title: Full bridge phase-shifted soft switching high-frequency inverter with boost PFC function for induction heating system

text: This paper is mainly concerned with a high frequency soft-switching PWM inverter suitable for consumer induction heating system. Proposed system is composed of soft switching chopper based voltage boost PFC input stage and phase shifted PWM controlled full bridge ZCZVS high frequency inverter stage. Its fundamentals of operating performances are illustrated and evaluated on the experimental results. Its effectiveness is substantially proved on the basis of the experimental results from a practical point of view.


title: Automatic Modulation Classification of Overlapped Sources Using Multi-Gene Genetic Programming With Structural Risk Minimization Principle

text: As the spectrum environment becomes increasingly crowded and complicated, primary users may be interfered by secondary users and other illegal users. Automatic modulation classification (AMC) of a single source cannot recognize the overlapped sources. Consequently, the AMC of overlapped sources attracts much attention. In this paper, we propose a genetic programming-based modulation classification method for overlapped sources (GPOS). The proposed GPOS consists of two stages, the training stage, and the classification stage. In the training stage, multi-gene genetic programming (MGP)-based feature engineering transforms sample estimates of cumulants into highly discriminative MGP-features iteratively, until optimal MGP-features (OMGP-features) are obtained, where the structural risk minimization principle (SRMP) is employed to evaluate the classification performance of MGP-features and train the classifier. Moreover, a self-adaptive genetic operation is designed to accelerate the feature engineering process. In the classification stage, the classification decision is made by the trained classifier using the OMGP-features. Through simulation results, we demonstrate that the proposed scheme outperforms other existing methods in terms of classification performance and robustness in case of varying power ratios and fading channel.


title: Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection

text: Recently, microblogging sites such as Twitter have garnered a great deal of attention as an advanced form of location-aware social network services, whereby individuals can easily and instantly share their most recent updates from any place. In this study, we aim to develop a geo-social event detection system by monitoring crowd behaviors indirectly via Twitter. In particular, we attempt to find out the occurrence of local events such as local festivals; a considerable number of Twitter users probably write many posts about these events. To detect such unusual geo-social events, we depend on geographical regularities deduced from the usual behavior patterns of crowds with geo-tagged microblogs. By comparing these regularities with the estimated ones, we decide whether there are any unusual events happening in the monitored geographical area. Finally, we describe the experimental results to evaluate the proposed unusuality detection method on the basis of geographical regularities obtained from a large number of geo-tagged tweets around Japan via Twitter.


title: Classic Hallucinogens and Mystical Experiences: Phenomenology and Neural Correlates.

text: This chapter begins with a brief review of descriptions and definitions of mystical-type experiences and the historical connection between classic hallucinogens and mystical experiences. The chapter then explores the empirical literature on experiences with classic hallucinogens in which claims about mystical or religious experiences have been made. A psychometrically validated questionnaire is described for the reliable measurement of mystical-type experiences occasioned by classic hallucinogens. Controlled laboratory studies show that under double-blind conditions that provide significant controls for expectancy bias, psilocybin can occasion complete mystical experiences in the majority of people studied. These effects are dose-dependent, specific to psilocybin compared to placebo or a psychoactive control substance, and have enduring impact on the moods, attitudes, and behaviors of participants as assessed by self-report of participants and ratings by community observers. Other studies suggest that enduring personal meaning in healthy volunteers and therapeutic outcomes in patients, including reduction and cessation of substance abuse behaviors and reduction of anxiety and depression in patients with a life-threatening cancer diagnosis, are related to the occurrence of mystical experiences during drug sessions. The final sections of the chapter draw parallels in human neuroscience research between the neural bases of experiences with classic hallucinogens and the neural bases of meditative practices for which claims of mystical-type experience are sometimes made. From these parallels, a functional neural model of mystical experience is proposed, based on changes in the default mode network of the brain that have been observed after the administration of classic hallucinogens and during meditation practices for which mystical-type claims have been made.


title: Sparsely sampled Fourier ptychography.

text: Fourier ptychography (FP) is an imaging technique that applies angular diversity functions for high-resolution complex image recovery. The FP recovery routine switches between two working domains: the spectral and spatial domains. In this paper, we investigate the spectral-spatial data redundancy requirement of the FP recovery process. We report a sparsely sampled FP scheme by exploring the sampling interplay between these two domains. We demonstrate the use of the reported scheme for bypassing the high-dynamic-range combination step in the original FP recovery routine. As such, it is able to shorten the acquisition time of the FP platform by ~50%. As a special case of the sparsely sample FP, we also discuss a sub-sampled scheme and demonstrate its application in solving the pixel aliasing problem plagued in the original FP algorithm. We validate the reported schemes with both simulations and experiments. This paper provides insights for the development of the FP approach.


title: Threats to Feminist Identity and Reactions to Gender Discrimination

text: The aim of this research was to examine conditions that modify feminists' support for women as targets of gender discrimination. In an experimental study we tested a hypothesis that threatened feminist identity will lead to greater differentiation between feminists and conservative women as victims of discrimination and, in turn, a decrease in support for non-feminist victims. The study was conducted among 96 young Polish female professionals and graduate students from Gender Studies programs in Warsaw who self-identified as feminists (Mage  = 22.23). Participants were presented with a case of workplace gender discrimination. Threat to feminist identity and worldview of the discrimination victim (feminist vs. conservative) were varied between research conditions. Results indicate that identity threat caused feminists to show conditional reactions to discrimination. Under identity threat, feminists perceived the situation as less discriminatory when the target held conservative views on gender relations than when the target was presented as feminist. This effect was not observed under conditions of no threat. Moreover, feminists showed an increase in compassion for the victim when she was portrayed as a feminist compared to when she was portrayed as conservative. Implications for the feminist movement are discussed.


title: Dynamic Filters in Graph Convolutional Networks

text: Convolutional neural networks (CNNs) have massively impacted visual recognition in 2D images, and are now ubiquitous in state-of-the-art approaches. While CNNs naturally extend to other domains, such as audio and video, where data is also organized in rectangular grids, they do not easily generalize to other types of data such as 3D shape meshes, social network graphs or molecular graphs. To handle such data, we propose a novel graph-convolutional network architecture that builds on a generic formulation that relaxes the 1-to-1 correspondence between filter weights and data elements around the center of the convolution. The main novelty of our architecture is that the shape of the filter is a function of the features in the previous network layer, which is learned as an integral part of the neural network. Experimental evaluations on digit recognition, semi-supervised document classification, and 3D shape correspondence yield state-of-the-art results, significantly improving over previous work for shape correspondence.


title: BINSEC/SE: A Dynamic Symbolic Execution Toolkit for Binary-Level Analysis

text: When it comes to software analysis, several approaches exist from heuristic techniques to formal methods, which are helpful at solving different kinds ofproblems. Unfortunately very few initiative seek to aggregate this techniques in the same platform. BINSEC intend to fulfill this lack of binary analysis platform by allowing to perform modular analysis. This work focusses on BINSEC/SE, the new dynamic symbolic execution engine (DSE) implemented in BINSEC. We will highlight the novelties of the engine, especially in terms of interactions between concrete and symbolic execution or optimization of formula generation. Finally, two reverse engineering applications are shown in order to emphasize the tool effectiveness.


title: Artificial intelligent firewall

text: Firewalls are now an integral part of network security. An intelligent firewall that prevents unauthorized access to a system has been developed. Artificial intelligence applications are uniquely suited for the ever-changing, ever-evolving world of network security. Typical firewalls are only as good as the information provided by the Network Administrator. A new type of attack creates vulnerabilities, which a static firewall does not have the ability to avoid without human direction. An AI-managed firewall service, however, can protect a computer network from known and future threats. We report in this paper on research in progress concerning the integration of different security techniques. A main purpose of the project is to integrate a smart detection engine into a firewall. The smart detection engine will aim at not only detecting anomalous network traffic as in classical IDSs, but also detecting unusual structures data packets that suggest the presence of virus data. We will report in this paper on the concept of an intelligent firewall that contains a smart detection engine for potentially malicious data packets.


title: Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking

text: Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. We present a benchmark for Multiple Object Tracking launched in the late 2014, with the goal of creating a framework for the standardized evaluation of multiple object tracking methods. This paper collects the two releases of the benchmark made so far, and provides an in-depth analysis of almost 50 state-of-the-art trackers that were tested on over 11000 frames. We show the current trends and weaknesses of multiple people tracking methods, and provide pointers of what researchers should be focusing on to push the field forward.


Personality Analysis Through Handwriting


title: Search Based Software Engineering

text: The use of evolutionary algorithms for solving multi-objective optimization problems has become increasingly popular, mainly within the last 15 years. From among the several research trends that have originated in recent years, one of the most promising is the use of hybrid approaches that allow to improve the performance of multi-objective evolutionary algorithms (MOEAs). In this talk, some of the most representative research on the use of hybrid approaches in evolutionary multi-objective optimization will be discussed. The topics discussed will include multi-objective memetic algorithms, hybridization of MOEAs with gradient-based methods and with direct search methods, as well as multi-objective hyperheuristics. Some applications of these approaches as well as some potential paths for future research in this area will also be briefly discussed. Towards Evolutionary Multitasking: A New Paradigm


title: Real-time 3D eyelids tracking from semantic edges

text: State-of-the-art real-time face tracking systems still lack the ability to realistically portray subtle details of various aspects of the face, particularly the region surrounding the eyes. To improve this situation, we propose a technique to reconstruct the 3D shape and motion of eyelids in real time. By combining these results with the full facial expression and gaze direction, our system generates complete face tracking sequences with more detailed eye regions than existing solutions in real-time. To achieve this goal, we propose a generative eyelid model which decomposes eyelid variation into two low-dimensional linear spaces which efficiently represent the shape and motion of eyelids. Then, we modify a holistically-nested DNN model to jointly perform semantic eyelid edge detection and identification on images. Next, we correspond vertices of the eyelid model to 2D image edges, and employ polynomial curve fitting and a search scheme to handle incorrect and partial edge detections. Finally, we use the correspondences in a 3D-to-2D edge fitting scheme to reconstruct eyelid shape and pose. By integrating our fast fitting method into a face tracking system, the estimated eyelid results are seamlessly fused with the face and eyeball results in real time. Experiments show that our technique applies to different human races, eyelid shapes, and eyelid motions, and is robust to changes in head pose, expression and gaze direction.


title: Recurrent Network Models for Human Dynamics

text: We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and prediction of human body pose in videos and motion capture. The ERD model is a recurrent neural network that incorporates nonlinear encoder and decoder networks before and after recurrent layers. We test instantiations of ERD architectures in the tasks of motion capture (mocap) generation, body pose labeling and body pose forecasting in videos. Our model handles mocap training data across multiple subjects and activity domains, and synthesizes novel motions while avoiding drifting for long periods of time. For human pose labeling, ERD outperforms a per frame body part detector by resolving left-right body part confusions. For video pose forecasting, ERD predicts body joint displacements across a temporal horizon of 400ms and outperforms a first order motion model based on optical flow. ERDs extend previous Long Short Term Memory (LSTM) models in the literature to jointly learn representations and their dynamics. Our experiments show such representation learning is crucial for both labeling and prediction in space-time. We find this is a distinguishing feature between the spatio-temporal visual domain in comparison to 1D text, speech or handwriting, where straightforward hard coded representations have shown excellent results when directly combined with recurrent units [31].


title: DeepPose: Human Pose Estimation via Deep Neural Networks

text: We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regres- sors which results in high precision pose estimates. The approach has the advantage of reasoning about pose in a holistic fashion and has a simple but yet powerful formula- tion which capitalizes on recent advances in Deep Learn- ing. We present a detailed empirical analysis with state-of- art or better performance on four academic benchmarks of diverse real-world images.


title: Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments

text: We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms. Besides increasing the size of the datasets in the current state-of-the-art by several orders of magnitude, we also aim to complement such datasets with a diverse set of motions and poses encountered as part of typical human activities (taking photos, talking on the phone, posing, greeting, eating, etc.), with additional synchronized image, human motion capture, and time of flight (depth) data, and with accurate 3D body scans of all the subject actors involved. We also provide controlled mixed reality evaluation scenarios where 3D human models are animated using motion capture and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide a set of large-scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future work in the research community. Our experiments show that our best large-scale model can leverage our full training set to obtain a 20% improvement in performance compared to a training set of the scale of the largest existing public dataset for this problem. Yet the potential for improvement by leveraging higher capacity, more complex models with our large dataset, is substantially vaster and should stimulate future research. The dataset together with code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, is available online at http://vision.imar.ro/human3.6m.


title: Sparse deep belief net model for visual area V2

text: Motivated in part by the hierarchical organization of the cortex, a number of algorithms have recently been proposed that try to learn hierarchical, or “deep,” structure from unlabeled data. While several authors have formally or informally compared their algorithms to computations performed in visual area V1 (and the cochlea), little attempt has been made thus far to evaluate these algorithms in terms of their fidelity for mimicking computations at deeper levels in the cortical hierarchy. This paper presents an unsupervised learning model that faithfully mimics certain properties of visual area V2. Specifically, we develop a sparse variant of the deep belief networks of Hinton et al. (2006). We learn two layers of nodes in the network, and demonstrate that the first layer, similar to prior work on sparse coding and ICA, results in localized, oriented, edge filters, similar to the Gabor functions known to model V1 cell receptive fields. Further, the second layer in our model encodes correlations of the first layer responses in the data. Specifically, it picks up both colinear (“contour”) features as well as corners and junctions. More interestingly, in a quantitative comparison, the encoding of these more complex “corner” features matches well with the results from the Ito & Komatsu’s study of biological V2 responses. This suggests that our sparse variant of deep belief networks holds promise for modeling more higher-order features.


title: Modeling Human Motion Using Binary Latent Variables

text: We propose a non-linear generative model for human motion data that uses an undirected model with binary latent variables and real-valued “visible” variables that represent joint angles. The latent and visible variables at each time step receive directed connections from the visible variables at the last few time-steps. Such an architecture makes on-line inference efficient and allows us to use a simple approximate learning procedure. After training, the model finds a single set of parameters that simultaneously capture several different kinds of motion. We demonstrate the power of our approach by synthesizing various motion sequences and by performing on-line filling in of data lost during motion capture. Website: http://www.cs.toronto.edu/ ∼gwtaylor/publications/nips2006mhmublv/


title: Efficient Large Message Broadcast using NCCL and CUDA-Aware MPI for Deep Learning

text: Emerging paradigms like High Performance Data Analytics (HPDA) and Deep Learning (DL) pose at least two new design challenges for existing MPI runtimes. First, these paradigms require an efficient support for communicating unusually large messages across processes. And second, the communication buffers used by HPDA applications and DL frameworks generally reside on a GPU's memory. In this context, we observe that conventional MPI runtimes have been optimized over decades to achieve lowest possible communication latency for relatively smaller message sizes (up-to 1 Megabyte) and that too for CPU memory buffers. With the advent of CUDA-Aware MPI runtimes, a lot of research has been conducted to improve performance of GPU buffer based communication. However, little exists in current state of the art that deals with very large message communication of GPU buffers. In this paper, we investigate these new challenges by analyzing the performance bottlenecks in existing CUDA-Aware MPI runtimes like MVAPICH2-GDR, and propose hierarchical collective designs to improve communication latency of the MPI_Bcast primitive by exploiting a new communication library called NCCL. To the best of our knowledge, this is the first work that addresses these new requirements where GPU buffers are used for communication with message sizes surpassing hundreds of megabytes. We highlight the design challenges for our work along with the details of design and implementation. In addition, we provide a comprehensive performance evaluation using a Micro-benchmark and a CUDA-Aware adaptation of Microsoft CNTK DL framework. We report up to 47% improvement in training time for CNTK using the proposed hierarchical MPI_Bcast design.


title: Bifurcation Analysis of a Mathematical Model for Malaria Transmission

text: We present an ordinary differential equation mathematical model for the spread of malaria in human and mosquito populations. Susceptible humans can be infected when they are bitten by an infectious mosquito. They then progress through the exposed, infectious, and recovered classes, before reentering the susceptible class. Susceptible mosquitoes can become infected when they bite infectious or recovered humans, and once infected they move through the exposed and infectious classes. Both species follow a logistic population model, with humans having immigration and disease-induced death. We define a reproductive number, R0, for the number of secondary cases that one infected individual will cause through the duration of the infectious period. We find that the disease-free equilibrium is locally asymptotically stable when R0 < 1 and unstable when R0 > 1. We prove the existence of at least one endemic equilibrium point for all R0 > 1. In the absence of disease-induced death, we prove that the transcritical bifurcation at R0 = 1 is supercritical (forward). Numerical simulations show that for larger values of the disease-induced death rate, a subcritical (backward) bifurcation is possible at R0 = 1.


title: The Distributed Dissimilar Redundancy Architecture of Fly-by-Wire Flight Control System

text: This article presents a distributed dissimilar redundancy architecture of fly-by-wire Flight Control System. It is based on many dissimilar flight control computer, redundancy structure which includes the cross arranged hydraulic and power supply, three types of data buses, lots of data correction monitors, a distributed control loop to avoid the correlated faults and achieve the rigid safety airworthiness requirements. A Fault Tree Analysis (FTA) is implemented to verify whether the system safety could meet system design targets.


title: Handwritten Digit Recognition: A Neural Network Demo

text: A handwritten digit recognition system was used in a demonstration project to visualize artificial neural networks, in particular Kohonen’s self-organizing feature map. The purpose of this project was to introduce neural networks through a relatively easy-to-understand application to the general public. This paper describes several techniques used for preprocessing the handwritten digits, as well as a number of ways in which neural networks were used for the recognition task. Whereas the main goal was a purely educational one, a moderate recognition rate of 98% was reached on a test set.


title: Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring

text: Marine environments are delicate ecosystems which directly influence local climates, flora, fauna, and human activities. Their monitorization plays a key role in their preservation, which is most commonly done through the use of environmental sensing buoy networks. These devices transmit data by means of satellite communications or close-range base stations, which present several limitations and elevated infrastructure costs. Unmanned Aerial Vehicles (UAV) are another alternative for remote environmental monitoring which provide new types of data and ease of use. These aircraft are mainly used in video capture related applications, in its various light spectrums, and do not provide the same data as sensing buoys, nor can they be used for such extended periods of time. The aim of this research is to provide a flexible, easy to deploy and cost-effective Wireless Sensor Network (WSN) for monitoring marine environments. This proposal uses a UAV as a mobile data collector, low-power long-range communications and sensing buoys as part of a single WSN. A complete description of the design, development, and implementation of the various parts of this system is presented, as well as its validation in a real-world scenario.


title: A Modularization Method for Battery Equalizers Using Multiwinding Transformers

text: This paper proposes a modularized global architecture using multi-winding transformers for battery cell balancing. The global balancing for a series-connected battery string is achieved based on forward conversion in each battery module and based on flyback conversion among modules. The demagnetization of the multiwinding transformers is also simultaneously achieved by the flyback conversion among modules without the need of additional demagnetizing circuits. Moreover, all MOSFET switches are driven by two complementary pulse width modulation signals without the requirement of cell voltage sensors, and energy can be automatically and simultaneously delivered from any high voltage cells to any low voltage cells. Compared with existing equalizers requiring additional balancing circuits for battery modules, the proposed modularized equalizer shares one circuit for the balancing among cells and modules. The balancing performance of the proposed equalizer is perfectly verified through experimental results, and the maximum balancing efficiency is up to 91.3%. In summary, the proposed modularized equalizer has the advantages of easier modularization, simpler control, higher efficiency, smaller size, and lower cost, ensuring the battery system higher reliability and easier implementation.


title: Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?

text: We present a direct extension of probabilistic diffusion tractography to the case of multiple fibre orientations. Using automatic relevance determination, we are able to perform online selection of the number of fibre orientations supported by the data at each voxel, simplifying the problem of tracking in a multi-orientation field. We then apply the identical probabilistic algorithm to tractography in the multi- and single-fibre cases in a number of example systems which have previously been tracked successfully or unsuccessfully with single-fibre tractography. We show that multi-fibre tractography offers significant advantages in sensitivity when tracking non-dominant fibre populations, but does not dramatically change tractography results for the dominant pathways.


title: CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision

text: Knowledge of 3D properties of objects is a necessity in order to build effective computer vision systems. However, lack of large scale 3D datasets can be a major constraint for datadriven approaches in learning such properties. We consider the task of single image 3D point cloud reconstruction, and aim to utilize multiple foreground masks as our supervisory data to alleviate the need for large scale 3D datasets. A novel differentiable projection module, called ‘CAPNet’, is introduced to obtain such 2D masks from a predicted 3D point cloud. The key idea is to model the projections as a continuous approximation of the points in the point cloud. To overcome the challenges of sparse projection maps, we propose a loss formulation termed ‘affinity loss’ to generate outlierfree reconstructions. We significantly outperform the existing projection based approaches on a large-scale synthetic dataset. We show the utility and generalizability of such a 2D supervised approach through experiments on a real-world dataset, where lack of 3D data can be a serious concern. To further enhance the reconstructions, we also propose a test stage optimization procedure to obtain reconstructions that display high correspondence with the observed input image.


title: A Learning Scheme for Microgrid Islanding and Reconnection

text: This paper introduces a robust learning scheme that can dynamically predict the stability of the reconnection of subnetworks to a main grid. As the future electrical power systems tend towards smarter and greener technology, the deploymen t of self sufficient networks, or microgrids, becomes more likel y. Microgrids may operate on their own or synchronized with the main grid, thus control methods need to take into account islandi ng and reconnecting said networks. The ability to optimally and safely reconnect a portion of the grid is not well understood and, as of now, limited to raw synchronization between interconnection points. A support vector machine (SVM) leveraging real-time data from phasor measurement units (PMUs) is proposed to predict in real time whether the reconnection of a sub-network to the main grid would lead to stability or instability. A dynam ics simulator fed with pre-acquired system parameters is used t o create training data for the SVM in various operating states. The classifier was tested on a variety of cases and operating poin ts to ensure diversity. Accuracies of approximately 90% were obs erved throughout most conditions when making dynamic predictions of a given network. Keywords—Synchrophasor, machine learning, microgrid, islanding, reconnection I. I NTRODUCTION As we make strides towards a smarter power system, it is important to explore new techniques and innovations to fully capture the potential of such a dynamic entity. Many large blackout events, such as the blackout of 2003, could have been prevented with smarter controls and better monito ring [1]. Phasor measurement units, or PMUs, are one such breakthrough that will allow progress to be made in both monitoring and implementing control to the system [2]. PMUs allow for direct measurement of bus voltages and angles at high sample rates which makes dynamic state estimation more feasible [3], [4]. With the use of PMUs, it is possible to improve upon current state estimation [5] and potentially o pen up new ways to control the grid. The addition of control techniques and dynamic monitoring will be important as we begin to integrate newer solutions, such as microgrids, int o the power network. With these advanced monitoring devices, microgrids become more feasible due to the potential for rea ltime monitoring schemes. The integration of microgrids bri ng many benefits such as the ability to operate while islanded as well as interconnected with the main grid; they provide a smooth integration for renewable energy sources that matc h C. Lassetter, E. Cotilla-Sanchez, and J. Kim are with the Sch ool of Electrical Engineering & Computer Science, Oregon State University, C orvallis, OR, 97331 USA e-mail:{lassettc,ecs,kimjinsu }@oregonstate.edu local demand. Unfortunately the implementation of microgr ids is still challenging due to lacking experience with the beha vior of control schemes during off-nominal operation. Currently, microgrids are being phased in slowly due in part to the difficulty of operating subnetworks independent ly as well as determining when they can be reconnected to the main grid. Works in the literature have focused on the potential of reconnecting microgrids to the main grid, in particular aiming at synchronizing the buses at points of interconnect with respects to their voltages, frequencies , and angles [6]. Effort has been directed at creating control sch emes to minimize power flow at the point of common coupling (PCC) using direct machine control, load shedding, as well a s energy storage, to aid in smooth reconnection [7], [8]. Upon reconnection of an islanded sub-network to the main grid, instability can cause damage on both ends. It is important to track instabilities on both the microgrid and main grid upon reconnection to accurately depict the outcome of reconnect ion. Most works focus on the synchronization of two networks before reconnection [9], [10]. In some cases we may need to look at larger microgrids or subnetworks in which multipl e PCCs exist. In such scenarios, it becomes much more difficult to implement a control scheme that satisfies goodreconnection tolerances in regards to minimizing bus frequency, angle, a nd voltage differences at each PCC. In addition to the possibil ity of multiple PCCs, it is possible that direct manipulation of the system becomes limited, compromised, or unsupported with respect to synchronization. In order to address these short comings, we implement an algorithm that dynamically tracks and makes predictions based on the system states, providing real-time stability information of potential reconnectio ns. An algorithm that tracks potential reconnection and island ing times needs to be robust to prevent incorrect operation during these scenarios. PMUs make use of GPS synchronization [11] which can create an attack platform for adversarie s by changing or shifting the time synchronization. Incorrec t or compromised usage could lead to incorrect predictions that would degrade system stability due to hidden failures that remain dormant until triggered by contingencies [12]. We propose an algorithm that can make accurate predictions in face of potentially compromised measurements. Due to the complexity of the power grid, it is difficult to come up with a verbatim standard depicting the potential sta bility after reconnection of a subnetwork. With advances in the artificial intelligence community, we can make use of machin e learning algorithms in order to explore vast combinations o f sensor inputs, states, and control actions. This can be done in a


title: Notification and awareness: synchronizing task-oriented collaborative activity

text: People working collaboratively must establish and maintain awareness of one another’s intentions, actions and results. Notification systems typically support awareness of the presence, tasks and actions of collaborators, but they do not adequately support awareness of persistent and complex activities. We analysed awareness breakdowns in use of our Virtual School system—stemming from problems related to the collaborative situation, group, task and tool support—to motivate the concept of activity awareness. Activity awareness builds on prior conceptions of social and action awareness, but emphasizes the importance of activity context factors like planning and coordination. This work suggests design strategies for notification systems to better support collaborative activity. r 2003 Elsevier Science Ltd. All rights reserved.


title: Design Challenges/Solutions for Environments Supporting the Analysis of Social Media Data in Crisis Informatics Research

text: Crisis informatics investigates how society's pervasive access to technology is transforming how it responds to mass emergency events. To study this transformation, researchers require access to large sets of data that because of their volume and heterogeneous nature are difficult to collect and analyze. To address this concern, we have designed and implemented an environment - EPIC Analyze - that supports researchers with the collection and analysis of social media data. Our research has identified the types of components - such as NoSQL, MapReduce, caching, and search - needed to ensure that these services are reliable, scalable, extensible, and efficient. We describe the design challenges encountered - such as data modeling, time vs. Space tradeoffs, and the need for a useful and usable system - when building EPIC Analyze and discuss its scalability, performance, and functionality.


title: Anomaly Detection and Attribution Using Bayesian Networks

text: We present a novel approach to anomaly detection in Bayesian networks, enabling both the detection and explanation of anomalous cases in a dataset. By exploiting the structure of a Bayesian network, our algorithm is able to efficiently search for local maxima of data conflict between closely related variables. Benchmark tests using data simulated from complex Bayesian networks show that our approach provides a significant improvement over techniques that search for anomalies using the entire network, rather than its subsets. We conclude with demonstrations of the unique explanatory power of our approach in determining the observation(s) responsible for an anomaly. APPROVED FOR PUBLIC RELEASE


title: Automatic Detection of Diabetic Retinopathy using Deep Convolutional Neural Network

text: The purpose of this project is to design an automated and efficient solution that could detect the symptoms of DR from a retinal image within seconds and simplify the process of reviewing and examination of images. Diabetic Retinopathy (DR) is a complication of diabetes that is caused by changes in the blood vessel of the retina and it is one of the leading causes of blindness in the developed world. Currently, detecting DR symptoms is a manual and time-consuming process. Recently, fullyconnected and convolutional neural networks have been trained to achieve state-of-the-art performance on a wide variety of tasks such as speech recognition, image classification, natural language processing, and bioinformatics. In our approach, we trained a deep Convolutional Neural Network model on a large dataset consisting around 35,000 images and used dropout layer techniques to achieve higher accuracy.


title: STDP-based spiking deep convolutional neural networks for object recognition

text: Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated - using rate-based neural networks trained with back-propagation - that having many layers increases the recognition robustness, an approach known as deep learning. We thus designed a deep SNN, comprising several convolutional (trainable with STDP) and pooling layers. We used a temporal coding scheme where the most strongly activated neurons fire first, and less activated neurons fire later or not at all. The network was exposed to natural images. Thanks to STDP, neurons progressively learned features corresponding to prototypical patterns that were both salient and frequent. Only a few tens of examples per category were required and no label was needed. After learning, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first layer to object prototypes in the last layer. Coding was very sparse, with only a few thousands spikes per image, and in some cases the object category could be reasonably well inferred from the activity of a single higher-order neuron. More generally, the activity of a few hundreds of such neurons contained robust category information, as demonstrated using a classifier on Caltech 101, ETH-80, and MNIST databases. We also demonstrate the superiority of STDP over other unsupervised techniques such as random crops (HMAX) or auto-encoders. Taken together, our results suggest that the combination of STDP with latency coding may be a key to understanding the way that the primate visual system learns, its remarkable processing speed and its low energy consumption. These mechanisms are also interesting for artificial vision systems, particularly for hardware solutions.


title: Weld : A Common Runtime for High Performance Data Analytics

text: Modern analytics applications combine multiple functions from different libraries and frameworks to build increasingly complex workflows. Even though each function may achieve high performance in isolation, the performance of the combined workflow is often an order of magnitude below hardware limits due to extensive data movement across the functions. To address this problem, we propose Weld, a runtime for data-intensive applications that optimizes across disjoint libraries and functions. Weld uses a common intermediate representation to capture the structure of diverse dataparallel workloads, including SQL, machine learning and graph analytics. It then performs key data movement optimizations and generates efficient parallel code for the whole workflow. Weld can be integrated incrementally into existing frameworks like TensorFlow, Apache Spark, NumPy and Pandas without changing their user-facing APIs. We show that Weld can speed up these frameworks, as well as applications that combine them, by up to 30×.


title: Passivity-Based Controller Design of Grid-Connected VSCs for Prevention of Electrical Resonance Instability

text: The time delay in the current control loop of a grid-connected voltage-source converter (VSC) may cause destabilization of electrical resonances in the grid or in the VSC's input filter. Instability is prevented if the input admittance of the VSC can be made passive. This paper presents an analytical controller design method for obtaining passivity. The method is equally applicable to single- and three-phase systems, i.e., in the latter case, for both stationary- and synchronous-frame control. Simulations and experiments verify the theoretical results.


title: Automatic Recognition of Deceptive Facial Expressions of Emotion

text: Humans modify facial expressions in order to mislead observers regarding their true emotional states. Being able to recognize the authenticity of emotional displays is notoriously difficult for human observers. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of genuine and deceptive facial expressions of emotions for automatic recognition1 . We show that overall the problem of recognizing deceptive facial expressions can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt feature space. Interesting additional results show that on average it is easier to distinguish among genuine expressions than deceptive ones and that certain emotion pairs are more difficult to distinguish than others.


title: Coalition Structure Generation: Dynamic Programming Meets Anytime Optimization

text: Coalition structure generation involves partitioning a set of agents into exhaustive and disjoint coalitions so as to maximize the social welfare. What makes this such a challenging problem is that the number of possible solutions grows exponentially as the number of agents increases. To date, two main approaches have been developed to solve this problem, each with its own strengths and weaknesses. The state of the art in the first approach is the Improved Dynamic Programming (IDP) algorithm, due to Rahwan and Jennings, that is guaranteed to find an optimal solution in O(3), but which cannot generate a solution until it has completed its entire execution. The state of the art in the second approach is an anytime algorithm called IP, due to Rahwan et al., that provides worst-case guarantees on the quality of the best solution found so far, but which is O(n). In this paper, we develop a novel algorithm that combines both IDP and IP, resulting in a hybrid performance that exploits the strength of both algorithms and, at the same, avoids their main weaknesses. Our approach is also significantly faster (e.g. given 25 agents, it takes only 28% of the time required by IP, and 0.3% of the time required by IDP).


title: Privacy-Preserving User-Auditable Pseudonym Systems

text: Personal information is often gathered and processed in a decentralized fashion. Examples include health records and governmental data bases. To protect the privacy of individuals, no unique user identifier should be used across the different databases. At the same time, the utility of the distributed information needs to be preserved which requires that it be nevertheless possible to link different records if they relate to the same user. Recently, Camenisch and Lehmann (CCS 15) have proposed a pseudonym scheme that addresses this problem by domain-specific pseudonyms. Although being unlinkable, these pseudonyms can be converted by a central authority (the converter). To protect the users' privacy, conversions are done blindly without the converter learning the pseudonyms or the identity of the user. Unfortunately, their scheme sacrifices a crucial privacy feature: transparency. Users are no longer able to inquire with the converter and audit the flow of their personal data. Indeed, such auditability appears to be diametral to the goal of blind pseudonym conversion. In this paper we address these seemingly conflicting requirements and provide a system where user-centric audits logs are created by the oblivious converter while maintaining all privacy properties. We prove our protocol to be UC-secure and give an efficient instantiation using novel building blocks.


title: Boosting slow oscillations during sleep potentiates memory

text: There is compelling evidence that sleep contributes to the long-term consolidation of new memories. This function of sleep has been linked to slow (<1 Hz) potential oscillations, which predominantly arise from the prefrontal neocortex and characterize slow wave sleep. However, oscillations in brain potentials are commonly considered to be mere epiphenomena that reflect synchronized activity arising from neuronal networks, which links the membrane and synaptic processes of these neurons in time. Whether brain potentials and their extracellular equivalent have any physiological meaning per se is unclear, but can easily be investigated by inducing the extracellular oscillating potential fields of interest. Here we show that inducing slow oscillation-like potential fields by transcranial application of oscillating potentials (0.75 Hz) during early nocturnal non-rapid-eye-movement sleep, that is, a period of emerging slow wave sleep, enhances the retention of hippocampus-dependent declarative memories in healthy humans. The slowly oscillating potential stimulation induced an immediate increase in slow wave sleep, endogenous cortical slow oscillations and slow spindle activity in the frontal cortex. Brain stimulation with oscillations at 5 Hz—another frequency band that normally predominates during rapid-eye-movement sleep—decreased slow oscillations and left declarative memory unchanged. Our findings indicate that endogenous slow potential oscillations have a causal role in the sleep-associated consolidation of memory, and that this role is enhanced by field effects in cortical extracellular space.


title: Gestational diabetes mellitus.

text: Gestational diabetes mellitus (GDM) is defined as glucose intolerance of various degrees that is first detected during pregnancy. GDM is detected through the screening of pregnant women for clinical risk factors and, among at-risk women, testing for abnormal glucose tolerance that is usually, but not invariably, mild and asymptomatic. GDM appears to result from the same broad spectrum of physiological and genetic abnormalities that characterize diabetes outside of pregnancy. Indeed, women with GDM are at high risk for having or developing diabetes when they are not pregnant. Thus, GDM provides a unique opportunity to study the early pathogenesis of diabetes and to develop interventions to prevent the disease.


title: From the Shortest Vector Problem to the Dihedral Hidden Subgroup Problem

text: In Quantum Computation and Lattice Problems [11] Oded Regev presented the first known connection between lattices and quantum computation, in the form of a quantum reduction from the poly(n)-unique shortest vector problem to the dihedral hidden subgroup problem by sampling cosets. This article contains a summary of Regev’s result.


title: Ontologies in E-Learning: Review of the Literature

text: We have witnessed a great interest in ontologies as an emerging technology within the Semantic Web. Its foundation that was laid in the last decade paved the way for the development of ontologies and systems that use ontologies in various domains, including the E-Learning domain. In this article, we survey key contributions related to the development and use of ontologies in the domain of E-Learning systems. We provide a framework for classification of the literature that is useful for the community of practice in categorizing work in this field and for determining possible research lines. We also discuss future trends in this area.


title: Vision-Based Gesture Recognition: A Review

text: The use of gesture as a natural interface serves as a motivating force for research in modeling, analyzing and recognition of gestures. In particular, human computer intelligent interaction needs vision-based gesture recognition, which involves many interdisciplinary studies. A survey on recent vision-based gesture recognition approaches is given in this paper. We shall review methods of static hand posture and temporal gesture recognition. Several application systems of gesture recognition are also described in this paper. We conclude with some thoughts about future research directions.


title: Methods for Designing Multiple Classifier Systems

text: In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this paper, the problem of design of multiple classifier system is discussed. Six design methods based on the so-called “overproduce and choose” paradigm are described and compared by experiments. Although these design methods exhibited some interesting features, they do not guarantee to design the optimal multiple classifier system for the classification task at hand. Accordingly, the main conclusion of this paper is that the problem of the optimal MCS design still remains open.


title: Bagging predictors

text: Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.


title: A Real-Time Continuous Gesture Recognition System for Sign Language

text: In this paper, a large vocabulary sign language interpreter is presented with real-time continuous gesture recognition of sign language using a DataGlove . The most critical problem, end-point detection in a stream of gesture input is first solved and then statistical analysis is done according to 4 parameters in a gesture : posture, position, orientation, and motion. We have implemented a prototype system with a lexicon of 250 vocabularies in Taiwanese Sign Language (TWL). This system uses hidden Markov models (HMMs) for 51 fundamental postures, 6 orientations, and 8 motion primitives. In a signerdependent way, a sentence of gestures based on these vocabularies can be continuously recognized in real-time and the average recognition rate is 80.4%.


title: Methods of combining multiple classifiers and their applications to handwriting recognition

text: Method of combining the classification powers of several classifiers is regarded as a general problem in various application areas of pattern recognition, and a systematic investigation has been made. Possible solutions to the problem can be divided into three categories according to the levels of information available from the various classifiers. Four approaches are proposed based on different methodologies for solving this problem. One is suitable for combining individual classifiers such as Bayesian, k-NN and various distance classifiers. The other three could be used for combining any kind of individual classifiers. On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers could be improved significantly. For example, on the U.S. zipcode database, the result of 98.9% recognition with 0.90% substitution and 0.2% rejection can be obtained, as well as a high reliability with 95% recognition, 0% substitution and 5% rejection. These results compared favorably to other research p u p s in Europe, Asia, and North America.


title: A single-stage three-phase high power factor rectifier with high-frequency isolation and regulated DC-bus based on the DCM SEPIC converter

text: In this paper, the main concepts related to the development of a single-stage three-phase high power factor rectifier, with high-frequency isolation and regulated DC bus are described. The structure operation is presented, being based on the DC-DC SEPIC converter operating in the discontinuous conduction mode. This operational mode provides to the rectifier a high power factor feature, with sinusoidal input current, without the use of any current sensors and current loop control. A design example and simulation results are presented in order to validate the theoretical analysis.


SYSTEM DYNAMICS : SYSTEMIC FEEDBACK MODELING FOR POLICY ANALYSIS


title: Practical Matrix Completion and Corruption Recovery Using Proximal Alternating Robust Subspace Minimization

text: Low-rank matrix completion is a problem of immense practical importance. Recent works on the subject often use nuclear norm as a convex surrogate of the rank function. Despite its solid theoretical foundation, the convex version of the problem often fails to work satisfactorily in real-life applications. Real data often suffer from very few observations, with support not meeting the randomness requirements, ubiquitous presence of noise and potentially gross corruptions, sometimes with these simultaneously occurring. This paper proposes a Proximal Alternating Robust Subspace Minimization method to tackle the three problems. The proximal alternating scheme explicitly exploits the rank constraint on the completed matrix and uses the $$\ell _0$$ ℓ 0 pseudo-norm directly in the corruption recovery step. We show that the proposed method for the non-convex and non-smooth model converges to a stationary point. Although it is not guaranteed to find the global optimal solution, in practice we find that our algorithm can typically arrive at a good local minimizer when it is supplied with a reasonably good starting point based on convex optimization. Extensive experiments with challenging synthetic and real data demonstrate that our algorithm succeeds in a much larger range of practical problems where convex optimization fails, and it also outperforms various state-of-the-art algorithms.


title: Self-calibration and visual SLAM with a multi-camera system on a micro aerial vehicle

text: The use of a multi-camera system enables a robot to obtain a surround view, and thus, maximize its perceptual awareness of its environment. An accurate calibration is a necessary prerequisite if vision-based simultaneous localization and mapping (vSLAM) is expected to provide reliable pose estimates for a micro aerial vehicle (MAV) with a multi-camera system. On our MAV, we set up each camera pair in a stereo configuration. We propose a novel vSLAM-based self-calibration method for a multi-sensor system that includes multiple calibrated stereo cameras and an inertial measurement unit (IMU). Our selfcalibration estimates the transform with metric scale between each camera and the IMU. Once the MAV is calibrated, the MAV is able to estimate its global pose via a multi-camera vSLAM implementation based on the generalized camera model. We propose a novel minimal and linear 3-point algorithm that uses inertial information to recover the relative motion of the MAV with metric scale. Our constant-time vSLAM implementation with loop closures runs on-board the MAV in real-time. To the best of our knowledge, no published work has demonstrated realtime on-board vSLAM with loop closures. We show experimental results in both indoor and outdoor environments. The code for both the self-calibration and vSLAM is available as a set of ROS packages at https://github.com/hengli/vmav-ros-pkg.


title: CAB: Connectionist Analogy Builder

text: The ability to make informative comparisons is central to human cognition. Comparison involves aligning two representations and placing their elements into correspondence. Detecting correspondences is a necessary component of analogical inference, recognition, categorization, schema formation, and similarity judgment. Connectionist Analogy Builder (CAB) determines correspondences through a simple iterative computation that matches elements in one representation with elements playing compatible roles in the other representation while simultaneously enforcing structural constraints. CAB shows promise as a process model of comparison as its performance can be related to human performance (e.g., solution trajectory, error patterns, time-on-task). Furthermore, CAB’s bounded working memory allows it to account for the inherent capacity limitations of human processing. CAB’s strengths are its parsimony, transparency of operations, and ability to generate performance predictions. In this paper, CAB is evaluated against benchmark phenomena from the analogy literature. © 2003 Cognitive Science Society, Inc. All rights reserved.


title: Co-fusion: Real-time segmentation, tracking and fusion of multiple objects

text: In this paper we introduce Co-Fusion, a dense SLAM system that takes a live stream of RGB-D images as input and segments the scene into different objects (using either motion or semantic cues) while simultaneously tracking and reconstructing their 3D shape in real time. We use a multiple model fitting approach where each object can move independently from the background and still be effectively tracked and its shape fused over time using only the information from pixels associated with that object label. Previous attempts to deal with dynamic scenes have typically considered moving regions as outliers, and consequently do not model their shape or track their motion over time. In contrast, we enable the robot to maintain 3D models for each of the segmented objects and to improve them over time through fusion. As a result, our system can enable a robot to maintain a scene description at the object level which has the potential to allow interactions with its working environment; even in the case of dynamic scenes.


title: Reverse Curriculum Generation for Reinforcement Learning

text: Many relevant tasks require an agent to reach a certain state, or to manipulate objects into a desired configuration. For example, we might want a robot to align and assemble a gear onto an axle or insert and turn a key in a lock. These tasks present considerable difficulties for reinforcement learning approaches, since the natural reward function for such goal-oriented tasks is sparse and prohibitive amounts of exploration are required to reach the goal and receive a learning signal. Past approaches tackle these problems by manually designing a task-specific reward shaping function to help guide the learning. Instead, we propose a method to learn these tasks without requiring any prior task knowledge other than obtaining a single state in which the task is achieved. The robot is trained in “reverse”, gradually learning to reach the goal from a set of starting positions increasingly far from the goal. Our method automatically generates a curriculum of starting positions that adapts to the agent’s performance, leading to efficient training on such tasks. We demonstrate our approach on difficult simulated fine-grained manipulation problems, not solvable by state-of-the-art reinforcement learning methods.


title: Error Mechanisms of the Oscillometric Fixed-Ratio Blood Pressure Measurement Method

text: The oscillometric fixed-ratio method is widely employed for non-invasive measurement of systolic and diastolic pressures (SP and DP) but is heuristic and prone to error. We investigated the accuracy of this method using an established mathematical model of oscillometry. First, to determine which factors materially affect the errors of the method, we applied a thorough parametric sensitivity analysis to the model. Then, to assess the impact of the significant parameters, we examined the errors over a physiologically relevant range of those parameters. The main findings of this model-based error analysis of the fixed-ratio method are that: (1) SP and DP errors drastically increase as the brachial artery stiffens over the zero trans-mural pressure regime; (2) SP and DP become overestimated and underestimated, respectively, as pulse pressure (PP) declines; (3) the impact of PP on SP and DP errors is more obvious as the brachial artery stiffens over the zero trans-mural pressure regime; and (4) SP and DP errors can be as large as 58 mmHg. Our final and main contribution is a comprehensive explanation of the mechanisms for these errors. This study may have important implications when using the fixed-ratio method, particularly in subjects with arterial disease.


title: 2 Conditional Saliency 2 . 1 Lossy Coding

text: By the guidance of attention, human visual system is able to locate objects of interest in complex scene. In this paper, we propose a novel visual saliency detection method the conditional saliency for both image and video. Inspired by biological vision, the definition of visual saliency follows a strictly local approach. Given the surrounding area, the saliency is defined as the minimum uncertainty of the local region, namely the minimum conditional entropy, when the perceptional distortion is considered. To simplify the problem, we approximate the conditional entropy by the lossy coding length of multivariate Gaussian data. The final saliency map is accumulated by pixels and further segmented to detect the proto-objects. Experiments are conducted on both image and video. And the results indicate a robust and reliable feature invariance saliency.


title: New reverse-conducting IGBT (1200V) with revolutionary compact package

text: Fuji Electric developed a 1200V class RC-IGBT based on our latest thin wafer process. The performance of this RC-IGBT shows the same relationship between conduction loss and switching loss as our 6th generation conventional IGBT and FWD. In addition its trade-off can be optimized for hard switching by lifetime killer. Calculations of the hard switching inverter loss and chip junction temperature (Tj) show that the optimized RC-IGBT can handle 35% larger current density per chip area. In order to utilize the high performance characteristics of the RC-IGBT, we assembled them in our newly developed compact package. This module can handle 58% higher current than conventional 100A modules at a 51% smaller footprint.


title: Saliency Unified: A Deep Architecture for simultaneous Eye Fixation Prediction and Salient Object Segmentation

text: Human eye fixations often correlate with locations of salient objects in the scene. However, only a handful of approaches have attempted to simultaneously address the related aspects of eye fixations and object saliency. In this work, we propose a deep convolutional neural network (CNN) capable of predicting eye fixations and segmenting salient objects in a unified framework. We design the initial network layers, shared between both the tasks, such that they capture the object level semantics and the global contextual aspects of saliency, while the deeper layers of the network address task specific aspects. In addition, our network captures saliency at multiple scales via inception-style convolution blocks. Our network shows a significant improvement over the current state-of-the-art for both eye fixation prediction and salient object segmentation across a number of challenging datasets.


title: Center-Fed Patch Antenna Array Excited by an Inset Dielectric Waveguide for 60-GHz Applications

text: A patch antenna array proximity-coupled fed by the inset dielectric waveguide (IDW) is proposed in 60-GHz frequency band. The antenna array consists of four series-fed subarrays each having eight pairs of circular patch elements, four substrate integrated IDWs, and a compact four-way power divider transmitting the signal from the grounded coplanar waveguide (GCPW) to the IDWs. The IDW is realized by metallized via-hole arrays in a printed circuit board (PCB). The parallel center-fed configuration is applied to suppress the frequency-dependent beam squinting of the series-fed subarray. Measured results show that the proposed antenna exhibits a gain variation between 15.8 and 19.2 dBi, fixed main beam in the direction of θ = 0°, and the reflection coefficient |S11| ≤ -10 dB over the frequency band from 58 to 64 GHz.


title: A systematic review and meta-analysis of dressings used for wound healing: the efficiency of honey compared to silver on burns.

text:
BACKGROUND
Honey has the antibacterial effect of silver without the toxic effect of silver on the skin. Even so, silver is the dominant antibacterial dressing used in wound healing.

OBJECTIVES
To evaluate the healing effects of honey dressings compared to silver dressings for acute or chronic wounds.

DESIGN
A systematic review with meta-analysis.

METHOD
The search, conducted in seven databases, resulted in six randomised controlled trial studies from South Asia focusing on antibacterial properties and healing times of honey and silver.

RESULT
Honey was more efficacious for wound healing than silver, as measured in the number of days needed for wounds to heal (pooled risk difference -20, 95% CI -0.29 to -0.11, p < .001). Honey turned out to have more antibacterial qualities than silver.

CONCLUSION
All the included studies based on burns showed the unequivocal result that honey had an even more positive effect than silver on wound healing.


title: A novel, compact, low-cost, impulse ground-penetrating radar for nondestructive evaluation of pavements

text: This paper reports on the development of a novel, compact, low-cost, impulse ground-penetrating radar (GPR) and demonstrate its use for nondestructive evaluation of pavement structures. This GPR consists of an ultrashort-monocycle-pulse transmitter (330 ps), an ultrawide-band (UWB) sampling receiver (0-6 GHz), and two UWB antennas (0.2-20 GHz)-completely designed using microwave-integrated circuits with seamless electrical connections between them. An approximate analysis is used to determine the signal loss and power budget. Performance of this GPR has been verified through the measurements of relative permittivity and thicknesses of various samples, and a good agreement between the experimental and theoretical results has been achieved.


title: Tracing Data Lineage Using Schema Transformation Pathways

text: With the increasing amount and diversity of information available on the Internet, there has been a huge growth in information systems that need to integrate data from distributed, heterogeneous data sources. Tracing the lineage of the integrated data is one of the current problems being addressed in data warehouse research. In this paper, we propose a new approach for tracing data linage based on schema transformation pathways. We show how the individual transformation steps in a transformation pathway can be used to trace the derivation of the integrated data in a step-wise fashion, thus simplifying the lineage tracing process.


title: Wireless sensor networks: a survey on recent developments and potential synergies

text: Wireless sensor network (WSN) has emerged as one of the most promising technologies for the future. This has been enabled by advances in technology and availability of small, inexpensive, and smart sensors resulting in cost effective and easily deployable WSNs. However, researchers must address a variety of challenges to facilitate the widespread deployment of WSN technology in real-world domains. In this survey, we give an overview of wireless sensor networks and their application domains including the challenges that should be addressed in order to push the technology further. Then we review the recent technologies and testbeds for WSNs. Finally, we identify several open research issues that need to be investigated in future. Our survey is different from existing surveys in that we focus on recent developments in wireless sensor network technologies. We review the leading research projects, standards and technologies, and platforms. Moreover, we highlight a recent phenomenon in WSN research that is to explore synergy between sensor networks and other technologies and explain how this can help sensor networks achieve their full potential. This paper intends to help new researchers entering the domain of WSNs by providing a comprehensive survey on recent developments.


title: Control of systems integrating logic, dynamics, and constraints

text: This paper proposes a framework for modeling and controlling systems described by interdependent physical laws, logic rules, and operating constraints, denoted as Mixed Logical Dynamical (MLD) systems. These are described by linear dynamic equations subject to linear inequalities involving real and integer variables. MLD systems include constrained linear systems, finite state machines, some classes of discrete event systems, and nonlinear systems which can be approximated by piecewise linear functions. A predictive control scheme is proposed which is able to stabilize MLD systems on desired reference trajectories while fulfilling operating constraints, and possibly take into account previous qualitative knowledge in the form of heuristic rules. Due to the presence of integer variables, the resulting on-line optimization procedures are solved through Mixed Integer Quadratic Programming (MIQP), for which efficient solvers have been recently developed. Some examples and a simulation case study on a complex gas supply system are reported.


title: Fabric-based actuator modules for building soft pneumatic structures with high payload-to-weight ratio

text: This paper introduces a new concept of building soft pneumatic structures by assembling modular units of fabric-based rotary actuators (FRAs) and beams. Upon pressurization, the inner folds of FRA would expand, which causes the FRA module to unfold, generating angular displacement. Hence, FRAs would enable mobility function of the structure and its range of motion. The modular nature of the actuator units enables customized configuration of pneumatic structures, which can be modified and scaled by selecting the appropriate modules. FRAs are also designed to be bladder-less, that is, they are made without an additional layer of inner bladder. Thus, a simple fabrication process can be used to prepare the actuators. In this paper, we studied how the performance of the FRA modules changes with their dimensions and demonstrated how a soft gripper can be constructed using these modules. The kinematic response of the actuator segments of the gripper was analyzed and a pressure control algorithm was developed to regulate the pneumatic pressure of the actuator. The modular based soft robotic gripper alone weighs about 140g. Yet, based on the grip tests, it is able to lift heavier objects (up to 2.4kg), achieving a high payload-to-weight ratio of about 1714%, which is higher than the value reported by previously developed soft pneumatic grippers using elastomeric materials. Lastly, we also demonstrated that the gripper is capable of performing two essential grasping modes, which are power grasping and fingertip grasping, for objects of various shapes.


title: A critical evaluation of the complex PTSD literature: implications for DSM-5.

text: Complex posttraumatic stress disorder (CPTSD) has been proposed as a diagnosis for capturing the diverse clusters of symptoms observed in survivors of prolonged trauma that are outside the current definition of PTSD. Introducing a new diagnosis requires a high standard of evidence, including a clear definition of the disorder, reliable and valid assessment measures, support for convergent and discriminant validity, and incremental validity with respect to implications for treatment planning and outcome. In this article, the extant literature on CPTSD is reviewed within the framework of construct validity to evaluate the proposed diagnosis on these criteria. Although the efforts in support of CPTSD have brought much needed attention to limitations in the trauma literature, we conclude that available evidence does not support a new diagnostic category at this time. Some directions for future research are suggested.


title: Agent based urban growth modeling framework on Apache Spark

text: The simulation of urban growth is an important part of urban planning and development. Due to large data and computational challenges, urban growth simulation models demand efficient data analytic frameworks for scaling them to large geographic regions. Agent-based models are widely used to observe and analyze the urban growth simulation at various scales. The incorporation of the agent-based model makes the scaling task even harder due to communication and coordination among agents. Many existing agent-based model frameworks were implemented using traditional shared and distributed memory programming models. On the other hand, Apache Spark is becoming a popular platform for distributed big data in-memory analytics. This paper presents an implementation of agent-based sub-model in Apache Spark framework. With the in-memory computation, Spark implementation outperforms the traditional distributed memory implementation using MPI. This paper provides (i) an overview of our framework capable of running urban growth simulations at a fine resolution of 30 meter grid cells, (ii) a scalable approach using Apache Spark to implement an agent-based model for simulating human decisions, and (iii) the comparative analysis of performance of Apache Spark and MPI based implementations.


title: KinectFaceDB: A Kinect Database for Face Recognition

text: The recent success of emerging RGB-D cameras such as the Kinect sensor depicts a broad prospect of 3-D data-based computer applications. However, due to the lack of a standard testing database, it is difficult to evaluate how the face recognition technology can benefit from this up-to-date imaging sensor. In order to establish the connection between the Kinect and face recognition research, in this paper, we present the first publicly available face database (i.e., KinectFaceDB1) based on the Kinect sensor. The database consists of different data modalities (well-aligned and processed 2-D, 2.5-D, 3-D, and video-based face data) and multiple facial variations. We conducted benchmark evaluations on the proposed database using standard face recognition methods, and demonstrated the gain in performance when integrating the depth data with the RGB data via score-level fusion. We also compared the 3-D images of Kinect (from the KinectFaceDB) with the traditional high-quality 3-D scans (from the FRGC database) in the context of face biometrics, which reveals the imperative needs of the proposed database for face recognition research.


title: Electromagnetic energy harvesting from train induced railway track vibrations

text: Anelectromagnetic energy harvester is designed to harness the vibrational power from railroad track deflections due to passing trains. Whereas typical existing vibration energy harvester technologies are built for low power applications of milliwatts range, the proposed harvester will be designed for higher power applications for major track-side equipment such as warning signals, switches, and health monitoring sensors, which typically require a power supply of 10 Watts or more. To achieve this goal, we implement a new patent pending motion conversion mechanism which converts irregular pulse-like bidirectional linear vibration into regulated unidirectional rotational motion. Features of the motion mechanism include bidirectional to unidirectional conversion and flywheel speed regulation, with advantages of improved reliability, efficiency, and quality of output power. It also allows production of DC power directly from bidirectional vibration without electronic diodes. Preliminary harvester prototype testing results illustrate the features and benefits of the proposed motion mechanism, showing reduction of continual system loading, regulation of generator speed, and capability for continuous DC power generation.


title: Sensorized pneumatic muscle for force and stiffness control

text: This paper presents the design and experimental validation of a soft pneumatic artificial muscle with position and force sensing capabilities. Conductive liquid-based soft sensors are embedded in a fiber-reinforced contractile actuator to measure two modes of deformation — axial strain and diametral expansion — which, together, are used to determine the stroke length and contractile force generated under internal pressure. We validate the proposed device by using data from the embedded sensors to estimate the force output of the actuator at fixed lengths and the stiffness and force output of a one degree-of-freedom hinge joint driven by an antagonist pair of the sensorized pneumatic muscles.


title: Spelling Error Trends and Patterns in Sindhi

text: Statistical error Correction technique is the most accurate and widely used approach today, but for a language like Sindhi which is a low resourced language the trained corpora’s are not available, so the statistical techniques are not possible at all. Instead a useful alternative would be to exploit various spelling error trends in Sindhi by using a Rule based approach. For designing such technique an essential prerequisite would be to study the various error patterns in a language. This paper presents various studies of spelling error trends and their types in Sindhi Language. The research shows that the error trends common to all languages are also encountered in Sindhi but their do exist some error patters that are catered specifically to a Sindhi language.


title: Induction motor fault diagnosis using labview

text: Now-a-days, manufacturing companies of electrical machines are in need of easy and early fault detection techniques as they are attire by the customers. Though many researchers have developed many fault diagnosing techniques, user friendly software with automated long measurement process is needed so that the non-technical persons also can operate to identify the faults occurred. In our work the healthy induction motor is modeled in stationary reference frame. Faulty conditions are included in the model to analyze the effect of fault in the motor using the user friendly software, LabVIEW.


title: Applications, challenges, and prospective in emerging body area networking technologies

text: Advances in wireless technology and supporting infrastructure provide unprecedented opportunity for ubiquitous real-time healthcare and fitness monitoring without constraining the activities of the user. Wirelessly connected miniaturized sensors and actuators placed in, on, and around the body form a body area network for continuous, automated, and unobtrusive monitoring of physiological signs to support medical, lifestyle and entertainment applications. BAN technology is in the early stage of development, and several research challenges have to be overcome for it to be widely accepted. In this article we study the core set of application, functional, and technical requirements of the BAN. We also discuss fundamental research challenges such as scalability (in terms of data rate, power consumption, and duty cycle), antenna design, interference mitigation, coexistence, QoS, reliability, security, privacy, and energy efficiency. Several candidate technologies poised to address the emerging BAN market are evaluated, and their merits and demerits are highlighted. A brief overview of standardization activities relevant to BANs is also presented.


title: Addressing Security and Privacy Risks in Mobile Applications

text: Applications for mobile platforms are being developed at a tremendous rate, but often without proper security implementation. Insecure mobile applications can cause serious information security and data privacy issues and can have severe repercussions on users and organizations alike.


title: Security Analysis of Wearable Fitness Devices ( Fitbit )

text: This report describes an analysis of the Fitbit Flex ecosystem. Our objectives are to describe (1) the data Fitbit collects from its users, (2) the data Fitbit provides to its users, and (3) methods of recovering data not made available to device owners. Our analysis covers four distinct attack vectors. First, we analyze the security and privacy properties of the Fitbit device itself. Next, we observe the Bluetooth traffic sent between the Fitbit device and a smartphone or personal computer during synchronization. Third, we analyze the security of the Fitbit Android app. Finally, we study the security properties of the network traffic between the Fitbit smartphone or computer application and the Fitbit web service. We provide evidence that Fitbit unnecessarily obtains information about nearby Flex devices under certain circumstances. We further show that Fitbit does not provide device owners with all of the data collected. In fact, we find evidence of per-minute activity data that is sent to the Fitbit web service but not provided to the owner. We also discovered that MAC addresses on Fitbit devices are never changed, enabling usercorrelation attacks. BTLE credentials are also exposed on the network during device pairing over TLS, which might be intercepted by MITM attacks. Finally, we demonstrate that actual user activity data is authenticated and not provided in plaintext on an end-to-end basis from the device to the Fitbit web service.


title: Versatile low power media access for wireless sensor networks

text: We propose B-MAC, a carrier sense media access protocol for wireless sensor networks that provides a flexible interface to obtain ultra low power operation, effective collision avoidance, and high channel utilization. To achieve low power operation, B-MAC employs an adaptive preamble sampling scheme to reduce duty cycle and minimize idle listening. B-MAC supports on-the-fly reconfiguration and provides bidirectional interfaces for system services to optimize performance, whether it be for throughput, latency, or power conservation. We build an analytical model of a class of sensor network applications. We use the model to show the effect of changing B-MAC's parameters and predict the behavior of sensor network applications. By comparing B-MAC to conventional 802.11-inspired protocols, specifically SMAC, we develop an experimental characterization of B-MAC over a wide range of network conditions. We show that B-MAC's flexibility results in better packet delivery rates, throughput, latency, and energy consumption than S-MAC. By deploying a real world monitoring application with multihop networking, we validate our protocol design and model. Our results illustrate the need for flexible protocols to effectively realize energy efficient sensor network applications.


title: Security and Privacy Issues in Wireless Sensor Networks for Healthcare Applications

text: The use of wireless sensor networks (WSN) in healthcare applications is growing in a fast pace. Numerous applications such as heart rate monitor, blood pressure monitor and endoscopic capsule are already in use. To address the growing use of sensor technology in this area, a new field known as wireless body area networks (WBAN or simply BAN) has emerged. As most devices and their applications are wireless in nature, security and privacy concerns are among major areas of concern. Due to direct involvement of humans also increases the sensitivity. Whether the data gathered from patients or individuals are obtained with the consent of the person or without it due to the need by the system, misuse or privacy concerns may restrict people from taking advantage of the full benefits from the system. People may not see these devices safe for daily use. There may also possibility of serious social unrest due to the fear that such devices may be used for monitoring and tracking individuals by government agencies or other private organizations. In this paper we discuss these issues and analyze in detail the problems and their possible measures.


title: New Beam Tracking Technique for Millimeter Wave-band Communications

text: In this paper, we propose an efficient beam tracking method for mobility scenario in mmWave-band communications. When the position of the mobile changes in mobility scenario , the base-station needs to perform beam training frequently to t rack the time-varying channel, thereby spending significant res ources for training beams. In order to reduce the training overhead, we propose a new beam training approach called “beam tracking” which exploits the continuous nature of time varying angle of departure (AoD) for beam selection. We show that transmissi on of only two training beams is enough to track the time-varying AoD at good accuracy. We derive the optimal selection of beam pai r which minimizes Cramer-Rao Lower Bound (CRLB) for AoD estimation averaged over statistical distribution of the AoD. Our numerical results demonstrate that the proposed beam track ing scheme produces better AoD estimation than the conventiona l beam training protocol with less training overhead. I. I NTRODUCTION The next generation wireless communication systems aim to achieve Giga bit/s throughput to support high speed multimedia data service [1], [2]. Since there exist ample amount of unutilized frequency spectrum in millimeter Wave (mmWave) band (30 GHz-300 GHz), wireless communication over mmWave band is considered as a promising solution to achieve significant leap in spectral efficiency [3]. However , one major limitation of mmWave communications is significant free space path loss, which causes large attenuation of sign al power at the receiver. Furthermore, the overall path loss ge t worse when the signal goes through obstacles, rain, foliage , and any blockage to mobile devices. Recently, active resear ch on mmWave communication has been conducted in order to overcome these limitations [1]–[5]. In mmWave band, many antenna elements can be integrated in a small form factor and hence, we can employ high directional beamforming using a large number of antennas to compensate high path loss. In order to perform high directional beamforming, it is necessary to estimate channels for all transmitter and rece iver antenna pair. While this step requires high computational c omplexity due to large number of antennas, channel estimation can be performed efficiently by using the angular domain representation of channels [6]. In angular domain, only a fe w angular bins contain the most of the received energy. Hence, if we identify the dominant angular bins (which correspond to the angle of arrival (AoA) and the angle of departure (AoD)), we can obtain the channel estimate without incurrin g computational complexity. Basically, both AoD and AoA can be estimated using so called “beam training” procedure. The base-station sends t he training beams at the designated direction and the receiver estimates the AoD/AoA based on the received signals. Widely used beam training method (called “beam cycling method”) is to allow the base-station to transmit N training beams one by one at the equally spaced directions. However, to ensure good estimate of AoD/AoA, N should be large, leading to significant training overhead. This problem becomes even more serious for the mobility scenario in mmWave communications. Since the location of mobiles keeps changing, th e base-station should transmit training beams more frequent ly to update AoD/AoA estimates, causing significant drop in data throughput [7]. Recently, several adaptive beam train ing schemes have been proposed to improve the conventional beam training method [10]–[14]. In this paper, we introduce a novel beam training method for mobility scenario in mmWave communications. Our idea is based on the observation that for mobility scenario, the AoD of the particular user does not change drastically so that continuous nature of the AoD change can be accounted to improve the efficacy of the beam training. Since this approach exploits temporal dynamics of AoD, we call such beam training scheme “beam tracking”. While the convenional method makes no assumption on the state of AoD, we use statistical distribution of the AoD given the previousl y state of AoD. Using the probabilistic model on AoD change, we derive effective beam tracking strategy which employs transmission of two training beams from the base-station. Optimal placement of two training beams in angular domain is sought by minimizing (the lower bound of) variance of the estimation error for AoD. As a result, we choose the best beam p ir from the beam codebook for the given prior knowledge on AoD. Our simulation results show that the proposed beam tracking method offers the channel estimation performance comparable to the conventional beam training methods with significantly reduced training overhead. The rest of this paper is organized as follows; In section II, we introduce the system and channel models for mmWave communications and in section III, we describe the proposed b am tracking method and the simulation results are provide d in section IV. Finally, the paper is concluded in section V. II. SYSTEM MODEL In this section, we describe the system model for mmWave communications. First, we describe the angular domain repr esentation of the mmWave channel and then we introduce the procedure for beam training and channel estimation. A. Channel Model Consider single user mmWave MIMO systems with the base-station withNb antennas and the mobile with Nm antennas. The MIMO channel model with L paths at timet is described by [8]


title: Curvature-controlled curve editing using piecewise clothoid curves

text: Two-dimensional curves are conventionally designed using splines or Bézier curves. Although formally they are C or higher, the variation of the curvature of (piecewise) polynomial curves is difficult to control; in some cases it is practically impossible to obtain the desired curvature. As an alternative we propose piecewise clothoid curves (PCCs). We show that from the design point of view they have many advantages: control points are interpolated, curvature extrema lie in the control points, and adding control points does not change the curve. We present a fast localized clothoid interpolation algorithm that can also be used for curvature smoothing, for curve fitting, for curvature blending, and even for directly editing the curvature. We give a physical interpretation of variational curvature minimization, from which we derive our scheme. Finally, we demonstrate the achievable quality with a range of examples. & 2013 Elsevier Ltd. All rights reserved.


title: Linking received packet to the transmitter through physical-fingerprinting of controller area network

text: The Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle data communication. Simplicity, robustness, and suitability for real-time systems are the salient features of the CAN bus protocol. However, it lacks the basic security features such as massage authentication, which makes it vulnerable to the spoofing attacks. In a CAN network, linking CAN packet to the sender node is a challenging task. This paper aims to address this issue by developing a framework to link each CAN packet to its source. Physical signal attributes of the received packet consisting of channel and node (or device) which contains specific unique artifacts are considered to achieve this goal. Material and design imperfections in the physical channel and digital device, which are the main contributing factors behind the device-channel specific unique artifacts, are leveraged to link the received electrical signal to the transmitter. Generally, the inimitable patterns of signals from each ECUs exist over the course of time that can manifest the stability of the proposed method. Uniqueness of the channel-device specific attributes are also investigated for time-and frequency-domain. Feature vector is made up of both time and frequency domain physical attributes and then employed to train a neural network-based classifier. Performance of the proposed fingerprinting method is evaluated by using a dataset collected from 16 different channels and four identical ECUs transmitting same message. Experimental results indicate that the proposed method achieves correct detection rates of 95.2% and 98.3% for channel and ECU classification, respectively.


title: Multi-objective model predictive control for grid-tied 15-level packed U cells inverter

text: This paper presents a multi-objective Model Predictive Control (MPC) for grid-tied 4-cell 15-level Packed U Cells (PUC) inverter. This challenging topology is characterized by high power quality with reduced number of components compared to conventional multilevel inverters. Compared to traditional PI controller, MPC is attracting more interest due to its good dynamic response and high accuracy of reference tracking, through the minimization of a flexible user-defined cost function. For the presented PUC topology, the grid current should be jointly controlled with the capacitors' voltages for ensuring proper operation of the inverter, which leads to an additional requirement of pre-charge circuits for the capacitors in case of using PI current controller (or using additional PI controllers). The proposed MPC achieves grid current injection, low current THD, unity PF, while balancing the capacitors' voltages.


title: Test anxiety and direction of attention.

text: The literature reviewed suggests an attentional interpretation, of the adverse effects which test anxiety has on task performance. During task performance the highly test-anxious person divides his attention between self-relevant and task-relevant variables, in contrast to the low-test-anxious person who focuses his attention more fully on the task. This interpretation was supported by literature from diverse areas suggesting that (a) highly anxious persons are generally more self-preoccupied than are people low in anxiety; (b) the selffocusing tendencies of highly test-anxious persons are activated in testing situations; (c) those situational conditions in which the greatest performance differences occur are ones which elicit the self-focusing tendencies of highly test-anxious subjects, and the task-focusing tendencies of low-anxious subjects; (d) research examining the relationship between anxiety and task variables suggests that anxiety reduces the range of task cues utilized in performance; (e) "worry," an attentionally demanding cognitive activity, is more debilitating of task performance than is autonomic arousal. Treatment and research implications of this attentional interpretation of test anxiety are briefly discussed.


title: GPS positioning in a multipath environment

text: We address the problem of GPS signal delay estimation in a multipath environment with a low-complexity constraint. After recalling the usual early–late estimator and its bias in a multipath propagation context, we study the maximum-likelihood estimator (MLE) based on a signal model including the parametric contribution of reflected components. It results in an efficient algorithm using the existing architecture, which is also very simple and cheap to implement. Simulations show that the results of the proposed algorithm, in a multipath environment, are similar to these of the early–late in a single-path environment. The performance are further characterized, for both MLEs (based on the single-path and multipath propagation) in terms of bias and standard deviation. The expressions of the corresponding Cramér—Rao (CR) bounds are derived in both cases to show the good performance of the estimators when unbiased.