title: Statistical and algebraic analysis of the transfer matrices errors

abstract: In this paper we consider two methods for assessing the impact of errors in the adjustment of control parameters in accelerator systems. These methods are based on two methods of investigation of errors of control parameters. The first of them is based on the well known method of the statistical analysis and the second - on the algebraic analysis of the transfer matrix. These approaches are naturally encapsulated in the Virtual Accelerator concept.


title: Microfluidic large-scale integration.

abstract: We developed high-density microfluidic chips that contain plumbing networks with thousands of micromechanical valves and hundreds of individually addressable chambers. These fluidic devices are analogous to electronic integrated circuits fabricated using large-scale integration. A key component of these networks is the fluidic multiplexor, which is a combinatorial array of binary valve patterns that exponentially increases the processing power of a network by allowing complex fluid manipulations with a minimal number of inputs. We used these integrated microfluidic networks to construct the microfluidic analog of a comparator array and a microfluidic memory storage device whose behavior resembles random-access memory.


title: An integrated flywheel energy storage system with homopolar inductor motor/generator and high-frequency drive

abstract: The design, construction, and test of an integrated flywheel energy storage system with a homopolar inductor motor/generator and high-frequency drive is presented in this paper. The work is presented as an integrated design of flywheel system, motor, drive, and controller. The motor design features low rotor losses, a slotless stator, construction from robust and low cost materials, and a rotor that also serves as the energy storage rotor for the flywheel system. A high-frequency six-step drive scheme is used in place of pulsewidth modulation because of the high electrical frequencies. A speed-sensorless controller that works without state estimation is also described. A prototype of the flywheel system has been demonstrated at a power level of 9.4 kW, with an average system efficiency of 83% over a 30000-60000 r/min speed range.


title: An innovative m-utility management and billing system

abstract: Purpose – Aims to provide a new wireless design for utility management and billing systems using GSM networks that can be used by large utility sites.Design/methodology/approach – Traditional energy and water meters are replaced or enhanced to produce digital readings. A hardware interface is designed and then connected via the GSM network to a server at the utility headquarters. The new system is compared with traditional utility systems.Findings – A prototype for the proposed systems was implemented. It was tested using a real GSM network in the United Arab Emirates. The performance of the systems was acceptable, with high accuracy results when compared with the existing systems.Research limitations/implications – Applying the system in large buildings with multiple subscribers is not a straightforward task; the system may need some modifications.Practical implications – This is a very useful system for utility companies which are interested in better management and billing systems. The system can be im...


title: Deep neural networks segment neuronal membranes in electron microscopy images

abstract: We address a central problem of neuroanatomy, namely, the automatic segmentation of neuronal structures depicted in stacks of electron microscopy (EM) images. This is necessary to efficiently map 3D brain structure and connectivity. To segment biological neuron membranes, we use a special type of deep artificial neural network as a pixel classifier. The label of each pixel (membrane or non-membrane) is predicted from raw pixel values in a square window centered on it. The input layer maps each window pixel to a neuron. It is followed by a succession of convolutional and max-pooling layers which preserve 2D information and extract features with increasing levels of abstraction. The output layer produces a calibrated probability for each class. The classifier is trained by plain gradient descent on a 512 × 512 × 30 stack with known ground truth, and tested on a stack of the same size (ground truth unknown to the authors) by the organizers of the ISBI 2012 EM Segmentation Challenge. Even without problem-specific postprocessing, our approach outperforms competing techniques by a large margin in all three considered metrics, i.e. rand error, warping error and pixel error. For pixel error, our approach is the only one outperforming a second human observer.


title: A Moving Least Squares Based Approach for Contour Visualization of Multi-Dimensional Data

abstract: Analysis of high dimensional data is a common task. Often, small multiples are used to visualize 1 or 2 dimensions at a time, such as in a scatterplot matrix. Associating data points between different views can be difficult though, as the points are not fixed. Other times, dimensional reduction techniques are employed to summarize the whole dataset in one image, but individual dimensions are lost in this view. In this paper, we present a means of augmenting a dimensional reduction plot with isocontours to reintroduce the original dimensions. By applying this to each dimension in the original data, we create multiple views where the points are consistent, which facilitates their comparison. Our approach employs a combination of a novel, graph-based projection technique with a GPU accelerated implementation of moving least squares to interpolate space between the points. We also present evaluations of this approach both with a case study and with a user study.


title: MRI texture features may predict differentiation and nodal stage of cervical cancer: a pilot study

abstract: BackgroundTexture analysis in oncological magnetic resonance imaging (MRI) may yield surrogate markers for tumor differentiation and staging, both of which are important factors in the treatment planning for cervical cancer.PurposeTo identify texture features which may predict tumor differentiation and nodal status in diffusion-weighted imaging (DWI) of cervical carcinomaMaterial and MethodsTwenty-three patients were enrolled in this prospective, institutional review board (IRB)-approved study. Pelvic MRI was performed at 3-T including a DWI echo-planar sequence with b-values 40, 300, and 800 s/mm2. Apparent diffusion coefficient (ADC) maps were used for region of interest (ROI)-based texture analysis (32 texture features) of tumor, muscle, and fat based on histogram and gray-level matrices (GLM). All features confounded by the ROI size (linear model) were excluded. The remaining features were examined for correlations with histological differentiation (Spearman) and nodal status (Kruskal–Wallis). Hierarc...


title: Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data

abstract: Human large-scale functional brain networks are hypothesized to undergo significant changes over development. Little is known about these functional architectural changes, particularly during the second half of the first year of life. We used multivariate pattern classification of resting-state functional connectivity magnetic resonance imaging (fcMRI) data obtained in an on-going, multi-site, longitudinal study of brain and behavioral development to explore whether fcMRI data contained information sufficient to classify infant age. Analyses carefully account for the effects of fcMRI motion artifact. Support vector machines (SVMs) classified 6 versus 12 month-old infants (128 datasets) above chance based on fcMRI data alone. Results demonstrate significant changes in measures of brain functional organization that coincide with a special period of dramatic change in infant motor, cognitive, and social development. Explorations of the most different correlations used for SVM lead to two different interpretations about functional connections that support 6 versus 12-month age categorization.


title: gFacet: A Browser for the Web of Data

abstract: This paper introduces a new approach to browsing the Web of data by combining graph-based visualization with faceted filtering techniques. The graph-based visualization of facets supports the integration of different domains and an efficient exploration of highly structured and interrelated datasets. It allows to access information from distant user-defined perspectives and thereby enables the exploration beyond the borders of Web pages.


title: Lithium and the Nature of Brown Dwarf Candidates

abstract: This paper discusses the ability of Li to confirm the substellar nature of a brown dwarf candidate. Theoretical computations using different interior models agree that brown dwarfs with masses below $\sim$ 0.065 M$_{\odot}$ preserve a significant fraction of their initial Li content while for higher masses total Li depletion occurs in very short timescales. Refined spectral synthesis using brown dwarf model atmospheres shows that the Li I resonance line at 670.8 nm should produce a conspicuous feature at effective temperatures higher than 2000 K, suitable for spectroscopic detection in present brown dwarf candidates. We summarize the results of our search for Li in many of the best candidates known so far.


title: Emergent computation in simple model of neural activity

abstract: We investigate the dynamics of a network consisting of an array of identical cortical units with nearest neighbor interactions under periodic arousal. Each unit consists of two interconnected populations of neurons tuned to a state in which many nonlinear resonances are available. The network is critically balanced due to short-ranged antisymmetric connections between units. For wide ranges of the network parameters, the patterns of activity resemble the dynamics of cellular automata. It is argued that these dynamical states may provide a template in which computation can be implemented.


title: Survey of failures and fault tolerance in cloud

abstract: Cloud computing provides support for hosting client's application. Cloud is a distributed platform that provides hardware, software and network resources to both execute consumer's application and also to store and mange user's data. Cloud is also used to execute scientific workflow applications that are in general complex in nature when compared to other applications. Since cloud is a distributed platform, it is more prone to errors and failures. In such an environment, avoiding a failure is difficult and identifying the source of failure is also complex. Because of this, fault tolerance mechanisms are implemented on the cloud platform. This ensures that even if there are failures in the environment, critical data of the client is not lost and user's application running on cloud is not affected in any manner. Fault tolerance mechanisms also help in improving the cloud's performance by proving the services to the users as required on demand. In this paper a survey of existing fault tolerance mechanisms for the cloud platform are discussed. This paper also discusses the failures, fault tolerant clustering methods and fault tolerant models that are specific for scientific workflow applications.


title: Structured Light-Based 3D Reconstruction System for Plants

abstract: Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants.This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance.


title: Educating the Disagreeable Extravert: Narcissism, the Big Five Personality Traits, and Achievement Goal Orientation

abstract: Despite the fact that longitudinal data have been compiled over the past 30 years among undergraduate students in higher education settings regarding narcissism, the literature is devoid of empirical investigations that explore the relationships between narcissism and learning. Because the data suggest that narcissism scores are increasing each year among this population, an exploration of the relationship between narcissism and learning is timely and warranted. Sampling from university undergraduate students, this study uses the Narcissistic Personality Inventory, the Big Five Inventory, and the Achievement Goal Questionnaire to verify the known relationships between narcissism and the Big Five personality traits of extraversion and agreeableness; to verify the known relationships between the Big Five personality traits of extraversion and agreeableness and goal orientation; and to explore a previously undocumented empirical relationship between narcissism and performance goal orientation. Results of this exploratory study indicate that while narcissism does contribute to a performance goal orientation, it is not a substantial variable in determining achievement goal orientation in general. The study addresses the implications and limitations of this research in addition to areas for additional investigation.


title: Leadership Behaviour Preferences of Student-Athletes: A Comparative Study of South Africa and India

abstract: The athlete-coach relationship is a vertical dyadic relationship in which the coach plays a critical leadership role. The direct influence of coaches on their athletes has been widely reported in many studies from various perspectives. These perspectives include sport involvement, enjoyment, and withdrawal, athlete satisfaction; athletes’ physical and psychological status; optimal sport performance; and successful sport performance. The majority of research in sport leadership has focused on the coach, with particular emphasis on personality traits, behavioural attributes, and situational determinants. The current study focuses on a comparison of coach leadership preferences of athletes in South Africa and India. The study used a quantitative research approach using the Leadership Scale for Sport (LSS) to collect data from student-athletes in South Africa and India. There were 221 South African participants with ages ranging between 18 years and 60 years (M = 23.8, SD = 6.0). On the other hand, there were 400 Indian participants with ages ranging between 18 years and 25 years (M = 21.4, SD = 2.1). To analyse data, descriptive statistics in terms of means and standard deviations were computed. Comparisons of different variables between the two countries were determined through computing unpaired samples t-tests. The results revealed that Indian participants had higher mean scores for all the leadership behaviour preferences compared to their South African counterparts. In fact, all the means were statistically significant (p DOI: 10.5901/mjss.2013.v4n11p81


Beyond The Cox Model : Artificial Neural Networks For Survival Analysis Part II


title: AKNOBAS: A knowledge-based segmentation recommender system based on intelligent data mining techniques

abstract: Recommender Systems have recently undergone an unwavering improvement in ::: terms of efficiency and pervasiveness. They have become a source of ::: competitive advantage in many companies which thrive on them as the ::: technological core of their business model. In recent years, we have made ::: substantial progress in those Recommender Systems outperforming the accuracy ::: and added-value of their predecessors, by using cutting-edge techniques such ::: as Data Mining and Segmentation. In this paper, we present AKNOBAS, a ::: Knowledge-based Segmentation Recommender System, which follows that trend ::: using Intelligent Clustering Techniques for Information Systems. The ::: contribution of this Recommender System has been validated through a business ::: scenario implementation proof-of-concept and provides a clear breakthrough of ::: marshaling information through AI techniques.


title: Mutations in PTPN11, encoding the protein tyrosine phosphatase SHP-2, cause Noonan syndrome

abstract: Noonan syndrome (MIM 163950) is an autosomal dominant disorder characterized by dysmorphic facial features, proportionate short stature and heart disease (most commonly pulmonic stenosis and hypertrophic cardiomyopathy). Webbed neck, chest deformity, cryptorchidism, mental retardation and bleeding diatheses also are frequently associated with this disease. This syndrome is relatively common, with an estimated incidence of 1 in 1,000-2,500 live births. It has been mapped to a 5-cM region (NS1) [corrected] on chromosome 12q24.1, and genetic heterogeneity has also been documented. Here we show that missense mutations in PTPN11 (MIM 176876)-a gene encoding the nonreceptor protein tyrosine phosphatase SHP-2, which contains two Src homology 2 (SH2) domains-cause Noonan syndrome and account for more than 50% of the cases that we examined. All PTPN11 missense mutations cluster in interacting portions of the amino N-SH2 domain and the phosphotyrosine phosphatase domains, which are involved in switching the protein between its inactive and active conformations. An energetics-based structural analysis of two N-SH2 mutants indicates that in these mutants there may be a significant shift of the equilibrium favoring the active conformation. This implies that they are gain-of-function changes and that the pathogenesis of Noonan syndrome arises from excessive SHP-2 activity.


title: SURGE: Surface Regularized Geometry Estimation from a Single Image

abstract: This paper introduces an approach to regularize 2.5D surface normal and depth predictions at each pixel given a single input image. The approach infers and reasons about the underlying 3D planar surfaces depicted in the image to snap predicted normals and depths to inferred planar surfaces, all while maintaining fine detail within objects. Our approach comprises two components: (i) a four-stream convolutional neural network (CNN) where depths, surface normals, and likelihoods of planar region and planar boundary are predicted at each pixel, followed by (ii) a dense conditional random field (DCRF) that integrates the four predictions such that the normals and depths are compatible with each other and regularized by the planar region and planar boundary information. The DCRF is formulated such that gradients can be passed to the surface normal and depth CNNs via backpropagation. In addition, we propose new planar-wise metrics to evaluate geometry consistency within planar surfaces, which are more tightly related to dependent 3D editing applications. We show that our regularization yields a 30% relative improvement in planar consistency on the NYU v2 dataset [24].


title: An Analysis of the Feasibility of Graph Compression Techniques for Indexing Regular Path Queries

abstract: Regular path queries (RPQs) are a fundamental part of recent graph query languages like SPARQL and PGQL. They allow the definition of recursive path structures through regular expressions in a declarative pattern matching environment. We study the use of the K2-tree graph compression technique to materialize RPQ results with low memory consumption for indexing. Compact index representations enable the efficient storage of multiple indexes for varying RPQs.


title: Effects of Loss Functions And Target Representations on Adversarial Robustness

abstract: Understanding and evaluating the robustness of neural networks under adversarial settings is a subject of growing interest. Attacks proposed in the literature usually work with models trained to minimize cross-entropy loss and output softmax probabilities. In this work, we present interesting experimental results that suggest the importance of considering other loss functions and target representations, specifically, (1) training on mean-squared error and (2) representing targets as codewords generated from a random codebook. We evaluate the robustness of neural networks that implement these proposed modifications using existing attacks, showing an increase in accuracy against untargeted attacks of up to 98.7\% and a decrease of targeted attack success rates of up to 99.8\%. Our model demonstrates more robustness compared to its conventional counterpart even against attacks that are tailored to our modifications. Furthermore, we find that the parameters of our modified model have significantly smaller Lipschitz bounds, an important measure correlated with a model's sensitivity to adversarial perturbations.


title: Multilayer Hybrid Deep-Learning Method for Waste Classification and Recycling

abstract: This study proposes a multilayer hybrid deep-learning system (MHS) to automatically sort waste disposed of by individuals in the urban public area. This system deploys a high-resolution camera to capture waste image and sensors to detect other useful feature information. The MHS uses a CNN-based algorithm to extract image features and a multilayer perceptrons (MLP) method to consolidate image features and other feature information to classify wastes as recyclable or the others. The MHS is trained and validated against the manually labelled items, achieving overall classification accuracy higher than 90% under two different testing scenarios, which significantly outperforms a reference CNN-based method relying on image-only inputs.


title: Can we build language-independent OCR using LSTM networks?

abstract: Language models or recognition dictionaries are usually considered an essential step in OCR. However, using a language model complicates training of OCR systems, and it also narrows the range of texts that an OCR system can be used with. Recent results have shown that Long Short-Term Memory (LSTM) based OCR yields low error rates even without language modeling. In this paper, we explore the question to what extent LSTM models can be used for multilingual OCR without the use of language models. To do this, we measure cross-language performance of LSTM models trained on different languages. LSTM models show good promise to be used for language-independent OCR. The recognition errors are very low (around 1%) without using any language model or dictionary correction.


title: Shoulder Surfing Defence for Recall-based Graphical Passwords

abstract: Graphical passwords are often considered prone to shoulder-surfing attacks, where attackers can steal a user's password by peeking over his or her shoulder in the authentication process. In this paper, we explore shoulder surfing defence for recall-based graphical password systems such as Draw-A-Secret and Background Draw-A-Secret, where users doodle their passwords (i.e. secrets) on a drawing grid. We propose three innovative shoulder surfing defence techniques, and conduct two separate controlled laboratory experiments to evaluate both security and usability perspectives of the proposed techniques. One technique was expected to work to some extent theoretically, but it turned out to provide little protection. One technique provided the best overall shoulder surfing defence, but also caused some usability challenges. The other technique achieved reasonable shoulder surfing defence and good usability simultaneously, a good balance which the two other techniques did not achieve. Our results appear to be also relevant to other graphical password systems such as Pass-Go.


Recurrent Neural Network Predictions in Sentiment Analysis


title: Unemployment duration and personality

abstract: This paper focuses on the role personality traits play in determining individual unemployment duration. We argue that a worker’s job search intensity is decisively driven by her personality traits, reflected in her propensity to motivate and control herself while searching for a job. Moreover, personality traits, in as far as they can be signaled to a potential employer, may also enhance the probability of receiving and accepting a job offer.


title: Multiview Deep Learning for Land-Use Classification

abstract: A multiscale input strategy for multiview deep learning is proposed for supervised multispectral land-use classification, and it is validated on a well-known data set. The hypothesis that simultaneous multiscale views can improve composition-based inference of classes containing size-varying objects compared to single-scale multiview is investigated. The end-to-end learning system learns a hierarchical feature representation with the aid of convolutional layers to shift the burden of feature determination from hand-engineering to a deep convolutional neural network (DCNN). This allows the classifier to obtain problem-specific features that are optimal for minimizing the multinomial logistic regression objective, as opposed to user-defined features which trade optimality for generality. A heuristic approach to the optimization of the DCNN hyperparameters is used, based on empirical performance evidence. It is shown that a single DCNN can be trained simultaneously with multiscale views to improve prediction accuracy over multiple single-scale views. Competitive performance is achieved for the UC Merced data set, where the 93.48% accuracy of multiview deep learning outperforms the 85.37% accuracy of SIFT-based methods and the 90.26% accuracy of unsupervised feature learning.


title: Low noise amplifier circuit design for 5 GHz to 6 GHz

abstract: The development of wireless portable electronics is moving towards smaller and lighter devices. Although low noise amplifier (LNA) performance is extremely good nowadays, the design engineer still has to make some complex system trades. Many LNA are large, heavy and consume a lot of power. The design of an LNA in radio frequency (RF) circuits requires the trade-off of many important characteristics, such as gain, noise figure (NF), stability, power consumption and complexity. This situation forces designers to make choices in the design of RF circuits. The designed simulation process is done using the Advance Design System (ADS), while FR4 strip board is used for fabrication purposes. A single stage LNA has successfully designed with 7.78 dB forward gain and 1.53 dB noise figure; it is stable along the UNII frequency band.


title: Density-Driven Cross-Lingual Transfer of Dependency Parsers

abstract: We present a novel method for the crosslingual transfer of dependency parsers. Our goal is to induce a dependency parser in a target language of interest without any direct supervision: instead we assume access to parallel translations between the target and one or more source languages, and to supervised parsers in the source language(s). Our key contributions are to show the utility of dense projected structures when training the target language parser, and to introduce a novel learning algorithm that makes use of dense structures. Results on several languages show an absolute improvement of 5.51% in average dependency accuracy over the state-of-the-art method of (Ma and Xia, 2014). Our average dependency accuracy of 82.18% compares favourably to the accuracy of fully supervised methods.


title: A 45$^{\circ}$ Linearly Polarized Hollow-Waveguide 16 $\,\times$16-Slot Array Antenna Covering 71–86 GHz Band

abstract: A 45° linearly polarized hollow-waveguide 16 × 16- slot array antenna is proposed for point-to-point wireless communication in the 71-76 GHz and 81-86 GHz bands. The antenna is composed of an equally-split corporate-feed circuit and 2 × 2-element sub-arrays which radiate the 45 ::: ° ::: linear polarization. Low sidelobe characteristics are obtained in the E-plane by diagonal placement of the square antenna rotating by 45 degrees. To suppress cross polarization, a radiating narrow-slot pair is adopted. The sub-array is designed by a genetic algorithm and 25.7% reflection bandwidth for is obtained by decreasing the Q of two eigenmodes of the radiating element. A 16 × 16-element array is fabricated by diffusion bonding of thin copper plates. The total thickness is 3.2 mm which is less than 1 free space wavelength. A high gain of 32.9 dBi, high antenna efficiency of 86.6% and low sidelobe characteristics of -27.1-dB first sidelobe levels are measured at the center frequency of 78.5 GHz. Further, the broadband characteristics of reflection , show a gain of more than 31.4 dBi, and low cross polarization of less than -30 dB are achieved over the 71-86 GHz band.


title: A 3.5-GHz PLL for fast low-IF/zero-IF LO switching in an 802.11 transceiver

abstract: A PLL technique is introduced that enables fast and accurate frequency switching, independent of the loop bandwidth. It uses separate tuning paths, each driving a separate VCO tune port. Different frequencies are produced by letting the VCO make different weighted combinations of the stable tuning voltages. The PLL converges to the stable tuning voltages by switching it a few times between the desired frequencies and tuning paths. Once the stabilized tuning voltages are found, one can switch between frequencies as fast as one can switch between K VCO s. The technique is applied to a 3.5-GHz integer-N PLL to enable fast jumping of the local oscillator (LO) frequency when an 802.11 transceiver is switched between a low and a zero intermediate frequency (LIF/ZIF). It uses dual phase/frequency detectors (PFD), charge pumps (CPs), and on-chip loop filters to control two separate low-leakage VCO tune ports. Each PFD/tune port combination can be (de)activated separately, without disturbing the loop filters' charge. The 50-kHz bandwidth PLL achieves a measured 7-MHz jump with ±20 kHz accuracy within 6 μs. The measured phase noise is -123 dBc/Hz at 1-MHz offset.


title: Net2Vec: Deep Learning for the Network

abstract: We present Net2Vec, a flexible high-performance platform that allows the execution of deep learning algorithms in the communication network. Net2Vec is able to capture data from the network at more than 60Gbps, transform it into meaningful tuples and apply predictions over the tuples in real time. This platform can be used for different purposes ranging from traffic classification to network performance analysis. ::: Finally, we showcase the use of Net2Vec by implementing and testing a solution able to profile network users at line rate using traces coming from a real network. We show that the use of deep learning for this case outperforms the baseline method both in terms of accuracy and performance.


Ayurvedic Medicine for the Treatment of Dementia: Mechanistic Aspects


title: Scientific Cloud Computing: Early Definition and Experience

abstract: Cloud computing emerges as a new computing paradigm which aims to provide reliable, customized and QoS guaranteed computing dynamic environments for end-users. This paper reviews recent advances of Cloud computing, identifies the concepts and characters of scientific Clouds, and finally presents an example of scientific Cloud for data centers


title: Incorporating Expert Feedback into Active Anomaly Discovery

abstract: Unsupervised anomaly detection algorithms search for outliers and then predict that these outliers are the anomalies. When deployed, however, these algorithms are often criticized for high false positive and high false negative rates. One cause of poor performance is that not all outliers are anomalies and not all anomalies are outliers. In this paper, we describe an Active Anomaly Discovery (AAD) method for incorporating expert feedback to adjust the anomaly detector so that the outliers it discovers are more in tune with the expert user's semantic understanding of the anomalies. The AAD approach is designed to operate in an interactive data exploration loop. In each iteration of this loop, our algorithm first selects a data instance to present to the expert as a potential anomaly and then the expert labels the instance as an anomaly or as a nominal data point. Our algorithm updates its internal model with the instance label and the loop continues until a budget of B queries is spent. The goal of our approach is to maximize the total number of true anomalies in the B instances presented to the expert. We show that when compared to other state-of-the-art algorithms, AAD is consistently one of the best performers.


title: Autonomic nervous system activity distinguishes among emotions

abstract: Emotion-specific activity in the autonomic nervous system was generated by constructing facial prototypes of emotion muscle by muscle and by reliving past emotional experiences. The autonomic activity produced distinguished not only between positive and negative emotions, but also among negative emotions. This finding challenges emotion theories that have proposed autonomic activity to be undifferentiated or that have failed to address the implications of autonomic differentiation in emotion.


title: The Effects of a DC Power Layer in a 10-Nb-Layer Device for SFQ LSIs

abstract: We have evaluated the effects of a DC power (DCP) layer in a 10-Nb-layer device using SQUIDs and large-scale Josephson transmission lines (LS-JTLs). The 10-Nb-layer device has recently been developed for SFQ LSIs, with an active layer including Josephson junctions (JJs) at the top, two passive transmission line layers in the middle, and the DCP layer for bias current feeds at the bottom. The evaluation with SQUIDs revealed that the 10-Nb-layer device structure drastically reduced the magnetic flux induced by DC currents flowing through the DCP line, in comparison with the previous advanced process device structure. A major factor for this reduction was an increase in the number of ground layers between the DCP layer and the active layer. In the test of the LS-JTLs containing about 12000 JJs, we obtained the operating margins as wide as numerically simulated ones. We also observed no difference in the margins between a method for extracting ground return currents through ground layers and that through the dedicated layer. These results demonstrated that the 10-Nb-layer device structure is suitable for SFQ-LSIs.


Providing IoT Services in Smart Cities through Dynamic Augmented Reality Markers


Correction of tear trough deformity with a cohesive polydensified matrix hyaluronic acid: a case series


title: Discovering Valuable items from Massive Data

abstract: Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? This task generalizes several important problems such as multi-arm bandits, active search and the knapsack problem. We present an algorithm, GP-SELECT, which utilizes prior knowledge about similarity between items, expressed as a kernel function. GP-SELECT uses Gaussian process prediction to balance exploration (estimating the unknown value of items) and exploitation (selecting items of high value). We extend GP-SELECT to be able to discover sets that simultaneously have high utility and are diverse. Our preference for diversity can be specified as an arbitrary monotone submodular function that quantifies the diminishing returns obtained when selecting similar items. Furthermore, we exploit the structure of the model updates to achieve an order of magnitude (up to 40X) speedup in our experiments without resorting to approximations. We provide strong guarantees on the performance of GP-SELECT and apply it to three real-world case studies of industrial relevance: (1) Refreshing a repository of prices in a Global Distribution System for the travel industry, (2) Identifying diverse, binding-affine peptides in a vaccine design task and (3) Maximizing clicks in a web-scale recommender system by recommending items to users.


title: Denaturation transition of stretched DNA

abstract: In the last two decades, single-molecule force measurements using optical and magnetic tweezers and atomic force spectroscopy have dramatically expanded our knowledge of nucleic acids and proteins. These techniques characterize the force on a biomolecule required to produce a given molecular extension. When stretching long DNA molecules, the observed force–extension relationship exhibits a characteristic plateau at approximately 65 pN where the DNA may be extended to almost twice its B-DNA length with almost no increase in force. In the present review, I describe this transition in terms of the Poland–Scheraga model and summarize recent related studies.


title: A deep-learning model-based and data-driven hybrid architecture for image annotation

abstract: Does adding more training data always help improve the effectiveness of a machine-learning or pattern-recognition task? Recent evidences in machine translation and speech recognition seem to suggest that the data-driven approach outperforms the traditional model-based approach. Instead of carefully modeling rules and their exceptions, the data-driven approach relies on identifying similar patterns in massive datasets and then uses the similar patterns to predict the labels (or other outcomes) of unseen instances. In this work, we compare representative data-driven and model-based schemes on an image annotation task. We enumerate pros and cons of these two approaches, and propose a hybrid approach, which can harness the strengths of the two.


title: Awareness, Interest, and Purchase : The Effects of User- and Marketer-Generated Content on Purchase Decision Processes

abstract: Companies use Facebook fan pages to promote their products or services. Recent research shows that user- (UGC) and marketer-generated content (MGC) created on fan pages affect online sales. But it is still unclear how exactly they affect consumers during their purchase process. We analyze field data from a large German e-tailer to investigate the effects of UGC and MGC in a multi-stage model of purchase decision processes: awareness creation, interest stimulation, and final purchase decision. We find that MGC and UGC create awareness by attracting users to the fan page. Increased numbers of active users stimulate user interest, and more users visit the e-tailer's online shop. Neutral UGC increase the conversion rate of online shop visitors. Comparisons between one-, two- and three-stage modes show that neglecting one or two stages hides several important effects of MGC and UGC on consumers and ultimately leads to inaccurate predictions of key business figures.


title: Through the Looking Glass: The Cardiac Care Network of Ontario 10 Years Later

abstract: he Cardiac Care Network of Ontario (CCN) is a pioneering partnership of health professionals, hospitals and government that focuses on appropriate and timely access to adult cardiac services by patients and their physicians. Established by the Ontario Ministry of Health and Long-Term Care in 1990, CCN advises the Ministry and coordinates the provision of advanced cardiac services for adult Ontarians.


title: Towards Data Quality Assessment in Online Advertising

abstract: In online advertising, our aim is to match the advertisers with the most relevant users to optimize the campaign performance. In the pursuit of achieving this goal, multiple data sources provided by the advertisers or third-party data providers are utilized to choose the set of users according to the advertisers' targeting criteria. In this paper, we present a framework that can be applied to assess the quality of such data sources in large scale. This framework efficiently evaluates the similarity of a specific data source categorization to that of the ground truth, especially for those cases when the ground truth is accessible only in aggregate, and the user-level information is anonymized or unavailable due to privacy reasons. We propose multiple methodologies within this framework, present some preliminary assessment results, and evaluate how the methodologies compare to each other. We also present two use cases where we can utilize the data quality assessment results: the first use case is targeting specific user categories, and the second one is forecasting the desirable audiences we can reach for an online advertising campaign with pre-set targeting criteria.


title: Interference Graph Construction for Cellular D2D Communications

abstract: This paper studies the interference graph construction for device-to-device (D2D) communications in cellular networks and proposes an interference graph construction protocol. The proposed interference graph construction protocol consists of two stages, i.e., announcement and collision resolution, which are performed in an iterative manner. The announcement announces the existence of each D2D link to its neighbor area by broadcasting announce messages. The collision-resolution stage resolves the collisions that could occur during the announce stage by broadcasting response messages. The theoretical analysis of the proposed construction protocol is presented, and the convergence of the protocol to an actual interference graph is studied. The theoretical analysis is validated by simulation results, and the performance of the proposed protocol is analyzed. It is shown that, for a number of D2D links up to as large as 1000 links, only two to seven iterations are needed to complete the interference graph construction procedure.


Transportation mode identification and real-time CO 2 emission estimation using smartphones How CO 2 GO works


title: [Globulomaxillary cyst].

abstract: Globulomaxillary cysts are located in the globular and maxillary process fusion area; they are not of odontogenic origin. Their etiology is similar to oral and facial clefts, which explains why they are classified as ontogenic fissural cysts. However, some clefts are not localized precisely on the premaxillary and maxillary process fusion area and their etiology as globulomaxillary cysts is questioned. In fact, those lesions could actually be of odontogenic origin.


Proteum - A Tool for the Assessment of Test Adequacy for C Programs: User's guide


title: A new, earth-based radar technique for the measurement of lunar topography

abstract: Radio interferometry is a new technique for the measurement of the surface topography of the Moon. Elevation data may be obtained directly without regard for unambiguously-identified features, for any lunar surface element that yields a recognizable radar echo.


title: A Novel Model-Based Driving Behavior Recognition System Using Motion Sensors

abstract: In this article, a novel driving behavior recognition system based on a specific physical model and motion sensory data is developed to promote traffic safety. Based on the theory of rigid body kinematics, we build a specific physical model to reveal the data change rule during the vehicle moving process. In this work, we adopt a nine-axis motion sensor including a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, and apply a Kalman filter for noise elimination and an adaptive time window for data extraction. Based on the feature extraction guided by the built physical model, various classifiers are accomplished to recognize different driving behaviors. Leveraging the system, normal driving behaviors (such as accelerating, braking, lane changing and turning with caution) and aggressive driving behaviors (such as accelerating, braking, lane changing and turning with a sudden) can be classified with a high accuracy of 93.25%. Compared with traditional driving behavior recognition methods using machine learning only, the proposed system possesses a solid theoretical basis, performs better and has good prospects.


title: Crowd Event Detection on Optical Flow Manifolds

abstract: Analyzing crowd events in a video is key to understanding the behavioral characteristics of people (humans). Detecting crowd events in videos is challenging because of articulated human movements and occlusions. The aim of this paper is to detect the events in a probabilistic framework for automatically interpreting the visual crowd behavior. In this paper, crowd event detection and classification in optical flow manifolds (OFMs) are addressed. A new algorithm to detect walking and running events has been proposed, which uses optical flow vector lengths in OFMs. Furthermore, a new algorithm to detect merging and splitting events has been proposed, which uses Riemannian connections in the optical flow bundle (OFB). The longest vector from the OFB provides a key feature for distinguishing walking and running events. Using a Riemannian connection, the optical flow vectors are parallel transported to localize the crowd groups. The geodesic lengths among the groups provide a criterion for merging and splitting events. Dispersion and evacuation events are jointly modeled from the walking/running and merging/splitting events. Our results show that the proposed approach delivers a comparable model to detect crowd events. Using the performance evaluation of tracking and surveillance 2009 dataset, the proposed method is shown to produce the best results in merging, splitting, and dispersion events, and comparable results in walking, running, and evacuation events when compared with other methods.


title: The Signals and Noise: Actionable Information in Improvised Social Media Channels During a Disaster

abstract: Web-based social and communication technologies enable citizens to self-organize relief efforts in response to crises. This work focuses on a question fundamental to the concept of collective intelligence: how effective are such self-organized channels, ungoverned by any central authority, in conforming to their intended function? In this study we examine the hashtag #PorteOuverte ("#OpenDoor") introduced during the 2015 Paris terrorist attacks, as an "improvised logistical channel" (ILC) to help individuals to find a safe shelter near the attack sites. We analyze the dynamics and effectiveness of #PorteOuverte by comparing its proportion of relevant logistical messages - individuals requesting or offering shelter - to other messages such as those offering emotional consolation or commenting on the hashtag itself. Our results reveal that the vast majority of messages are not relevant, however the crowd senses and spreads relevant messages more than others. We further demonstrate that relevant messages can be automatically detected and thus algorithmic promotion may be possible.


title: Separate Vertical Wiring for the Fixation of Comminuted Fractures of the Inferior Pole of the Patella

abstract: Purpose ::: Among patients over 50 years of age, separate vertical wiring alone may be insufficient for fixation of fractures of the inferior pole of the patella. Therefore, mechanical and clinical studies were performed in patients over the age of 50 to test the strength of augmentation of separate vertical wiring with cerclage wire (i.e., combined technique).


title: Object-based visual attention for computer vision

abstract: In this paper, a novel model of object-based visual attention extending Duncan's Integrated Competition Hypothesis [Phil. Trans. R. Soc. London B 353 (1998) 1307-1317] is presented. In contrast to the attention mechanisms used in most previous machine vision systems which drive attention based on the spatial location hypothesis, the mechanisms which direct visual attention in our system are object-driven as well as feature-driven. The competition to gain visual attention occurs not only within an object but also between objects. For this purpose, two new mechanisms in the proposed model are described and analyzed in detail. The first mechanism computes the visual salience of objects and groupings; the second one implements the hierarchical selectivity of attentional shifts. The results of the new approach on synthetic and natural images are reported.


title: The RSR2015: Database for Text-Dependent Speaker Verification using Multiple Pass-Phrases

abstract: This paper describes a new speech corpus, the RSR2015 database designed for text-dependent speaker recognition with scenario based on fixed pass-phrases. This database consists of over 71 hours of speech recorded from English speakers covering the diversity of accents spoken in Singapore. Acquisition has been done using a set of six portable devices including smart phones and tablets. The pool of speakers consists of 300 participants (143 female and 157 male speakers) from 17 to 42 years old. We propose a protocol for the case of user-dependent pass-phrases in text-dependent speaker recognition and we also report speaker recognition experiments on RSR2015 database. Index Terms: database, speaker recognition, text-dependent


title: A 28-GHz 4-channel dual-vector receiver phased array in SiGe BiCMOS technology

abstract: This paper presents a 28-GHz four-channel phased-array receiver in 120-nm SiGe BiCMOS technology for 5G cellular application. The phased-array receiver employs scalar-only weighting functions within each front-end and then global quadrature power combining to realize beamforming. Differential LNAs and dual-vector variable-gain amplifiers are used to realize each front-end with compact area. Each front-end achieves 5.1 to 7 dB noise figure, −16.8 to −13.8 dBm input compression point, −10.5 to −8.9 dBm input third-order intercept point across 4-bit phase settings and a 3-dB bandwidth of 26.5 to 33.9GHz, while consuming 136 mW per element. RMS gain and phase errors are < 0.6 dB and < 5.4° at 28–32 GHz respectively, and all four elements reveal well-matched responses.


title: Multi-anticipation and heterogeneity in car-following empirics and a first exploration of their implications

abstract: This paper considers multi-anticipative car-following behavior, i.e. longitudinal driving behavior that includes driver responses to multiple vehicles ahead. The paper addresses the results of a trajectory data based empirical study with respect to multi-anticipation. The results of this part of the study provide empirical evidence of multi-anticipative car-following. A second important conclusion is that there is a large inter-driver variability in multi-leader driving behavior, both with respect to model parameters and the degree of multi-anticipation. These results lead to the question what the implications of these findings are for microscopic modeling? The paper therefore also addresses this important question by performing first explorations of the results with respect to both heterogeneity with respect to optimal parameters and the degree of multi-anticipation by performing simulations where the prime focus is on the speed of the propagation of a disturbance and platoon stability


title: Drone Delivery Models for Healthcare

abstract: Just as the mobile phone allowed developing ::: countries to leapfrog technology in personal ::: communication, the delivery drone has the potential ::: to have the same effect on traditional transportation ::: infrastructure. Inaccessible roads no longer will ::: prevent delivery of blood, medications or other ::: healthcare items. This paper reviews the current ::: status of innovative drone delivery with a particular ::: emphasis on healthcare. The leading companies in ::: this field and their different strategies are studied. ::: Further we review the latest decision models that ::: facilitate management decision making for operating ::: a drone fleet. Our contribution in this paper of two ::: new models associated with the design of a drone ::: healthcare delivery network will facilitate more ::: timely, efficient and economical drone healthcare ::: delivery to potentially save lives.


title: An advanced multi-carrier modulation for future radio systems

abstract: Many alternative modulation schemes have been investigated to replace OFDM for radio systems. But they all have some weak points. In this paper, we present a novel modulation scheme, which minimizes the predecessors’ drawbacks, while still keeping their advantages.


A Survey on Feature Extraction and Classification of ECG Signal


title: Isolation and characterization of five novel high molecular weight subunit of glutenin genes from Triticum timopheevi and Aegilops cylindrica

abstract: Analysis by SDS-PAGE of total protein fractions from single seeds of Aegilops cylindrica (genomes C and D) and Triticum timopheevi (genomes A and G) showed the presence of three bands corresponding to high molecular weight subunits of glutenin (HMW subunits) in the former and two major bands and a minor band corresponding to HMW subunits in the latter. Three Ae. cylindrica and two T. timopheevi HMW subunit gene sequences, each comprising the entire coding region, were amplified by polymerase chain reaction (PCR) and their complete nucleotide sequences determined. A combination of N-terminal amino acid sequencing of the proteins identified by SDS-PAGE and alignments of the derived amino acid sequences of the proteins encoded by the PCR products identified the Ae. cylindrica HMW subunits as 1Cx, 1Cy and 1Dy, and the T. timopheevi HMW subunits as 1Gx, 1Ax and 1Ay. It was not clear whether or not a 1Gy HMW subunit was present in T. timopheevi. The PCR products from Ae. cyclindrica were derived from 1Cy and 1Dy genes and a silent 1Dx gene containing an in-frame internal stop codon, while those from T. timopheevi were derived from 1Ax and 1Ay genes. The 1Cx, 1Gx and 1Gy sequences were not amplified successfully. The proteins encoded by the five novel genes had similar structures to previously characterized HMW subunits of bread wheat (Triticum aestivum). Differences and similarities in sequence and structure, and in the distribution of cysteine residues (relevant to the ability of HMW subunits to form high Mr polymers) distinguished the HMW subunits of x- and y-type and of each genome rather than those of the different species. There was no evidence of a change in HMW subunit expression or structure resulting from selective breeding of bread wheat. The novel 1Ax, 1Ay, 1Cy and 1Dy HMW subunits were expressed in Escherichia coli, and the expressed proteins were shown to have very similar mobilities to the endogenous HMW subunits on SDS-PAGE. The truncated 1Dx gene from Ae. cylindrica failed to express in E. coli, and no HMW subunit-related protein of the size predicted for the truncated 1Dx subunit could be identified by immunodetection in seed extracts.


title: Control Strategy for Flexible Microgrid Based on Parallel Line-Interactive UPS Systems

abstract: In this paper, the control strategy for a flexible microgrid is presented. The microgrid presented here consists of several line-interactive uninterruptible power supply (UPS) systems connected in parallel. The control technique is based on the droop method to avoid critical communications among UPS units. Thus, a flexible microgrid is obtained to operate in either grid-connected or islanded mode. A small-signal analysis is presented in order to analyze the system stability, which gives rules to design the main control parameters. Simulation and experimental results are presented, showing the feasibility of the proposed controller.


title: Gate Injection Transistor (GIT)—A Normally-Off AlGaN/GaN Power Transistor Using Conductivity Modulation

abstract: We have developed a normally-off GaN-based transistor using conductivity modulation, which we call a gate injection transistor (GIT). This new device principle utilizes hole-injection from the p-AlGaN to the AlGaN/GaN heterojunction, which simultaneously increases the electron density in the channel, resulting in a dramatic increase of the drain current owing to the conductivity modulation. The fabricated GIT exhibits a threshold voltage of 1.0 V with a maximum drain current of 200 mA/mm, in which a forward gate voltage of up to 6 V can be applied. The obtained specific ON-state resistance (RON . A) and the OFF-state breakdown voltage (BV ds) are 2.6 mOmega . cm2 and 800 V, respectively. The developed GIT is advantageous for power switching applications.


title: Atmospheric aerosols: composition, transformation, climate and health effects.

abstract: Aerosols are of central importance for atmospheric chemistry and physics, the biosphere, climate, and public health. The airborne solid and liquid particles in the nanometer to micrometer size range influence the energy balance of the Earth, the hydrological cycle, atmospheric circulation, and the abundance of greenhouse and reactive trace gases. Moreover, they play important roles in the reproduction of biological organisms and can cause or enhance diseases. The primary parameters that determine the environmental and health effects of aerosol particles are their concentration, size, structure, and chemical composition. These parameters, however, are spatially and temporally highly variable. The quantification and identification of biological particles and carbonaceous components of fine particulate matter in the air (organic compounds and black or elemental carbon, respectively) represent demanding analytical challenges. This Review outlines the current state of knowledge, major open questions, and research perspectives on the properties and interactions of atmospheric aerosols and their effects on climate and human health.


title: Learning with Drift Detection

abstract: Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem of learning when the distribution that generate the examples changes over time. We present a method for detection of changes in the probability distribution of examples. The idea behind the drift detection method is to control the online error-rate of the algorithm. The training examples are presented in sequence. When a new training example is available, it is classified using the actual model. Statistical theory guarantees that while the distribution is stationary, the error will decrease. When the distribution changes, the error will increase. The method controls the trace of the online error of the algorithm. For the actual context we define a warning level, and a drift level. A new context is declared, if in a sequence of examples, the error increases reaching the warning level at example k w , and the drift level at example k d . This is an indication of a change in the distribution of the examples. The algorithm learns a new model using only the examples since k w . The method was tested with a set of eight artificial datasets and a real world dataset. We used three learning algorithms: a perceptron, a neural network and a decision tree. The experimental results show a good performance detecting drift and with learning the new concept. We also observe that the method is independent of the learning algorithm.


title: From e-commerce to social commerce: A close look at design features

abstract: E-commerce is undergoing an evolution through the adoption of Web 2.0 capabilities to enhance customer participation and achieve greater economic value. This new phenomenon is commonly referred to as social commerce, however it has not yet been fully understood. In addition to the lack of a stable and agreed-upon definition, there is little research on social commerce and no significant research dedicated to the design of social commerce platforms. This study offers literature review to explain the concept of social commerce, tracks its nascent state-of-the-art, and discusses relevant design features as they relate to e-commerce and Web 2.0. We propose a new model and a set of principles for guiding social commerce design. We also apply the model and guidelines to two leading social commerce platforms, Amazon and Starbucks on Facebook. The findings indicate that, for any social commerce website, it is critical to achieve a minimum set of social commerce design features. These design features must cover all the layers of the proposed model, including the individual, conversation, community and commerce levels.


title: Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach

abstract: We present a new approach for modeling and rendering existing architectural scenes from a sparse set of still photographs. Our modeling approach, which combines both geometry-based and imagebased techniques, has two components. The first component is a photogrammetricmodeling method which facilitates the recovery of the basic geometry of the photographed scene. Our photogrammetric modeling approach is effective, convenient, and robust because it exploits the constraints that are characteristic of architectural scenes. The second component is a model-based stereo algorithm, which recovers how the real scene deviates from the basic model. By making use of the model, our stereo technique robustly recovers accurate depth from widely-spaced image pairs. Consequently, our approach can model large architectural environments with far fewer photographs than current image-based modeling approaches. For producing renderings, we present view-dependent texture mapping, a method of compositing multiple views of a scene that better simulates geometric detail on basic models. Our approach can be used to recover models for use in either geometry-based or image-based rendering systems. We present results that demonstrate our approach’s ability to create realistic renderings of architectural scenes from viewpoints far from the original photographs. CR Descriptors: I.2.10 [Artificial Intelligence]: Vision and Scene Understanding Modeling and recovery of physical attributes; I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism Color, shading, shadowing, and texture I.4.8 [Image Processing]: Scene Analysis Stereo; J.6 [Computer-Aided Engineering]: Computer-aided design (CAD).


title: Chinese Sentiment Analysis Using Bidirectional LSTM with Word Embedding

abstract: Long Short-Term Memory network have been successfully applied to sequence modeling task and obtained great achievements. However, Chinese text contains richer syntactic and semantic information and has strong intrinsic dependency between words and phrases. In this paper, we propose Bidirectional Long Short-Term Memory (BLSTM) with word embedding for Chinese sentiment analysis. BLSTM can learn past and future information and capture stronger dependency relationship. Word embedding mainly extract words’ feature from raw characters input and carry important syntactic and semantic information. Experimental results show that our model achieves 91.46 % accuracy for sentiment analysis task.


title: Local trajectory planning and control for autonomous vehicles using the adaptive potential field

abstract: In this paper, a new potential field approach is proposed for trajectory planning and control in autonomous vehicles. The potential field of the surrounding environment is generated including vehicles, road boundaries and lane centers. Based on the predicted positions of the vehicles, the location of the ego vehicle and the surrounding potentials are synchronized. In addition, the potential fields of the surrounding vehicles are adaptively modified in shape depending on the relative velocity of the surrounding vehicles. The longitudinal distance required for the lateral avoidance is mathematically calculated and reflected in the potential field. Based on the proposed potential field, the trajectory of the autonomous vehicle is selected as the suboptimal path and the MPC (Model Predictive Control) method is applied for tracking control and the lateral stability of the vehicle. The performance of the proposed algorithm is verified in simulations under various conditions.


title: Virtual Community Attraction: Why People Hang Out Online

abstract: Understanding the attraction of virtual communities is crucial to organizations that want to tap into their enormous information potential. Existing literature theorizes that people join virtual communities to exchange information and/or social support. Theories of broader Internet use have indicated both entertainment and searching for friendship as motivational forces. This exploratory study empirically examines the importance of these reasons in assessing why people come to virtual communities by directly asking virtual community members why they joined. ::: ::: The responses to the open-ended question “Why did you join?” were categorized based upon the reasons suggested in the literature. Across 27 communities in 5 different broad types, 569 different reasons from 399 people indicated that most sought either friendship or exchange of information, and a markedly lower percent sought social support or recreation. The reasons were significantly dependent on the grouping of the communities into types. In all the community types information exchange was the most popular reason for joining. Thereafter, however, the reason varied depending on community type. Social support was the second most popular reason for members in communities with health/wellness and professional/occupational topics, but friendship was the second most popular reason among members in communities dealing with personal interests/hobbies, pets, or recreation. These findings suggest that virtual community managers should emphasize not only the content but also encourage the friendship and social support aspects as well if they wish to increase the success of their virtual community.


title: Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors

abstract: Alcohol use in young adults is common, with high rates of morbidity and mortality largely due to periodic, heavy drinking episodes (HDEs). Behavioral interventions delivered through electronic communication modalities (e.g., text messaging) can reduce the frequency of HDEs in young adults, but effects are small. One way to amplify these effects is to deliver support materials proximal to drinking occasions, but this requires knowledge of when they will occur. Mobile phones have built-in sensors that can potentially be useful in monitoring behavioral patterns associated with the initiation of drinking occasions. The objective of our work is to explore the detection of daily-life behavioral markers using mobile phone sensors and their utility in identifying drinking occasions. We utilized data from 30 young adults aged 21-28 with past hazardous drinking and collected mobile phone sensor data and daily Experience Sampling Method (ESM) of drinking for 28 consecutive days. We built a machine learning-based model that is 96.6% accurate at identifying non-drinking, drinking and heavy drinking episodes. We highlight the most important features for detecting drinking episodes and identify the amount of historical data needed for accurate detection. Our results suggest that mobile phone sensors can be used for automated, continuous monitoring of at-risk populations to detect drinking episodes and support the delivery of timely interventions.


title: Adaptive Quantization for Deep Neural Network

abstract: In recent years Deep Neural Networks (DNNs) have been rapidly developed in various applications, together with increasingly complex architectures. The performance gain of these DNNs generally comes with high computational costs and large memory consumption, which may not be affordable for mobile platforms. Deep model quantization can be used for reducing the computation and memory costs of DNNs, and deploying complex DNNs on mobile equipment. In this work, we propose an optimization framework for deep model quantization. First, we propose a measurement to estimate the effect of parameter quantization errors in individual layers on the overall model prediction accuracy. Then, we propose an optimization process based on this measurement for finding optimal quantization bit-width for each layer. This is the first work that theoretically analyse the relationship between parameter quantization errors of individual layers and model accuracy. Our new quantization algorithm outperforms previous quantization optimization methods, and achieves 20-40% higher compression rate compared to equal bit-width quantization at the same model prediction accuracy.


title: You'll never walk alone: Modeling social behavior for multi-target tracking

abstract: Object tracking typically relies on a dynamic model to predict the object's location from its past trajectory. In crowded scenarios a strong dynamic model is particularly important, because more accurate predictions allow for smaller search regions, which greatly simplifies data association. Traditional dynamic models predict the location for each target solely based on its own history, without taking into account the remaining scene objects. Collisions are resolved only when they happen. Such an approach ignores important aspects of human behavior: people are driven by their future destination, take into account their environment, anticipate collisions, and adjust their trajectories at an early stage in order to avoid them. In this work, we introduce a model of dynamic social behavior, inspired by models developed for crowd simulation. The model is trained with videos recorded from birds-eye view at busy locations, and applied as a motion model for multi-people tracking from a vehicle-mounted camera. Experiments on real sequences show that accounting for social interactions and scene knowledge improves tracking performance, especially during occlusions.


title: 3D model matching with Viewpoint-Invariant Patches (VIP)

abstract: The robust alignment of images and scenes seen from widely different viewpoints is an important challenge for camera and scene reconstruction. This paper introduces a novel class of viewpoint independent local features for robust registration and novel algorithms to use the rich information of the new features for 3D scene alignment and large scale scene reconstruction. The key point of our approach consists of leveraging local shape information for the extraction of an invariant feature descriptor. The advantages of the novel viewpoint invariant patch (VIP) are: that the novel features are invariant to 3D camera motion and that a single VIP correspondence uniquely defines the 3D similarity transformation between two scenes. In the paper we demonstrate how to use the properties of the VIPs in an efficient matching scheme for 3D scene alignment. The algorithm is based on a hierarchical matching method which tests the components of the similarity transformation sequentially to allow efficient matching and 3D scene alignment. We evaluate the novel features on real data with known ground truth information and show that the features can be used to reconstruct large scale urban scenes.


title: SMS spam detection for Indian messages

abstract: The growth of the mobile phone users has led to a dramatic increase in SMS spam messages. Though in most parts of the world, mobile messaging channel is currently regarded as “clean” and trusted, on the contrast recent reports clearly indicate that the volume of mobile phone spam is dramatically increasing year by year. It is an evolving setback especially in the Middle East and Asia. SMS spam filtering is a comparatively recent errand to deal such a problem. It inherits many concerns and quick fixes from Email spam filtering. However it fronts its own certain issues and problems. This paper inspires to work on the task of filtering mobile messages as Ham or Spam for the Indian Users by adding Indian messages to the worldwide available SMS dataset. The paper analyses different machine learning classifiers on large corpus of SMS messages for Indian people.


title: Brushables: Example-based Edge-aware Directional Texture Painting

abstract: In this paper we present Brushables-a novel approach to example-based painting that respects user-specified shapes at the global level and preserves textural details of the source image at the local level. We formulate the synthesis as a joint optimization problem that simultaneously synthesizes the interior and the boundaries of the region, transferring relevant content from the source to meaningful locations in the target. We also provide an intuitive interface to control both local and global direction of textural details in the synthesized image. A key advantage of our approach is that it enables a "combing" metaphor in which the user can incrementally modify the target direction field to achieve the desired look. Based on this, we implement an interactive texture painting tool capable of handling more complex textures than ever before, and demonstrate its versatility on difficult inputs including vegetation, textiles, hair and painting media.


title: Micromechanics based multi-level model for predicting the coefficients of thermal expansion of hybrid fiber reinforced concrete

abstract: Abstract This study is involved with presenting a multi-level micromechanical model for predicting the coefficients of thermal expansion (CTE) of randomly distributed and oriented short hybrid fiber reinforced concrete (HFRC), which includes the cement paste level, concrete level, and HFRC level. In this micromechanical model, the inner products (IP), outer products (OP), calcium hydroxide (CH), unhydrated clinker, sands, aggregates and hybrid fibers are comprehensively considered. A substepping homogenization framework is presented to realize the upscaling from the microstructure to macro HFRC, based on which the overall CTE of HFRC is determined. In addition, the volume fractions of phases at each level are also presented to facilitate the prediction of CTE. Comparisons with experimental data from previous studies are implemented level by level. Subsequently, the influences of the water-cement ratio, the hydration degree, the aggregate property and the fiber property on the CTE are discussed carefully.


title: On Availability for Blockchain-Based Systems

abstract: Blockchain has recently gained momentum. Startups, enterprises, banks, and government agencies around the world are exploring the use of blockchain for broad applications including public registries, supply chains, health records, and voting. Dependability properties, like availability, are critical for many of these applications, but the guarantees offered by the blockchain technology remain unclear, especially from an application perspective. In this paper, we identify the availability limitations of two mainstream blockchains, Ethereum and Bitcoin. We demonstrate that while read availability of blockchains is typically high, write availability - for transaction management - is actually low. For Ethereum, we collected 6 million transactions over a period of 97 days. First, we measured the time for transactions to commit as required by the applications. Second, we observed that some transactions never commit, due to the inherent blockchain design. Third and perhaps even more dramatically, we identify the consequences of the lack of built-in options for explicit abort or retry that can maintain the application in an uncertain state, where transactions remain pending (neither aborted nor committed) for an unknown duration. Finally we propose techniques to mitigate the availability limitations of existing blockchains, and experimentally test the efficacy of these techniques.


title: Simultaneous Localization, Mapping and Moving Object Tracking

abstract: Simultaneous localization, mapping and moving object tracking (SLAMMOT) involves both simultaneous localization and mapping (SLAM) in dynamic environments and detecting and tracking these dynamic objects. In this paper, a mathematical framework is established to integrate SLAM and moving object tracking. Two solutions are described: SLAM with generalized objects, and SLAM with detection and tracking of moving objects (DATMO). SLAM with generalized objects calculates a joint posterior over all generalized objects and the robot. Such an approach is similar to existing SLAM algorithms, but with additional structure to allow for motion modeling of generalized objects. Unfortunately, it is computationally demanding and generally infeasible. SLAM with DATMO decomposes the estimation problem into two separate estimators. By maintaining separate posteriors for stationary objects and moving objects, the resulting estimation problems are much lower dimensional than SLAM with generalized objects. Both SLAM and moving object tracking from a moving vehicle in crowded urban areas are daunting tasks. Based on the SLAM with DATMO framework, practical algorithms are proposed which deal with issues of perception modeling, data association, and moving object detection. The implementation of SLAM with DATMO was demonstrated using data collected from the CMU Navlab11 vehicle at high speeds in crowded urban environments. Ample experimental results shows the feasibility of the proposed theory and algorithms.


Hardware Implementation of Sine Wave Generator using CORDIC Algorithm


title: Mitochondrial DNA Profiling of Illegal Tortoiseshell Products Derived from Hawksbill Sea Turtles

abstract: The hawksbill sea turtle (Eretmochelys imbricata) is a highly endangered species, commonly poached for its ornate shell. “Tortoiseshell” products made from the shell are widely, although illegally, available in many countries. Hawksbills have a circumglobal distribution; thus, determining their origin is difficult, although genetic differences exist geographically. In the research presented, a procedure was developed to extract and amplify mitochondrial DNA from tortoiseshell items, in an effort to better understand where the species is being poached. Confiscated tortoiseshell items were obtained from the U.S. Fish and Wildlife Service, and DNA from 56 of them was analyzed. Multiple mitochondrial haplotypes were identified, including five not previously reported. Only one tortoiseshell item proved to be of Atlantic origin, while all others corresponded to genetic stocks in the Indo-Pacific region. The developed methodology allows for unique, and previously unattainable, genetic information on the illegal poaching of sea turtles for the decorative tortoiseshell trade.


title: Terrain Synthesis from Digital Elevation Models

abstract: In this paper, we present an example-based system for terrain synthesis. In our approach, patches from a sample terrain (represented by a height field) are used to generate a new terrain. The synthesis is guided by a user-sketched feature map that specifies where terrain features occur in the resulting synthetic terrain. Our system emphasizes large-scale curvilinear features (ridges and valleys) because such features are the dominant visual elements in most terrains. Both the example height field and user's sketch map are analyzed using a technique from the field of geomorphology. The system finds patches from the example data that match the features found in the user's sketch. Patches are joined together using graph cuts and Poisson editing. The order in which patches are placed in the synthesized terrain is determined by breadth-first traversal of a feature tree and this generates improved results over standard raster-scan placement orders. Our technique supports user-controlled terrain synthesis in a wide variety of styles, based upon the visual richness of real-world terrain data.


title: Regenerative braking strategy for electric vehicles

abstract: Regenerative braking is an effective approach for electric vehicles to extend their driving range. The control strategy of regenerative braking plays an important role in maintaining the vehicle's stability and recovering energy. In this paper, the main properties that have influence on brake energy regeneration are analyzed. Mathematical model of brake energy regenerating electric vehicles is established. By analyzing the charge and discharge characteristics of the battery and motor, a simple regenerative braking strategy is proposed. The strategy takes the braking torque required, the motor available braking torque, and the braking torque limit into account, and it can make the best use of the motor braking torque. Simulation results show higher energy regeneration compared to a parallel strategy when the proposed strategy is adopted.


title: The Effects of Hyperparameters on SGD Training of Neural Networks

abstract: The performance of neural network classifiers is determined by a number of hyperparameters, including learning rate, batch size, and depth. A number of attempts have been made to explore these parameters in the literature, and at times, to develop methods for optimizing them. However, exploration of parameter spaces has often been limited. In this note, I report the results of large scale experiments exploring these different parameters and their interactions.


title: Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II

abstract: This paper proposes a new evolutionary algorithm, called DSMGA-II, to efficiently solve optimization problems via exploiting problem substructures. The proposed algorithm adopts pairwise linkage detection and stores the information in the form of dependency structure matrix (DSM). A new linkage model, called the incremental linkage set, is then constructed by using the DSM. Inspired by the idea of optimal mixing, the restricted mixing and the back mixing are proposed. The former aims at efficient exploration under certain constrains. The latter aims at exploitation by refining the DSM so as to reduce unnecessary evaluations. Experimental results show that DSMGA-II outperforms LT-GOMEA and hBOA in terms of number of function evaluations on the concatenated/folded/cyclic trap problems, NK-landscape problems with various degrees of overlapping, 2D Ising spin-glass problems, and MAX-SAT. The investigation of performance comparison with P3 is also included.


title: Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems

abstract: The increasing scale and wealth of inter-connected data, such as those accrued by social network applications, demand the design of new techniques and platforms to efficiently derive actionable knowledge from large-scale graphs. However, real-world graphs are famously difficult to process efficiently. Not only they have a large memory footprint, but also most graph algorithms entail memory access patterns with poor locality, data-dependent parallelism and a low compute-to-memory access ratio. Moreover, most real-world graphs have a highly heterogeneous node degree distribution, hence partitioning these graphs for parallel processing and simultaneously achieving access locality and load-balancing is difficult. ::: This work starts from the hypothesis that hybrid platforms (e.g., GPU-accelerated systems) have both the potential to cope with the heterogeneous structure of real graphs and to offer a cost-effective platform for high-performance graph processing. This work assesses this hypothesis and presents an extensive exploration of the opportunity to harness hybrid systems to process large-scale graphs efficiently. In particular, (i) we present a performance model that estimates the achievable performance on hybrid platforms; (ii) informed by the performance model, we design and develop TOTEM - a processing engine that provides a convenient environment to implement graph algorithms on hybrid platforms; (iii) we show that further performance gains can be extracted using partitioning strategies that aim to produce partitions that each matches the strengths of the processing element it is allocated to, finally, (iv) we demonstrate the performance advantages of the hybrid system through a comprehensive evaluation that uses real and synthetic workloads (as large as 16 billion edges), multiple graph algorithms that stress the system in various ways, and a variety of hardware configurations.


Naive Bayesian classifier within ILP-R


title: VDR polymorphisms influence immunological response in HIV-1+ individuals undergoing antiretroviral therapy

abstract: Vitamin D exerts an immuno-modulatory activity on several immune system cells through the vitamin D receptor (VDR). Herein, we verified that age and a therapeutic regimen containing protease inhibitors are associated with failures in antiretroviral therapies (ARVs). In addition, we assessed whether a VDR SNP (rs11568820: C allele and CC genotype) and GC (rs2228570-rs11568820) allelic combinations are associated with immunological failure (p < 0.05). Our findings suggest a possible role of VDR SNPs on immunological failure in HIV-1+ individuals undergoing regular ARVs.


title: Role Discovery in Networks

abstract: Roles represent node-level connectivity patterns such as star-center, star-edge nodes, near-cliques or nodes that act as bridges to different regions of the graph. Intuitively, two nodes belong to the same role if they are structurally similar. Roles have been mainly of interest to sociologists, but more recently, roles have become increasingly useful in other domains. Traditionally, the notion of roles were defined based on graph equivalences such as structural, regular, and stochastic equivalences. We briefly revisit these early notions and instead propose a more general formulation of roles based on the similarity of a feature representation (in contrast to the graph representation). This leads us to propose a taxonomy of three general classes of techniques for discovering roles that includes(i) graph-based roles, (ii) feature-based roles, and (iii) hybrid roles. We also propose a flexible framework for discovering roles using the notion of similarity on a feature-based representation. The framework consists of two fundamental components: (a) role featureconstruction and (b) role assignment using the learned feature representation. We discuss the different possibilities for discoveringfeature-based roles and the tradeoffs of the many techniques for computing them. Finally, we discuss potential applications and future directions and challenges.


title: T/R-module technologies today and future trends

abstract: After many years of development the active electronically scanned array (AESA) radar technology has reached a mature technology level. Many of today's and future radar systems will be equipped with the ASEA technology. T/R-modules are key elements in active phased array antennas for radar and electronic warfare applications. Meanwhile T/R-modules using GaAs MMICs are in mass production with high quantities. Top priority is on continuous improvement of yield figures by optimizing the spread of key performance parameters to come down with cost. To fulfill future demands on power, bandwidth, robustness, weight, multifunctional sensor capability, and overall sensor cost, new emerging semiconductor and packaging technologies have to be implemented for the next generation T/R-modules. Using GaN MMICs as HPAs and also as robust LNAs is a promising approach. Higher integration at the amplitude and phase setting section of the T/R-module is realized with GaAs core chips or even with SiGe multifunction chips. With increasing digital signal processing capability the digital beam forming will get more importance with a high impact on the T/R-modules. For lower production costs but also for sensor integration new packaging concepts are necessary. This includes the transition towards organic packages and using low cost surface-mount soldering technology.


title: A Modularized Charge Equalizer Using a Battery Monitoring IC for Series-Connected Li-Ion Battery Strings in Electric Vehicles

abstract: In the lithium-ion battery systems for electric vehicles (EVs), a battery management system (BMS) is essential for enhancing the battery's life cycle and safety. As a result, a BMS is required to realize both the effective cell monitoring and balancing. Moreover, individual cell balancing and monitoring circuit with a smaller size are required in a large number of battery cells. To meet these requirements, a modularized charge equalizer using the monitoring integrated circuit (IC) is proposed for EV battery strings. The proposed scheme exhibits efficient charge equalization with simple control of the monitoring IC. In the proposed equalizer, the battery string is modularized into a master module and multiple slave modules. A central equalization converter in the master module is shared by all of the battery cells through the module and cell switches in the slave module, instead of a dedicated charge equalizer for each of the cells. Individual charge equalization can be controlled by the cell monitoring IC in the slave module. With this configuration, the battery monitoring and balancing can be effectively merged into one controller. Moreover, the individual charge equalizer can be effectively implemented without affecting the size or cost based on the numbers of cells. In this paper, a prototype for 88 lithium-ion battery cells is optimally designed and implemented. Experimental results verify that the proposed method exhibits outstanding balancing performance with simple operation methods.


Compressive Sensing


title: Block-Row Sparse Multiview Multilabel Learning for Image Classification

abstract: In image analysis, the images are often represented by multiple visual features (also known as multiview features), that aim to better interpret them for achieving remarkable performance of the learning. Since the processes of feature extraction on each view are separated, the multiple visual features of images may include overlap, noise, and redundancy. Thus, learning with all the derived views of the data could decrease the effectiveness. To address this, this paper simultaneously conducts a hierarchical feature selection and a multiview multilabel (MVML) learning for multiview image classification, via embedding a proposed a new block-row regularizer into the MVML framework. The block-row regularizer concatenating a Frobenius norm (F-norm) regularizer and an l(2,1)-norm regularizer is designed to conduct a hierarchical feature selection, in which the F-norm regularizer is used to conduct a high-level feature selection for selecting the informative views (i.e., discarding the uninformative views) and the l(2,1)-norm regularizer is then used to conduct a low-level feature selection on the informative views. The rationale of the use of a block-row regularizer is to avoid the issue of the over-fitting (via the block-row regularizer), to remove redundant views and to preserve the natural group structures of data (via the F-norm regularizer), and to remove noisy features (the l(2,1)-norm regularizer), respectively. We further devise a computationally efficient algorithm to optimize the derived objective function and also theoretically prove the convergence of the proposed optimization method. Finally, the results on real image datasets show that the proposed method outperforms two baseline algorithms and three state-of-the-art algorithms in terms of classification performance.


Teachers Job Satisfaction and Motivation for School Effectiveness: An Assessment


title: Scalable Blockchain Based Smart Contract Execution

abstract: Blockchain, or distributed ledger, provides a way to build various decentralized systems without relying on any single trusted party. This is especially attractive for smart contracts, that different parties do not need to trust each other to have a contract, and the distributed ledger can guarantee correct execution of the contract. Most existing distributed ledger based smart contract systems process smart contracts in a serial manner, i.e., all users have to run a contract before its result can be accepted by the system. Although this approach is easy to implement and manage, it is not scalable and greatly limits the system's capability of handling a large number of smart contracts. In order to address this problem, we propose a scalable smart contract execution scheme that can run multiple smart contract in parallel to improve throughput of the system. Our scheme relies on two key techniques: a fair contract partition algorithm leveraging integer linear programming to partition a set of smart contracts into multiple subsets, and a random assignment protocol assigning subsets randomly to a subgroup of users. We prove that, our scheme is secure as long as more than $50\%$ of the computational power is possessed by honest nodes. We then conduct experiments with data from existing smart contract system to evaluate the efficiency of our scheme. The results demonstrate that our approach is scalable and much more efficient than the existing smart contract platform.


title: A 4 by 10 series 60 GHz microstrip array antenna fed by butler matrix for 5G applications

abstract: This paper presents a low-cost, beam-steerable 4 × 10 antenna array system operating at 60 GHz. The proposed antenna system is fed by a 4 × 10 Butler Matrix network designed using microstrip line (ML) structure. Chebyshev tapered microstrip antenna arrays with 10 series-fed elements are connected to four output ports of the feed network. Four steerable beams with maximum 16.5 dBi system gain and 1GHz bandwidth(BW) satisfy the requirements of millimeter wave propagation study and handset application for 5G communication.


title: Performance characteristics of anthropomorphic prosthetic hands

abstract: In this paper we set forth a review of performance characteristics for both common commercial prosthetics as well as anthropomorphic research devices. Based on these specifications as well as surveyed results from prosthetic users, ranges of hand attributes are evaluated and discussed. End user information is used to describe the performance requirements for prosthetic hands for clinical use.


title: Guided Policy Search as Approximate Mirror Descent

abstract: Guided policy search algorithms can be used to optimize complex nonlinear policies, such as deep neural networks, without directly computing policy gradients in the high-dimensional parameter space. Instead, these methods use supervised learning to train the policy to mimic a "teacher" algorithm, such as a trajectory optimizer or a trajectory-centric reinforcement learning method. Guided policy search methods provide asymptotic local convergence guarantees by construction, but it is not clear how much the policy improves within a small, finite number of iterations. We show that guided policy search algorithms can be interpreted as an approximate variant of mirror descent, where the projection onto the constraint manifold is not exact. We derive a new guided policy search algorithm that is simpler and provides appealing improvement and convergence guarantees in simplified convex and linear settings, and show that in the more general nonlinear setting, the error in the projection step can be bounded. We provide empirical results on several simulated robotic navigation and manipulation tasks that show that our method is stable and achieves similar or better performance when compared to prior guided policy search methods, with a simpler formulation and fewer hyperparameters.


title: Orthostatic Intolerance and Postural Orthostatic Tachycardia Syndrome in Joint Hypermobility Syndrome/Ehlers-Danlos Syndrome, Hypermobility Type: Neurovegetative Dysregulation or Autonomic Failure?

abstract: Background. Joint hypermobility syndrome/Ehlers-Danlos syndrome, hypermobility type (JHS/EDS-HT), is a hereditary connective tissue disorder mainly characterized by generalized joint hypermobility, skin texture abnormalities, and visceral and vascular dysfunctions, also comprising symptoms of autonomic dysfunction. This study aims to further evaluate cardiovascular autonomic involvement in JHS/EDS-HT by a battery of functional tests. Methods. The response to cardiovascular reflex tests comprising deep breathing, Valsalva maneuver, 30/15 ratio, handgrip test, and head-up tilt test was studied in 35 JHS/EDS-HT adults. Heart rate and blood pressure variability was also investigated by spectral analysis in comparison to age and sex healthy matched group. Results. Valsalva ratio was normal in all patients, but 37.2% of them were not able to finish the test. At tilt, 48.6% patients showed postural orthostatic tachycardia, 31.4% orthostatic intolerance, 20% normal results. Only one patient had orthostatic hypotension. Spectral analysis showed significant higher baroreflex sensitivity values at rest compared to controls. Conclusions. This study confirms the abnormal cardiovascular autonomic profile in adults with JHS/EDS-HT and found the higher baroreflex sensitivity as a potential disease marker and clue for future research.


title: A Capacitive Coupled Patch Antenna Array With High Gain and Wide Coverage for 5G Smartphone Applications

abstract: In this paper, a novel capacitive coupled patch antenna array capable of providing high gain and 360° coverage in the elevation plane is proposed. The proposed antenna element is studied in detail using the theory of characteristic modes to enhance the bandwidth. It exhibits a uniform radiation pattern with a stable gain when integrated with the mobile phone chassis. These elements are positioned in the mobile phone chassis as a set of four sub-arrays each with 12 antenna elements to provide high realized gain around 16.5 dBi with each sub-array providing 90° coverage. The antenna array is fabricated, and the beam steering performance of the array is evaluated using a beam-steering test bed. The results show that it covers the frequency range of 24–28 GHz, which is a promising band for future 5G-based smartphone services as predicted and could be a strong candidate for 5G applications.