title: Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection

abstract: We propose a new method for fusing a LIDAR point cloud and camera-captured images in the deep convolutional neural network (CNN). The proposed method constructs a new layer called non-homogeneous pooling layer to transform features between bird view map and front view map. The sparse LIDAR point cloud is used to construct the mapping between the two maps. The pooling layer allows efficient fusion of the bird view and front view features at any stage of the network. This is favorable for the 3D-object detection using camera-LIDAR fusion in autonomous driving scenarios. A corresponding deep CNN is designed and tested on the KITTI bird view object detection dataset, which produces 3D bounding boxes from the bird view map. The fusion method shows particular benefit for detection of pedestrians in the bird view compared to other fusion-based object detection networks.


title: Constructing optimal IP routing tables

abstract: The Border Gateway Protocol (BGP) populates Internet backbone routers with routes or prefixes. We present an algorithm to locally compute (without any modification to BGP) equivalent forwarding tables that provably contain the minimal number of prefixes. For large backbone routers, the Optimal Routing Table Constructor (ORTC) algorithm that we present produces routing tables with roughly 60% of the original number of prefixes. The publicly available MaeEast database with 41315 prefixes reduces to 23007 prefixes when ORTC is applied. We present performance measurements on four publicly available databases and a formal proof that ORTC does produce the optimal set of routes.


title: Steady-State Analysis of the Series Resonant Converter

abstract: The series resonant converter is analyzed in steady state, and for constant switching frequency the output current and voltage characteristics are found to be ellipses. The converter operating point can then be easily obtained by superimposing a load line on these elliptical characteristics. Peak resonant capacitor voltage and inductor current are also plotted in the output plane and are dependent to first order only on output current. When peak voltage and current are plotted in this manner, the dependence of component stresses on operating point is clearly revealed. The output characteristics are modified to include the effect of transistor and diode voltage drops, and experimental verification is presented.


title: Fully homomorphic encryption with polylog overhead, Cryptology ePrint Archive

abstract: We show that homomorphic evaluation of (wide enough) arithmetic circuits can be accomplished with only polylogarithmic overhead. Namely, we present a construction of fully homomorphic encryption (FHE) schemes that for security parameter λ can evaluate any width-Ω(λ) circuit with t gates in time t· polylog(λ). ::: ::: To get low overhead, we use the recent batch homomorphic evaluation techniques of Smart-Vercauteren and Brakerski-Gentry-Vaikuntanathan, who showed that homomorphic operations can be applied to "packed" ciphertexts that encrypt vectors of plaintext elements. In this work, we introduce permuting/routing techniques to move plaintext elements across these vectors efficiently. Hence, we are able to implement general arithmetic circuit in a batched fashion without ever needing to "unpack" the plaintext vectors. ::: ::: We also introduce some other optimizations that can speed up homomorphic evaluation in certain cases. For example, we show how to use the Frobenius map to raise plaintext elements to powers of p at the "cost" of a linear operation.


title: Hand-Crafted Feature Guided Deep Learning for Facial Expression Recognition

abstract: A number of facial expression recognition algorithms based on hand-crafted features and deep neutral networks have been developed. Motivated by the similarity between the hand-crafted features and features learned by deep network, a new feature loss is proposed to embed the information of hand-crafted features into the training process of network, which tries to reduce the difference between the two features. Based on the feature loss, a general framework for embedding the traditional feature information was developed and tested using CK+, JAFFE and FER2013 datasets. Experimental results show that the proposed network achieves much better accuracy than the original hand-crafted feature and the network without using our feature loss. When compared with other algorithms in literature, our network also achieved the best performance on CK+ dataset, i.e. 97.35% accuracy has been achieved.


title: A critical consideration of social networking sites’ addiction potential

abstract: AbstractThe evolution of the Internet and its applications has led to a notable increase in concern about social networking sites (SNSs). SNSs have had global mass appeal and their often frequent use – usually by young people – has triggered worries, discussions and studies on the topic of technological and social networking addictions. In addressing this issue, we have to ask to what extent technological and social networking addictions are of the same nature as substance addictions, and whether the consequences they lead to, if any, are severe enough to merit clinical attention. We can summarize our position on the topic by saying that SNSs are primarily used to increase social capital and that there is not currently enough empirical evidence on SNSs’ addiction potential to claim that SNS addition exists. Although SNSs can provoke certain negative consequences in a subset of their users or provide a platform for the expression of preexisting conditions, this is not sufficient support for their standalon...


title: Design and Implementation of a Wireless Sensor Network for Precision Agriculture Operating in API Mode

abstract: This paper presents a Wireless Sensor Network (WSN) solution applied to Precision Agriculture (PA) capable of collecting real time data on different parameters related to agriculture. The technological advances related to this activity are of great importance since agriculture is considered an economic and social pillar essential for the wellbeing of society. In addition, the growing demand for real-time information in the agriculture field has driven the development of efficient wireless communications via sensor networks. WSN provides low cost, low energy consumption, easy to implement solution in places of difficult access for the implementation of wired networks. Precision agriculture is a way to optimize resources and improve cultivation through the collection of relevant information through the deployment of sensor networks, providing government authorities with statistical information to make appropriate decisions based on reliable data. The present research describes the design, implementation and validation process of a WSN technological solution which can collect information related to humidity, environment and soil temperature, atmospheric pressure, level of luminosity and UV radiation; this information is relevant to determine optimal parameters for cultivation and farming methods using IoT technologies for Big Data analysis at the government level.


title: Flexible Graphene Films via the Filtration of Water-Soluble Noncovalent Functionalized Graphene Sheets

abstract: Flexible graphene films were prepared by the filtration of water-soluble noncovalently functionalized graphene sheets with pyrenebutyrate. The work presented here will not only open a new way for preparing water-soluble graphene dispersions but also provide a general route for fabricating conducting films based on graphene.


title: Enabling drone communications with WiMAX Technology

abstract: With the popularity of winter tourism, the winter recreation activities have been increased day by day in alpine environments. However, large numbers of people and rescuers are injured and lost in this environment due to the avalanche accidents every year. Drone-based rescue systems are envisioned as a viable solution for saving lives in this hostile environment. To this aim, a European project named “Smart collaboration between Humans and ground-aErial Robots for imProving rescuing activities in Alpine environments (SHERPA)” has been launched with the objective to develop a mixed ground and aerial drone platform to support search and rescue activities in a real-world hostile scenarios. In this paper, we study the challenges of existing wireless technologies for enabling drone wireless communications in alpine environment. We extensively discuss about the positive and negative aspects of the standards according to the SHERPA network requirements. Then based on that, we choose Worldwide interoperability for Microwave Access network (WiMAX) as a suitable technology for drone communications in this environment. Finally, we present a brief discussion about the use of operating band for WiMAX and the implementation issues of SHERPA network. The outcomes of this research assist to achieve the goal of the SHERPA project.


title: Improvement on Agglomerative Hierarchical Clustering Algorithm Based on Tree Data Structure with Bidirectional Approach

abstract: Hierarchical clustering algorithms take an input of pair wise data-item similarities and output a hierarchy of the data-items. This paper presents bi-directional agglomerative hierarchical clustering algorithm to create a bottom-up hierarchy, by iteratively merging the closest pair of data-items into one cluster. The result is a rooted AVL tree. The n leafs correspond to input data-items that need to n/2 or n/2+1 steps to merge into one cluster, correspond to groupings of items in coarser granularities climbing towards the root. As observed from the time complexity and number of steps needed to cluster all data points into one cluster perspective, the performance of the bi-directional agglomerative algorithm using tree data structure is better than the current agglomerative algorithms. Analysis on the experimental results indicates that the improved algorithm has a higher efficiency than previous methods.


title: Tamper Detection of JPEG Image Due to Seam Modifications

abstract: Content-aware image retargeting has been investigated since the last decade as a paradigm of image modification for proper display on the different screen sizes. Modifications, such as seam carving or seam insertion, have been introduced to achieve aforesaid image retargeting. The changes in an image are not easily recognizable by human eyes. Inspired by the blocking artifact characteristics matrix (BACM), a method to detect tampers caused by seam modification on JPEG retargeted images without knowledge of the original image is proposed in this paper. In a BACM block matrix, we found that the original JPEG image demonstrates a regular symmetrical data, whereas the symmetrical data in a block reconstructed by seam modification is destroyed. Twenty-two features are proposed to train the data using a support vector machine classification method. The experimental results clearly demonstrate that the proposed method provides a very high recognition rate for those JPEG retargeted images. The source codes and the complete experimental data can be accessed at http://video.minelab.tw/DETS/ .


title: A Comparative Study of Texture Measures for Terrain Classification

abstract: Three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively. Feature sets of these types, all designed analogously, were used to classify two sets of terrain samples. It was found that the Fourier features generally performed more poorly, while the other feature sets all performned comparably.


title: Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing

abstract: Background: Data sharing accelerates scientific progress but sharing individual-level data while preserving patient privacy presents a barrier. Methods and Results: Using pairs of deep neural netwo...


Worldmaking Agency–Worldmaking Authority: The Sovereign Constitutive Role of Tourism


title: 9.8 An 860μW 2.1-to-2.7GHz all-digital PLL-based frequency modulator with a DTC-assisted snapshot TDC for WPAN (Bluetooth Smart and ZigBee) applications

abstract: Ultra-low-power (ULP) transceivers enable short-range networks of autonomous sensor nodes for wireless personal-area-network (WPAN) applications. RF PLLs for frequency synthesis and modulation consume a significant share of the total transceiver power, making sub-mW PLLs key to realize ULP WPAN radios. Compared to analog PLLs [1], all-digital PLLs (ADPLLs) are preferred in nanoscale CMOS as they offer benefits of smaller area, programmability, capability of extensive self-calibrations, and easy portability [2]. However, analog PLLs dominate the field of ULP WPAN radios [1], since the time-to-digital-converter (TDC) of an ADPLL has traditionally been power hungry. We present a 2.1-to-2.7GHz 860μW fractional-N ADPLL in 40nm CMOS for WPAN applications, which breaks the 1mW barrier and consumes at least 5× lower power compared to state-of-the-art ADPLLs.


title: Slow-Wave Sleep Estimation for Healthy Subjects and OSA Patients Using R–R Intervals

abstract: We developed an automatic slow-wave sleep (SWS) detection algorithm that can be applied to groups of healthy subjects and patients with obstructive sleep apnea (OSA). This algorithm detected SWS based on autonomic activations derived from the heart rate variations of a single sensor. An autonomic stability, which is an SWS characteristic, was evaluated and quantified using R-R intervals from an electrocardiogram (ECG). The thresholds and the heuristic rule to determine SWS were designed based on the physiological backgrounds for sleep process and distribution across the night. The automatic algorithm was evaluated based on a fivefold cross validation using data from 21 healthy subjects and 24 patients with OSA. An epoch-by-epoch (30 s) analysis showed that the overall Cohen's kappa, accuracy, sensitivity, and specificity of our method were 0.56, 89.97%, 68.71%, and 93.75%, respectively. SWS-related information, including SWS duration (min) and percentage (%), were also calculated. A significant correlation in these parameters was found between automatic and polysomnography scorings. Compared with similar methods, the proposed algorithm convincingly discriminated SWS from non-SWS. The simple method using only R-R intervals has the potential to be utilized in mobile and wearable devices that can easily measure this information. Moreover, when combined with other sleep staging methods, the proposed method is expected to be applicable to long-term sleep monitoring at home and ambulatory environments.


title: Clinical Characteristics of Comorbid Narcissistic Personality Disorder in Patients With Borderline Personality Disorder

abstract: This study examines psychopathology and clinical characteristics of patients with borderline personality disorder (BPD) and comorbid narcissistic personality disorder (NPD) from two international randomized controlled trials. From a combined sample of 188 patients with BPD, 25 also fulfilled criteria for a comorbid diagnosis of NPD according to DSM-IV. The BPD patients with comorbid NPD, compared to the BPD patients without comorbid NPD, showed significantly more BPD criteria (M = 7.44 vs. M = 6.55, p < .001), fulfilled more criteria of comorbid histrionic (M = 3.84 vs. M = 1.98, p < .001), paranoid (M = 3.12 vs. M = 2.27, p = .014), and schizotypal (M = 1.64 vs. M = 1.02, p = .018) personality disorders, and were more likely to meet criteria for full histrionic PD diagnosis (44.0% vs. 14.2%, p < .001). The BPD-NPD group also reported significantly fewer psychiatric hospitalizations in the previous year (M = 0.40 vs. M = 0.82, p = .019) and fewer axis I disorders (M = 2.68 vs. M = 3.75, p = .033). No diff...


title: Morphology of puparia of flesh flies in Thailand.

abstract: Puparia of five flesh fly species were investigated for forensic study. Boettcherisca nathani (Lopes, 1961), Boettcherisca peregrina (Robineau-Desvoidy, 1830), Lioproctia pattoni (Senior-White, 1924), Liopygia ruficornis (Fabricius, 1794) and Parasarcophaga (Liosarcophaga) dux (Thomson, 1869) were examined with a scanning electron microscopy (SEM). Differences between species were found in the number and arrangement of papillae in the anterior spiracle, the shape of intersegmental spines between the prothorax and mesothorax and the pattern of spiracular tufts at the posterior spiracle. The anterior spiracle of B. nathani had two rows, comprising 21-27 papillae; while those of B. peregrina and L. pattoni had one or two irregular rows with 24-26 and 20-28 papillae, respectively. Anterior spiracle of L. ruficornis and P. dux had one row of 10-15 papillae. Intersegmental spines between the prothorax and mesothorax and pattern of spiracular tufts at the posterior spiracle are morphologically different. L. ruficornis and P. dux puparia are similar, but the position of the interslit plate between the inner and middle spiracular slits was found to be an important attribute to separate both species. Morphometric analysis on the length and width of puparia of these species revealed statistically different among them. The key for identifying puparia of forensically important flesh flies has been provided.


title: Millimeter-Wave Integrated Pyramidal Horn Antenna Made of Multilayer Printed Circuit Board (PCB) Process

abstract: Due to the low atmospheric absorption over W-band, numerous applications are expected, which should be developed at low cost. Short wavelength makes the dimension of antennas in this frequency range small, which usually requires sophisticated and expensive fabrication process. This communication presents a class of integrated wideband pyramidal horn antennas which can be made of low-cost multilayered printed circuit board (PCB) process. The proposed horn antenna radiates along the broadside to the substrate and uses substrate integrated waveguide (SIW) as its feeder. Transverse slot on the top metallic surface at the end of SIW is deployed to drive the horn antenna. Metalized via holes are used to synthesize the horn walls. The opening of the horn antenna is discretely flared from the bottom to the top layer. Measured bandwidth of the antenna is 35 GHz (70-105 GHz) while a relatively constant gain of 10 ± 1 dB is obtained over most of the bandwidth.


title: Microscopic and Macroscopic Spatio-Temporal Topic Models for Check-in Data

abstract: Twitter, together with other online social networks, such as Facebook, and Gowalla have begun to collect hundreds of millions of check-ins. Check-in data captures the spatial and temporal information of user movements and interests. To model and analyze the spatio-temporal aspect of check-in data and discover temporal topics and regions, we first propose a spatio-temporal topic model, i.e., Upstream Spatio-Temporal Topic Model (USTTM). USTTM can discover temporal topics and regions, i.e., a user’s choice of region and topic is affected by time in this model. We use continuous time to model check-in data, rather than discretized time, avoiding the loss of information through discretization. In addition, USTTM captures the property that user’s interests and activity space will change over time, and users have different region and topic distributions at different times in USTTM. However, both USTTM and other related models capture “microscopic patterns” within a single city, where users share POIs, and cannot discover “macroscopic” patterns in a global area, where users check-in to different POIs. Therefore, we also propose a macroscopic spatio-temporal topic model, MSTTM, employing words of tweets that are shared between cities to learn the topics of user interests. We perform an experimental evaluation on Twitter and Gowalla data sets from New York City and on a Twitter US data set. In our qualitative analysis, we perform experiments with USTTM to discover temporal topics, e.g., how topic “tourist destinations” changes over time, and to demonstrate that MSTTM indeed discovers macroscopic, generic topics. In our quantitative analysis, we evaluate the effectiveness of USTTM in terms of perplexity, accuracy of POI recommendation, and accuracy of user and time prediction. Our results show that the proposed USTTM achieves better performance than the state-of-the-art models, confirming that it is more natural to model time as an upstream variable affecting the other variables. Finally, the performance of the macroscopic model MSTTM is evaluated on a Twitter US dataset, demonstrating a substantial improvement of POI recommendation accuracy compared to the microscopic models.


title: A STAP overview

abstract: This tutorial provides a brief overview of space-time adaptive processing (STAP) for radar applications. We discuss space-time signal diversity and various forms of the adaptive processor, including reduced-dimension and reduced-rank STAP approaches. Additionally, we describe the space-time properties of ground clutter and noise-jamming, as well as essential STAP performance metrics. We conclude this tutorial with an overview of some current STAP topics: space-based radar, bistatic STAP, knowledge-aided STAP, multi-channel synthetic aperture radar and non-sidelooking array configurations.


title: An Integrated Environment for the Development of Knowledge-Based Recommender Applications

abstract: The complexity of the product assortments offered by on-line selling platforms makes selection a challenging task. Customers differ in respect to expertise and product knowledge, but intelligent recommender systems offer personalized dialogues that support the product-selection process. This paper describes CWAdvisor, a domain-independent, knowledge-based recommender environment that provides users with consistently appropriate solutions, identifies additional selling opportunities, and explains solutions. The discussion uses examples from several application domains to show how model-based diagnosis, personalization, and intuitive knowledge-acquisition techniques support customer-oriented sales dialogues. Experience obtained in industrial projects is reported, and successfully deployed recommender applications are evaluated.


title: Agricultural waste Annona squamosa peel extract: biosynthesis of silver nanoparticles.

abstract: Development of reliable and eco-friendly process for the synthesis of metallic nanoparticles is an important step in the field of application of nanotechnology. We have developed modern method by using agriculture waste to synthesize silver nanoparticles by employing an aqueous peel extract of Annona squamosa in AgNO(3). Controlled growth of silver nanoparticles was formed in 4h at room temperature (25°C) and 60°C. AgNPs were irregular spherical in shape and the average particle size was about 35±5 nm and it is consistent with particle size obtained by XRD Scherer equation.


title: Determining visitor engagement through augmented reality at science festivals: An experience economy perspective

abstract: Augmented reality (AR) has been increasingly implemented to enhance visitor experiences, and tourism research has long understood the importance of creating memorable experiences, leading to the research era of experience economy. Although technology-enhanced visitor engagement is crucial for science festivals, research focusing on visitor engagement through AR using the experience economy perspective is limited. Therefore, the aim of this study is to examine how the educational, esthetics, escapist and entertainment experience using AR affect visitor satisfaction and memorable experience, and eventually, lead to visitor engagement with science experiences in the context of science festivals. A total of 220 data inputs were collected as part of the European City of Science festivities and Manchester Science Festival 2016 and analyzed using structural equation modelling. Findings show that the four realms of experience economy influence satisfaction and memory and, ultimately, the intention for visitor engagement with science research at science festivals. Theoretical contributions and practical implications are presented and discussed.


title: Generative Cooperative Net for Image Generation and Data Augmentation

abstract: How to build a good model for image generation given an abstract concept is a fundamental problem in computer vision. In this paper, we explore a generative model for the task of generating unseen images with desired features. We propose the Generative Cooperative Net (GCN) for image generation. The idea is similar to generative adversarial networks except that the generators and discriminators are trained to work accordingly. Our experiments on hand-written digit generation and facial expression generation show that GCN's two cooperative counterparts (the generator and the classifier) can work together nicely and achieve promising results. We also discovered a usage of such generative model as an data-augmentation tool. Our experiment of applying this method on a recognition task shows that it is very effective comparing to other existing methods. It is easy to set up and could help generate a very large synthesized dataset.


Rulelog: Highly Expressive Semantic Rules with Scalable Deep Reasoning


title: A Multiphase Class-D Automotive Audio Amplifier With Integrated Low-Latency ADCs for Digitized Feedback After the Output Filter

abstract: A $5 \times 80$ W class-D audio power amplifier for automotive applications is presented. The amplifier is implemented in a 140-nm bipolar CMOS DMOS SOI. Configurable digital-loop filters can compensate for a range of LC output filters and their high loop gain (>50 dB between 20 Hz and 20 kHz) suppresses non-idealities of the output filter and enables low-cost output filter components. Key elements are the integrated low-latency $\Delta \Sigma$ analog to digital converters (ADCs) which digitize the output signals directly at the speaker load, after the output filter. The ADCs use filtering finite impulse response digital to analog converters in their feedback path to create an input–output transfer with a negative group delay at low frequencies. The ADCs have 116-dBA DR and −108 dB total harmonic distortion (THD). The bridge-tied load amplifier supports multiphase pulse-width modulation for lower electromagnetic interference. It operates with supplies from 6 to 25 V and with loads down to $1\Omega$ and achieves 19- $\mu \text{V}$ idle noise (Awtd) and 0.004% THD + N.


title: RCNF: Real-time Collaborative Network Forensic Scheme for Evidence Analysis

abstract: Network forensic techniques help in tracking different types of cyber attack by monitoring and inspecting network traffic. However, with the high speed and large sizes of current networks, and the sophisticated philosophy of attackers, in particular mimicking normal behaviour and/or erasing traces to avoid detection, investigating such crimes demands intelligent network forensic techniques. This paper suggests a real-time collaborative network Forensic scheme (RCNF) that can monitor and investigate cyber intrusions. The scheme includes three components of capturing and storing network data, selecting important network features using chi-square method and investigating abnormal events using a new technique called correntropy-variation. We provide a case study using the UNSW-NB15 dataset for evaluating the scheme, showing its high performance in terms of accuracy and false alarm rate compared with three recent state-of-the-art mechanisms.


title: An ultra-wideband miniaturized printed dipole antenna for EMC measurements

abstract: A modified dipole form of the PICA (planar inverted cone antenna) is presented for EMC measurements in this communication. By simply getting the whole structure halved, a further step towards miniaturization has been taken. The half-cut antenna shows over a decade impedance bandwidth while offering a 50% reduction in the size compared to the full version and having stable directional radiation pattern. The performance and characteristics of the antenna are investigated through both simulation and measurement.


title: The social side of gaming: a study of interaction patterns in a massively multiplayer online game

abstract: Playing computer games has become a social experience. Hundreds of thousands of players interact in massively multiplayer online games (MMORPGs), a recent and successful genre descending from the pioneering multi-user dungeons (MUDs). These new games are purposefully designed to encourage interactions among players, but little is known about the nature and structure of these interactions. In this paper, we analyze player-to-player interactions in two locations in the game Star Wars Galaxies. We outline different patterns of interactivity, and discuss how they are affected by the structure of the game. We conclude with a series of recommendations for the design and support of social activities within multiplayer games.


title: A New Approach for Modeling Darrieus-Type Vertical Axis Wind Turbine Rotors Using Electrical Equivalent Circuit Analogy: Basis of Theoretical Formulations and Model Development

abstract: Models are crucial in the engineering design process because they can be used for both the optimization of design parameters and the prediction of performance. Thus, models can significantly reduce design, development and optimization costs. This paper proposes a novel equivalent electrical model for Darrieus-type vertical axis wind turbines (DTVAWTs). The proposed model was built from the mechanical description given by the Paraschivoiu double-multiple streamtube model and is based on the analogy between mechanical and electrical circuits. This work addresses the physical concepts and theoretical formulations underpinning the development of the model. After highlighting the working principle of the DTVAWT, the step-by-step development of the model is presented. For assessment purposes, simulations of aerodynamic characteristics and those of corresponding electrical components are performed and compared.


title: Stargazer: Automated regression-based GPU design space exploration

abstract: Graphics processing units (GPUs) are of increasing interest because they offer massive parallelism for high-throughput computing. While GPUs promise high peak performance, their challenge is a less-familiar programming model with more complex and irregular performance trade-offs than traditional CPUs or CMPs. In particular, modest changes in software or hardware characteristics can lead to large or unpredictable changes in performance. In response to these challenges, our work proposes, evaluates, and offers usage examples of Stargazer1, an automated GPU performance exploration framework based on stepwise regression modeling. Stargazer sparsely and randomly samples parameter values from a full GPU design space and simulates these designs. Then, our automated stepwise algorithm uses these sampled simulations to build a performance estimator that identifies the most significant architectural parameters and their interactions. The result is an application-specific performance model which can accurately predict program runtime for any point in the design space. Because very few initial performance samples are required relative to the extremely large design space, our method can drastically reduce simulation time in GPU studies. For example, we used Stargazer to explore a design space of nearly 1 million possibilities by sampling only 300 designs. For 11 GPU applications, we were able to estimate their runtime with less than 1.1% average error. In addition, we demonstrate several usage scenarios of Stargazer.


title: Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks

abstract: Future sensor networks will be composed of a large number of densely deployed sensors/actuators. A key feature of such networks is that their nodes are untethered and unattended. Consequently, energy efficiency is an important design consideration for these networks. Motivated by the fact that sensor network queries may often be geographical, we design and evaluate an energy efficient routing algorithm that propagates a query to the appropriate geographical region, without flooding. The proposed Geographic and Energy Aware Routing (GEAR) algorithm uses energy aware neighbor selection to route a packet towards the target region and Recursive Geographic Forwarding or Restricted Flooding algorithm to disseminate the packet inside the destina-


Multi-view Multi-illuminant Intrinsic Dataset


title: An Empirical Investigation of the Impact of Individual and Work Characteristics on Telecommuting Success

abstract: Individual and work characteristics are used in telecommuting plans; however, their impact on telecommuting success is not well known. We studied how employee tenure, work experience, communication skills, task interdependence, work output measurability, and task variety impact telecommuter productivity, performance, and satisfaction after taking into account the impact of communication technologies. Data collected from 89 North American telecommuters suggest that in addition to the richness of the media, work experience, communication skills, and task interdependence impact telecommuting success. These characteristics are practically identifiable and measurable; therefore, we expect our findings to help managers convert increasing telecommuting adoption rates to well-defined and measurable gains.


TAKING EMPOWERMENT TO THE NEXT LEVEL: A MULTIPLE-LEVEL MODEL OF EMPOWERMENT, PERFORMANCE, AND SATISFACTION


title: Grounding the component of cyber terrorism framework using the grounded theory

abstract: The current framework of cyber terrorism consists of non-tested components which are related to physical terrorism as well as cyber security threats. However, there is no universally accepted definition of cyber terrorism, which seems to be a fundamental issue and challenge in countering threats from cyber terrorism. Grounded theory research begins by focusing on area of study and gathers data from various sources, including literatures, interviews and field observations. A grounded theory study can result in empirically grounded hypothesis that can be further tested and verified with new data using qualitative methodology. In this research, semi-structured interview with purposive sampling procedures is the primary data collection. Twenty-two (22) participants took part in the interview session. The analysis indicated that the framework describing cyber terrorism can be considered from six components: motivation, target, method of attack, domain, action by perpetrator and impact. Continued research in this area can be further conducted, which may lead to the development of strategic and technological framework to counter cyber terrorism.


Self-Efficacy and Performance of Grade 7 Students


title: Layered foil as an alternative to litz wire: Multiple methods for equal current sharing among layers

abstract: Litz wire is useful for high-frequency power but has high cost, limited thermal conductivity, and decreased effectiveness above 1 MHz. Multiple parallel layers of foil have the potential to overcome these limitations, but techniques are needed to enforce current sharing between the foil layers, as is accomplished by twisting in litz wire. Four strategies for this include already known techniques for interchanging foil layer positions, which are reviewed, and three new approaches: 1) balancing flux linkage by adjusting spacing between layers, 2) using capacitance between layers to provide ballast impedance, and 3) adding miniature balancing transformers to the foil terminations. The methods are analyzed and their scope of applicability is compared.


Agent-based knowledge analysis framework in DM


title: The Metabolic Syndrome Is Frequent in Klinefelter's Syndrome and Is Associated With Abdominal Obesity and Hypogonadism

abstract: OBJECTIVE ::: Klinefelter's syndrome is associated with an increased prevalence of diabetes, but the pathogenesis is unknown. Accordingly, the aim of this study was to investigate measures of insulin sensitivity, the metabolic syndrome, and sex hormones in patients with Klinefelter's syndrome and an age-matched control group. RESEARCH DESIGN AN METHODS: In a cross-sectional study, we examined 71 patients with Klinefelter's syndrome, of whom 35 received testosterone treatment, and 71 control subjects. Body composition was evaluated using dual-energy X-ray absorptiometry scans. Fasting blood samples were analyzed for sex hormones, plasma glucose, insulin, C-reactive protein (CRP), and adipocytokines. We analyzed differences between patients with untreated Klinefelter's syndrome and control subjects and subsequently analyzed differences between testosterone-treated and untreated Klinefelter's syndrome patients. ::: ::: ::: RESULTS ::: Of the patients with Klinefelter's syndrome, 44% had metabolic syndrome (according to National Cholesterol Education Program/Adult Treatment Panel III criteria) compared with 10% of control subjects. Insulin sensitivity (assessed by homeostasis model assessment 2 modeling), androgen, and HDL cholesterol levels were significantly decreased, whereas total fat mass and LDL cholesterol, triglyceride, CRP, leptin, and fructosamine levels were significantly increased in untreated Klinefelter's syndrome patients. In treated Klinefelter's syndrome patients, LDL cholesterol and adiponectin were significantly decreased, whereas no difference in body composition was found in comparison with untreated Klinefelter's syndrome patients. Multivariate analyses showed that truncal fat was the major determinant of metabolic syndrome and insulin sensitivity. ::: ::: ::: CONCLUSIONS ::: The prevalence of metabolic syndrome was greatly increased, whereas insulin sensitivity was decreased in Klinefelter's syndrome. Both correlated with truncal obesity. Hypogonadism in Klinefelter's syndrome may cause an unfavorable change in body composition, primarily through increased truncal fat and decreased muscle mass. Testosterone treatment in Klinefelter's syndrome only partly corrected the unfavorable changes observed in untreated Klinefelter's syndrome, perhaps due to insufficient testosterone doses.


title: A Critique of Structure-from-Motion Algorithms

abstract: I review current approaches to structure from motion (SFM) and suggest a framework for designing new algorithms. The discussion focuses on reconstruction rather than on correspondence and on algorithms reconstructing from many images. I argue that it is important to base experiments and algorithm design on theoretical analyses of algorithm behavior and on an understanding of the intrinsic, algorithm-independent properties of SFM optimal estimation. I propose new theoretical analyses as examples, which suggest a range of experimental questions about current algorithms as well as new types of algorithms. The paper begins with a review of several of the important multi-image-based approaches to SFM, including optimization, fusing (e.g., Kalman filtering), projective methods, and invariant-based algorithms. I suggest that optimization by means of general minimization techniques needs to be supplemented by a theoretical understanding of the SFM least-squares error surface. I argue that fusing approaches are essentially no more robust than algorithms reconstructing from a small number of images and advocate experiments to determine the limitations of fusing. I also propose that fusing may be one of the best reconstruction strategies in situations where few-image algorithms give reasonable results, and suggest that an experimental understanding of the properties of few-image algorithms is important for designing good fusing methods. I emphasize the advantages of an approach based on fusing image-pair reconstructions. With regard to the projective approach, I argue that its trade-off of simplicity versus accuracy/robustness needs more careful experimental examination, and I advocate more research on the effects of calibration error on Euclidean reconstruction. I point out the relative lack of research on adapting Euclidean approaches to deal with incomplete knowledge of the calibration. I argue that invariant-based algorithms could be more nonrobust and inaccurate, and not necessarily much faster, than an approach fusing two-image optimizations. Based on recent results showing that two-image reconstructions are nearly as accurate as multi-image ones, I suggest that the authors of invariants methods conduct careful comparisons of their algorithms to two-image-based results. The remainder of the paper discusses the issues involved in designing a generally applicable SFM algorithm. I argue that current SFM algorithms perform well only in restricted domains, and that different types of algorithms do well on quite different types of sequences. I present examples of three domains that are important in applications and describe three types of algorithms, each of which performs well in just one of the three domains. I advocate testing current algorithms on a wider variety of sequences to determine their limits of applicability. More generally, I propose that SFM is a messy problem and that it could require a flexible “intermediate-level” system incorporating a variety of different algorithms and sophisticated decision rules for combining them. The paper concludes with a general discussion of experiments, pointing out that real-image experiments are not always the best means of evaluating reconstruction algorithms.


title: Robotic techniques for upper limb evaluation and rehabilitation of stroke patients

abstract: This paper presents two robot devices for use in the rehabilitation of upper limb movements and reports the quantitative parameters obtained to characterize the rate of improvement, thus allowing a precise monitoring of patient's recovery. A one degree of freedom (DoF) wrist manipulator and a two-DoF elbow-shoulder manipulator were designed using an admittance control strategy; if the patient could not move the handle, the devices completed the motor task. Two groups of chronic post-stroke patients (G1 n=7, and G2 n=9) were enrolled in a three week rehabilitation program including standard physical therapy (45 min daily) plus treatment by means of robot devices, respectively, for wrist and elbow-shoulder movements (40 min, twice daily). Both groups were evaluated by means of standard clinical assessment scales and a new robot measured evaluation metrics that included an active movement index quantifying the patient's ability to execute the assigned motor task without robot assistance, the mean velocity, and a movement accuracy index measuring the distance of the executed path from the theoretic one. After treatment, both groups improved their motor deficit and disability. In G1, there was a significant change in the clinical scale values (p 0.53 p<0.03). Our findings suggest that robot-aided neurorehabilitation may improve the motor outcome and disability of chronic post-stroke patients. The new robot measured parameters may provide useful information about the course of treatment and its effectiveness at discharge.


title: Defeating Kyoto: the conservative movement’s impact on U.S. climate change policy

abstract: In this article, we argue that a major reason the United States failed to ratify the Kyoto Protocol to ameliorate global warming is the opposition of the American conservative movement, a key segment of the anti-environmental counter-movement. We examine how the conservative movement mobilized between 1990 and 1997 to construct the "non-problematicity" of global warming. After we describe how conservative think tanks mobilized to challenge the global warming claims of mainstream climate science, we examine how these countermovement organizations aligned themselves with prominent American climate change skeptics known for their staunch criticism of mainstream climate research and their affiliations with the fossil fuels industry. We then examine how the efforts of these conservative think tanks were enhanced by the shift in the political opportunity structure created by the 1994 Republican takeover of Congress. This study demonstrates how a powerful countermovement effectively challenged the environmental community's definition of global warming as a social problem and blocked the passage of any significant climate change policy.


title: Iron-rich rhythmically laminated sediments in Lake of the Clouds, northeastern Minnesota1

abstract: Sediments from the deepest part of Lake of the Clouds (31 m) are rhythmically laminated throughout all but the basal part of the column. The laminations can be correlated with events in the lake cluring the annual cycle. Ferrous iron (maximum 620 ppm ) accumulates in a layer of water about 2 m thick in the deepest part of the lake. The laminations form in the sediments only where overlain by this stagnant iron-rich water. The lightcolored portion of the couplet presumably forms from iron oxides precipitated from partially oxygenated bottom water during fall and spring overturns, the dark layer from organic remains that settle out during summer. Chemical analysis and electron-microprobe studies reveal a much higher concentration of iron and manganese in the light layers than in the dark. Vivianite [Fe:!( POa)z * 8H,O] and probably limonite ( FcaOs * nHzO) occur extensively in nodules throughout the core. Siderite (FeCG) was identified by X-ray diffraction. Numerous orange spherical aggregates occur in the light layers. An iron and manganese profile of the sediment column suggests that the most distinct laminae are found where the iron content of the sediment is at least 4%.


title: Instructional design for learning path identification in an e-learning environment using felder-silverman learning styles model

abstract: Developing instructional design for e-content and checking its effectiveness among a group of learners is very significant in e-learning. In the current educational system, teachers are giving instructions or delivering learning contents to learners, without understanding the learner profile parameters such as learning style, motivation, attitude, aptitude etc. In an e-learning environment, providing pre-defined learning content to all learners, without understanding their profile parameters, affect their learning process. This paper proposes a system for providing personalized e-content for learners (PEL) based on Felder-Silverman Learning Styles model (FSLSM). As a case study E-content is developed for students of graduate course in Computer Applications in the subject Computer Graphics. Effectiveness of the e-content is checked by identifying the learning path among a group of visual learners.


Acupuncture for chronic fatigue syndrome and idiopathic chronic fatigue: a multicenter, nonblinded, randomized controlled trial


title: Neural network techniques for financial performance prediction: integrating fundamental and technical analysis

abstract: This research project investigates the ability of neural networks, specifically, the backpropagation algorithm, to integrate fundamental and technical analysis for financial performance prediction. The predictor attributes include 16 financial statement variables and 11 macroeconomic variables. The rate of return on common shareholders' equity is used as the to-be-predicted variable. Financial data of 364 S&P companies are extracted from the CompuStat database, and macroeconomic variables are extracted from the Citibase database for the study period of 1985-1995. Used as predictors in Experiments 1, 2, and 3 are the 1 year's, the 2 years', and the 3 years' financial data, respectively. Experiment 4 has 3 years' financial data and macroeconomic data as predictors. Moreover, in order to compensate for data noise and parameter misspecification as well as to reveal prediction logic and procedure, we apply a rule extraction technique to convert the connection weights from trained neural networks to symbolic classification rules. The performance of neural networks is compared with the average return from the top one-third returns in the market (maximum benchmark) that approximates the return from perfect information as well as with the overall market average return (minimum benchmark) that approximates the return from highly diversified portfolios. Paired t tests are carried out to calculate the statistical significance of mean differences. Experimental results indicate that neural networks using 1 year's or multiple years' financial data consistently and significantly outperform the minimum benchmark, but not the maximum benchmark. As for neural networks with both financial and macroeconomic predictors, they do not outperform the minimum or maximum benchmark in this study. The experimental results also show that the average return of 0.25398 from extracted rules is the only compatible result to the maximum benchmark of 0.2786. Consequentially, we demonstrate rule extraction as a postprocessing technique for improving prediction accuracy and for explaining the prediction logic to financial decision makers.


title: Analyzing the Facebook Friendship Graph

abstract: Online Social Networks (OSN) during last years acquired a ::: huge and increasing popularity as one of the most important emerging Web phenomena, deeply modifying the behavior of users and contributing to build a solid substrate of connections and relationships among people using the Web. In this preliminary work paper, our purpose is to analyze Facebook, considering a signi�cant sample of data re ::: ecting relationships among subscribed users. Our goal is to extract, from this platform, relevant information about the distribution of these relations and exploit tools and algorithms provided by the Social Network Analysis (SNA) to discover and, possibly, understand underlying similarities ::: between the developing of OSN and real-life social networks.


title: Fast Statistical Learning Algorithm for Feature Generation

abstract: This paper presents an improved statistical learning algorithm for feature generation in pattern recognition and signal processing. It is approximately derived from geometrical interpretation of the Fisher linear discriminant analysis (FLDA). The principal component analysis (PCA) is popular for data compression and feature extraction. Furthermore, iterative learning algorithms for obtaining eigenvectors in PCA have been presented in such fields. Their effectiveness has been demonstrated in many applications. However, recently FLDA has been often used in many fields, especially face image recognition. The drawback of FLDA is a long computational time based on a large-sized covariance matrix and the issue that the within-class covariance matrix is usually singular. Generally, in FLDA, the inverse matrix of the within-class covariance matrix cannot be obtained, since data dimension is generally higher than the number of data and then it includes many zero eigenvalues. In order to overcome this difficulty, a new iterative feature generation method, the simple-FLDA was proposed by authors. In this paper, further improvement is introduced into the simple-FLDA and its effectiveness is demonstrated for preliminary personal identification problem.


title: The examination of pole geometry of consequent pole type ferrite PM axial gap motor with field winding

abstract: This paper presents the examination of consequent pole type ferrite PM axial gap motor with field winding. Especially the configuration of magnet and rotor core is analyzed with equivalent magnetic circuit and 3D FEA. The prototype was assembled and the comparison between test result and analysis is also provided. It is confirmed that high torque and efficiency characteristic is achieved when the width of magnet and rotor core are 21 deg. and 15 deg. (mechanical angle) respectively.


title: Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group

abstract: Currently, the most widely used criteria for assessing response to therapy in high-grade gliomas are based on two-dimensional tumor measurements on computed tomography (CT) or magnetic resonance imaging (MRI), in conjunction with clinical assessment and corticosteroid dose (the Macdonald Criteria). It is increasingly apparent that there are significant limitations to these criteria, which only address the contrast-enhancing component of the tumor. For example, chemoradiotherapy for newly diagnosed glioblastomas results in transient increase in tumor enhancement (pseudoprogression) in 20% to 30% of patients, which is difficult to differentiate from true tumor progression. Antiangiogenic agents produce high radiographic response rates, as defined by a rapid decrease in contrast enhancement on CT/MRI that occurs within days of initiation of treatment and that is partly a result of reduced vascular permeability to contrast agents rather than a true antitumor effect. In addition, a subset of patients treated with antiangiogenic agents develop tumor recurrence characterized by an increase in the nonenhancing component depicted on T2-weighted/fluid-attenuated inversion recovery sequences. The recognition that contrast enhancement is nonspecific and may not always be a true surrogate of tumor response and the need to account for the nonenhancing component of the tumor mandate that new criteria be developed and validated to permit accurate assessment of the efficacy of novel therapies. The Response Assessment in Neuro-Oncology Working Group is an international effort to develop new standardized response criteria for clinical trials in brain tumors. In this proposal, we present the recommendations for updated response criteria for high-grade gliomas.


title: NESMO: Network slicing management and orchestration framework

abstract: This paper presents a novel network slicing management and orchestration architectural framework. A brief description of business scenarios and potential customers of network slicing is provided, illustrating the need for ordering network services with very different requirements. Based on specific customer goals (of ordering and building an end-to-end network slice instance) and other requirements gathered from industry and standardization associations, a solution is proposed enabling the automation of end-to-end network slice management and orchestration in multiple resource domains. This architecture distinguishes between two main design time and runtime components: Network Slice Design and Multi-Domain Orchestrator, belonging to different competence service areas with different players in these domains, and proposes the required interfaces and data structures between these components.


title: Heterogeneous Environment Aware Streaming Graph Partitioning

abstract: With the increasing availability of graph data and widely adopted cloud computing paradigm, graph partitioning has become an efficient pre-processing technique to balance the computing workload and cope with the large scale of input data. Since the cost of partitioning the entire graph is strictly prohibitive, there are some recent tentative works towards streaming graph partitioning which run faster, are easily parallelized, and can be incrementally updated. Most of the existing works on streaming partitioning assume that worker nodes within a cluster are homogeneous in nature. Unfortunately, this assumption does not always hold. Experiments show that these homogeneous algorithms suffer a significant performance degradation when running at heterogeneous environment. In this paper, we propose a novel adaptive streaming graph partitioning approach to cope with heterogeneous environment. We first formally model the heterogeneous computing environment with the consideration of the unbalance of computing ability (e.g., the CPU frequency) and communication ability (e.g., the network bandwidth) for each node. Based on this model, we propose a new graph partitioning objective function that aims to minimize the total execution time of the graph-processing job. We then explore some simple yet effective streaming algorithms for this objective function that can achieve balanced and efficient partitioning result. Extensive experiments are conducted on a moderate sized computing cluster with real-world web and social network graphs. The results demonstrate that the proposed approach achieves significant improvement compared with the state-of-the-art solutions.


title: Ensemble learning in Bayesian neural networks

abstract: Bayesian treatments of learning in neural networks are typically based either on a local Gaussian approximation to a mode of the posterior weight distribution, or on Markov chain Monte Carlo simulations. A third approach, called ensemble learning, was introduced by Hinton and van Camp (1993). It aims to approximate the posterior distribution by minimizing the Kullback-Leibler divergence between the true posterior and a parametric approximating distribution. The original derivation of a deterministic algorithm relied on the use of a Gaussian approximating distribution with a diagonal covariance matrix and hence was unable to capture the posterior correlations between parameters. In this chapter we show how the ensemble learning approach can be extended to full-covariance Gaussian distributions while remaining computationally tractable. We also extend the framework to deal with hyperparameters, leading to a simple re-estimation procedure. One of the benefits of our approach is that it yields a strict lower bound on the marginal likelihood, in contrast to other approximate procedures.


Tuning of PID Controller for DC Servo Motor using Genetic Algorithm


Non-Functional Requirements in Software Engineering


title: A New Metal Control Gate Last process (MCGL process) for high performance DC-SF (Dual Control gate with Surrounding Floating gate) 3D NAND flash memory

abstract: A new Metal Control Gate Last process (MCGL process) has been successfully developed for the DC-SF (Dual Control gate with Surrounding Floating gate cell)[1] three-dimensional (3D) NAND flash memory. The MCGL process can realize a low resistive tungsten (W) metal word-line with high-k IPD, a low damage on tunnel oxide/IPD, and a preferable FG shape. And also, a conventional bulk erase can be used, replaced GIDL erase in BiCS[3][4], due to direct connection between channel poly and p-well by the channel contact holes. Therefore, by using MCGL process, high performance and high reliability of DC-SF cell can be achieved for MLC/TLC 256Gb/512Gb 3D NAND flash memories.


title: Micro-determination of inorganic phosphorus and alkaline phosphatase activity in biological fluids.

abstract: Inorganic phosphorus and alkaline phosphatase activities were measured in serum samples from goats, buffaloes, cows, bullocks and also in amniotic and allantoic fluid of goats using ascorbic acid as reductant for phosphomolybdate. The results were compared with conventional method of FISKE and SUBBAROW (1925). There were no significant differences between the 2 methods. It was possible to measure micro-amounts of inorganic phosphorus and alkaline phosphatase activity with precision even in low cost colorimeters.


title: ‘Δ’ slotted compact printable orientation insensitive chipless RFID tag for long range applications

abstract: A novel design concept of a compact printable chipless RFID tag is presented. The tag consists of a triangular patch loaded with multiple triangular ‘Δ’ shaped slot resonators. Six copies of the identical patches are placed together to achieve a very compact long range chipless RFID tag. This symmetric frequency domain based tag has the advantage to be read from any orientation with the reader antennas. The tag can be read by chipless RFID tag reader in both near field and far-field measurements. This tag does not have a ground plane and has higher data density compared to most of the existing printable chipless tags.


title: An Algorithm for Many-Objective Optimization With Reduced Objective Computations: A Study in Differential Evolution

abstract: In this paper we have developed an algorithm for many-objective optimization problems, which will work more quickly than existing ones, while offering competitive performance. The algorithm periodically reorders the objectives based on their conflict status and selects a subset of conflicting objectives for further processing. We have taken differential evolution multiobjective optimization (DEMO) as the underlying metaheuristic evolutionary algorithm, and implemented the technique of selecting a subset of conflicting objectives using a correlation-based ordering of objectives. The resultant method is called α-DEMO, where α is a parameter determining the number of conflicting objectives to be selected. We have also proposed a new form of elitism so as to restrict the number of higher ranked solutions that are selected in the next population. The α-DEMO with the revised elitism is referred to as α-DEMO-revised. Extensive results of the five DTLZ functions show that the number of objective computations required in the proposed algorithm is much less compared to the existing algorithms, while the convergence measures are competitive or often better. Statistical significance testing is also performed. A real-life application on structural optimization of factory shed truss is demonstrated.


title: A Low-Noise, Large-Dynamic-Range-Enhanced Amplifier Based on JFET Buffering Input and JFET Bootstrap Structure

abstract: We design a low-noise, large-dynamic-range amplifier based on transimpedance amplifier (TIA) for detecting shot noise of 1064-nm laser in quantum optical experiments. Compared with a single-junction field-effect transistor (JFET) buffered TIA, the electronic noise is effectively 2.8 dB suppressed at the analysis of 2 MHz, and 4 dB suppressed at 4 MHz by means of a JFET bootstrap structure. Under the transimpedance gain of 200 k $\Omega $ and 0.5 pF, the measured shot noise at the injected laser power of $51~\mu $ W is 12.5 dB above the electronic noise at 2 MHz, and 9.8 dB at 4 MHz. With adjustment of bias condition of the bootstrap structure and application of a $L$ – $C$ (inductance and capacitance) structure, the dynamic range is largely boosted from the original 1.52 to 11.22 mW. The amplifier meets the requirement of SNR for Bell-state detection in quantum optical experiments.


title: The role of virtualization in embedded systems

abstract: System virtualization, which enjoys immense popularity in the enterprise and personal computing spaces, is recently gaining significant interest in the embedded domain. Starting from a comparison of key characteristics of enterprise systems and embedded systems, we will examine the difference in motivation for the use of system virtual machines, and the resulting differences in the requirements for the technology. We find that these differences are quite substantial, and that virtualization is unable to meet the special requirements of embedded systems. Instead, more general operating-systems technologies are required, which support virtualization as a special case. We argue that high-performance microkernels, specifically L4, are a technology that provides a good match for the requirements of next-generation embedded systems.


title: End-Effect Magnetic Field Analysis of the Halbach Array Permanent Magnet Spherical Motor

abstract: The Halbach array permanent magnet (PM) spherical motor (PMSM) consists of a spherical rotor and a spherical-shell stator. The magnetic field distribution surrounding the rotor PMs is closely related to the spherical structure of the Halbach array PMSM. The Halbach array PM configuration characteristics lead to the end leakage magnetic field in the latitudinal direction of the spherical structure, which is end effect. Establishment of an accurate 3-D magnetic field analytical model containing the end leakage magnetic field of the Halbach array PMSM is the foundation of magnetic field distortion research with its influence on eddy-current loss. The magnetic field distribution of the Halbach array PMs in the air gap is calculated using the scalar magnetic potential. Laplace and Poisson equations of the scalar magnetic potential in the spherical coordinate system are derived. To analyze the end leakage magnetic field of the Halbach array PMSM in the latitudinal direction, concept of mechanical pseudo-cycle in the motor latitudinal direction is proposed, and the cycle of Fourier series is redefined. A completed 3-D analytical expression of the Halbach array PMSM magnetic field distribution is derived by the new Fourier series and spatial integral technology, in which the end leakage magnetic field is contained. The results obtained by the proposed analytical method are compared with the numerical results, i.e., the results obtained by the finite-element method, which verify the effectiveness of the analytical method. Magnetic field distribution containing the end leakage magnetic field in different magnetization methods is compared and analyzed, which shows that the Halbach array magnetization has a larger main air-gap magnetic field and a smaller end leakage magnetic field. The effect of motor structure parameters on the end leakage magnetic field is analyzed, and the effect of end leakage magnetic field on eddy-current loss is preliminarily discussed, which shows that eddy-current loss caused by end effect almost accounts for half of the total eddy-current loss. The research on end effect and the effect of motor parameters on end effect are of great significance. In the end, the leakage magnetic field results obtained by the proposed analytical method are verified by the experimental results.


title: miRTarBase: a database curates experimentally validated microRNA–target interactions

abstract: MicroRNAs (miRNAs), i.e. small non-coding RNA molecules (∼22 nt), can bind to one or more target sites on a gene transcript to negatively regulate protein expression, subsequently controlling many cellular mechanisms. A current and curated collection of miRNA–target interactions (MTIs) with experimental support is essential to thoroughly elucidating miRNA functions under different conditions and in different species. As a database, miRTarBase has accumulated more than 3500 MTIs by manually surveying pertinent literature after data mining of the text systematically to filter research articles related to functional studies of miRNAs. Generally, the collected MTIs are validated experimentally by reporter assays, western blot, or microarray experiments with overexpression or knockdown of miRNAs. miRTarBase curates 3576 experimentally verified MTIs between 657 miRNAs and 2297 target genes among 17 species. miRTarBase contains the largest amount of validated MTIs by comparing with other similar, previously developed databases. The MTIs collected in the miRTarBase can also provide a large amount of positive samples to develop computational methods capable of identifying miRNA–target interactions. miRTarBase is now available on http://miRTarBase.mbc.nctu.edu.tw/, and is updated frequently by continuously surveying research articles.


title: Localizing Heart Sounds in Respiratory Signals Using Singular Spectrum Analysis

abstract: Respiratory sounds are always contaminated by heart sound interference. An essential preprocessing step in some of the heart sound cancellation methods is localizing primary heart sound components. Singular spectrum analysis (SSA), a powerful time series analysis technique, is used in this paper. Despite the frequency overlap of the heart and lung sound components, two different trends in the eigenvalue spectra are recognizable, which leads to find a subspace that contains more information about the underlying heart sound. Artificially mixed and real respiratory signals are used for evaluating the performance of the method. Selecting the appropriate length for the SSA window results in good decomposition quality and low computational cost for the algorithm. The results of the proposed method are compared with those of well-established methods, which use the wavelet transform and entropy of the signal to detect the heart sound components. The proposed method outperforms the wavelet-based method in terms of false detection and also correlation with the underlying heart sounds. Performance of the proposed method is slightly better than that of the entropy-based method. Moreover, the execution time of the former is significantly lower than that of the latter.


title: On-Board Calibration Methods for Mechanical Distortions of Satellite Phased Array Antennas

abstract: In satellite phased-array antennas, mechanical distortions, which may be due to vibrations caused when launching the satellite into the orbit or thermal variations in the orbit, result in significant degradation of electrical performance. In this paper, two on-board calibration methods are proposed for such mechanical distortions: One compensates for the element phase differences resulting from mechanical distortions, and the other is used to set the best-fit phase distribution obtained from the measured phases. The latter method is effective even at a low signal-to-noise ratio (SNR). The proposed methods have been validated by experiments, through which the theoretically predicted calibration accuracy of the methods has been successfully reproduced.


title: Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!

abstract: The global bandwidth shortage facing wireless carriers has motivated the exploration of the underutilized millimeter wave (mm-wave) frequency spectrum for future broadband cellular communication networks. There is, however, little knowledge about cellular mm-wave propagation in densely populated indoor and outdoor environments. Obtaining this information is vital for the design and operation of future fifth generation cellular networks that use the mm-wave spectrum. In this paper, we present the motivation for new mm-wave cellular systems, methodology, and hardware for measurements and offer a variety of measurement results that show 28 and 38 GHz frequencies can be used when employing steerable directional antennas at base stations and mobile devices.


title: Measuring technological distance for patent mapping

abstract: Recent works in the information science literature have presented cases of using patent databases and patent classification information to construct network maps of technology fields, which aim to aid in competitive intelligence analysis and innovation decision making. Constructing such a patent network requires a proper measure of the distance between different classes of patents in the patent classification systems. Despite the existence of various distance measures in the literature, it is unclear how to consistently assess and compare them, and which ones to select for constructing patent technology network maps. This ambiguity has limited the development and applications of such technology maps. Herein, we propose to compare alternative distance measures and identify the superior ones by analyzing the differences and similarities in the structural properties of resulting patent network maps. Using United States patent data from 1976 to 2006 and the International Patent Classification IPC system, we compare 12 representative distance measures, which quantify interfield knowledge base proximity, field-crossing diversification likelihood or frequency of innovation agents, and co-occurrences of patent classes in the same patents. Our comparative analyses suggest the patent technology network maps based on normalized coreference and inventor diversification likelihood measures are the best representatives.


title: Extracting Temporal and Causal Relations between Events

abstract: A notably challenging problem related to event processing is recognizing the relations holding between events in a text, in particular temporal and causal relations. While there has been some research on temporal relations, the aspect of causality between events from a Natural Language Processing (NLP) perspective has hardly been touched. We propose an annotation scheme to cover different types of causality between events, techniques for extracting such relations and an investigation into the connection between temporal and causal relations. In this thesis work we aim to focus especially on the latter, because causality is presumed to have a temporal constraint. We conjecture that injecting this presumption may be beneficial for the recognition of both temporal and causal relations.


title: Cake Cutting Algorithms: Be Fair If You Can

abstract: The challenge of dividing an asset fairly, from cakes to more important properties, is of great practical importance in many situations. Since the famous Polish school of mathematicians (Steinhaus, Banach, and Knaster) introduced and described algorithms for the fair division problem in the 1940s, the concept has been widely popularized. This book gathers into one readable and inclusive source a comprehensive discussion of the state of the art in cake-cutting problems for both the novice and the professional. It offers a complete treatment of all cake-cutting algorithms under all the considered definitions of "fair" and presents them in a coherent, reader-friendly manner. Robertson and Webb have brought this elegant problem to life for both the bright high school student and the professional researcher.


abstract: Principal components analysis and, more generally, the Singular Value Decomposition are fundamental data analysis tools that express a data matrix in terms of a sequence of orthogonal or uncorrelated vectors of decreasing importance. Unfortunately, being linear combinations of up to all the data points, these vectors are notoriously difficult to interpret in terms of the data and processes generating the data. In this article, we develop CUR matrix decompositions for improved data analysis. CUR decompositions are low-rank matrix decompositions that are explicitly expressed in terms of a small number of actual columns and/or actual rows of the data matrix. Because they are constructed from actual data elements, CUR decompositions are interpretable by practitioners of the field from which the data are drawn (to the extent that the original data are). We present an algorithm that preferentially chooses columns and rows that exhibit high “statistical leverage” and, thus, in a very precise statistical sense, exert a disproportionately large “influence” on the best low-rank fit of the data matrix. By selecting columns and rows in this manner, we obtain improved relative-error and constant-factor approximation guarantees in worst-case analysis, as opposed to the much coarser additive-error guarantees of prior work. In addition, since the construction involves computing quantities with a natural and widely studied statistical interpretation, we can leverage ideas from diagnostic regression analysis to employ these matrix decompositions for exploratory data analysis.


title: The limits of expert performance using hierarchic marking menus

abstract: A marking menu allows a user to perform a menu selection by either popping-up a radial (or pie) menu, or by making a straight mark in the direction of the desired menu item without popping-up the menu. A hierarchic marking menu uses hierarchic radial menus and “zig-zag” marks to select from the hierarchy. This paper experimentally investigates the bounds on how many items can be in each level, and how deep the hierarchy can be, before using a marking to select an item becomes too slow or prone to errors.


title: Optical communications using orbital angular momentum beams

abstract: Orbital angular momentum (OAM), which describes the “phase twist” (helical phase pattern) of light beams, has recently gained interest due to its potential applications in many diverse areas. Particularly promising is the use of OAM for optical communications since: (i) coaxially propagating OAM beams with different azimuthal OAM states are mutually orthogonal, (ii) inter-beam crosstalk can be minimized, and (iii) the beams can be efficiently multiplexed and demultiplexed. As a result, multiple OAM states could be used as different carriers for multiplexing and transmitting multiple data streams, thereby potentially increasing the system capacity. In this paper, we review recent progress in OAM beam generation/detection, multiplexing/demultiplexing, and its potential applications in different scenarios including free-space optical communications, fiber-optic communications, and RF communications. Technical challenges and perspectives of OAM beams are also discussed.


title: Personalized QoS-Aware Web Service Recommendation and Visualization

abstract: With the proliferation of web services, effective QoS-based approach to service recommendation is becoming more and more important. Although service recommendation has been studied in the recent literature, the performance of existing ones is not satisfactory, since (1) previous approaches fail to consider the QoS variance according to users' locations; and (2) previous recommender systems are all black boxes providing limited information on the performance of the service candidates. In this paper, we propose a novel collaborative filtering algorithm designed for large-scale web service recommendation. Different from previous work, our approach employs the characteristic of QoS and achieves considerable improvement on the recommendation accuracy. To help service users better understand the rationale of the recommendation and remove some of the mystery, we use a recommendation visualization technique to show how a recommendation is grouped with other choices. Comprehensive experiments are conducted using more than 1.5 million QoS records of real-world web service invocations. The experimental results show the efficiency and effectiveness of our approach.


title: Wireless Sensor Networks for Cost-Efficient Residential Energy Management in the Smart Grid

abstract: Wireless sensor networks (WSNs) will play a key role in the extension of the smart grid towards residential premises, and enable various demand and energy management applications. Efficient demand-supply balance and reducing electricity expenses and carbon emissions will be the immediate benefits of these applications. In this paper, we evaluate the performance of an in-home energy management (iHEM) application. The performance of iHEM is compared with an optimization-based residential energy management (OREM) scheme whose objective is to minimize the energy expenses of the consumers. We show that iHEM decreases energy expenses, reduces the contribution of the consumers to the peak load, reduces the carbon emissions of the household, and its savings are close to OREM. On the other hand, iHEM application is more flexible as it allows communication between the controller and the consumer utilizing the wireless sensor home area network (WSHAN). We evaluate the performance of iHEM under the presence of local energy generation capability, prioritized appliances, and for real-time pricing. We show that iHEM reduces the expenses of the consumers for each case. Furthermore, we show that packet delivery ratio, delay, and jitter of the WSHAN improve as the packet size of the monitoring applications, that also utilize the WSHAN, decreases.


title: Lifelong Craniofacial Growth and the Implications for Osseointegrated Implants

abstract: Purpose: The sequence of observations presented is intended to alert the dental profession to complications that may occur when teeth and implants co-exist and subtle adult craniofacial growth occurs. Materials and Methods: The authors’ observations of partially edentulous implant restorations with more than 20 years of follow-up included some observed changes relative to patients’ remaining teeth and jaw structures. These changes, which were not easily explained and appeared to be random deviations from expected implantrestorative stability, conformed with research ! ndings of craniofacial growth continuing into adulthood. The authors identi! ed several distinct areas in which such adult craniofacial growth could potentially in" uence the relationship of implant restorations to the remaining teeth and jaw structure. Results: Potential esthetic, occlusal, and periodontal rami! cations of continued adult craniofacial growth were found to include changes in occlusion, opened contact as a result of teeth migration, and changes in anterior esthetic results. The latter may include labialization of the anterior implant restoration and a progressive discrepancy of the cervical gingival margin of the implant restoration relative to the adjacent teeth. Cases are presented showing poor sequellae of treatment due to growth occurring after the assumption was made that a stable jaw dimension had been reached. While continued adult craniofacial growth suf! cient to cause clinical problems is not common, it is also presently not predictable. Conclusion: When changes in tooth position relative to implant restorations secondary to long-term adult growth occur, they can cause problems that are dif! cult or even impossible to correct. Future research will ideally enable identi! cation of patients at risk for developing such problems. INT J ORAL MAXILLOFAC IMPLANTS 2012;28:163‐169. doi: 10.11607/jomi.2827


title: Laplacian Echo State Network for Multivariate Time Series Prediction

abstract: Echo state network is a novel kind of recurrent neural networks, with a trainable linear readout layer and a large fixed recurrent connected hidden layer, which can be used to map the rich dynamics of complex real-world data sets. It has been extensively studied in time series prediction. However, there may be an ill-posed problem caused by the number of real-world training samples less than the size of the hidden layer. In this brief, a Laplacian echo state network (LAESN), is proposed to overcome the ill-posed problem and obtain low-dimensional output weights. First, an echo state network is used to map the multivariate time series into a large reservoir. Then, assuming that an unknown underlying manifold is inside the reservoir, we employ the Laplacian eigenmaps to estimate the manifold by constructing an adjacency graph associated with the reservoir states. Finally, the output weights are calculated by the low-dimensional manifold. In addition, some criteria of transient stability, local controllability, and local observability are given. Experimental results based on two real-world data sets substantiate the effectiveness and characteristics of the proposed LAESN model.


title: A CONJOINT STUDY OF THE RELATIONSHIP BETWEEN WEBSITE ATTRIBUTES AND CONSUMER PURCHASE INTENTIONS

abstract: Many scholars have developed different conceptual models to capture effects of factors on consumers’ preferences for on-line shopping websites (e.g., Ranganathan and Gananpathy, 2002; Schaupp and Belanger, 2005; Shih, 2004; Straub and Watson, 2001) because the on-line shopping website has become one of the major interfaces between companies and their consumers. A review of extant literatures suggests that a survey method has been widely used. However, a survey method used for exploring consumers’ preferences for on-line shopping websites may bear potential problems of validity and transferability due to respondents’ potential lack of both the knowledge and experience associated with on-line technologies. To address the above problem, a refined methodology is imperative in the investigation into on-line shopping experiences. In this paper, we extended and modified the Schaupp and Belanger model (2005) to develop and propose a model of on-line shopping intentions that includes three categories of factors: technology, shopping, and product. Next, we developed four on-line websites that incorporate and operationalize the relevant factors to serve as test platforms. All respondents of this study were asked to visit these web-sites that represent differing shopping scenarios before they completed our on-line survey. By simulating different shopping scenarios on our four test platforms, prior experiences of respondents are not required in this study. Findings of this study are as follows. First, on-line contract signing mechanism was ranked as the most desired option in building trust in on-line shopping websites by all respondents. Second, respondents of this study were found able to be categorized into three groups according to the website factors they valued most. The three were logistic/security, security/trust, and convenience/trust. The specific attributes of on-line shopping websites valued most within each of the three groups are discussed in this paper. This study presents findings that support the use of many existing on-line technologies that have been found little used in on-line shopping websites such as digital certificate and on-line contracting. The inclusion of these technologies in on-line shopping websites may increase the cost of building and maintaining them and may create worries about user inconvenience. However, it is important that these technologies increase the attractiveness of the on-line shopping website and web surfers purchase intentions.


Design and realisation of Low leakage 1-bit CMOS based Full Adder Cells for Mobile Applications


title: Modeling Pain Using fMRI: From Regions to Biomarkers

abstract: Pain is a subjective and complex phenomenon. Its complexity is related to its heterogeneity: multiple component processes, including sensation, affect, and cognition, contribute to pain experience and reporting. These components are likely to be encoded in distributed brain networks that interact to create pain experience and pain-related decision-making. Therefore, to understand pain, we must identify these networks and build models of these interactions that yield testable predictions about pain-related outcomes. We have developed several such models or ‘signatures’ of pain, by (1) integrating activity across multiple systems, and (2) using pattern-recognition to identify processes related to pain experience. One model, the Neurologic Pain Signature, is sensitive and specific to pain in individuals, involves brain regions that receive nociceptive afferents, and shows little effect of expectation or self-regulation in tests to date. Another, the ‘Stimulus Intensity-Independent Pain Signature’, explains substantial additional variation in trial-to-trial pain reports. It involves many brain regions that do not show increased activity in proportion to noxious stimulus intensity, including medial and lateral prefrontal cortex, nucleus accumbens, and hippocampus. Responses in this system mediate expectancy and perceived control effects in several studies. Overall, this approach provides a pathway to understanding pain by identifying multiple systems that track different aspects of pain. Such componential models can be combined in unique ways on a subject-by-subject basis to explain an individual’s pain experience.


title: The 2017 international classification of the Ehlers–Danlos syndromes

abstract: The Ehlers–Danlos syndromes (EDS) are a clinically and genetically heterogeneous group of heritable connective tissue disorders (HCTDs) characterized by joint hypermobility, skin hyperextensibility, and tissue fragility. Over the past two decades, the Villefranche Nosology, which delineated six subtypes, has been widely used as the standard for clinical diagnosis of EDS. For most of these subtypes, mutations had been identified in collagen-encoding genes, or in genes encoding collagen-modifying enzymes. Since its publication in 1998, a whole spectrum of novel EDS subtypes has been described, and mutations have been identified in an array of novel genes. The International EDS Consortium proposes a revised EDS classification, which recognizes 13 subtypes. For each of the subtypes, we propose a set of clinical criteria that are suggestive for the diagnosis. However, in view of the vast genetic heterogeneity and phenotypic variability of the EDS subtypes, and the clinical overlap between EDS subtypes, but also with other HCTDs, the definite diagnosis of all EDS subtypes, except for the hypermobile type, relies on molecular confirmation with identification of (a) causative genetic variant(s). We also revised the clinical criteria for hypermobile EDS in order to allow for a better distinction from other joint hypermobility disorders. To satisfy research needs, we also propose a pathogenetic scheme, that regroups EDS subtypes for which the causative proteins function within the same pathway. We hope that the revised International EDS Classification will serve as a new standard for the diagnosis of EDS and will provide a framework for future research purposes. © 2017 Wiley Periodicals, Inc.


title: Vertebrae Detection and Labelling in Lumbar MR Images

abstract: We describe a method to automatically detect and label the vertebrae in human lumbar spine MRI scans. Our contribution is to show that marrying two strong algorithms (the DPM object detector of Felzenszwalb et al. [1], and inference using dynamic programming on chains) together with appropriate modelling, results in a simple, computationally cheap procedure, that achieves state-of-the-art performance. The training of the algorithm is principled, and heuristics are not required. The method is evaluated quantitatively on a dataset of 371 MRI scans, and it is shown that the method copes with pathologies such as scoliosis, joined vertebrae, deformed vertebrae and disks, and imaging artifacts. We also demonstrate that the same method is applicable (without retraining) to CT scans.


When is a linguistic metaphor a conceptual metaphor


title: Piezoelectric Vibration-Type Tactile Sensor Using Elasticity and Viscosity Change of Structure

abstract: We propose a new tactile sensor utilizing piezoelectric vibration. This tactile sensor has a high sensitivity, wide measurement range, pressure resistance, flexibility, and self-sensing function. This tactile sensor comprises two piezoelectric materials. One is used for the vibration of the sensor element and the other is used for the measurement of the change in mechanical impedance induced by an external force. We achieved the wide measurement range by implementing two ideas. One was to apply the external force to the sensor element through an elastic body and the other was to use two or more modes of vibration. Moreover, for the elastic body, it is preferable to use a material whose elasticity and viscosity are easily changed by an external force, such as a gel. In this study, first, this tactile sensor was analyzed, and then its characteristics were derived. The analytical results qualitatively corresponded to the experimental results. Next, a prototype tactile sensor was fabricated and evaluated. The evaluation results showed that this tactile sensor can measure a pressure of 2.5 [Pa] or less and a pressure of 10 [kPa] or more and its pressure resistance is 1 [MPa] or more.


title: Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration

abstract: Tracking of multiple objects is an important application in AI City geared towards solving salient problems related to safety and congestion in an urban environment. Frequent occlusion in traffic surveillance has been a major problem in this research field. In this challenge, we propose a model-based vehicle localization method, which builds a kernel at each patch of the 3D deformable vehicle model and associates them with constraints in 3D space. The proposed method utilizes shape fitness evaluation besides color information to track vehicle objects robustly and efficiently. To build 3D car models in a fully unsupervised manner, we also implement evolutionary camera self-calibration from tracking of walking humans to automatically compute camera parameters. Additionally, the segmented foreground masks which are crucial to 3D modeling and camera self-calibration are adaptively refined by multiple-kernel feedback from tracking. For object detection/classification, the state-of-the-art single shot multibox detector (SSD) is adopted to train and test on the NVIDIA AI City Dataset. To improve the accuracy on categories with only few objects, like bus, bicycle and motorcycle, we also employ the pretrained model from YOLO9000 with multi-scale testing. We combine the results from SSD and YOLO9000 based on ensemble learning. Experiments show that our proposed tracking system outperforms both state-of-the-art of tracking by segmentation and tracking by detection.


title: Design, implementation and evaluation of congestion control for multipath TCP

abstract: Multipath TCP, as proposed by the IETF working group mptcp, allows a single data stream to be split across multiple paths. This has obvious benefits for reliability, and it can also lead to more efficient use of networked resources. We describe the design of a multipath congestion control algorithm, we implement it in Linux, and we evaluate it for multihomed servers, data centers and mobile clients. We show that some 'obvious' solutions for multipath congestion control can be harmful, but that our algorithm improves throughput and fairness compared to single-path TCP. Our algorithmis a drop-in replacement for TCP, and we believe it is safe to deploy.


Adaptive prediction time for sequence classification


title: The Tree-to-Tree Correction Problem

abstract: The tree-to-tree correctmn problem Is to determine, for two labeled ordered trees T and T', the distance from T to T' as measured by the mlmmum cost sequence of edit operaUons needed to transform T into T' The edit operations investigated allow changing one node of a tree into another node, deleting one node from a tree, or inserting a node into a tree An algorithm Is presented which solves this problem m time O(V* V'*LZ* L'2), where V and V' are the numbers of nodes respectively of T and T', and L and L' are the maximum depths respectively of T and T' Possible apphcatmns are to the problems of measuring the similarity between trees, automatic error recovery and correction for programming languages, and determining the largest common substructure of two trees


title: An extensive comparison of species-abundance distribution models

abstract: The SAD is one of the most widely studied patterns in ecology, leading to a proliferation of models that attempt to characterize the shape of the distribution and identify potential mechanisms for the pattern (see McGill et al. 2007 for a recent review of SADs). These models range from arbitrary distributions that are chosen based on providing a good fit to the data (Fisher et al. 1943), to distributions chosen based on combinatorics and the most likely state of the system, (Frank 2011, Harte 2011, Locey and White 2013), to models based on ecological process (Tokeshi 1993, Hubbell 2001, Volkov et al. 2003).


title: Recurrent Pixel Embedding for Instance Grouping

abstract: We introduce a differentiable, end-to-end trainable framework for solving pixel-level grouping problems such as instance segmentation consisting of two novel components. First, we regress pixels into a hyper-spherical embedding space so that pixels from the same group have high cosine similarity while those from different groups have similarity below a specified margin. We analyze the choice of embedding dimension and margin, relating them to theoretical results on the problem of distributing points uniformly on the sphere. Second, to group instances, we utilize a variant of mean-shift clustering, implemented as a recurrent neural network parameterized by kernel bandwidth. This recurrent grouping module is differentiable, enjoys convergent dynamics and probabilistic interpretability. Backpropagating the group-weighted loss through this module allows learning to focus on only correcting embedding errors that won't be resolved during subsequent clustering. Our framework, while conceptually simple and theoretically abundant, is also practically effective and computationally efficient. We demonstrate substantial improvements over state-of-the-art instance segmentation for object proposal generation, as well as demonstrating the benefits of grouping loss for classification tasks such as boundary detection and semantic segmentation.


title: Modelling Context with User Embeddings for Sarcasm Detection in Social Media

abstract: We introduce a deep neural network for automated sarcasm detection. Recent work has emphasized the need for models to capitalize on contextual features, beyond lexical and syntactic cues present in utterances. For example, different speakers will tend to employ sarcasm regarding different subjects and, thus, sarcasm detection models ought to encode such speaker information. Current methods have achieved this by way of laborious feature engineering. By contrast, we propose to automatically learn and then exploit user embeddings, to be used in concert with lexical signals to recognize sarcasm. Our approach does not require elaborate feature engineering (and concomitant data scraping); fitting user embeddings requires only the text from their previous posts. The experimental results show that our model outperforms a state-of-the-art approach leveraging an extensive set of carefully crafted features.


title: Design of Dual Band Sleeve Dipole Antenna for Mobile Jammer Applications

abstract: A multi band sleeve dipole antenna for jammer application is proposed. The antenna can cover band in which mobile and other wireless devices operate. The design antenna shows very good omni-directional characteristics and scattering parameter S11 value less than -10db. The dipole is made of two concentric hallow cylinder with different radius and different lengths. The antenna is fed at the center with coaxial cable pass through the bottom cylinder and thus avoids the use of complicated balun network. The impedance bandwidth and the radiation pattern are measured and analyzed both at 900 MHz and 1.8 GHz. The antenna can also be used GSM/DCS/WCDMA/CDMA2000/ TD-SCDMA /WLAN applications at both indoor and outdoor platform.