title: Impact of distributed generation on the stability of electrical power system

abstract: This paper aims at analysing the potential impacts that distributed generation might have on the stability of electrical power networks. In particular, the performance of a power system with significant penetration of distributed resources is described to assess different types of stability of the bulk network. For this purpose, a hypothetical network is simulated, assuming a large number of fuel cells and micro-turbines as dispersed units in the low-voltage area. The investigation is carried out at constant load demands but with different contributions from fuel cells and micro-turbines. Thus, the rated and supplied powers of the conventional synchronous generators are adjusted to achieve the power balance in the network. With each penetration level of the distributed generation, the performance of the network is studied and different stability classes are analysed. The results are compared with the performance of the network without any distributed generation, as a reference case, to highlight the influence of penetration levels of such units on the stability of the entire network.


title: Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard

abstract: An enterprise resource planning system (ERP) is the information backbone of a company that integrates and automates all business operations. It is a critical issue to select the suitable ERP system which meets all the business strategies and the goals of the company. This study presents an approach to select a suitable ERP system for textile industry. Textile companies have some difficulties to implement ERP systems such as variant structure of products, production variety and unqualified human resources. At first, the vision and the strategies of the organization are checked by using balanced scorecard. According to the company's vision, strategies and KPIs, we can prepare a request for proposal. Then ERP packages that do not meet the requirements of the company are eliminated. After strategic management phase, the proposed methodology gives advice before ERP selection. The criteria were determined and then compared according to their importance. The rest ERP system solutions were selected to evaluate. An external evaluation team consisting of ERP consultants was assigned to select one of these solutions according to the predetermined criteria. In this study, the fuzzy analytic hierarchy process, a fuzzy extension of the multi-criteria decision-making technique AHP, was used to compare these ERP system solutions. The methodology was applied for a textile manufacturing company.


title: Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification

abstract: Convolutional Neural Networks (CNN) are state-of-theart models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN.


title: Stochastic Park-and-Charge Balancing for Fully Electric and Plug-in Hybrid Vehicles

abstract: Motivated by the need to provide services to alleviate range anxiety of electric vehicles, we consider the problem of balancing charging demand across a network of charging stations. Our objective is to reduce the potential for excessively long queues to build up at some charging stations, although other charging stations are underutilized. A stochastic balancing algorithm is presented to achieve these goals. A further feature of this algorithm is that it is fully decentralized and facilitates a plug-and-play type of behavior. Using our system, the charging stations can join and leave the network without any changes to, or communication with, a centralized infrastructure. Analysis and simulations are presented to illustrate the efficacy of our algorithm.


title: Segmentation Accuracy for Offline Arabic Handwritten Recognition Based on Bounding Box Algorithm

abstract: Character segmentation plays an important role in the Arabic optical character recognition (OCR) system, because the letters incorrectly segmented perform to unrecognized character. Accuracy of character recognition depends mainly on the segmentation algorithm used. The domain of off-line handwriting in the Arabic script presents unique technical challenges and has been addressed more recently than other domains. Many different segmentation algorithms for off-line Arabic handwriting recognition have been proposed and applied to various types of word images. This paper provides modify segmentation algorithm based on bounding box to improve segmentation accuracy using two main stages: preprocessing stage and segmentation stage. In preprocessing stage, used a set of methods such as noise removal, binarization, skew correction, thinning and slant correction, which retains shape of the character. In segmentation stage, the modify bounding box algorithm is done. In this algorithm a distance analysis use on bounding boxes of two connected components (CCs): main (CCs), auxiliary (CCs). The modified algorithm is presented and taking place according to three cases. Cut points also determined using structural features for segmentation character. The modified bounding box algorithm has been successfully tested on 450 word images of Arabic handwritten words. The results were very promising, indicating the efficiency of the suggested approach.


title: Comparison of three manual coccydynia treatments: a pilot study.

abstract: STUDY DESIGN ::: A prospective pilot study with independent assessment and a 2-year follow-up period was conducted. ::: ::: ::: OBJECTIVES ::: To compare and assess the efficacy of three manual coccydynia treatments, and to identify factors predictive of a good outcome. ::: ::: ::: SUMMARY OF BACKGROUND DATA ::: Various manual medicine treatments have been described in the literature. In an open study, the addition of manipulation to injection treatment produced a 25% increase in satisfactory results. Dynamic radiographs of the coccyx allow breakdown of coccydynia into four etiologic groups based on coccygeal mobility: luxation, hypermobility, immobility, and normal mobility. These groups may respond differently to manual treatments. ::: ::: ::: METHODS ::: The patients were randomized into three groups, each of which received three to four sessions of a different treatment: levator anus massage, joint mobilization, or mild levator stretch. Assessment with a visual analog scale was performed by an independent observer at 7 days, 30 days, 6 months, and 2 years. ::: ::: ::: RESULTS ::: The results of the manual treatments were satisfactory for 25.7% of the cases at 6 months, and for 24.3% of the cases at 2 years. The results varied with the cause of the coccydynia. The patients with an immobile coccyx had the poorest results, whereas those with a normally mobile coccyx fared the best. The patients with luxation or hypermobility had results somewhere between these two rates. Levator anus massage and stretch were more effective than joint mobilization, which worked only for patients with a normally mobile coccyx. Pain when patients stood up from sitting and excessive levator tone were associated with a good outcome. However, none of the results was significant because of the low success rate associated with manual treatment. ::: ::: ::: CONCLUSIONS ::: There is a need for a placebo-controlled study to establish conclusively whether manual treatments are effective. This placebo must be an external treatment. A sample size of 190 patients would be required for 80% confidence in detecting a difference.


title: Prediction for Mobile Application Usage Patterns

abstract: Advances in smartphone technology have enabled the prevalence of mobile applications. Such a variety of mobile applications make the smartphone more interesting and more humanized, and running these applications has become the major function of smartphones. However, the limited resources of current smartphone requires both researchers and companies paying more attention to the way of effectively managing mobile applications. A challenging task is how to predict mobile user’s application usage patterns for improving smartphone performance. Although the usage patterns can be treated as the time series prediction problem, traditional time series models are usually too complex to be directly adopted by the smartphone environment. Novel methods that can be running on smartphones, are desperately needed. To this end, in this paper we propose a simple and efficient method named Prediction Algorithm with Fixed Cycle Length (PAFCL), which can be used for predicting mobile application usage patterns in real-time and mobile environment. The experiments on the collected realworld data validate the ability of our prediction methods.


title: Novel membrane-less two-way MEMS loudspeaker based on piezoelectric dual-concentric actuators

abstract: In this paper a novel type of piezoelectric MEMS loudspeaker is presented. The device concept is based on concentrically cascaded PZT actuators making it the first two-way MEMS speaker reported. As a further novelty, the device is designed to operate without a closed membrane offering significant improvements with respect to acoustic performance, energy efficiency and manufacturability. Extensive FEA studies have revealed a very high SPL of more than 85 dB at 500 Hz for a device 10 × 10 mm2 in size. At higher frequencies even larger SPL values are achieved enabling a flat frequency response with 95 dB for frequencies above 800 Hz. Based on the concept speaker prototypes have been fabricated using MEMS technology and are currently being characterized.


title: A linear streaming algorithm for community detection in very large networks

abstract: In this paper, we introduce a novel community detection algorithm in graphs, called SCoDA (Streaming Community Detection Algorithm), based on an edge streaming setting. This algorithm has an extremely low memory footprint and a lightning-fast execution time as it only stores two integers per node and processes each edge strictly once. The approach is based on the following simple observation: if we pick an edge uniformly at random in the network, this edge is more likely to connect two nodes of the same community than two nodes of distinct communities. We exploit this idea to build communities by local changes at each edge arrival. Using theoretical arguments, we relate the ability of SCoDA to detect communities to usual quality metrics of these communities like the conductance. Experimental results performed on massive real-life networks ranging from one million to more than one billion edges shows that SCoDA runs more than ten times faster than existing algorithms and leads to similar or better detection scores on the largest graphs.


Rules or connections in past-tense inflections: what does the evidence rule out?


title: Convex Sparse PCA for Unsupervised Feature Learning

abstract: Principal component analysis (PCA) has been widely applied to dimensionality reduction and data pre-processing for different applications in engineering, biology, social science, and the like. Classical PCA and its variants seek for linear projections of the original variables to obtain the low-dimensional feature representations with maximal variance. One limitation is that it is difficult to interpret the results of PCA. Besides, the classical PCA is vulnerable to certain noisy data. In this paper, we propose a Convex Sparse Principal Component Analysis (CSPCA) algorithm and apply it to feature learning. First, we show that PCA can be formulated as a low-rank regression optimization problem. Based on the discussion, the l2, 1-normminimization is incorporated into the objective function to make the regression coefficients sparse, thereby robust to the outliers. Also, based on the sparse model used in CSPCA, an optimal weight is assigned to each of the original feature, which in turn provides the output with good interpretability. With the output of our CSPCA, we can effectively analyze the importance of each feature under the PCA criteria. Our new objective function is convex, and we propose an iterative algorithm to optimize it. We apply the CSPCA algorithm to feature selection and conduct extensive experiments on seven benchmark datasets. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art unsupervised feature selection algorithms.


title: Content fingerprinting using wavelets

abstract: In this paper, we introduce Waveprint, a novel method for audio identification. Waveprint uses a combination of computer-vision techniques and large-scale-data-stream processing algorithms to create compact fingerprints of audio data that can be efficiently matched. The resulting system has excellent identification capabilities for small snippets of audio that have been degraded in a variety of manners, including competing noise, poor recording quality, and cell-phone playback. We explicitly measure the tradeoffs between performance, memory usage, and computation through extensive experimentation.


title: An Incremental CFS Algorithm for Clustering Large Data in Industrial Internet of Things

abstract: With the rapid advances of sensing technologies and wireless communications, large amounts of dynamic data pertaining to industrial production are being collected from many sensor nodes deployed in the industrial Internet of Things. Analyzing those data effectively can help to improve the industrial services and mitigate the system unprepared breakdowns. As an important technique of data analysis, clustering attempts to find the underlying pattern structures embedded in unlabeled information. Unfortunately, most of the current clustering techniques that could only deal with static data become infeasible to cluster a significant volume of data in the dynamic industrial applications. To tackle this problem, an incremental clustering algorithm by fast finding and searching of density peaks based on k-mediods is proposed in this paper. In the proposed algorithm, two cluster operations, namely cluster creating and cluster merging, are defined to integrate the current pattern into the previous one for the final clustering result, and k-mediods is employed to modify the clustering centers according to the new arriving objects. Finally, experiments are conducted to validate the proposed scheme on three popular UCI datasets and two real datasets collected from industrial Internet of Things in terms of clustering accuracy and computational time.


title: Fast and Accurate OOV Decoder on High-Level Features

abstract: This work proposes a novel approach to out-of-vocabulary (OOV) keyword search (KWS) task. The proposed approach is based on using high-level features from an automatic speech recognition (ASR) system, so called phoneme posterior based (PPB) features, for decoding. These features are obtained by calculating time-dependent phoneme posterior probabilities from word lattices, followed by their smoothing. For the PPB features we developed a special novel very fast, simple and efficient OOV decoder. Experimental results are presented on the Georgian language from the IARPA Babel Program, which was the test language in the OpenKWS 2016 evaluation campaign. The results show that in terms of maximum term weighted value (MTWV) metric and computational speed, for single ASR systems, the proposed approach significantly outperforms the state-of-the-art approach based on using in-vocabulary proxies for OOV keywords in the indexed database. The comparison of the two OOV KWS approaches on the fusion results of the nine different ASR systems demonstrates that the proposed OOV decoder outperforms the proxy-based approach in terms of MTWV metric given the comparable processing speed. Other important advantages of the OOV decoder include extremely low memory consumption and simplicity of its implementation and parameter optimization.


title: Lightweight wrinkle synthesis for 3D facial modeling and animation

abstract: We present a lightweight non-parametric method to generate wrinkles for 3D facial modeling and animation. The key lightweight feature of the method is that it can generate plausible wrinkles using a single low-cost Kinect camera and one high quality 3D face model with details as the example. Our method works in two stages: (1) offline personalized wrinkled blendshape construction. User-specific expressions are recorded using the RGB-Depth camera, and the wrinkles are generated through example-based synthesis of geometric details. (2) Online 3D facial performance capturing. These reconstructed expressions are used as blendshapes to capture facial animations in real-time. Experiments on a variety of facial performance videos show that our method can produce plausible results, approximating the wrinkles in an accurate way. Furthermore, our technique is low-cost and convenient for common users. We present a lightweight non-parametric approach to generate wrinkles for 3D facial modeling and animation.Our method represents a convenient approach for generating plausible facial wrinkles with low-cost.Our method enables the reconstruction of captured expressions with wrinkles in real-time.


title: Multilinear Regression for Embedded Feature Selection with Application to fMRI Analysis

abstract: Embedded feature selection is effective when both prediction ::: and interpretation are needed. The Lasso and its extensions ::: are standard methods for selecting a subset of features while ::: optimizing a prediction function. In this paper, we are interested ::: in embedded feature selection for multidimensional ::: data, wherein (1) there is no need to reshape the multidimensional ::: data into vectors and (2) structural information from ::: multiple dimensions are taken into account. Our main contribution ::: is a new method called Regularized multilinear regression ::: and selection (Remurs) for automatically selecting a subset ::: of features while optimizing prediction for multidimensional ::: data. Both nuclear norm and the `1-norm are carefully ::: incorporated to derive a multi-block optimization algorithm ::: with proved convergence. In particular, Remurs is motivated ::: by fMRI analysis where the data are multidimensional and it ::: is important to find the connections of raw brain voxels with ::: functional activities. Experiments on synthetic and real data ::: show the advantages of Remurs compared to Lasso, Elastic ::: Net, and their multilinear extensions.


title: Total sleep deprivation, chronic sleep restriction and sleep disruption

abstract: Abstract Sleep loss may result from total sleep deprivation (such as a shift worker might experience), chronic sleep restriction (due to work, medical conditions or lifestyle) or sleep disruption (which is common in sleep disorders such as sleep apnea or restless legs syndrome). Total sleep deprivation has been widely researched, and its effects have been well described. Chronic sleep restriction and sleep disruption (also known as sleep fragmentation) have received less experimental attention. Recently, there has been increasing interest in sleep restriction and disruption as it has been recognized that they have a similar impact on cognitive functioning as a period of total sleep deprivation. Sleep loss causes impairments in cognitive performance and simulated driving and induces sleepiness, fatigue and mood changes. This review examines recent research on the effects of sleep deprivation, restriction and disruption on cognition and neurophysiologic functioning in healthy adults, and contrasts the similarities and differences between these three modalities of sleep loss.


Treatments for primary delusional infestation


title: A 128-GS/s 63-GHz-bandwidth InP-HBT-based analog-MUX module for ultra-broadband D/A conversion subsystem

abstract: A 128-GS/s 63-GHz-bandwidth 2:1 analog-multiplexer (AMUX) module has been developed for ultrabroadband digital-to-analog (D/A) conversion subsystems. The AMUX IC was fabricated using 0.5-μm-emitter InP HBTs, which have a peak ft and fmax of 290 and 320 GHz, respectively. The IC has a through bandwidth of 67 GHz. We developed an ultra-low-loss metal package equipped with G3PO (SMPS) connectors. The AMUX module based on our new package has a through bandwidth of 63 GHz and operates at a sampling rate of up to 128 GS/s. We then constructed an over-50-GHz-bandwidth D/A conversion subsystem based on two 26-GHz-bandwidth sub-DACs and this AMUX module. In addition, we successfully demonstrated a 214-Gb/s discrete multi-tone (DMT) signal generation.


title: A SVM Method for Continuous Blood Pressure Estimation from a PPG Signal

abstract: There is not always a linear relationship between the blood pressure and the pulse duration obtained from photoplethysmography (PPG) signal. In order to estimate the blood pressure from the PPG signal, A Support Vector Machine (SVM) method for continuous blood pressure estimation from a PPG Signal is applied in this paper. Training data were extracted from The University of Queensland Vital Signs Dataset for better representation of possible pulse and pressure variation. In total there were more than 7000 heartbeats and 9 parameters to be extracted from each other for analysis, then these features were defined as the input vector for training. The comparison between estimated and reference values shows better accuracy than the linear regression method and also shows better accuracy than the ANN method in diastolic blood pressure, which brings great significance in the field of mobile wearable.


title: Hiding information and signatures in trapdoor knapsacks

abstract: The knapsack problem is an NP-complete combinatorial problem that is strongly believed to be computationally difficult to solve in general. Specific instances of this problem that appear very difficult to solve unless one possesses "trapdoor information" used in the design of the problem are demonstrated. Because only the designer can easily solve problems, others can send him information hidden in the solution to the problems without fear that an eavesdropper will be able to extract the information. This approach differs from usual cryptographic systems in that a secret key is not needed. Conversely, only the designer can generate signatures for messages, but anyone can easily check their authenticity.


title: Quantification of network structural dissimilarities

abstract: Identifying and quantifying dissimilarities among graphs is a problem of practical importance, but current approaches are either limited or computationally demanding. Here, the authors propose an efficiently computable measure for network comparison that can identify structural topological differences.


title: Modeling secrecy and deception in a multiple-period attacker-defender signaling game

abstract: In this paper, we apply game theory to model strategies of secrecy and deception in a multiple-period attacker-defender resource-allocation and signaling game with incomplete information. At each period, we allow one of the three possible types of defender signals--truthful disclosure, secrecy, and deception. We also allow two types of information updating--the attacker updates his knowledge about the defender type after observing the defender's signals, and also after observing the result of a contest (if one occurs in any given time period). Our multiple-period model provides insights into the balance between capital and expense for defensive investments (and the effects of defender private information, such as defense effectiveness, target valuations, and costs), and also shows that defenders can achieve more cost-effective security through secrecy and deception (possibly lasting more than one period), in a multiple-period game. This paper helps to fill a significant gap in the literature. In particular, to our knowledge, no past work has studied defender secrecy and deception in a multiple-period game. Moreover, we believe that the solution approach developed and applied in this paper would prove useful in other types of multiple-period games.


title: Design of Multiway Power Divider by Using Stepped-Impedance Transformers

abstract: In this paper, the design of multiway power dividers by interconnecting power dividers with fewer output ports is studied by transforming them into multisection stepped-impedance transformers. By using this approach, it is easy to design multiway power dividers with required equal ripples of input reflection (S11 ) within a wide passband. The interconnecting lines can be used as additional matching sections to improve the input matching. General properties of this type of multiway power dividers can be easily obtained. Design of multiway power divider with required input reflection level can be readily performed by using the published data and tables of stepped-impedance transformer in the literature. Both even- and odd-mode analyses are performed to obtain the optimal isolation resistor values. A prototype of a wideband four-way power divider with a rhombic architecture is designed, fabricated, and measured. Simulation and measurement results are in good agreement and validate the proposed approach.


title: “Mask-Bot 2i”: An active customisable robotic head with interchangeable face

abstract: This paper describes the development of “Mask-Bot 2i” (codenamed: Kabuto), a robotic head designed for research into Human-Robot-Interactions. The uniqueness of our new robotic head is that the appearance of its face can be alter on-the-fly. Different faces can be projected onto the active head system. The head is fully active with a 3-DOF neck, with support for biannual hearing as well as video camera for seeing. An interchangeable face is the main feature of this new Mask-Bot, the head can be equipped with an average face mask as well as a highly customised individualised face that can be easily exchanged. Additionally, the actuation of the head has been designed to match the natural head movements of an average human. Thus, enabling the head and face to be articulated synchronously to the speech production while the natural head movement matches that of the animated face. The design and realisation of this new system is presented in details in this paper.


title: Significant triples: Adjective+Noun+Verb combinations

abstract: A hybrid tea rose plant having a low petal count; thick, leathery petals; strong, vigorous, upright growth; unusual orange-red flower color, and a very long vase life.


title: What characterizes late-life depression?

abstract: This article analyzes late-life depression, looking carefully at what defines a person as elderly, the incidence of late-life depression, complications and differences in symptoms between young and old patients with depression, subsyndromal depression, bipolar depression in the elderly, the relationship between grief and depression, along with sleep disturbances and suicidal ideation. Language: en


title: CloniZER Spell Checker Adaptive, Language Independent Spell Checker

abstract: Abstract CloniZER spell checker is an adaptive, language independent and 'built-in error pattern free' spell checker tool which is based on 'Ternary Search Tree' data structure. It suggests the proper form of the misspelled words using nondeterministic traverse. In other words the problem of spell checking is addressed by traverse a tree with variable weighted edges. The proposed method learns media error pattern and improves its suggestions as time goes by. Instead of using expert knowledge for error pattern modelling, the proposed algorithm learns error pattern by interaction with user. 1 Keywords: Spell Checking, Error Pattern, Learning and Adaptation, Lexicon, Ternary Search Tree (TST). 1. Introduction Nowadays, having general access to Internet as a universal phenomenon, electronic texts development and using the text based query interfaces have made the existence of assisting tools for text manipulation inevitable. For example, about 10 to 12 percent of the queries entered in search engines, have dictation errors [1]. Spell Checker have a vast application zone, such as internet search improvement [2][3], correction of errors caused by OCR


title: What can digital technologies take from and bring to research in mathematics education. innovation

abstract: In this review, we distinguish two categories of digital technologies, programmable microworlds and expressive tools, chosen to highlight the different ways in which software shapes and is shaped by its incorporation into mathematical teaching and learning environments. The review indicates that software tools do indeed shape learning, but often do so in unpredicted ways. Furthermore, apart from its unsurprising dependence on tasks and activity structures, research suggests that learning is highly sensitive to small changes in technologies, and that the design of tools and learning have tended to co-evolve. The chapter identifies a common research trajectory for the study of any particular software in relation to mathematical education as well as drawing attention to two emergent issues: the openness of tools, and the reconceptualisation of mathematical teaching and learning.


title: Vision-Based Front Vehicle Detection and Its Distance Estimation

abstract: Vision-based driver assistant systems are very promising in Intelligent Transportation System (ITS). This paper will propose a system that can detect front vehicles and estimate the nearest car distance from the host car. In a companion paper [1], we have developed a scene analysis module that deals with scene segmentation and natural object labeling of forward-looking images by the use of fuzzy Adaptive Resonance Theory (ART) and fuzzy inference techniques. Based on this technique, the proposed system can detect the front vehicles and then estimate the distance of the nearest car from us. The validity of our proposed scheme in car detection and the distance estimation was verified to be very successful by field-test experiments.


title: Finite Element Analysis of Donning Procedure of a Prosthetic Transfemoral Socket

abstract: Lower limb amputation is a severe psychological and physical event in a patient. A prosthetic solution can be provided but should respond to a patient-specific need to accommodate for the geometrical and biomechanical specificities. A new approach to calculate the stress-strain state at the interaction between the socket and the stump of five transfemoral amputees is presented. In this study the socket donning procedure is modeled using an explicit finite element method based on the patient-specific geometry obtained from CT and laser scan data. Over stumps the mean maximum pressure is 4 kPa (SD 1.7) and the mean maximum shear stresses are 1.4 kPa (SD 0.6) and 0.6 kPa (SD 0.3) in longitudinal and circumferential directions, respectively. Locations of the maximum values are according to pressure zones at the sockets. The stress-strain states obtained in this study can be considered more reliable than others, since there are normal and tangential stresses associated to the socket donning procedure.


title: [Measures and achievements of schistosomiasis control in the Yangtze River Basin].

abstract: This paper reviewed the prevention and control of schistosomiasis in the Yangtze River Basin in three stages, centered on the measures and achievements. It pointed out the key work of prevention and control which are infectious source control and effective surveillance at present stage. It will be beneficial to schistosomiasis control in China.


title: Development of a Multi-Channel Concentric Tube Robotic System With Active Vision for Transnasal Nasopharyngeal Carcinoma Procedures

abstract: Minimally invasive surgery-based nasopharyngeal cancer treatment is promising, but currently, it is not a common treatment choice because of the absence of suitable tools. In this letter, a multi-channel concentric tube robot is proposed for the treatment of nasopharyngeal cancer based on natural orifice translumenal endoscopic surgery. The proposed system has three channels, i.e., two manipulation channels and one vision channel, and all the three channels are confined by a 10 mm active sheath. The robot is controlled by human-in-the-loop bimanual teleoperation under active endoscopic guidance. The reduced sheath diameter and the steerable vision channel improve the functionality of the system and distinguish our design from the prior art. The feasibility of the system has been evaluated through a series of simulations and experiments. Results show that the proposed system is capable to conduct cooperative tasks in a confined space and the miniaturized manipulator is suitable for transnasal procedures. Besides, comparisons with other types of flexible surgical robots are discussed to further demonstrate the superiority of the proposed system in the target clinical applications.


title: An Efficient VLSI Architecture for Discrete Hadamard Transform

abstract: This paper proposes an efficient VLSI architecture for discrete Hadamard transform, which is used in real time digital signal processing applications like image coding, MPEG, and CDMA etc. The proposed N-point Hadamard transform architecture consists of signed carry save adder tree. So the depth of the architecture falls within the bounds of O (log2 N). The same proposed architecture is implemented for 2D-discrete Hadamard transform, where the hardware utilization is full. In other words, row/column processing of 2D-transform is carried with same 1D-hardware. The performance results show that the proposed architecture gives better performance compared with existing architectures using 45 nm CMOS TSMC library. The proposed 8-point 1D-DHT achieves an improvement of 55.14% and 42.07% in worst path delay over conventional and linear systolic array based 1D-DHT architectures respectively. Similarly proposed 8x8-point 2D-DHT achieves an improvement of 50.7%, 21.7%, and 51.1% in worst path delay over conventional, linear systolic array, and distributed arithmetic based 2D-DHT architectures respectively.


title: Bounding box splitting for robust shape classification

abstract: This paper presents a fast method to compute homeomorphisms between 2D lattices and shapes found in binary images. Unlike many other methods, this mapping is not restricted to simply connected shapes but applies to arbitrary topologies. Moreover, it provides an avenue to the embedding of shapes in vectorspaces over R and C and thus enables robust shape recognition.


title: Cognitive architectures: a way forward for the psychology of programming

abstract: Programming language and library designers often debate the usability of particular design choices. These choices may impact many developers, yet scientific evidence for them is rarely provided. Cognitive models of program comprehension have existed for over thirty years, but the lack of quantitative (operational) validations for their internal components limits their utility for usability studies. To ease the burden of quantifying these existing models, we recommend using the ACT-R cognitive architecture - a simulation framework for psychological models. In this paper, we review the history of cognitive modeling in the psychology of programming. We provide an overview of the ACT-R cognitive architecture, and show how it could be used to fill in the gaps of an existing, yet incomplete, quantitative model (the Cognitive Complexity Metric). Lastly, we discuss the potential benefits and challenges associated with building a comprehensive cognitive model on top of a cognitive architecture.


title: Toward innate leg stability on unmodeled and natural terrain: hexapod walking

abstract: A legged system that can negotiate moderately uneven terrain without human intervention or a vision-based control system can allow those resources, if available, to be concentrated on high-level tasks. This paper presents results of preliminary research on the application of a Force Threshold-based Position (FTP) Controller on an upright quadruped system blindly walking on irregular terrain. The algorithm requires no interleg communication, such as needed for a multi-leg Jacobian, or inertial sensing of the body. Instead, each leg relies upon local feedback and simple time triggers for gait phasing. Results are shown for sagittal plane implementation in a dynamics-based simulator, while an experimental system is being designed for further testing. Concurrent research applies the FTP controller to a sprawled-leg hexapod system in hopes of extracting general leg control principles for innate stability over moderate terrain.


title: O-CNN: octree-based convolutional neural networks for 3D shape analysis

abstract: We present O-CNN, an Octree-based Convolutional Neural Network (CNN) for 3D shape analysis. Built upon the octree representation of 3D shapes, our method takes the average normal vectors of a 3D model sampled in the finest leaf octants as input and performs 3D CNN operations on the octants occupied by the 3D shape surface. We design a novel octree data structure to efficiently store the octant information and CNN features into the graphics memory and execute the entire O-CNN training and evaluation on the GPU. O-CNN supports various CNN structures and works for 3D shapes in different representations. By restraining the computations on the octants occupied by 3D surfaces, the memory and computational costs of the O-CNN grow quadratically as the depth of the octree increases, which makes the 3D CNN feasible for high-resolution 3D models. We compare the performance of the O-CNN with other existing 3D CNN solutions and demonstrate the efficiency and efficacy of O-CNN in three shape analysis tasks, including object classification, shape retrieval, and shape segmentation.


The Great Grevy ’ s Rally : The Need , Methods , Findings , Implications and Next Steps


title: A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion

abstract: We develop and present a novel deep convolutional neural network architecture, where heterogeneous pooling is used to provide constrained frequency-shift invariance in the speech spectrogram while minimizing speech-class confusion induced by such invariance. The design of the pooling layer is guided by domain knowledge about how speech classes would change when formant frequencies are modified. The convolution and heterogeneous-pooling layers are followed by a fully connected multi-layer neural network to form a deep architecture interfaced to an HMM for continuous speech recognition. During training, all layers of this entire deep net are regularized using a variant of the “dropout” technique. Experimental evaluation demonstrates the effectiveness of both heterogeneous pooling and dropout regularization. On the TIMIT phonetic recognition task, we have achieved an 18.7% phone error rate, lowest on this standard task reported in the literature with a single system and with no use of information about speaker identity. Preliminary experiments on large vocabulary speech recognition in a voice search task also show error rate reduction using heterogeneous pooling in the deep convolutional neural network.


title: Nightmares, Demons, and Slaves Exploring the Painful Metaphors of Workplace Bullying

abstract: Although considerable research has linked workplace bullying with psychosocial and physical costs, the stories and conceptualizations of mistreatment by those targeted are largely untold. This study uses metaphor analysis to articulate and explore the emotional pain of workplace bullying and, in doing so, helps to translate its devastation and encourage change. Based on qualitative data gathered from focus groups, narrative interviews, and target drawings, the analysis describes how bullying can feel like a battle, water torture, nightmare, or noxious substance. Abused workers frame bullies as narcissistic dictators, two-faced actors, and devil figures. Employees targeted with workplace bullying liken themselves to vulnerable children, slaves, prisoners, animals, and heartbroken lovers. These metaphors highlight and delimit possibilities for agency and action. Furthermore, they may serve as diagnostic cues, providing shorthand necessary for early intervention.


Challenges and Limitations Concerning Automatic Child Pornography Classification


EteRNA-RL : Using reinforcement learning to design RNA secondary structures


title: ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia.

abstract: Chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-seq) has become a valuable and widely used approach for mapping the genomic location of transcription-factor binding and histone modifications in living cells. Despite its widespread use, there are considerable differences in how these experiments are conducted, how the results are scored and evaluated for quality, and how the data and metadata are archived for public use. These practices affect the quality and utility of any global ChIP experiment. Through our experience in performing ChIP-seq experiments, the ENCODE and modENCODE consortia have developed a set of working standards and guidelines for ChIP experiments that are updated routinely. The current guidelines address antibody validation, experimental replication, sequencing depth, data and metadata reporting, and data quality assessment. We discuss how ChIP quality, assessed in these ways, affects different uses of ChIP-seq data. All data sets used in the analysis have been deposited for public viewing and downloading at the ENCODE (http://encodeproject.org/ENCODE/) and modENCODE (http://www.modencode.org/) portals.


title: Learning cross-modality similarity for multinomial data

abstract: Many applications involve multiple-modalities such as text and images that describe the problem of interest. In order to leverage the information present in all the modalities, one must model the relationships between them. While some techniques have been proposed to tackle this problem, they either are restricted to words describing visual objects only, or require full correspondences between the different modalities. As a consequence, they are unable to tackle more realistic scenarios where a narrative text is only loosely related to an image, and where only a few image-text pairs are available. In this paper, we propose a model that addresses both these challenges. Our model can be seen as a Markov random field of topic models, which connects the documents based on their similarity. As a consequence, the topics learned with our model are shared across connected documents, thus encoding the relations between different modalities. We demonstrate the effectiveness of our model for image retrieval from a loosely related text.


title: Short-term and long-term consequences of achievement goals: Predicting interest and performance over time.

abstract: Why do some students excel in their college classes and develop interest in an academic discipline? The authors examined both the short-term and long-term consequences of students' achievement goals in an introductory psychology course. Mastery goals positively predicted subsequent interest in the course, but not course grades. Performance goals positively predicted grades, but not interest. Three semesters later, the authors obtained measures of continued interest in die discipline and long-term performance. Mastery goals predicted subsequent enrollment in psychology courses, whereas performance goals predicted long-term academic performance. These positive and complementary effects of mastery and performance goals on different measures of academic success are consistent with a multiple-goals perspective in which both goals can have beneficial consequences in college education.


title: Distributed Simulation of Hybrid Systems with HLA Support

abstract: In the paper we discuss one of the approaches to modeling and distributed simulation of hybrid systems. We use hybrid state machines to model complex interdependencies between discrete and continuous time behaviors. This framework is fully supported by UML-RT/Java tool AnyLogic developed at Experimental Object Technologies (http://www.xjtek.com). We use high level architecture (HLA) standard for distributed simulation, as a communication and synchronization media for distributed hybrid simulation components. We present a new technique (remote predicate evaluation (RPE)) for distributed modeling of hybrid systems that is applicable to a wide class of simulations. RPE integration with AnyLogic tool and HLA is also considered.


title: Altmetric: enriching scholarly content with article-level discussion and metrics

abstract: Scholarly content is increasingly being discussed, shared, and bookmarked online by researchers. Altmetric is a start-up that focuses on tracking, collecting, and measuring this activity on behalf of publishers; here we describe our approach and general philosophy. Over the past year we have seen sharing and discussion activity around approximately 750,000 articles. The average number of articles shared each day grows by 5–10% a month. We look at examples of how people are interacting with papers online and at how publishers can collect and present the resulting data to deliver real value to their authors and readers.


title: Voice Pathology Detection Using Deep Learning on Mobile Healthcare Framework

abstract: The feasibility and popularity of mobile healthcare are currently increasing. The advancement of modern technologies, such as wireless communication, data processing, the Internet of Things, cloud, and edge computing, makes mobile healthcare simpler than before. In addition, the deep learning approach brings a revolution in the machine learning domain. In this paper, we investigate a voice pathology detection system using deep learning on the mobile healthcare framework. A mobile multimedia healthcare framework is also designed. In the voice pathology detection system, voices are captured using smart mobile devices. Voice signals are processed before being fed to a convolutional neural network (CNN). We use a transfer learning technique to use the existing robust CNN models. In particular, the VGG-16 and CaffeNet models are investigated in the paper. The Saarbrucken voice disorder database is used in the experiments. Experimental results show that the voice pathology detection accuracy reaches up to 97.5% using the transfer learning of CNN models.


title: MuNN: Mutation Analysis of Neural Networks

abstract: Deep neural networks have made amazing progress in many areas over the past few years. After training deep neural networks, a common way is to build a set of test samples to evaluate the networks. However, test adequacy of deep neural networks is overlooked in both research and practice. This paper proposes MuNN, a mutation analysis method for (deep) neural networks, inspired by the success of mutation analysis in conventional software testing. Five mutation operators are designed on the characteristics of neural networks. The well known dataset MNIST has been used in our experiment. We study two research questions: (1) How does mutation affect neural networks; (2) How does neural depth affect mutation analysis. The experimental results show that mutation analysis of neural networks has strong domain characteristics. This indicates that domain mutation operators are needed to enhance mutation analysis. The experimental results also show that the mutation effects are gradually weakened with the deepening of neurons. We need to design new mutation mechanism for deep neural networks. We believe that mutation analysis can not only be used to measure test adequacy, but also be used to understand neural network operation.


title: On Optical Mark-Sense Scanning

abstract: Optical mark-sense scanning has lead to a resurgence in the use of paper ballots in the United States, despite a century of strong competition from paperless direct-recording voting systems. By the time mark-sense technology emerged, procedural measures had been developed to counter most of the vulnerabilities of paper ballots. Automatic counting of paper ballots poses technical and legal problems, but by counting the paper ballots automatically in the presence of the voter, mark-sense systems address some of the remaining problems with paper ballots. The best current technology uses precinct-count optical scanners to capture pixelized images of each ballot and then process the marks on that image. While this technology may be among the best voting technologies available today for the conduct of complex general elections, it faces one significant problem, access to voters with disabilities. There are promising solutions to this problem, but work remains to be done.


title: 3D skeleton based action recognition by video-domain translation-scale invariant mapping and multi-scale dilated CNN

abstract: In this paper, we present an image classification approach to action recognition with 3D skeleton videos. First, we propose a video domain translation-scale invariant image mapping, which transforms the 3D skeleton videos to color images, namely skeleton images. Second, a multi-scale dilated convolutional neural network (CNN) is designed for the classification of the skeleton images. Our multi-scale dilated CNN model could effectively improve the frequency adaptiveness and exploit the discriminative temporal-spatial cues for the skeleton images. Even though the skeleton images are very different from natural images, we show that the fine-tuning strategy still works well. Furthermore, we propose different kinds of data augmentation strategies to improve the generalization and robustness of our method. Experimental results on popular benchmark datasets such as NTU RGB + D, UTD-MHAD, MSRC-12 and G3D demonstrate the superiority of our approach, which outperforms the state-of-the-art methods by a large margin.


title: Detectability thresholds and optimal algorithms for community structure in dynamic networks

abstract: We study the fundamental limits on learning latent community structure in dynamic networks. Specifically, we study dynamic stochastic block models where nodes change their community membership over time, but where edges are generated independently at each time step. In this setting (which is a special case of several existing models), we are able to derive the detectability threshold exactly, as a function of the rate of change and the strength of the communities. Below this threshold, we claim that no algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this limit. The first uses belief propagation (BP), which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the BP equations. We verify our analytic and algorithmic results via numerical simulation, and close with a brief discussion of extensions and open questions.


title: Ground plane segmentation for mobile robot visual navigation

abstract: We describe a method of mobile robot monocular visual navigation, which uses multiple visual cues to detect and segment the ground plane in the robot's field of view. Corner points are tracked through an image sequence and grouped into coplanar regions using a method which we call an H-based tracker. The H-based tracker employs planar homographies and is initialised by 5-point planar projective invariants. This allows us to detect ground plane patches and the colour within such patches is subsequently modelled. These patches are grown by colour classification to give a ground plane segmentation, which is then used as an input to a new variant of the artificial potential field algorithm.


title: Semantifying Triples from Open Information Extraction Systems

abstract: The last few years have witnessed some remarkable success of the stateof- ::: the art unsupervised knowledge extraction systems like NELL and REVERB. ::: These systems are gifted with typically web-scale coverage but are often plagued ::: with ambiguity due to lack of proper schema or unique identifiers for the instances. ::: This classifies them apart from extraction systems like DBPEDIA, YAGO or FREEBASE ::: which have precise information content but have smaller coverage. In this ::: work we bring together the former to enrich the later with high precision novel ::: facts and present a statistical approach to discover new knowledge. In particular, ::: we semantify NELL triples using DBPEDIA.


title: MIMO Performance of Realistic UE Antennas in LTE Scenarios at 750 MHz

abstract: Multiple-input-multiple-output (MIMO) is a technique to achieve high data rates in mobile communication networks. Simulations are performed at both the antenna level and Long-Term Evolution (LTE) system level to assess the performance of realistic handheld devices with dual antennas at 750 MHz. It is shown that MIMO works very well and gives substantial performance gain in user devices with a quarter-wavelength antenna separation.


HARMONIC SINGLE CHANNEL SOURCE SEPARATION


title: An improved novel driving scheme of synchronous rectifiers for LLC resonant converters

abstract: This paper proposes an improved novel synchronous rectifier (SR) driving scheme for resonant converters. It is very suitable for high frequency, high efficiency and high power density dc-dc resonant converters with SRs. An LLC resonant converter with proposed synchronous rectification is designed and analyzed. With proposed driving scheme, SR body diode conduction is almost reduced to none. The reverse recovery problem of SRs is eliminated. Both current and voltage stress are greatly decreased. Conduction loss and switching loss of SRs are considerably reduced. The experimental results show that the proposed LLC resonant converter with SR can achieve low stresses, high efficiency and high power density.


title: Mining Chat-room Conversations for Social and Semantic Interactions

abstract: The ephemeral nature of human communication via networks today poses interesting and challenging problems for information technologists. The Intelink intelligence network, for example, has a need to monitor chat-room conversations to ensure the integrity of sensitive data being transmitted via the network. However, the sheer volume of communication in venues such as email, newsgroups, and chat precludes manual techniques of information management. It has been estimated that over 430 million instant messages, for example, are exchanged each day on the America Online network [3]. Although a not insignificant fraction of such data may be temporarily archived (e.g., newsgroups), no systematic mechanisms exist for accumulating these artifacts of communication in a form that lends itself to the construction of models of semantics [12]. In essence, dynamic techniques of analysis are needed if textual data of this nature is to be effectively mined. This article reports our progress in developing a text mining tool for analysis of chat-room conversations. Central to our efforts is the development of functionality to answer questions such as "What topics are being discussed in a chat-room?", "Who is discussing which topics?" and "Who is interacting with whom?" The objective of our research is to develop technology that can automatically identify such patterns of interaction in both social and semantic terms. In this article we report our preliminary findings in identifying threads of conversation in multi-topic, multi-person chat-rooms. We have achieved promising results in terms of precision and recall by employing pattern recognition techniques based on finite state automata. We also report the design of our approach to building models of social and semantic interactions based on our HDDI text mining infrastructure [13].


NUMERICAL THERMAL ANALYSIS IN ELECTRON BEAM ADDITIVE MANUFACTURING WITH PREHEATING EFFECTS


Combining Open Innovation and Knowledge Management for a Community of Practice - An Analytics Driven Approach


title: Optimistic Personality and Psychosocial Well-Being During Treatment Predict Psychosocial Well-Being Among Long-Term Survivors of Breast Cancer.

abstract: Summary of Multivariate Analyses (Simultaneous Entry) forFour Follow-Up Outcomes, Incorporating Autocorrelations PlusSignificant Bivariate Demographic and Medical Predictors Adjusted R 2 Predictor variable tp Distress emotion index ( df 152).29 Initial distress emotions .48 7.01 .001Partnered (2) vs. single (1) .18 2.68 .01Time since treatment .25 3.62 .001Chemotherapy .13 1.83 .07Subsequent cancer event .05 0.80 .43Depression (CES–D) a ( df 132).35 Initial CES–D .40 5.43 .001Partnered (2) vs. single (1) .24 3.38 .001Time since treatment .07 0.98 .33Years of education .17 2.27 .03Hispanic (2) vs. others (1) .14 1.88 .07Subsequent cancer event .12 1.73 .09Quality of life ( df 152).41 Initial quality of life .61 9.67 .001Partnered (2) vs. single (1) .15 2.45 .02Subsequent cancer event .04 0.61 .54Social disruption a ( df 125).19 Initial social disruption .29 3.27 .002Partnered (2) vs. single (1) .16 1.95 .06Time since treatment .01 0.12 .91Years of education .02 0.24 .81Employed (2) vs. not (1) .14 1.65 .11Hispanic (2) vs. others (1) .14 1.51 .14Subsequent cancer event .14 1.73 .09


title: Electrochemical Sensors for Soil Nutrient Detection: Opportunity and Challenge

abstract: Soil testing is the basis for nutrient recommendation and formulated fertilization. This study presented a brief overview of potentiometric electrochemical sensors (ISE and ISFET) for soil NPK detection. The opportunities and challenges for electrochemical sensors in soil testing were discussed.


title: Series pneumatic artificial muscles (sPAMs) and application to a soft continuum robot

abstract: We describe a new series pneumatic artificial muscle (sPAM) and its application as an actuator for a soft continuum robot. The robot consists of three sPAMs arranged radially around a tubular pneumatic backbone. Analogous to tendons, the sPAMs exert a tension force on the robot's pneumatic backbone, causing bending that is approximately constant curvature. Unlike a traditional tendon driven continuum robot, the robot is entirely soft and contains no hard components, making it safer for human interaction. Models of both the sPAM and soft continuum robot kinematics are presented and experimentally verified. We found a mean position accuracy of 5.5 cm for predicting the end-effector position of a 42 cm long robot with the kinematic model. Finally, closed-loop control is demonstrated using an eye-in-hand visual servo control law which provides a simple interface for operation by a human. The soft continuum robot with closed-loop control was found to have a step-response rise time and settling time of less than two seconds.


title: Enhancing Visual Question Answering Using Dropout

abstract: Using dropout in Visual Question Answering (VQA) is a common practice to prevent overfitting. However, in multi-path networks, the current way to use dropout may cause two problems: the co-adaptations of neurons and the explosion of output variance. In this paper, we propose the coherent dropout and the siamese dropouy to solve the two problems, respectively. Specifically, in coherent dropout, all relevant dropout layers in multiple paths are forced to work coherently to maximize the ability of preventing neuron co-adaptations. We show that the coherent dropout is simple in implementation but very effective to overcome overfitting. As for the explosion of output variance, we develop a siamese dropout mechanism to explicitly minimize the difference between the two output vectors produced from the same input data during training phase. Such mechanism can reduce the gap between training and inference phases and make the VQA model more robust. Extensive experiments are conducted to verify the effectiveness of coherent dropout and siamese dropout. And the results also show that our methods can bring additional improvements on the state-of-the-art VQA models.


title: Reliability study of complex physical systems using SysML

abstract: The development of safety critical systems becomes even harder since the complexity of these systems grows continuously. Moreover, this kind of process involves the use of powerful design methods and precise reliability techniques that utilize dissimilar models and construction policy. In this article we propose a method to unify and enhance this process by linking functional design phase using SysML with commonly used reliability techniques such as FMEA and dysfunctional models construction in AltaRica Data Flow. We present how SysML models can be analyzed automatically in order to produce an FMEA and expose a parallel between SysML models and AltaRica Data Flow ones. The given approach is structured around a database of dysfunctional behaviors that supports the studies and is updated by the obtained results. We exemplify the approach to analyze a system of level controlling of a tank.


title: Hydrothermal liquefaction of biomass: developments from batch to continuous process.

abstract: This review describes the recent results in hydrothermal liquefaction (HTL) of biomass in continuous-flow processing systems. Although much has been published about batch reactor tests of biomass HTL, there is only limited information yet available on continuous-flow tests, which can provide a more reasonable basis for process design and scale-up for commercialization. High-moisture biomass feedstocks are the most likely to be used in HTL. These materials are described and results of their processing are discussed. Engineered systems for HTL are described; however, they are of limited size and do not yet approach a demonstration scale of operation. With the results available, process models have been developed, and mass and energy balances determined. From these models, process costs have been calculated and provide some optimism as to the commercial likelihood of the technology.


title: Free versus for-a-fee: the impact of a paywall on the pattern and effectiveness of word-of-mouth via social media

abstract: Information goods providers such as print newspapers are experimenting with different pricing models for their online content. Despite research on the topic, it is still not clear how information pricing strategy influences word-of-mouth (WOM) via social media, which has become a dominant channel for raising awareness about a newspaper's articles and attracting new visitors to its website. Using The New York Times' paywall rollout as a natural experiment, our study examines how the implementation of paywall by a firm (i.e., a shift from "free" to "for-a-fee") influences the pattern and effectiveness of online WOM in social media. Our results indicate that implementing a paywall (i.e., charging for content that was earlier available for free) has a disproportionate impact on WOM for popular and niche articles, creating a longer tail in the WOM (i.e., content sharing) distribution. Further, we find that the impact of WOM on the NYT's website traffic weakens significantly after the introduction of a paywall. These results show that a paywall has implications for product and promotion strategies. The study offers novel and important implications for the theory and practice of strategic use of social media and paywall.


title: Composite right-/left-handed interdigital leaky-wave antenna on a substrate integrated waveguide

abstract: The successful concept of a composite right-/left-handed (CRLH) leaky-wave antenna based on a periodically loaded and dielectrically-filled rectangular hollow waveguide as proven in [1]-[4] is realised by single layer printed technology, which simplifies fabrication substantially. The clue is the constitution of one cell with an interdigital series capacitor, which produces an enhanced series capacitance. The eigenvalues are determined by the method presented in [5] to circumvent a rather time-consuming simulation with a fully numerical eigenmode solver. The dispersion results are moreover validated by the estimation technique Matrix-Pencil (MP) [6] and the input reflection as well as the radiation behaviour of a fabricated prototype is measured.


title: The Big Five personality traits, learning styles, and academic achievement

abstract: Personality and learning styles are both likely to play significant roles in influencing academic achievement. College students (308 undergraduates) completed the Five Factor Inventory and the Inventory of Learning Processes and reported their grade point average. Two of the Big Five traits, conscientiousness and agreeableness, were positively related with all four learning styles (synthesis analysis, methodical study, fact retention, and elaborative processing), whereas neuroticism was negatively related with all four learning styles. In addition, extraversion and openness were positively related with elaborative processing. The Big Five together explained 14% of the variance in grade point average (GPA), and learning styles explained an additional 3%, suggesting that both personality traits and learning styles contribute to academic performance. Further, the relationship between openness and GPA was mediated by reflective learning styles (synthesis-analysis and elaborative processing). These latter results suggest that being intellectually curious fully enhances academic performance when students combine this scholarly interest with thoughtful information processing. Implications of these results are discussed in the context of teaching techniques and curriculum design.


title: Polymorph: a model for dynamic level generation

abstract: Players begin games at different skill levels and develop their skill at different rates—so that even the best-designed games are uninterestingly easy for some players and frustratingly difficult for others. A proposed answer to this challenge is Dynamic Difficulty Adjustment (DDA), a general category of approaches that alter games during play, in response to player performance. However, nearly all these techniques are focused on basic parameter tweaking, while the difficulty of many games is connected to aspects that are more challenging to adjust dynamically, such as level design. Further, most DDA techniques are based on designer intuition, which may not reflect actual play patterns. Responding to these challenges, we have created Polymorph, which employs techniques from level generation and machine learning to understand level difficulty and player skill, dynamically constructing levels for a 2D platformer game with continually-appropriate challenge. We present the results of the user study on which Polymorph's model of level difficulty is based, as well as a discussion of the unique features of the model. We believe Polymorph creates a play experience that is unique because the changes are both personalized and structural, while also providing an example of a new application of machine learning to aid game design.


title: Emotion and decision-making explained: A précis

abstract: Emotion and Decision-Making Explained aims to explain and advance understanding of emotion and decision-making at two levels of explanation. One is the proximate, mechanistic level of explanation, in terms of brain mechanisms. In terms of decision-making, this results in emphasis on biologically plausible integrate-and-fire models of the attractor neuronal network processes that underlie decision-making, rather than the phenomenological models such as the drift-diffusion model, although these phenomenological models of decision-making are covered in Appendix B. The second is the ultimate level of explanation, the level of adaptive value in evolution, in which emotion is explained as an efficient and simple way for genes to influence behaviour for their own ‘selfish’ reproductive success, by specifying the rewarding and punishing goals for instrumental actions, with the resulting states when the rewards or punishers are or are not obtained being emotional states (Chapters 2 and 3). This is simpler and more efficient in terms of evolutionary adaptive value than for genes to specify the details of the actions, the behavioural responses, to be produced by stimuli.


title: Product Quantization for Nearest Neighbor Search

abstract: This paper introduces a product quantization-based approach for approximate nearest neighbor search. The idea is to decompose the space into a Cartesian product of low-dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace quantization indices. The euclidean distance between two vectors can be efficiently estimated from their codes. An asymmetric version increases precision, as it computes the approximate distance between a vector and a code. Experimental results show that our approach searches for nearest neighbors efficiently, in particular in combination with an inverted file system. Results for SIFT and GIST image descriptors show excellent search accuracy, outperforming three state-of-the-art approaches. The scalability of our approach is validated on a data set of two billion vectors.


title: A review: Can robots reshape K-12 STEM education?

abstract: Can robots in classroom reshape K-12 STEM education, and foster new ways of learning? To sketch an answer, this article reviews, side-by-side, existing literature on robot-based learning activities featuring mathematics and physics (purposefully putting aside the well-studied field of “robots to teach robotics”) and existing robot platforms and toolkits suited for classroom environment (in terms of cost, ease of use, orchestration load for the teacher, etc.). Our survey suggests that the use of robots in classroom has indeed moved from purely technology to education, to encompass new didactic fields. We however identified several shortcomings, in terms of robotic platforms and teaching environments, that contribute to the limited presence of robotics in existing curricula; the lack of specific teacher training being likely pivotal. Finally, we propose an educational framework merging the tangibility of robots with the advanced visibility of augmented reality.


The Terrascope Dataset: A Scripted Multi-Camera Indoor Video Surveillance Dataset with Ground-truth, The Second Joint


title: Tunable barium strontium titanate thin film capacitors for RF and microwave applications

abstract: In this paper, we report on the fabrication and testing of thin film barium strontium titanate (BST) based capacitors for RF and microwave components. At 7 V DC applied, MOCVD BST parallel plate capacitors yielded 60% (2.4:1) tunability between 45 MHz and 200 MHz. In this frequency range, the dielectric loss tangent was typically 0.004. Measured results for the frequency dependent dielectric permittivity and tunability at large RF voltage amplitudes are presented.


Cubrick : A Scalable Distributed MOLAP Database for Fast Analytics


title: Third-Party Web Tracking: Policy and Technology

abstract: In the early days of the web, content was designed and hosted by a single person, group, or organization. No longer. Webpages are increasingly composed of content from myriad unrelated "third-party" websites in the business of advertising, analytics, social networking, and more. Third-party services have tremendous value: they support free content and facilitate web innovation. But third-party services come at a privacy cost: researchers, civil society organizations, and policymakers have increasingly called attention to how third parties can track a user's browsing activities across websites. This paper surveys the current policy debate surrounding third-party web tracking and explains the relevant technology. It also presents the FourthParty web measurement platform and studies we have conducted with it. Our aim is to inform researchers with essential background and tools for contributing to public understanding and policy debates about web tracking.


title: A hierarchical genetic fuzzy system based on genetic programming for addressing classification with highly imbalanced and borderline data-sets

abstract: Lots of real world applications appear to be a matter of classification with imbalanced data-sets. This problem arises when the number of instances from one class is quite different to the number of instances from the other class. Traditionally, classification algorithms are unable to correctly deal with this issue as they are biased towards the majority class. Therefore, algorithms tend to misclassify the minority class which usually is the most interesting one for the application that is being sorted out. Among the available learning approaches, fuzzy rule-based classification systems have obtained a good behavior in the scenario of imbalanced data-sets. In this work, we focus on some modifications to further improve the performance of these systems considering the usage of information granulation. Specifically, a positive synergy between data sampling methods and algorithmic modifications is proposed, creating a genetic programming approach that uses linguistic variables in a hierarchical way. These linguistic variables are adapted to the context of the problem with a genetic process that combines rule selection with the adjustment of the lateral position of the labels based on the 2-tuples linguistic model. An experimental study is carried out over highly imbalanced and borderline imbalanced data-sets which is completed by a statistical comparative analysis. The results obtained show that the proposed model outperforms several fuzzy rule based classification systems, including a hierarchical approach and presents a better behavior than the C4.5 decision tree.


title: An increasing the power transmission efficiency of flat spiral coils by using ferrite materials for wireless power transfer applications

abstract: The optimal power transmission of a flat spiral coils are presented for wireless power transfer applications. It is a technique to increase the power transmission efficiency of magnetic field for wireless power transfer system by applying a flat spiral coils in conjunction with ferrite materials. The magnetic field distribution of flat spiral ferrite core materials and flat spiral air core will be analyzed by using the CST EM STUDIO program. Theoretical analyses investigate that a novel flat spiral coil applicator is effective for radiating the magnetic field or increasing the power transmission efficiency of wireless power transfer system. In the simulations, we competitions the magnetic field radiation and longest distance between the two kinds of induction coils with ferrite core and air core. The results of the simulation showed that the flat spiral ferrite core material has increased the power transmission efficiency about 42% when compared to the flat spiral air core. The advantage of a flat spiral coils by using ferrite materials are presented is that it can be applied to wireless power transfer system to high effectively.


title: Improved approximation for vector bin packing

abstract: We study the d-dimensional vector bin packing problem, a well-studied generalization of bin packing arising in resource allocation and scheduling problems. Here we are given a set of d-dimensional vectors v1,..., vn in [0,1]d, and the goal is to pack them into the least number of bins so that for each bin B, the sum of the vectors in it is at most 1 in every dimension, i.e., ||ΣvieB vi||∞ ≤ 1. For the 2-dimensional case we give an asymptotic approximation guarantee of 1 + ln(1.5) + e a (1.405 + e), improving upon the previous bound of 1 + ln 2 + e a (1.693 + e). We also give an almost tight (1.5 + e) absolute approximation guarantee, improving upon the previous bound of 2 [23]. For the d-dimensional case, we get a 1.5 + ln([EQUATION]) + e a 0.807 + ln(d + 1) + e guarantee, improving upon the previous (1+ln d+e) guarantee [2]. Here (1 + ln d) was a natural barrier as rounding-based algorithms can not achieve better than d approximation. We get around this by exploiting various structural properties of (near)-optimal packings, and using multi-objective multi-budget matching based techniques and expanding the Round & Approx framework to go beyond rounding-based algorithms. Along the way we also prove several results that could be of independent interest.


title: BIOINFORMATICS Genes and Genomes The fragment assembly string graph

abstract: We present a concept and formalism, the string graph, which represents all that is inferable about a DNA sequence from a collection of shotgun sequencing reads collected from it. We give time and space efficient algorithms for constructing a string graph given the collection of overlaps between the reads and, in particular, present a novel linear expected time algorithm for transitive reduction in this context. The result demonstrates that the decomposition of reads into kmers employed in the de Bruijn graph approach described earlier is not essential, and exposes its close connection to the unitig approach we developed at Celera. This paper is a preliminary piece giving the basic algorithm and results that demonstrate the efficiency and scalability of the method. These ideas are being used to build a next-generation whole genome assembler called BOA (Berkeley Open Assembler) that will easily scale to mammalian genomes. ::: ::: Contact: gene@eecs.berkeley.edu


title: Bridgeless flyback converter for low power lighting application

abstract: Lighting system management and optimization have turned into the most important research trend in lighting electronics. Solar LED lighting scheme is a major kind of light sources in the coming decades because of the efficiency concerns and renewable energy utilization. Conventional LED driving circuit is the combination of diode bridge rectifier and input PFC stage. Diode bridge rectifier at the front end of driving circuit will cause reduced conversion efficiency and power factor. This paper deals with reliable lighting scheme which can be supplied from both solar panel and conventional supply. A battery storage unit is also used to store the energy from PV panel and it also reduces the peak loading while it charging from conventional supply. The dc supply from grid connection is obtained by using bridgeless flyback converter which has inherent power factor correction capability. A MATLAB/Simulink model of proposed scheme is developed for validating the theoretical explanation and the relevant results are also discussed.


title: Domain-specific languages in Prolog for declarative expert knowledge in rules and ontologies

abstract: Abstract Declarative if–then rules have proven very useful in many applications of expert systems. They can be managed in deductive databases and evaluated using the well-known forward-chaining approach. For domain-experts, however, the syntax of rules becomes complicated quickly, and already many different knowledge representation formalisms exist. Expert knowledge is often acquired in story form using interviews. In this paper, we discuss its representation by defining domain-specific languages ( Dsl s) for declarative expert rules. They can be embedded in Prolog systems in internal Dsl s using term expansion and as external Dsl s using definite clause grammars and quasi-quotations – for more sophisticated syntaxes. Based on the declarative rules and the integration with the Prolog -based deductive database system DDbase , multiple rules acquired in practical case studies can be combined, compared, graphically analysed by domain-experts, and evaluated, resulting in an extensible system for expert knowledge. As a result, the actual modeling Dsl becomes executable; the declarative forward-chaining evaluation of deductive databases can be understood by the domain experts. Our Dsl for rules can be further improved by integrating ontologies and rule annotations.


title: Multimedia Data Management for Disaster Situation Awareness

abstract: To raise awareness in disaster situations, the quality and analysis of disaster-related big data are essential. Recent developments in the collection, analysis, and visualization of multimedia data have led to a significant enhancement in disaster management systems. Crowdsourcing tools, for instance, allow citizens to perform an active role in reporting information relevant to disaster events at a global scale through popular social media sites such as Twitter and Facebook. As multimedia data analysis becomes further advanced, it can augment the disaster situation awareness and provide an efficient and timely response. This paper describes how multimedia data management plays a prominent role in improving the capabilities to readily manage disaster situations. Specifically, visualization provides a more convenient and user-friendly means for individuals who have limited experience in disaster situations. A case study introducing a 3D animation system is presented, which simulates the impacts of storm surge near coastal areas.


title: Automatic Extraction of Facts, Relations, and Entities for Web-Scale Knowledge Base Population

abstract: Equipping machines with knowledge, through the construction of machinereadable knowledge bases, presents a key asset for semantic search, machine translation, question answering, and other formidable challenges in artificial intelligence. However, human knowledge predominantly resides in books and other natural language text forms. This means that knowledge bases must be extracted and synthesized from natural language text. When the source of text is the Web, extraction methods must cope with ambiguity, noise, scale, and updates. The goal of this dissertation is to develop knowledge base population methods that address the afore mentioned characteristics of Web text. The dissertation makes three contributions. The first contribution is a method for mining high-quality facts at scale, through distributed constraint reasoning and a pattern representation model that is robust against noisy patterns. The second contribution is a method for mining a large comprehensive collection of relation types beyond those commonly found in existing knowledge bases. The third contribution is a method for extracting facts from dynamic Web sources such as news articles and social media where one of the key challenges is the constant emergence of new entities. All methods have been evaluated through experiments involving Web-scale text collections.


title: Neuropsychological functioning in adolescent marijuana users: Subtle deficits detectable after a month of abstinence

abstract: In adults, studies examining the long-lasting cognitive effects of marijuana use demonstrate subtle deficits in attention, executive function, and memory. Because neuromaturation continues through adolescence, these results cannot necessarily generalize to adolescent marijuana users. The goal of this study was to examine neuropsychological functioning in abstinent marijuana using and demographically similar control adolescents. Data were collected from 65 adolescent marijuana users (n = 31, 26% females) and controls (n = 34, 26% females) 16–18 years of age. Extensive exclusionary criteria included independent psychiatric, medical, and neurologic disorders. Neuropsychological assessments were conducted after >23 days of monitored abstinence. After controlling for lifetime alcohol use and depressive symptoms, adolescent marijuana users demonstrated slower psychomotor speed (p < .05), and poorer complex attention (p < .04), story memory (p < .04), and planning and sequencing ability (p < .001) compared with controls. Post hoc analysis revealed that the number of lifetime marijuana use episodes was associated with poorer cognitive function, even after controlling for lifetime alcohol use. The general pattern of results suggested that, even after a month of monitored abstinence, adolescent marijuana users demonstrate subtle neuropsychological deficits compared with nonusers. It is possible that frequent marijuana use during adolescence may negatively influence neuromaturation and cognitive development.


title: Detection of Saccades and Postsaccadic Oscillations in the Presence of Smooth Pursuit

abstract: A novel algorithm for detection of saccades and postsaccadic oscillations in the presence of smooth pursuit movements is proposed. The method combines saccade detection in the acceleration domain with specialized on- and offset criteria for saccades and postsaccadic oscillations. The performance of the algorithm is evaluated by comparing the detection results to those of an existing velocity-based adaptive algorithm and a manually annotated database. The results show that there is a good agreement between the events detected by the proposed algorithm and those in the annotated database with Cohen's kappa around 0.8 for both a development and a test database. In conclusion, the proposed algorithm accurately detects saccades and postsaccadic oscillations as well as intervals of disturbances.


title: High-Speed FMCW Radar Frequency Synthesizer With DDS Based Linearization

abstract: Recently , we introduced a software linearization technique for frequency-modulated continuous-wave (FMCW) radar applications using a nonlinear direct digital synthesizer based frequency source. In this letter, we present a method that uses this unconventional, cost efficient, basically nonlinear synthesizer concept, but is capable of linearizing the frequency chirp directly in hardware by means of defined sweep predistortion. Additionally, the concept is extended for the generation of defined nonlinear frequency courses and verified on measurements with a 2.45-GHz FMCW radar prototype


title: Review of various methods in improvement in speed, power & efficiency of induction motor

abstract: Induction motors are mostly used motor in industrial motion control system. Earlier, the nature of industrial application of induction motor where off constant speed mechanical drives due to different speed control systems but the recent development in power electronics component have given the way for the development for the power electronics based variable speed induction motor drives replacing DC drives. This paper contains study of the different methodology of induction motor drives. The speed, power & efficiency of induction motor controlled by the process industrial motor drivesusing a various techniques. Among the no of methods of induction motor drive techniques, this paper explain the details about slip power recovery scheme by using multilevel inverters.


title: A review of hybrid evolutionary multiple criteria decision making methods

abstract: For real-world problems, the task of decision-makers is to identify a solution that can satisfy a set of performance criteria, which are often in conflict with each other. Multi-objective evolutionary algorithms tend to focus on obtaining a family of solutions that represent the trade-offs between the criteria; however ultimately a single solution must be selected. This need has driven a requirement to incorporate decision-maker preference models into such algorithms - a technique that is very common in the wider field of multiple criteria decision making. This paper reviews techniques which have combined evolutionary multi-objective optimization and multiple criteria decision making. Three classes of hybrid techniques are presented: a posteriori, a priori, and interactive, including methods used to model the decision-makers preferences and example algorithms for each category. To encourage future research directions, a commentary on the remaining issues within this research area is also provided.


title: Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques

abstract: Assessment as a dynamic process produces data that reasonable conclusions are derived by stakeholders for decision making that expectedly impact on students' learning outcomes. The data mining methodology while extracting useful, valid patterns from higher education database environment contribute to proactively ensuring students maximize their academic output. This paper develops a methodology by the derivation of performance prediction indicators to deploying a simple student performance assessment and monitoring system within a teaching and learning environment by mainly focusing on performance monitoring of students' continuous assessment (tests) and examination scores in order to predict their final achievement status upon graduation. Based on various data mining techniques (DMT) and the application of machine learning processes, rules are derived that enable the classification of students in their predicted classes. The deployment of the prototyped solution, integrates measuring, 'recycling' and reporting procedures in the new system to optimize prediction accuracy.


Music genre preference and tempo alter alpha and beta waves in human non-musicians


Ameliorative Effect of Calocybe indica, a Tropical Indian Edible Mushroom on Hyperglycemia Induced Oxidative Stress


title: BFQA: A Bengali Factoid Question Answering System

abstract: Question Answering (QA) research for factoid questions has recently achieved great success. Presently, QA systems developed for European, Middle Eastern and Asian languages are capable of providing answers with reasonable accuracy. However, Bengali being among the most spoken languages in the world, no factoid question answering system is available for Bengali till date. This paper describes the first attempt on building a factoid question answering system for Bengali language. The challenges in developing a question answering system for Bengali have been discussed. Extraction and ranking of relevant sentences have also been proposed. Also extraction strategy of the ranked answers from the relevant sentences are suggested for Bengali question answering system.


title: Airways: Optimization-Based Planning of Quadrotor Trajectories according to High-Level User Goals

abstract: In this paper we propose a computational design tool that allows end-users to create advanced quadrotor trajectories with a variety of application scenarios in mind. Our algorithm allows novice users to create quadrotor based use-cases without requiring deep knowledge in either quadrotor control or the underlying constraints of the target domain. To achieve this goal we propose an optimization-based method that generates feasible trajectories which can be flown in the real world. Furthermore, the method incorporates high-level human objectives into the planning of flight trajectories. An easy to use 3D design tool allows for quick specification and editing of trajectories as well as for intuitive exploration of the resulting solution space. We demonstrate the utility of our approach in several real-world application scenarios, including aerial-videography, robotic light-painting and drone racing.


title: A Survey of IoT-Enabled Cyberattacks: Assessing Attack Paths to Critical Infrastructures and Services

abstract: As the deployment of Internet of Things (IoT) is experiencing an exponential growth, it is no surprise that many recent cyber attacks are IoT-enabled : the attacker initially exploits some vulnerable IoT technology as a first step toward compromising a critical system that is connected, in some way, with the IoT. For some sectors, like industry, smart grids, transportation, and medical services, the significance of such attacks is obvious, since IoT technologies are part of critical back-end systems. However, in sectors where IoT is usually at the end-user side, like smart homes, such attacks can be underestimated, since not all possible attack paths are examined. In this paper, we survey IoT-enabled cyber attacks, found in all application domains since 2010. For each sector, we emphasize on the latest, verified IoT-enabled attacks, based on known real-world incidents and published proof-of-concept attacks. We methodologically analyze representative attacks that demonstrate direct, indirect, and subliminal attack paths against critical targets. Our goal is threefold: 1) to assess IoT-enabled cyber attacks in a risk-like approach, in order to demonstrate their current threat landscape; 2) to identify hidden and subliminal IoT-enabled attack paths against critical infrastructures and services; and 3) to examine mitigation strategies for all application domains.


title: Characterizing and Avoiding Negative Transfer

abstract: When labeled data is scarce for a specific target task, transfer learning often offers an effective solution by utilizing data from a related source task. However, when transferring knowledge from a less related source, it may inversely hurt the target performance, a phenomenon known as negative transfer. Despite its pervasiveness, negative transfer is usually described in an informal manner, lacking rigorous definition, careful analysis, or systematic treatment. This paper proposes a formal definition of negative transfer and analyzes three important aspects thereof. Stemming from this analysis, a novel technique is proposed to circumvent negative transfer by filtering out unrelated source data. Based on adversarial networks, the technique is highly generic and can be applied to a wide range of transfer learning algorithms. The proposed approach is evaluated on six state-of-the-art deep transfer methods via experiments on four benchmark datasets with varying levels of difficulty. Empirically, the proposed method consistently improves the performance of all baseline methods and largely avoids negative transfer, even when the source data is degenerate.


title: Prevalence and clinical classification of tremor in elderly—A community‐based survey in Brazil

abstract: Prevalence data on different types of tremor among the elderly population are very scarce. The objective of this study was to study the prevalence of tremor in a community-dwelling elderly population in the town of Bambui, Brazil. The authors studied 1186 inhabitants aged≥64 years. This was a 2-phase study in which all participants who screened positive in a questionnaire for tremor and parkinsonism or who used drugs capable of causing/suppressing tremor were examined. In this population, the prevalence rate was 17.4% for tremor, 7.4% for essential tremor, 5.6% for parkinsonian tremor, 2.8% for enhanced physiological tremor, and 1.6% for other causes. There were no gender differences in prevalence rates for all types. Patients who had Parkinson's disease with tremor were older than those who had essential tremor, whereas patients who had enhanced physiological tremor were significantly younger. The age-specific prevalence of tremor increased with advancing age for both men and women. The prevalence of tremor in the studied population was high and increased with advancing age. Essential tremor, parkinsonian tremor, and enhanced physiological tremor were the most commonly identified causes. © 2013 Movement Disorder Society


Plant genome editing with TALEN and CRISPR


Tau oligomers in cerebrospinal fluid in Alzheimer's disease


title: Towards Low-Latency and Ultra-Reliable Virtual Reality

abstract: Virtual Reality (VR) is expected to be one of the killer-applications in 5G networks. However, many technical bottlenecks and challenges need to be overcome to facilitate its wide adoption. In particular, VR requirements in terms of high-throughput, low-latency and reliable communication call for innovative solutions and fundamental research cutting across several disciplines. In view of this, this article discusses the challenges and enablers for ultra-reliable and low-latency VR. Furthermore, in an interactive VR gaming arcade case study, we show that a smart network design that leverages the use of mmWave communication, edge computing and proactive caching can achieve the future vision of VR over wireless.