a $\Delta V_{\mathrm {{TH}}}$ of $\text {TFT} = \pm 0.5$ V and a $\Delta V_{\mathrm {{TH}}}$ of OLED = 0.5 V with the emission current ranging from 7 nA to $1.13~\mu \text{A}$ .


UDPipe: Trainable Pipeline for Processing CoNLL-U Files Performing Tokenization, Morphological Analysis, POS Tagging and Parsing.


title: Parental social support, coping strategies, resilience factors, stress, anxiety and depression levels in parents of children with MPS III (Sanfilippo syndrome) or children with intellectual disabilities (ID)

abstract: Mucopolysaccharidosis type III (MPS III, Sanfilippo syndrome) is a lysosomal storage disorder, caused by a deficiency in one of four enzymes involved in the catabolism of the glycosaminoglycan heparan sulphate. It is a degenerative disorder, with a progressive decline in children's intellectual and physical functioning. There is currently no cure for the disorder. To date there is a paucity of research on how this disorder impacts parents psychological functioning. Specifically, research in the area has failed to employ adequate control groups to assess if the impact of this disorder on parents psychological functioning differs from parenting a child with intellectual disability (ID). The current study examined child behaviour and parental psychological functioning in 23 parents of children with MPS III and 23 parents of children with ID. Parents completed postal questionnaires about their child's behaviour and abilities and their own psychological functioning. Parents of children with MPS III reported fewer behavioural difficulties as their child aged, more severe level of intellectual disability, and similar levels of perceived social support, coping techniques, stress, anxiety and depression levels as parents of children with ID. Both groups of parents scored above the clinical cut off for anxiety and depression. Parents of children with MPS III rated themselves as significantly less future-orientated and goal directed than parents of children with ID. Services should develop support packages for parents of children with MPS III that incorporate an understanding of the unique stressors and current-difficulty approach of this population. Future research should examine gender differences between parental psychological functioning, using mixed qualitative and quantitative approaches, and utilise matched developmental level and typically developing control groups.


title: Transient Liquid Phase Sintering Using Copper-Solder-Resin Composite for High-Temperature Power Modules

abstract: Transient liquid phase sintering (TLPS) using copper (Cu)-solder-resin composite was investigated for dieattach applications of high-temperature power modules. The copper particles were connected with Cu-Sn IMCs through transient liquid phase reaction at about 220 °C. The Cu-IMC skeleton structure was partially filled with polyimide resin. The joint strengths of die-attached specimens were tested. The die-attached specimens showed superior reliability through thermal cycling tests. These results indicate the potential of TLPS using Cu-solder-resin composite for power module dieattach applications.


title: A 3-layer architecture for smart environment models

abstract: Enriched with more and more intelligent devices modern homes rapidly transform into smart environments. Their growing capabilities enable the implementation of a new generation of ubiquitous applications, but also raise the complexity of the development. Developers of applications for smart environments must cope with a multitude of sensors, devices, users and thus contexts. We present a model-based approach for modeling of, reasoning about and controlling smart environments. A context model provides adaptive applications with a unified access to the smart home environment and, through a unique approach of utilizing executable models, also reflects its state at runtime. The presented approach supports runtime user interface adaption and reconfiguration for seamless interaction and has been successfully utilized to build several context-adaptive applications running in our smart home testbed.


title: Deep contextual language understanding in spoken dialogue systems.

abstract: We describe a unified multi-turn multi-task spoken language understanding (SLU) solution capable of handling multiple context sensitive classification (intent determination) and sequence labeling (slot filling) tasks simultaneously. The proposed architecture is based on recurrent convolutional neural networks (RCNN) with shared feature layers and globally normalized sequence modeling components. The temporal dependencies within and across different tasks are encoded succinctly as recurrent connections. The dialog system responses beyond SLU component are also exploited as effective external features. We show with extensive experiments on a number of datasets that the proposed joint learning framework generates state-of-the-art results for both classification and tagging, and the contextual modeling based on recurrent and external features significantly improves the context sensitivity of SLU models.


title: Metacognitive Control of the Spacing of Study Repetitions.

abstract: Rememberers play an active role in learning, not only by committing material more or less faithfully to memory, but also by selecting judicious study strategies (or not). In three experiments, subjects chose whether to mass or space the second presentation of to-be-learned paired-associate terms that were either normatively difficult or easy to remember, under the constraint that subjects needed to space exactly half of the items (and mass the other half). In contrast with recent findings that implemented no such constraint (Son, 2004), subjects chose to space more of the difficult pairs (in Experiments 1 and 2). Reduction in exposure time eliminated but did not reverse this effect (Experiment 3). Subjects who spaced more of the difficult pairs were more likely to exhibit superior memory performance, but, because subjects who made spacing selections that had no effect on the actual scheduling of items also showed this effect (Experiment 2), that enhancement in performance is more likely to reflect subject selection than strategy efficacy. Overall, these results suggest that choice constraints strongly elicit a discrepancy-reduction approach (Dunlosky & Hertzog, 1998) to strategic decision-making, but that reduced study time can eliminate this effect.


title: Genetic Algorithm Solution of the Knapsack Problem Used in Finding Full Issues in the Holy Quran Based on the Number (19)

abstract: The Holy Quran is the biggest Miracle of Muslims everywhere and at every time; therefore, it is valid for every time and place. Actually, researches and studies into the Holy Quran that aim to uncover new miracles within are considered as a kind of worship for Muslims researchers since it facilitates the Islamic mission and clarifies the vague picture of Islam throughout the world. From this perspective, the researchers have selected the vague miracle of the number 19 in the Holy Quran to examine through this study. In this study, the researchers describe a special algorithm that facilitates the retrieval process of the full-issues according to the conditions of number 19 miracle in the Holy Quran. This algorithm is derived from the genetic algorithm that is used to solve the knapsack problem, since it is so similar to the case of this study. However, in the results of this study many full-issues have appeared that insure the truth of the number 19 miracle in the Holy Quran and thus support the miracle of the whole Holy Quran.


title: Developing An Ontology Of Cyber-Operations In Networks Of Computers

abstract: Many detection techniques have been proposed until now that struggle to keep up with the inherent complexity of applications, networks and protocols, resulting also in the growing rate of attacks that exploit them. Security frameworks that are created using an ontological approach are the next-gen systems of defense that have some advantages over the conventional techniques because they can capture the context of information and are capable to filter these contents depending on some certain factors. This paper proposes a method of creating an ontology that can be used for improving detection capabilities of attacks at all application levels. The ontology serves as a data model and knowledge base of the cyberoperations domain that conceptualizes and stores various types of data needed in the process of detecting an aware situation, such as information about attacks (types), OSI stack levels to which are targeted (software, network, hardware), countermeasure methods, resources necessary, knowledge required etc. The quality of the proposed model was assessed using a methodology known as OntoClean, that is a comprehensive suite of metrics for ontology evaluation that can comprise up to 15 criteria, as will be discussed during this paper. The ontology was tested in attack detection using a prototype web application firewall. In the evaluation process we used the famous dataset Kyoto2006+ proposed by the University of Kyoto in this scope. The results yielded for attacks detection by our proposed system were compared to other existing security solutions, like ModSecurit and Snort. In the conclusion section are stated the future directions of this research towards constructing reliable systems for cyber-security.


title: GekkoFS - A Temporary Distributed File System for HPC Applications

abstract: We present GekkoFS, a temporary, highly-scalable burst buffer file system which has been specifically optimized for new access patterns of data-intensive High-Performance Computing (HPC) applications. The file system provides relaxed POSIX semantics, only offering features which are actually required by most (not all) applications. It is able to provide scalable I/O performance and reaches millions of metadata operations already for a small number of nodes, significantly outperforming the capabilities of general-purpose parallel file systems.


title: Hybrid Augmented Reality for Participatory Learning: The Hidden Efficacy of Multi-User Game-Based Simulation

abstract: The goal for this research is to articulate and test a new hybrid Augmented Reality (AR) environment for conceptual understanding. From the theoretical lens of embodied interaction, we have designed a multi-user participatory simulation called ARfrac t where visitors in a science museum can learn about complex scientific concepts on the refraction of light through full-body immersion using optical see-through AR glasses, projection-based AR, and gesture technology. In particular, we developed two different types of simulations for ARfract , namely a game-based simulation and a non-game simulation to explore how the order of different AR simulations influences the perceived usability, user behaviors, learning experiences, and learning outcomes. For the experiment, 10 dyads were randomly assigned to one of the two experimental conditions: 1) the game-to-non-game condition and 2) the non-game-to-game condition. The results indicate that the learners who experienced the game-based simulation before the non-game simulation performed better than did the other group with the reversed experience order. This paper also reports the usability, user behaviors, and learning experience issues regarding the affordances of hybrid AR technologies. The major contribution of this proof-of concept research is that it articulates our understanding of how particular configurations (i.e., order) of the emerging technologies (i.e., hybrid Augmented Reality systems) and its use can lead to different learning outcomes.


title: An 80-mVpp Linear-Input Range, 1.6- $\text{G}\Omega $ Input Impedance, Low-Power Chopper Amplifier for Closed-Loop Neural Recording That Is Tolerant to 650-mVpp Common-Mode Interference

abstract: Closed-loop neuromodulation is essential for the advance of neuroscience and for administering therapy in patients suffering from drug-resistant neurological conditions. Neural stimulation generates large differential and common-mode (CM) artifacts at the recording sites, which easily saturate traditional recording front ends. This paper presents a neural recording chopper amplifier capable of handling in-band 80-mVpp differential artifacts and 650-mVpp CM artifacts while preserving the accompanying small neural signals. New techniques have been proposed that introduce immunity to CM interference, increase the input impedance of the chopper amplifier to 1.6 $\text{G}\Omega $ , and increase the maximum realizable resistance of duty-cycled resistors (DCR) to 90 $\text{G}\Omega $ . These techniques enable our recording front-end to achieve a dynamic range of 74 dB (200 Hz–5 kHz) and 81 dB (1–200 Hz). Implemented in a 40-nm CMOS process, the prototype occupies an area of 0.069 mm2/channel, and consumes 2.8 $\mu \text{W}$ from a 1.2-V supply. The input-referred noise is 5.3 $\mu \text{V}_{\mathrm {\mathbf {rms}}}$ (200 Hz–5 kHz) and 1.8 $\mu \text{V}_{\mathrm {\mathbf {rms}}}$ (1–200Hz). The total harmonic distortion for a 40-mV $_{\mathrm {\mathbf {p}}}$ input at 1 kHz is −76 dB. This work improves the input impedance by 5.3 $\times $ for chopped front-ends, linear-input range by 2 $\times $ , maximum resistance of DCR by 32 $\times $ , and tolerance to CM interferers by 6.5 $\times $ , while maintaining comparable power and noise performance.


title: Synthesis for multi-objective stochastic games: an application to autonomous urban driving

abstract: We study strategy synthesis for stochastic two-player games with multiple objectives expressed as a conjunction of LTL and expected total reward goals. For stopping games, the strategies are constructed from the Pareto frontiers that we compute via value iteration. Since, in general, infinite memory is required for deterministic winning strategies in such games, our construction takes advantage of randomised memory updates in order to provide compact strategies. We implement our methods in PRISM-games, a model checker for stochastic multi-player games, and present a case study motivated by the DARPA Urban Challenge, illustrating how our methods can be used to synthesise strategies for high-level control of autonomous vehicles.


title: Monocular semantic SLAM in dynamic street scene based on multiple object tracking

abstract: Semantic information has been agreed to be a key complement for more accurate SLAM or navigation and higher-level behavior planning. In dynamic scene, it also has the potential to improve performance. In this paper, we combine monocular SLAM with multiple object tracking to obtain robust static semantic map in complicated environment. This system utilizes up-to-date CNN object detector to detect possible moving objects in current view. Without the features located in these objects, monocular SLAM get consecutive pose transformation between two adjacent frames, which help multiple object tracking. With these trackers, this system can filter out dynamic objects, retain static objects for further sematic data association and graph optimization. We evaluate our approaches on the popular KITTI dataset and high dynamic RobotCar dataset.


title: A performance comparison of dickson and fibonacci charge pumps

abstract: This paper presents an analysis of two types of integrated charge pumps, Dickson and Fibonacci. The two circuits are compared in slow-switching conditions and at equal area occupation. A formula is developed for optimizing the capacitor sizes and improving the performance of the Fibonacci charge pump. The performance is evaluated with focus on voltage gain and output resistance and including the effects of parasitic capacitances.


title: Are there two types of malnutrition in chronic renal failure? Evidence for relationships between malnutrition, inflammation and atherosclerosis (MIA syndrome)

abstract: Many methods have been used to assess the presence It is believed that malnutrition is common in patients with chronic renal failure (CRF). They have reduced of malnutrition in patients with CRF. A history of weight loss and symptoms such as anorexia, nausea body weight, depleted energy (fat tissue) stores, loss of somatic protein ( low muscle mass) and low levels of and vomiting may indicate impending or established malnutrition. Anthropometric measurements, such as serum albumin, transferrin, pre-albumin and other visceral proteins. Various studies show signs of malnumid-arm muscle circumference, skinfold thickness and hand-grip strength may all be useful tools for estimattrition in 23–76% of haemodialysis (HD) and 18–50% of peritoneal dialysis (PD) patients [1–4]. Such variing malnutrition. Hand-grip strength, in particular, has been shown to be an inexpensive, reliable and easily ations in the prevalence of malnutrition may be related to factors such as age, case mix, co-morbid conditions performed parameter of nutrition [4,8] that also predicts mortality in CRF patients (unpublished observaand quality of dialysis therapy. The aetiology of malnutrition in CRF is complex and may include many tion). Creatinine kinetics have also been advocated as a method to assess nutritional status. However, recent factors, e.g. poor food intake because of anorexia, nausea and vomiting due to uraemic toxicity, hormonal evidence suggests that it is unreliable in individual CRF patients [8,9]. More sophisticated methods used derangements, acidosis and increased resting energy expenditure. to evaluate nutritional status include bio-electrical impedance, dual-emission X-ray absorptiometry While malnutrition by definition is caused by poor nutritional intake, laboratory or anthropometric meas(DXA), nuclear magnetic resonance, computerized tomography, total body potassium and total body urements are generally used to define it clinically. Other factors can cause the same changes in body and plasma nitrogen, but mostly as research tools. Finally, several biochemical markers [e.g. serum albumin, pre-albumin, protein composition, especially inflammatory and infectious complications [5,6 ] and chronic heart failure insulin-like growth factor-1 (IGF-1) and transferrin] have been used to evaluate nutritional status. Of these (CHF). In addition, factors directly associated with the dialytic procedure, such as bio-incompatibility, biochemical markers, serum albumin so far has been the most common to assess malnutrition, and hyponutrient losses in the dialysate and, during PD, poor appetite due to abdominal discomfort and uptake of albuminaemia has sometimes, perhaps erroneously (see below), been used to diagnose malnutrition [10]. glucose may also contribute to what we define as malnourishment in CRF. These may exert their action either by direct nutrient loss or by triggering the Protein and energy requirements in chronic renal inflammatory response. However, since malnutrition failure also occurs in pre-dialysis patients [7], it is evident that dialysis-unrelated factors, e.g. infectious and The protein requirements in maintenance dialysis inflammatory complications as well as co-morbidity, patients are not well defined. It can be assumed that may also be important contributors to malnutrition the variation in protein requirements is much greater in CRF. among dialysis patients than in healthy subjects, due to additional causes of variation, such as endocrine Correspondence and offprint requests to: Peter Stenvinkel, MD, and biochemical abnormalities, anaemia, drugs, physDepartment of Renal Medicine, K56, Huddinge University Hospital, S-141 86 Huddinge, Sweden. ical inactivity and co-morbid conditions, e.g. cardiovas-


title: Online Promotion and Its Influence on Destination Awareness and Loyalty in the Tourism Industry

abstract: Internet is commonly accepted as an important aspect in successful tourism promotion as well as destination marketing in this era. The main aim of this study is to explore how online promotion and its influence on destination awareness and loyalty in the tourism industry. This study proposes a structural model of the relationships among online promotion (OP), destination awareness (DA), tourist satisfaction (TS) and destination loyalty (DL). Randomly-selected respondents from the population of international tourists departing from Vietnamese international airports were selected as the questionnaire samples in the study. Initially, the exploratory factor analysis (EFA) was performed to test the validity of constructs, and the confirmatory factor analysis (CFA), using AMOS, was used to test the significance of the proposed hypothesizes model. The results show that the relationships among OP, DA, TS and DL appear significant in this study. The result also indicates that online promotion could improve the destination loyalty. Finally, the academic contribution, implications of the findings for tourism marketers and limitation are also discussed in this study.


title: Mining Competitors from Large Unstructured Datasets

abstract: In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the competitiveness between two items? Who are the main competitors of a given item? What are the features of an item that most affect its competitiveness? Despite the impact and relevance of this problem to many domains, only a limited amount of work has been devoted toward an effective solution. In this paper, we present a formal definition of the competitiveness between two items, based on the market segments that they can both cover. Our evaluation of competitiveness utilizes customer reviews, an abundant source of information that is available in a wide range of domains. We present efficient methods for evaluating competitiveness in large review datasets and address the natural problem of finding the top-k competitors of a given item. Finally, we evaluate the quality of our results and the scalability of our approach using multiple datasets from different domains.


EthDrive: A Peer-to-Peer Data Storage with Provenance


High Resolution CCD Polarization Imaging Sensor


title: 10W ultra-broadband power amplifier

abstract: We report the design and performance of an ultra-broadband power amplifier. It achieves 10 Watts output power with 21dB ± 1.5dB gain from 20 MHz to 3000 MHz. At lower frequencies from 20 to 1000 MHz the output power is 15 Watts with 22% efficiency. To achieve this performance, we employ a new design concept to control the device impedance and the power combiner impedance to be naturally 50 Ohms, such that no impedance matching is needed. Also, we developed a broadband microwave balun as a push-pull power combiner, which doubles as an impedance transformer. We believe the combination of output power, bandwidth and efficiency is the best reported to date.


Trends in satellite communications and the role of optical free-space communications


title: Autoencoder-based feature learning for cyber security applications

abstract: This paper presents a novel feature learning model for cyber security tasks. We propose to use Auto-encoders (AEs), as a generative model, to learn latent representation of different feature sets. We show how well the AE is capable of automatically learning a reasonable notion of semantic similarity among input features. Specifically, the AE accepts a feature vector, obtained from cyber security phenomena, and extracts a code vector that captures the semantic similarity between the feature vectors. This similarity is embedded in an abstract latent representation. Because the AE is trained in an unsupervised fashion, the main part of this success comes from appropriate original feature set that is used in this paper. It can also provide more discriminative features in contrast to other feature engineering approaches. Furthermore, the scheme can reduce the dimensionality of the features thereby signicantly minimising the memory requirements. We selected two different cyber security tasks: networkbased anomaly intrusion detection and Malware classication. We have analysed the proposed scheme with various classifiers using publicly available datasets for network anomaly intrusion detection and malware classifications. Several appropriate evaluation metrics show improvement compared to prior results.


Listening in the Dips: Comparing Relevant Features for Speech Recognition in Humans and Machines.


title: Microvasculature segmentation of arterioles using deep CNN

abstract: Segmenting microvascular structures is an important requirement in understanding angioadaptation by which vascular networks remodel their morphological structures. Accurate segmentation for separating microvasculature structures is important in quantifying remodeling process. In this work, we utilize a deep convolutional neural network (CNN) framework for obtaining robust segmentations of microvasculature from epifluorescence microscopy imagery of mice dura mater. Due to the inhomogeneous staining of the microvasculature, different binding properties of vessels under fluorescence dye, uneven contrast and low texture content, traditional vessel segmentation approaches obtain sub-optimal accuracy. We consider a deep CNN for the purpose keeping small vessel segments and handle the challenges posed by epifluorescence microscopy imaging modality. Experimental results on ovariectomized — ovary removed (OVX) — mice dura mater epifluorescence microscopy images show that the proposed modified CNN framework obtains an highest accuracy of 99% and better than other vessel segmentation methods.


title: Analysis of Self-Heating Effects in Ultrathin-Body SOI MOSFETs by Device Simulation

abstract: This paper discusses self-heating (SHE) effects in silicon-on-insulator (SOI) CMOS technology and applies device simulation to analyze the impact of thermal effects on the operation of nanoscale SOI n-MOSFETs. A 2-D drift-diffusion electrothermal simulation, using an electron transport model calibrated against Monte Carlo simulations at various temperatures, is employed in the analysis. We report the effects of device-structure parameters, such as SOI layer thickness, buried-oxide (BOX) thickness, source/drain (S/D) extension length, and thickness of the elevated S/D region, on the SHE of nanoscale MOSFETs. The SHE effects become significant due to the adoption of thin silicon layers and to the low thermal conductivity of the BOX, leading to the rise of large temperature under nominal operation conditions for high-performance digital circuits. The ac performance of SOI MOSFETs is influenced as well, and in particular, a severe degradation of the cutoff frequency of very short MOSFETs is predicted by numerical electrothermal device simulations. Although the effects of SHE on device performance are found to be somewhat modest and might be mitigated through device design, they may result in a degradation of the long-term reliability.


title: When time is space: Evidence for a mental time line

abstract: Abstract Time and space are tightly linked in the physical word. Recently, several lines of evidence have suggested that the mental representation of time might be spatial in nature. For instance, time–space interactions have been described as a strong preference to associate the past with the left space and the future with the right space. Here we review the growing evidence of interactions between time and space processing, systematized according to the type of interaction being investigated. We present the empirical findings supporting the possibility that humans represent the subjective time flow on a spatially oriented “mental time line” that is accessed through spatial attention mechanisms. The heterogeneous time–space interactions are then compared with the number–space interactions described in the numerical cognition literature. An alternative hypothesis, which maintains a common system for magnitude processing, including time, space, and number, is also discussed. Finally, we extend the discussion to the more general issue of how the representation of these concepts might be grounded into the cortical circuits that support spatial attention and sensorimotor transformations.


title: Evidence in Management and Organizational Science: Assembling the Field's Full Weight of Scientific Knowledge through Syntheses

abstract: This chapter advocates the good scientific practice of systematic research syntheses in Management and Organizational Science (MOS). A research synthesis is the systematic accumulation, analysis and reflective interpretation of the full body of relevant empirical evidence related to a question. It is the critical first step in effective use of scientific evidence. Synthesis is not a conventional literature review. Literature reviews are often position papers, cherry-picking studies to advocate a point of view. Instead, syntheses systematically identify where research findings are clear (and where they aren't), a key first step to establishing the conclusions science supports. Syntheses are also important for identifying contested findings and productive lines for future research. Uses of MOS evidence, that is, the motives for undertaking a research synthesis include scientific discovery and explanation, improved management practice guidelines, and formulating public policy. We identify six criteria for establishing the evidentiary value of a body of primary studies in MOS. We then pinpoint the stumbling blocks currently keeping the field from making effective use of its ever-expanding base of empirical studies. Finally, this chapter outlines a) an approach to research synthesis suitable to the domain of MOS and b) supporting practices to make synthesis a collective MOS project.


title: Treatment delivery platform for conformal catheter-based ultrasound hyperthermia

abstract: A clinical treatment delivery platform has been developed for providing 3D controlled hyperthermia with catheter-based ::: ultrasound applicators in conjunction with high dose rate (HDR) brachytherapy. This integrated system consists of ::: hardware and software components required for thermal therapy delivery, treatment monitoring and control, and realtime ::: and post-treatment analysis; and interstitial and endocavity ultrasound heating applicators. Hardware includes a ::: 32-channel RF amplifier with independent power (0-25 W) and frequency (5-10 MHz) control for ultrasound power ::: delivery and a 48-channel thermometry system compatible with 0.4 mm OD multi-sensor thermocouple probes. ::: Software graphical user interfaces (GUI) are used to monitor and control both the amplifier and the thermometry system. ::: The amplifier GUI controls, monitors, and records individual channel frequency and power values in real-time; the ::: thermometry GUI monitors and records temperature and thermal dose values in real-time, as well as displaying and ::: allowing dynamic control for temperature and thermal dose target thresholds. The thermometry GUI also incorporates ::: registration of thermocouple positions relative to target anatomy and applicator transducers based on HDR planning ::: tools (CT/MRI/US overlays) for improved treatment control and documentation. The interstitial (2.4 mm) and ::: endocavity (6 mm) ultrasound hyperthermia applicators are composed of linear arrays of 1-4 tubular piezoceramic ::: transducers - sectored at 90°, 180°, 270°, and 360° for single or dual directional heating patterns - that are compatible ::: with plastic implant catheters. QA techniques specific to these catheter-based ultrasound applicators have been devised ::: and implemented, and include rotational beam plots and dynamic force balance efficiency measurements, which are ::: critical to establish applicator performance. A quality assurance test matrix has been devised and used to evaluate and ::: characterize all components of this system prior to clinical implementation.