title: From natural language requirements to UML class diagrams

abstract: Unified Modeling Language (UML) is the most popular modeling language for analysis, design and development of the software system. There has been a lot of research interest in generating these UML models, especially class diagrams, automatically from Natural Language requirements. The interest in class diagrams can be attributed to the fact that classes represent the abstractions present in the system to be developed. However, automated generation of UML class diagrams is a challenging task as it involves lot of pre-processing or manual intervention at times. In this paper, we present dependency analysis based approach to derive UML class diagrams automatically from Natural Language requirements. We transform the requirements statements to an intermediary frame-based structured representation using dependency analysis of requirements statements and the Grammatical Knowledge Patterns. The knowledge stored in the frame-based structured representation is used to derive class diagrams using rule-based algorithm. Our approach has generated similar class diagrams as reported in earlier works based on linguistic analysis with either annotation or manual intervention. We present the effectiveness of our approach in terms of recall and precision for the case-studies presented in earlier works.


Communicating emotions in instant messaging , an overview


Hidden Markov Model ( HMM ) based Speech Synthesis for Urdu Language


title: Hand motion identification of grasp-and-lift task from electroencephalography recordings using recurrent neural networks

abstract: People who suffer from neuromuscular disorders and amputated limbs require prosthetic devices that are maneuvered through brain computer interfaces. Electroencephalography is a method to record the activity of the brain that is used for inputs for a brain computer interface. In this paper we propose a method for predicting hand motion phases in grasp-and-lift task from electroencephalography recordings using recurrent networks. Various architectures of recurrent neural networks are compared in terms of performance. For consistent prediction, moving average is applied.


title: Abstraction Levels for Robotic Imitation: Overview and Computational Approaches

abstract: This chapter reviews several approaches to the problem of learning by imitation in robotics. We start by describing several cognitive processes identified in the literature as necessary for imitation. We then proceed by surveying different approaches to this problem, placing particular emphasys on methods whereby an agent first learns about its own body dynamics by means of self-exploration and then uses this knowledge about its own body to recognize the actions being performed by other agents. This general approach is related to the motor theory of perception, particularly to the mirror neurons found in primates. We distinguish three fundamental classes of methods, corresponding to three abstraction levels at which imitation can be addressed. As such, the methods surveyed herein exhibit behaviors that range from raw sensory-motor trajectory matching to high-level abstract task replication. We also discuss the impact that knowledge about the world and/or the demonstrator can have on the particular behaviors exhibited.


title: Lightweight application classification for network management

abstract: Traffic application classification is an essential step in the network management process to provide high availability of network services. However, network management has seen limited use of traffic classification because of the significant overheads of existing techniques. In this context we explore the feasibility and performance of lightweight traffic classification based on NetFlow records. In our experiments, the NetFlow records are created from packet-trace data and pre-tagged based upon packet content. This provides us with NetFlow records that are tagged with a high accuracy for ground-truth. Our experiments show that NetFlow records can be usefully employed for application classification. We demonstrate that our machine learning technique is able to provide an identification accuracy (a 91%) that, while a little lower than that based upon previous packet-based machine learning work (> 95%), is significantly higher than the commonly used port-based approach (50--70%). Trade-offs such as the complexity of feature selection and packet sampling are also studied. We conclude that a lightweight mechanism of classification can provide application information with a considerably high accuracy, and can be a useful practice towards more effective network management.


title: Broadband SIW-to-Waveguide Transition in Multilayer LCP Substrates at W-Band

abstract: In this letter, we present a substrate-integrated waveguide (SIW) to air-filled rectangular waveguide (WG) transition in multilayer liquid crystal polymer substrates at W-band. The proposed transition is achieved by using a SIW-fed linearly flared antipodal slot line inserted into a WG. To minimize packaging leakage, no guided vias are utilized in the slot line region. Full-wave simulation reveals a low insertion loss of 0.8 dB for the proposed transition at W-band. To experimentally demonstrate this design, a WG-to-SIW-to-WG transition was fabricated and characterized. The measured insertion and reflection loss of the back-to-back transition is less than 1.8 and −11 dB, respectively, at W-band.


title: A comprehensive map of molecular drug targets

abstract: The success of mechanism-based drug discovery depends on the definition of the drug target. This definition becomes even more important as we try to link drug response to genetic variation, understand stratified clinical efficacy and safety, rationalize the differences between drugs in the same therapeutic class and predict drug utility in patient subgroups. However, drug targets are often poorly defined in the literature, both for launched drugs and for potential therapeutic agents in discovery and development. Here, we present an updated comprehensive map of molecular targets of approved drugs. We curate a total of 893 human and pathogen-derived biomolecules through which 1,578 US FDA-approved drugs act. These biomolecules include 667 human-genome-derived proteins targeted by drugs for human disease. Analysis of these drug targets indicates the continued dominance of privileged target families across disease areas, but also the growth of novel first-in-class mechanisms, particularly in oncology. We explore the relationships between bioactivity class and clinical success, as well as the presence of orthologues between human and animal models and between pathogen and human genomes. Through the collaboration of three independent teams, we highlight some of the ongoing challenges in accurately defining the targets of molecular therapeutics and present conventions for deconvoluting the complexities of molecular pharmacology and drug efficacy.


title: High Accuracy Arabic Handwritten Characters Recognition Using Error Back Propagation Artificial Neural Networks

abstract: This manuscript considers a new architecture to handwritten characters recognition based on simulation of the behavior of one type of artificial neural network, called the Error Back Propagation Artificial Neural Network (EBPANN). We present an overview of our neural network to be optimized and tested on 12 offline isolated Arabic handwritten characters (???????????????? ?,?,???) because the similarity of some Arabic characters and the location of the points in the character. Accuracy of 93.61% is achieved using EBPANN which is the highest accuracy achieved during Offline Handwritten Arabic Character Recognition. It is noted that the EBPANN in general generates an optimized comparison between the input samples and database samples which improves the final recognition rate. Experimental results show that the EBPANN is convergent and more accurate in solutions that minimize the error recognition rate.


title: Efficient and effective Querying by Image Content

abstract: In the QBIC (Query By Image Content) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, shape, position, and dominant edges of image objects and regions. Potential applications include medical (“Give me other images that contain a tumor with a texture like this one”), photo-journalism (“Give me images that have blue at the top and red at the bottom”), and many others in art, fashion, cataloging, retailing, and industry. We describe a set of novel features and similarity measures allowing query by image content, together with the QBIC system we implemented. We demonstrate the effectiveness of our system with normalized precision and recall experiments on test databases containing over 1000 images and 1000 objects populated from commercially available photo clip art images, and of images of airplane silhouettes. We also present new methods for efficient processing of QBIC queries that consist of filtering and indexing steps. We specifically address two problems: (a) non Euclidean distance measures; and (b) the high dimensionality of feature vectors. For the first problem, we introduce a new theorem that makes efficient filtering possible by bounding the non-Euclidean, full cross-term quadratic distance expression with a simple Euclidean distance. For the second, we illustrate how orthogonal transforms, such as Karhunen Loeve, can help reduce the dimensionality of the search space. Our methods are general and allow some “false hits” but no false dismissals. The resulting QBIC system offers effective retrieval using image content, and for large image databases significant speedup over straightforward indexing alternatives. The system is implemented in X/Motif and C running on an RS/6000.


title: Converting Cascade-Correlation Neural Nets into Probabilistic Generative Models

abstract: Humans are not only adept in recognizing what class an input instance belongs to (i.e., classification task), but perhaps more remarkably, they can imagine (i.e., generate) plausible instances of a desired class with ease, when prompted. Inspired by this, we propose a framework which allows transforming Cascade-Correlation Neural Networks (CCNNs) into probabilistic generative models, thereby enabling CCNNs to generate samples from a category of interest. CCNNs are a well-known class of deterministic, discriminative NNs, which autonomously construct their topology, and have been successful in giving accounts for a variety of psychological phenomena. Our proposed framework is based on a Markov Chain Monte Carlo (MCMC) method, called the Metropolis-adjusted Langevin algorithm, which capitalizes on the gradient information of the target distribution to direct its explorations towards regions of high probability, thereby achieving good mixing properties. Through extensive simulations, we demonstrate the efficacy of our proposed framework.


title: Multi-Atlas Segmentation of Biomedical Images: A Survey

abstract: Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, et al. (2004), Klein, et al. (2005), and Heckemann, et al. (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003-2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.


title: Usage and attribution of Stack Overflow code snippets in GitHub projects

abstract: Stack Overflow (SO) is the most popular question-and-answer website for software developers, providing a large amount of copyable code snippets. Using those snippets raises maintenance and legal issues. SO’s license (CC BY-SA 3.0) requires attribution, i.e., referencing the original question or answer, and requires derived work to adopt a compatible license. While there is a heated debate on SO’s license model for code snippets and the required attribution, little is known about the extent to which snippets are copied from SO without proper attribution. We present results of a large-scale empirical study analyzing the usage and attribution of non-trivial Java code snippets from SO answers in public GitHub (GH) projects. We followed three different approaches to triangulate an estimate for the ratio of unattributed usages and conducted two online surveys with software developers to complement our results. For the different sets of projects that we analyzed, the ratio of projects containing files with a reference to SO varied between 3.3% and 11.9%. We found that at most 1.8% of all analyzed repositories containing code from SO used the code in a way compatible with CC BY-SA 3.0. Moreover, we estimate that at most a quarter of the copied code snippets from SO are attributed as required. Of the surveyed developers, almost one half admitted copying code from SO without attribution and about two thirds were not aware of the license of SO code snippets and its implications.


title: From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots

abstract: Learning from demonstration for motion planning is an ongoing research topic. In this paper we present a model that is able to learn the complex mapping from raw 2D-laser range findings and a target position to the required steering commands for the robot. To our best knowledge, this work presents the first approach that learns a target-oriented end-to-end navigation model for a robotic platform. Furthermore, the supervised model training is based on expert demonstrations generated in simulation with an existing motion planner. We demonstrate that the learned navigation model is directly transferable to previously unseen virtual and, more interestingly, real-world environments. It can safely navigate the robot through obstacle-cluttered environments to reach the desired targets. We present an extensive quantitative and qualitative evaluation of the neural network-based motion planner, and compare it to a grid-based global approach, both in simulation and in real-world experiments.


title: Size Reduction of a Low-Profile Spiral Antenna Using Inductive and Dielectric Loading

abstract: This letter discusses a miniature low profile ultrawideband (UWB) spiral. The antenna is miniaturized using a combination of dielectric and inductive loading. In addition, a ferrite coated ground plane is adopted in place of the traditional metallic ground plane for profile reduction. Using full-wave simulations and measurements, it is shown that the miniaturized spiral can achieve similar performance to a traditional planar spiral twice its size.


title: PeerDB: A P2P-based System for Distributed Data Sharing

abstract: We present the design and evaluation of PeerDB, a peer-to-peer (P2P) distributed data sharing system. PeerDB distinguishes itself from existing P2P systems in several ways. First, it is a full-fledge data management system that supports fine-grain content-based searching. Second, it facilitates sharing of data without shared schema. Third, it combines the power of mobile agents into P2P systems to perform operations at peers' sites. Fourth, PeerDB network is self-configurable, i.e., a node can dynamically optimize the set of peers that it can communicate directly with based on some optimization criterion. By keeping peers that provide most information or services in close proximity (i.e., direct communication), the network bandwidth can be better utilized and system performance can be optimized. We implemented and evaluated PeerDB on a cluster of 32 Pentium II PCs. Our experimental results show that PeerDB can effectively exploit P2P technologies for distributed data sharing.


title: Understanding the regenerative comparator circuit

abstract: The regenerative comparator circuit which lies at the heart of A/D conversion, slicer circuits, and memory sensing, is unstable, time-varying, nonlinear, and with multiple equilibria. That does not mean, as this paper shows, that it cannot be understood with simple equivalent circuits that reveal its dynamics completely, and enable it to be designed to specifications on static and dynamic offset and noise. The analysis is applied to the StrongArm latch. I. INTRODUCTION Flip-flops used as regenerative amplifiers are found every- where in electronic circuits. It is well-understood that they have two stable states, and that given sufficient time, the circuit will regenerate an input voltage unbalance to reach one of these states. The latched comparator must be symmetric by its very nature, since its binary states are symmetrical. Practical unbal- ances in the circuit arising from transistor and load mismatch lead to uncertainty in the regenerated binary output when a small analog input is applied. This problem of offset is well- known to circuit designers, and since the observed offsets can be much larger than in simple linear amplifiers, a certain mystery attends to "dynamic offsets" that appear only in a latched comparator. This paper clears much of the mystery by visualizing regen- eration. By using one trajectory as a frame of reference, circuit unbalances can in most cases be modelled by a sequence of linear, time-varying equivalent circuits that capture, piecewise, the variation of the circuit with time. Thus, simple expressions are obtained for all offsets, and a straightforward method emerges to null them to a desired accuracy at very small cost in power or chip area.


title: Face recognition in unconstrained videos with matched background similarity

abstract: Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this paper we make the following contributions. (a) We present a comprehensive database of labeled videos of faces in challenging, uncontrolled conditions (i.e., ‘in the wild’), the ‘YouTube Faces’ database, along with benchmark, pair-matching tests1. (b) We employ our benchmark to survey and compare the performance of a large variety of existing video face recognition techniques. Finally, (c) we describe a novel set-to-set similarity measure, the Matched Background Similarity (MBGS). This similarity is shown to considerably improve performance on the benchmark tests.


title: Fetal Amelia: A Case Report

abstract: Congenital limb defects are rare fetal anomalies with a birth prevalence of 0.55 per 1,000. Amelia is an extremely rare birth defect marked by the complete absence of one or more limbs. We report a case of fetal amelia, ultrasound findings, manifestations and the fetal outcome.


title: Learning from positive and unlabeled data: a survey

abstract: Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.


title: Multi-Source Neural Variational Inference

abstract: Learning from multiple sources of information is an important problem in machine-learning research. The key challenges are learning representations and formulating inference methods that take into account the complementarity and redundancy of various information sources. In this paper we formulate a variational autoencoder based multi-source learning framework in which each encoder is conditioned on a different information source. This allows us to relate the sources via the shared latent variables by computing divergence measures between individual source’s posterior approximations. We explore a variety of options to learn these encoders and to integrate the beliefs they compute into a consistent posterior approximation. We visualise learned beliefs on a toy dataset and evaluate our methods for learning shared representations and structured output prediction, showing trade-offs of learning separate encoders for each information source. Furthermore, we demonstrate how conflict detection and redundancy can increase robustness of inference in a multi-source setting.


title: Morphometric and dynamic measurements of muscular fascia in healthy individuals using ultrasound imaging: a summary of the discrepancies and gaps in the current literature

abstract: PURPOSE ::: The objectives of this work was to conduct a comprehensive state-of-the art review of the current literature to identify any gaps or discrepancies and summarize the main challenges for obtaining a homogeneous evaluation of muscular fascia in healthy individuals. ::: ::: ::: METHODS ::: An electronic document search using key words and MeSH terms was performed with various databases. Two independent investigators were tasked with the screening of articles and data extraction. A critical appraisal of what is known was then conducted. ::: ::: ::: RESULTS ::: The literature search identified 65 articles related to healthy facia in the various databases consulted and 20 articles were kept for the review. The thickest portion of the fascia lata (the iliotibial tract) and the plantar fascia are the most often studied muscular fasciae whereas there is paucity of studies on fascia related to other muscles in the body. ::: ::: ::: CONCLUSION ::: US imaging is suitable to complement physical examination and for evaluating treatment outcomes. However, the small number of studies and the heterogeneity of the methods did not allow us to establish normal reference values for muscular fascia thickness and to provide strong recommendations about measurement protocols.


title: Stability of switched neural networks with time-varying delays

abstract: This paper investigates the globally exponential stability of switched neural networks with time-varying delays. By virtue of mode-dependent average dwell time, some significant criteria of exponential stability are obtained for delayed switched neural networks with only stable subsystems or with both stable subsystems and unstable subsystems. The proposed theoretical results could be utilized not only to verify the globally exponential stability of switched neural networks, but also to design appropriate switching signal to guarantee the globally exponential stabilization. They can explicitly reflect the effect of mode-dependent average dwell time on the stability of switched neural networks. In contrast to the previous results, the proposed criteria are more straightforward and effective in the real-world application. Three numerical examples are introduced to illustrate the effectiveness of the proposed results.


title: A 10-MHz Isolated Synchronous Class-Φ2 Resonant Converter

abstract: Because of the forward recovery, the performance of the diodes degrades seriously at multimegahertz, causing extremely high power loss. So, the synchronous rectification (SR) is strongly desired in the multimegahertz resonant converters. This paper proposes a self-driven level-shifted resonant gate driver (RGD) for the SR FET in a 10-MHz isolated class-Φ2 resonant converter. The proposed RGD provides precise switching timing for the SR so that the body diode conduction loss can be minimized. A control stage is introduced to the proposed RGD to block the circulating current and the low-impedance path in the driver to realize ON–OFF control of the converter with high efficiency. The proposed RGD also generates a tunable dc bias to increase the peak gate voltage and extend the conduction time with the optimal $R_{{\rm DS}({\rm on})}$ so that the average $R_{{\rm DS}({\rm on})}$ and the associated conduction loss can be reduced significantly. A 10-MHz prototype with 18-V input and 5-V/2-A output was built. At full load of 2 A, the proposed RGD improves the efficiency from 80.2% using the conventional RGD to 82% (an improvement of 1.8%). Compared to the efficiency of 77.3% using the diode rectification, the efficiency improvement is 4.7% at full load.


title: The Alternate Arm Converter: A New Hybrid Multilevel Converter With DC-Fault Blocking Capability

abstract: This paper explains the working principles, supported by simulation results, of a new converter topology intended for HVDC applications, called the alternate arm converter (AAC). It is a hybrid between the modular multilevel converter, because of the presence of H-bridge cells, and the two-level converter, in the form of director switches in each arm. This converter is able to generate a multilevel ac voltage and since its stacks of cells consist of H-bridge cells instead of half-bridge cells, they are able to generate higher ac voltage than the dc terminal voltage. This allows the AAC to operate at an optimal point, called the “sweet spot,” where the ac and dc energy flows equal. The director switches in the AAC are responsible for alternating the conduction period of each arm, leading to a significant reduction in the number of cells in the stacks. Furthermore, the AAC can keep control of the current in the phase reactor even in case of a dc-side fault and support the ac grid, through a STATCOM mode. Simulation results and loss calculations are presented in this paper in order to support the claimed features of the AAC.


title: The Development and Maintenance of Drug Addiction

abstract: What is the defining property of addiction? We dust off a several-decades-long debate about the relative importance of two forms of reinforcement—positive reinforcement, subjectively linked to drug-induced euphoria, and negative reinforcement, subjectively linked to the alleviation of pain—both of which figure importantly in addiction theory; each of these forms has dominated addiction theory in its time. We agree that addiction begins with the formation of habits through positive reinforcement and that drug-opposite physiological responses often establish the conditions for negative reinforcement to come into play at a time when tolerance, in the form of increasing reward thresholds, appears to develop into positive reinforcement. Wise's work has tended to focus on positive-reinforcement mechanisms that are important for establishing drug-seeking habits and reinstating them quickly after periods of abstinence, whereas Koob's work has tended to focus on the negative-reinforcement mechanisms that become most obvious in the late stages of sustained addiction. While we tend to agree with each other about the early and late stages of addiction, we hold different views as to (i) the point between early and late at which the diagnosis of ‘addiction' should be invoked, (ii) the relative importance of positive and negative reinforcement leading up to this transition, and (iii) the degree to which the specifics of negative reinforcement can be generalized across the range of addictive agents.


title: Music-based respiratory biofeedback in visually-demanding tasks

abstract: Biofeedback tools generally use visualizations to display physiological information to the user. As such, these tools are incompatible with visually demanding tasks such as driving. While auditory or haptic biofeedback may be used in these cases, the additional sensory channels can increase workload or act as a nuisance to the user. A number of studies, however, have shown that music can improve mood and concentration, while also reduce aggression and boredom. Here, we propose an intervention that combines the benefits of biofeedback and music to help users regulate their stress response while performing a visual task (driving a car simulator). Our approach encourages slow breathing by adjusting the quality of the music in response to the user's breathing rate. We evaluate the intervention on a 2 X 2 design with music and auditory biofeedback as independent variables. Our results indicate that our music-biofeedback intervention leads to lower arousal (reduced electrodermal activity and increased heart rate variability) than music alone, auditory biofeedback alone and a control condition.


title: A new user similarity model to improve the accuracy of collaborative filtering

abstract: We first analyze the shortages of the existing similarity measures in collaborative filtering.And second, we propose a new user similarity model to overcome these drawbacks.We compare the new model with many other similarity measures on two real data sets.Experiments show that the new model can reach better performance than many existing similarity measures. Collaborative filtering has become one of the most used approaches to provide personalized services for users. The key of this approach is to find similar users or items using user-item rating matrix so that the system can show recommendations for users. However, most approaches related to this approach are based on similarity algorithms, such as cosine, Pearson correlation coefficient, and mean squared difference. These methods are not much effective, especially in the cold user conditions. This paper presents a new user similarity model to improve the recommendation performance when only few ratings are available to calculate the similarities for each user. The model not only considers the local context information of user ratings, but also the global preference of user behavior. Experiments on three real data sets are implemented and compared with many state-of-the-art similarity measures. The results show the superiority of the new similarity model in recommended performance.


title: A Systematic Review of Adaptivity in Human-Robot Interaction

abstract: As the field of social robotics is growing, a consensus has been made on the design and implementation of robotic systems that are capable of adapting based on the user actions. These actions may be based on their emotions, personality or memory of past interactions. Therefore, we believe it is significant to report a review of the past research on the use of adaptive robots that have been utilised in various social environments. In this paper, we present a systematic review on the reported adaptive interactions across a number of domain areas during Human-Robot Interaction and also give future directions that can guide the design of future adaptive social robots. We conjecture that this will help towards achieving long-term applicability of robots in various social domains.


title: Performance evaluation of NewSQL databases

abstract: For over forty years, relational databases have been the leading model for data storage, retrieval and management. However, due to increasing needs for scalability and performance, alternative systems have emerged, namely NewSQL technology. NewSQL is a class of modern relational database management systems (RDBMS) that provide the same scalable performance of NoSQL systems for online transaction processing (OLTP) read-write workloads while still maintaining the ACID guarantees of a traditional database system. The rising interest in NewSQL technology, over the last few years resulted in an increasing number of evaluations and comparisons among competing NewSQL technologies. Some of the NewSQL databases currently used in various popular web applications are VoltDB, Google Spanner, MemSQL, SAP HANA, NuoDB, and TokuDB. This work is trying to comment on the various NewSQL database systems which describes benefits, characteristics, and classification of NewSQL databases for online transaction processing (OLTP) for Big data management. It also provides the list of popular NewSQL databases in categorized tables. This dissertation work mainly covers evaluation comparison between four NewSQL databases: NuoDB, VoltDB, MemSQL, and Cockroach DB on the basis of various parameters like read latency, write latency, update latency, and execution time. The experiments do not cover only performance (latency and execution time) but also focus on ease of use and flexibility of the used NewSQL databases.


title: Data fusion

abstract: The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation. This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data fusion, namely, uncertain and conflicting data values. We give an overview and classification of different ways of fusing data and present several techniques based on standard and advanced operators of the relational algebra and SQL. Finally, the article features a comprehensive survey of data integration systems from academia and industry, showing if and how data fusion is performed in each.


title: Time slice video synthesis by robust video alignment

abstract: Time slice photography is a popular effect that visualizes the passing of time by aligning and stitching multiple images capturing the same scene at different times together into a single image. Extending this effect to video is a difficult problem, and one where existing solutions have only had limited success. In this paper, we propose an easy-to-use and robust system for creating time slice videos from a wide variety of consumer videos. The main technical challenge we address is how to align videos taken at different times with substantially different appearances, in the presence of moving objects and moving cameras with slightly different trajectories. To achieve a temporally stable alignment, we perform a mixed 2D-3D alignment, where a rough 3D reconstruction is used to generate sparse constraints that are integrated into a pixelwise 2D registration. We apply our method to a number of challenging scenarios, and show that we can achieve a higher quality registration than prior work. We propose a 3D user interface that allows the user to easily specify how multiple videos should be composited in space and time. Finally, we show that our alignment method can be applied in more general video editing and compositing tasks, such as object removal.


title: Comparison of fiducial marker detection and object interaction in activities of daily living utilising a wearable vision sensor

abstract: Summary ::: This paper presents a comparison between algorithms (Oriented FAST and Rotated BRIEF (ORB) and Aruco) for the detection of fiducial markers placed throughout a smart environment. A series of activities of daily living (ADL) were conducted while monitoring a first-person perspective of the situation; this was achieved through the usage of the Google Glass platform. Fiducial markers were employed, as a means to assist with the detection of specific objects of interest, within the environment. Each marker was assigned unique Identification (ID) and was used to identify the object. Three activities were performed by a participant within the environment. On subsequent trials of the solution, lighting conditions were modified to assess fiducial marker detection rates on a frame-by-frame basis. This paper presents the results from this investigation, detailing performance measure for each object detected under various lighting conditions, motion blur and distance from the objects. An intelligent system was developed to specifically consider distance estimation in order to aid with the filtering out of false interactions. A linear filtering method was applied along with a fuzzy membership function to estimate the degree of user interaction, which assists in removing false positives generated by the occupant. The intelligent system returns an average precision, recall and an F-Measure of 0.99, 0.62 and 0.49, respectively. Copyright © 2016 John Wiley & Sons, Ltd.


title: Correcting astigmatism with toric intraocular lenses: Effect of posterior corneal astigmatism

abstract: Purpose To evaluate the impact of posterior corneal astigmatism on outcomes with toric intraocular lenses (IOLs). Setting Cullen Eye Institute, Baylor College of Medicine, Houston, Texas, USA. Design Case series. Methods Corneal astigmatism was measured using 5 devices before and 3 weeks after cataract surgery. Toric IOL alignment was recorded at surgery and at the slitlamp 3 weeks postoperatively. The actual corneal astigmatism was calculated based on refractive astigmatism 3 weeks postoperatively and the effective toric power calculated with the Holladay 2 formula. The prediction error was calculated as the difference between the astigmatism measured by each device and the actual corneal astigmatism. Vector analysis was used in all calculations. Results With the IOLMaster, Lenstar, Atlas, manual keratometer, and Galilei (combined Placido–dual Scheimpflug analyzer), the mean prediction errors (D) were, respectively, 0.59 @ 89.7, 0.48 @ 91.2, 0.51 @ 78.7, 0.62 @ 97.2, and 0.57 @ 93.9 for with-the-rule (WTR) astigmatism (60 to 120 degrees), and 0.17 @ 86.2, 0.23 @ 77.7, 0.23 @ 91.4, 0.41 @ 58.4, and 0.12 @ 7.3 for against-the-rule (ATR) astigmatism (0 to 30 degrees and 150 to 180 degrees). In the WTR eyes, there were significant WTR prediction errors (0.5 to 0.6 diopters [D]) by all devices. In ATR eyes, WTR prediction errors were 0.2 to 0.3 D by all devices except the Placido–dual Scheimpflug analyzer (all P Conclusions Corneal astigmatism was overestimated in WTR by all devices and underestimated in ATR by all except the Placido–dual Scheimpflug analyzer. A new toric IOL nomogram is proposed. Financial Disclosure Drs. Koch, Weikert, and Wang received research support from Ziemer USA, Inc. Dr. Koch has a financial interest with Alcon Laboratories, Inc., Abbott Medical Optics, Inc., Optimedica Corp., and Ziemer USA, Inc. No other author has a financial or proprietary interest in any material or method mentioned.


title: Hybrid structural health monitoring for in-service highway bridges using wireless multiscale sensors

abstract: With the rapid development of electrical circuits, Micro electromechanical system (MEMS) and network technology, ::: wireless smart sensor networks (WSSN) have shown significant potential for replacing existing wired Structural health ::: monitoring (SHM) systems due to their cost effectiveness and versatility. A few structural systems have been ::: monitored using WSSN measuring acceleration, temperature, wind speed, humidity; however, a multi-scale sensing ::: device which has the capability to measure the displacement has not been yet developed. In this project, a new highaccuracy ::: displacement sensing system has been developed combining a high resolution analog displacement sensor ::: and MEMS-based wireless microprocessor platform. The wireless sensor is calibrated in the laboratory to get the high ::: precision displacement data from analog sensor. The developed multi-scale sensing system is evaluated in a laboratory ::: bridge structure to check its performance. Finally, the developed hybrid multi-scale displacement sensing system was ::: deployed on in-service highway bridge for SHM.


title: AutoScale: Dynamic, Robust Capacity Management for Multi-Tier Data Centers

abstract: Energy costs for data centers continue to rise, already exceeding $15 billion yearly. Sadly much of this power is wasted. Servers are only busy 10--30p of the time on average, but they are often left on, while idle, utilizing 60p or more of peak power when in the idle state. We introduce a dynamic capacity management policy, AutoScale, that greatly reduces the number of servers needed in data centers driven by unpredictable, time-varying load, while meeting response time SLAs. AutoScale scales the data center capacity, adding or removing servers as needed. AutoScale has two key features: (i) it autonomically maintains just the right amount of spare capacity to handle bursts in the request rate; and (ii) it is robust not just to changes in the request rate of real-world traces, but also request size and server efficiency. We evaluate our dynamic capacity management approach via implementation on a 38-server multi-tier data center, serving a web site of the type seen in Facebook or Amazon, with a key-value store workload. We demonstrate that AutoScale vastly improves upon existing dynamic capacity management policies with respect to meeting SLAs and robustness.


title: On Netflix catalog dynamics and caching performance

abstract: Multimedia streaming applications have substantially changed the market policy of an increasing number of content providers that offer streaming services to the users. The need for effective video content delivery re-fueled interest for caching: since the Web-like workload of the 90s are not longer fit to describe the new Web of videos, in this work we investigate the suitability of the publicly available Netflix dataset for caching studies. Our analysis shows that, as the dataset continuously evolves (i) a steady state description is not statistically meaningful and (ii) despite the cache hit ratio decreases due to the growth of active movies in the catalog, simple caching replacement approaches are close to the optimum given the growing skew in the popularity distribution over the time. Additionally, we point out that, since the dataset reports logs of movie ratings, anomalies arise when ratings are considered to be movie views. At the same time, we show anomalies yield conservative caching results, that reinforces the soundness of our study.


title: A SURVEY OF TEXT CLUSTERING ALGORITHMS

abstract: Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this chapter, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the text domain. We will discuss the key methods used for text clustering, and their relative advantages. We will also discuss a number of recent advances in the area in the context of social network and linked data.


N ov 2 01 8 Gradient Descent Finds Global Minima of Deep Neural Networks


title: Current mental health program evaluation in San Mateo County

abstract: Examples of program evaluation studies are presented to illustrate the wide range of questions under review in the San Mateo County Mental Health Program. The variety of questions investigated is a reflection of the comprehensiveness and community orientation of the services included in this program. These studies are classified from four points of view, depending upon the focus of their activity: intraservice, interservice, division, and community. Some administrative factors related to the research function are discussed.


title: Unawareness of dyskinesias in Parkinson's and Huntington's diseases

abstract: We performed a clinical study to evaluate the unawareness of dyskinesias in patients affected by Parkinson's disease (PD) and Huntington's disease (HD). Thirteen PD patients with levodopa-induced dyskinesias and 9 HD patients were enrolled. Patients were asked to evaluate the presence of dyskinesias while performing specific motor tasks. The Abnormal Involuntary Movement Scale (AIMS) and Goetz dyskinesia rating scale were administered to determine the severity of dyskinesias. The Unified Parkinson's disease rating scale (UPDRS) and Unified Huntington's Disease Rating Scale (UHDRS) were used in PD and HD patients, respectively. In PD we found a significant negative relationship between unawareness score at hand pronation-supination and AIMS score for upper limbs. In HD we found a significant positive relationship between total unawareness score and disease duration. In PD the unawareness seems to be inversely related with severity of dyskinesias, while in HD it is directly related to disease duration and severity.


title: Diversity of Colletotrichum spp. isolated from chili pepper fruit exhibiting symptoms of anthracnose in Thailand

abstract: Colletotrichum spp. are causal agents of anthracnose disease in chili fruits and other tropical crops. The disease is increasing in chili fruits in Thailand and significantly reduces fruit quality and fruit production. Forty-eight isolates of Colletotrichum spp. associated with chili anthracnose were collected from different areas of Thailand during 2010–2015. Based on morphological characteristic identification, 10 isolates were shown to belong to the C. gloeosporioides species complex, 24 isolates belong to the C. acutatum species complex and 14 isolates to C. capsici. For molecular identification, two primer sets, ITS1/ITS4 and ACT528/ACT738, were used for amplification of the internal transcribed spacer of rRNA gene (ITS1–5.8S–ITS2) and partial region actin gene (ACT), respectively. The phylogenetic analysis of individual and combined ITS region and actin nucleotide sequences identified the collected isolates into 4 species: C. gloeosporioides, C. siamense, C. acutatum and C. capsici. The pathogenicity test demonstrated that all four species were pathogenic on intact unwounded and healthy fruits. These results indicated that C. capsici, C. acutatum, C. gloeosporioides and C. siamense were the causal agents of chili anthracnose disease.


title: Nonlinear Current Detection Based on Magnetic Modulation Technology

abstract: This paper proposes a sensor for leakage-current detection based on the magnetic modulation technology. The sensor can measure both nonlinear current with an arbitrary waveform and linear current from dc to several kilohertz. The current sensor consists of only one magnetic core and one winding, which significantly reduces the sensor size. The structure and operating principle of the current sensor are introduced, and the dynamic process of the current sensor is analyzed. In addition, the mathematical model of dynamic process with hysteresis considered for the current sensor is presented. Both the simulation and the experimental results are presented to verify the effectiveness of proposed current sensor.


title: Localized Multiple Kernel Learning for Anomaly Detection: One-class Classification

abstract: Multi-kernel learning has been well explored in the recent past and has exhibited promising outcomes for multi-class classification and regression tasks. In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection. Recently, the basic multi-kernel approach has been proposed to solve the OCC problem, which is simply a convex combination of different kernels with equal weights. This paper proposes a Localized Multiple Kernel learning approach for Anomaly Detection (LMKAD) using OCC, where the weight for each kernel is assigned locally. Proposed LMKAD approach adapts the weight for each kernel using a gating function. The parameters of the gating function and one-class classifier are optimized simultaneously through a two-step optimization process. We present the empirical results of the performance of LMKAD on 25 benchmark datasets from various disciplines. This performance is evaluated against existing Multi Kernel Anomaly Detection (MKAD) algorithm, and four other existing kernel-based one-class classifiers to showcase the credibility of our approach. Our algorithm achieves significantly better Gmean scores while using a lesser number of support vectors compared to MKAD. Friedman test is also performed to verify the statistical significance of the results claimed in this paper.


title: Small and Adaptive Antennas and Arrays for GNSS Applications

abstract: This paper reviews Global Navigation Satellite Systems (GNSS) antennas and arrays for robust coverage in presence of interference signals with a focus on antenna design aspects. A number of small antenna designs are presented that cover multiple current and future GNSS frequency bands (1150–1610 MHz) with sizes as small as 1 in (25 mm) in diameter. Several arrays are presented with a size as small as 3.5 in (8.9 cm) in diameter and approximately 0.5 in (13 mm) thick as compared to commercially available apertures 14 in (35.6 cm) and 5.5 in (14.0 cm) in diameter. These small arrays are shown to satisfy the gain requirements of GNSS receivers while simultaneously offering four to six antenna elements for adaptive interference suppression.


title: Production and Characterization of Nanoparticle Based Hyaluronate Gel Containing Retinyl Palmitate for Wound Healing

abstract: BACKGROUND ::: Wound healing is a biological process that can get in a state of pathologic inflammation, requiring the use of specific medications able to promote proper tissue repair. ::: ::: ::: OBJECTIVE ::: The study describes the production and characterization of nanoparticle based gel for wound healing treatment designed to deliver hyaluronic acid and retinyl palmitate onto the skin. ::: ::: ::: METHODS ::: Tristearin solid lipid nanoparticles and nanostructured lipid carriers based on a tristearin and caprylic/capric triglyceride mixture were produced and characterized. Gel spreadability and viscosity were investigated. Drug diffusion and "in vitro" wound healing were assessed by Franz cell and scratch wound assay in keratinocytes. ::: ::: ::: RESULTS ::: Cryogenic transmission electron microscopy evidenced flat discoid nanoparticles. Photon correlation spectroscopy analysis indicated homogeneous dimensional distribution and mean diameter 132±46 nm. X-ray evidenced a lamellar inner structure of lipid nanoparticles. Nanostructured lipid carriers, being based on a heterogeneous solid/ liquid lipid mixture, could better solubilize retinyl palmitate and control its stability. The hyaluronic acid directly added into nanoparticles' dispersion enabled to obtain a shear-thinning gel suitable for cutaneous administration. Retynil palmitate diffusion was slower from the nanoparticulate gel with respect to the plain nanoparticle dispersion. The "wound healing" effect of nanoparticulate gel containing retinyl palmitate and hyaluronic acid, analyzed in HaCaT cells, showed significant differences in wounded areas between treated and control cells during the first 24 h postwounding suggesting a synergic effect of retinyl palmitate and hyaluronic acid in "in vitro" wound healing. ::: ::: ::: CONCLUSIONS ::: This study suggests that a nanoparticle based hyaluronate gel containing retinyl palmitate can be efficiently used for wound healing.


title: Compact Implementations of FPGA-based PUFs with Enhanced Performance

abstract: The Physically Unclonable Functions (PUFs) are used in numerous security applications such as device authentication, secret key generation, FPGA intellectual property (IP) protection, and trusted computing. In this paper, compact implementations of Ring oscillator-based PUF (RO-PUF), Arbiter-based PUF (A-PUF) and RS Latch-based PUF (RS-LPUF) on an FPGA (Field Programmable Gate Array) platform are presented. The proposed schemes provide very competitive area trade-offs and effectively enable smallest FPGA implementations, reported so far, of RS-LPUF, RO-PUF, and A-PUF respectively. The designs have been validated by developing prototypes on Xilinx Spartan-6 FPGAs at core voltage of 1.2V and normal operating temperature. Finally, a detailed comparison of statistical analysis on their performance using measured PUF data have been carried out. It has been demonstrated that the proposed designs exhibit significantly improved performance in terms of statistical properties when compared to the existing works.


title: DEAP: a python framework for evolutionary algorithms

abstract: DEAP (Distributed Evolutionary Algorithms in Python) is a novel volutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black box type of frameworks. It also incorporates easy parallelism where users need not concern themselves with gory implementation details like synchronization and load balancing, only functional decomposition. Several examples illustrate the multiple properties of DEAP.


title: Exploring the effects of game elements in m-participation

abstract: Emerging technologies make mobile devices promising tools to be used for e-participation. Some municipalities have already recognized this and created m-participation apps. One of the greatest challenges when it comes to civic engagement is encouraging citizens to become involved. This study explores the potential of game elements as motivational factor. An overview of e-participation tools that already employ game elements is given. The main contribution of this study are the findings of an experiment that investigated the effects of introducing game elements to an m-participation prototype. Our results show that while game elements provide an initial incentive to become active, the most important aspect for engaging people in urban governance processes is for the governance to show that they are listening and take civic input serious. We further provide recommendations for future research.


title: 28 GHz and 73 GHz Millimeter-Wave Indoor Propagation Measurements and Path Loss Models

abstract: This paper presents 28 GHz and 73 GHz millimeter-wave propagation measurements performed in a typical office environment using a 400 Megachip-per-second broadband sliding correlator channel sounder and highly directional steerable 15 dBi (30° beamwidth) and 20 dBi (15° beamwidth) horn antennas. Power delay profiles were acquired for 48 transmitter-receiver location combinations over distances ranging from 3.9 m to 45.9 m with maximum transmit powers of 24 dBm and 12.3 dBm at 28 GHz and 73 GHz, respectively. Directional and omnidirectional path loss models and RMS delay spread statistics are presented for line-of-sight and non-line-of-sight environments for both co- and cross-polarized antenna configurations. The LOS omnidirectional path loss exponents were 1.1 and 1.3 at 28 GHz and 73 GHz, and 2.7 and 3.2 in NLOS at 28 GHz and 73 GHz, respectively, for vertically-polarized antennas. The mean directional RMS delay spreads were 18.4 ns and 13.3 ns, with maximum values of 193 ns and 288 ns at 28 GHz and 73 GHz, respectively.


Survey of Convolutional Neural Network


title: Correlation between morphological MRI findings and specific diagnostic categories in fetal alcohol spectrum disorders.

abstract: Fetal alcohol spectrum disorders (FASD) include physical and neurodevelopmental abnormalities related to prenatal alcohol exposure. Some neuroimaging findings have been clearly related to FASD, including corpus callosum and cerebellar anomalies. However, detailed studies correlating with specific FASD categories, that is, the fetal alcohol syndrome (FAS), partial FAS (pFAS) and alcohol related neurodevelopmental disorders (ARND), are lacking. We prospectively performed clinical assessment and brain MR imaging to 72 patients with suspected FASD, and diagnosis was confirmed in 62. The most frequent findings were hypoplasia of the corpus callosum and/or of the cerebellar vermis. Additional findings were vascular anomalies, gliosis, prominent perivascular spaces, occipito-cervical junction and cervical vertebral anomalies, pituitary hypoplasia, arachnoid cysts, and cavum septum pellucidum.


title: Reproducibility of the 14C urea breath test repeated after 1 week.

abstract: Objectives : The aim of this study was to determine the numerical and categorical reproducibility of the 14 C urea breath test in a routine clinical setting. Background : The 14 C urea breath test is used for diagnosis of Helicobacter pylori infection and is regarded by some centers as the gold standard. Methods : In 140 referrals, duplicate tests were performed 1 wk apart. Eighteen patients were excluded because they had taken antibiotics or had changed medication that could influence the gastric urease activity between the tests. Results : The limits of agreement between the repeated tests were 45-234%. Hence, the numerical result of a repeated test was 95% likely to differ from the first with a factor of approximately two or less. However, when patients were classified as positive or negative according to our previously determined cut-off limit, only three patients were discordantly classified by the two tests. The observed agreement between the tests was 98%, and the chance-corrected proportional agreement (K) was 95%, which is far better than that reported for the histological diagnosis of gastric H. pylori infection. Conclusion : The diagnostic reproducibility of the urea breath test was very good.


title: Long-term prediction for seismic hazard for radioactive waste disposal

abstract: Abstract We consider possible approaches to the long-term prediction for seismic hazard in relation to the practical need for the safety of geological disposal of long-lived radioactive waste. The required period of prediction significantly exceeds the one reflected in the set of maps of General Seismic Zoning of the territory of the Russian Federation (GSZ-97). The first geological repository in Russia is planned to be set up in the Nizhnii Kan granite massif in the Krasnoyarsk Krai. This region is an intraplate territory with a relatively high seismic activity. We summarize the analysis of the known empirical generalizations and theoretical principles underlying the seismic hazard prediction. Real seismic events constantly violate forward-looking statements even for relatively short periods of time. These and other arguments suggest that the hypothesis of stationarity of the seismic regime, which is the basis of long-term prediction today, has limited and uncertain applicability in time. Intraplate earthquake prediction is especially uncertain because of the uncertainty in the factor responsible for generating tectonic stresses in these regions. The short horizon of the prediction, based on statistical methods, can be attributed to the nonlinearity of seismic geodynamic processes. Fundamental laws of tectonic processes should be used as the scientific basis for long-term predictions for seismic hazard at the sites chosen for geological disposal of long-lived radioactive waste. These processes can be reflected in models for the migration of the seismically active boundaries of lithospheric plates and the occurrence of seismic activity in intraplate regions.


title: Greedy Layer-Wise Training of Deep Networks

abstract: Complexity theory of circuits strongly suggests that deep architectures can be much more ef cient (sometimes exponentially) than shallow architectures, in terms of computational elements required to represent some functions. Deep multi-layer neural networks have many levels of non-linearities allowing them to compactly represent highly non-linear and highly-varying functions. However, until recently it was not clear how to train such deep networks, since gradient-based optimization starting from random initialization appears to often get stuck in poor solutions. Hinton et al. recently introduced a greedy layer-wise unsupervised learning algorithm for Deep Belief Networks (DBN), a generative model with many layers of hidden causal variables. In the context of the above optimization problem, we study this algorithm empirically and explore variants to better understand its success and extend it to cases where the inputs are continuous or where the structure of the input distribution is not revealing enough about the variable to be predicted in a supervised task. Our experiments also confirm the hypothesis that the greedy layer-wise unsupervised training strategy mostly helps the optimization, by initializing weights in a region near a good local minimum, giving rise to internal distributed representations that are high-level abstractions of the input, bringing better generalization.


title: Proximal renal tubular dysfunction in primary distal renal tubular acidosis

abstract: Low-molecular-weight (LMW) proteinuria has been described in patients with primary distal renal tubular acidosis (dRTA). However, other proximal renal tubular dysfunctions have rarely been reported. In this report we describe reversible and multiple proximal renal tubular cell dysfunctions in a patient with dRTA. A 4-year-old girl was admitted to our hospital for investigation of short stature and proteinuria. Laboratory studies revealed a hyperchloremic metabolic acidosis without aciduria, hypokalemia, hypouricemia with uricosuria, hypercalciuria, LMW proteinuria, phosphaturia, and generalized aminoaciduria. The patient was diagnosed as having dRTA with multiple proximal renal tubular dysfunctions. All proximal renal tubular dysfunction subsided 1.5 years after starting alkali therapy. The precise pathogenic mechanisms underlying the development of multiple proximal renal tubular dysfunctions in dRTA remained unclear. However, proximal renal tubular endosomal dysfunction resulting from a profound intracellular acidosis caused by vacuolar H+-ATPase dysfunction or hypokalemic nephropathy might contribute to the development of proximal renal tubular dysfunctions in patients with dRTA.


title: IoT based SCADA integrated with Fog for power distribution automation

abstract: Electrical Grid we have is more than 50 years old. The integration of information technology to the electric grid system is expected to address many shortcomings of the current and traditional electrical grids that have resulted in Smart Grid which is gaining lot of interest and momentum. In Smart Grid, Power Distribution is one part which requires monitoring and control. Lot of technologies and have been applied in Smart grid towards sensing and action. Supervisory Control and Data Acquisition (SCADA) system is very well proven within the substation region. There has been very less monitoring done on the distribution side due to the geographical distribution. Currently, Internet of Things has paved way for connecting huge number of devices to the Internet which would be very much effective and beneficial for power distribution and Automation. So accordingly, an IoT based SCADA integrated with Fog for Distribution Automation system has been proposed which takes care of the consumer utilization, outage management, power quality control and pole transformer health. This is supported by fog computing which does real-time streaming analytics. This helps in reducing the internet bandwidth and latency for immediate control action.


title: A Hierarchical Bayesian Model for Unsupervised Induction of Script Knowledge

abstract: Scripts representing common sense knowledge about stereotyped sequences of events have been shown to be a valuable resource for NLP applications. We present a hierarchical Bayesian model for unsupervised learning of script knowledge from crowdsourced descriptions of human activities. Events and constraints on event ordering are induced jointly in one unified framework. We use a statistical model over permutations which captures event ordering constraints in a more flexible way than previous approaches. In order to alleviate the sparsity problem caused by using relatively small datasets, we incorporate in our hierarchical model an informed prior on word distributions. The resulting model substantially outperforms a state-of-the-art method on the event ordering task.


title: Printed MIMO-Antenna System Using Neutralization-Line Technique for Wireless USB-Dongle Applications

abstract: A printed two-multiple-input multiple-output (MIMO)-antenna system incorporating a neutralization line for antenna port decoupling for wireless USB-dongle applications is proposed. The two monopoles are located on the two opposite corners of the system PCB and spaced apart by a small ground portion, which serves as a layout area for antenna feeding network and connectors for the use of standalone antennas as an optional scheme. It was found that by removing only 1.5 mm long inwards from the top edge in the small ground portion and connecting the two antennas therein with a thin printed line, the antenna port isolation can be effectively improved. The neutralization line in this study occupies very little board space, and the design requires no conventional modification to the ground plane for mitigating mutual coupling. The behavior of the neutralization line was rigorously analyzed, and the MIMO characteristics of the proposed antennas was also studied and tested in the reverberation chamber. Details of the constructed prototype are described and discussed in this paper.


title: Multibiometric Cryptosystems Based on Feature-Level Fusion

abstract: Multibiometric systems are being increasingly de- ployed in many large-scale biometric applications (e.g., FBI-IAFIS, UIDAI system in India) because they have several advantages such as lower error rates and larger population coverage compared to unibiometric systems. However, multibiometric systems require storage of multiple biometric templates (e.g., fingerprint, iris, and face) for each user, which results in increased risk to user privacy and system security. One method to protect individual templates is to store only the secure sketch generated from the corresponding template using a biometric cryptosystem. This requires storage of multiple sketches. In this paper, we propose a feature-level fusion framework to simultaneously protect multiple templates of a user as a single secure sketch. Our main contributions include: (1) practical implementation of the proposed feature-level fusion framework using two well-known biometric cryptosystems, namery,fuzzy vault and fuzzy commitment, and (2) detailed analysis of the trade-off between matching accuracy and security in the proposed multibiometric cryptosystems based on two different databases (one real and one virtual multimodal database), each containing the three most popular biometric modalities, namely, fingerprint, iris, and face. Experimental results show that both the multibiometric cryptosystems proposed here have higher security and matching performance compared to their unibiometric counterparts.


title: L4Android: a generic operating system framework for secure smartphones

abstract: Smartphones became many people's primary means of communication. Emerging applications such as Near Field Communication require new levels of security that cannot be enforced by current smartphone operating systems. Therefore vendors resort to hardware extensions that have limitations in flexibility and increase the bill of materials. In this work we present a generic operating system framework that does away with the need for such hardware extensions. We encapsulate the original smartphone operating system in a virtual machine. Our framework allows for highly secure applications to run side-by-side with the virtual machine. It is based on a state-of-the-art microkernel that ensures isolation between the virtual machine and secure applications. We evaluate our framework by sketching how it can be used to solve four problems in current smartphone security.


title: AROMA: automated resource allocation and configuration of mapreduce environment in the cloud

abstract: Distributed data processing framework MapReduce is increasingly deployed in Clouds to leverage the pay-per-usage cloud computing model. Popular Hadoop MapReduce environment expects that end users determine the type and amount of Cloud resources for reservation as well as the configuration of Hadoop parameters. However, such resource reservation and job provisioning decisions require in-depth knowledge of system internals and laborious but often ineffective parameter tuning. We propose and develop AROMA, a system that automates the allocation of heterogeneous Cloud resources and configuration of Hadoop parameters for achieving quality of service goals while minimizing the incurred cost. It addresses the significant challenge of provisioning ad-hoc jobs that have performance deadlines in Clouds through a novel two-phase machine learning and optimization framework. Its technical core is a support vector machine based performance model that enables the integration of various aspects of resource provisioning and auto-configuration of Hadoop jobs. It adapts to ad-hoc jobs by robustly matching their resource utilization signature with previously executed jobs and making provisioning decisions accordingly. We implement AROMA as an automated job provisioning system for Hadoop MapReduce hosted in virtualized HP ProLiant blade servers. Experimental results show AROMA's effectiveness in providing performance guarantee of diverse Hadoop benchmark jobs while minimizing the cost of Cloud resource usage.


title: Improv: a system for scripting interactive actors in virtual worlds

abstract: Improv is a system for the creation of real−time behavior−based animated actors. There have been several recent efforts to build network distributed autonomous agents. But in general these efforts do not focus on the author’s view. To create rich interactive worlds inhabited by believable animated actors, authors need the proper tools. Improv provides tools to create actors that respond to users and to each other in real−time, with personalities and moods consistent with the author’s goals and intentions. Improv consists of two subsystems. The first subsystem is an Animation Engine that uses procedural techniques to enable authors to create layered, continuous, non−repetitive motions and smooth transitions between them. The second subsystem is a Behavior Engine that enables authors to create sophisticated rules governing how actors communicate, change, and make decisions. The combined system provides an integrated set of tools for authoring the "minds" and "bodies" of interactive actors. The system uses an english−style scripting language so that creative experts who are not primarily programmers can create powerful interactive applications.


title: Brain–Computer Interface Technology: A Review of the First International Meeting

abstract: Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromus- cular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuropros- thesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achieve- ment of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications,


title: Simultaneous Detection and Segmentation

abstract: We aim to detect all instances of a category in an image and, for each instance, mark the pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS). Unlike classical bounding box detection, SDS requires a segmentation and not just a box. Unlike classical semantic segmentation, we require individual object instances. We build on recent work that uses convolutional neural networks to classify category-independent region proposals (R-CNN [16]), introducing a novel architecture tailored for SDS. We then use category-specific, top-down figure-ground predictions to refine our bottom-up proposals. We show a 7 point boost (16% relative) over our baselines on SDS, a 5 point boost (10% relative) over state-of-the-art on semantic segmentation, and state-of-the-art performance in object detection. Finally, we provide diagnostic tools that unpack performance and provide directions for future work.


AFFECTIVE AGILE DESIGN A PROPOSAL FOR A NEW SOFTWARE DEVELOPMENT MODEL


title: Defining Media Enjoyment as the Satisfaction of Intrinsic Needs

abstract: This article presents a model of enjoyment rooted in self-determination theory (Deci & Ryan, 1985) that includes the satisfaction of three needs related to psychological wellbeing: autonomy, competence, and relatedness. In an experiment designed to validate this conceptualization of enjoyment, we manipulate video game characteristics related to the satisfaction of these needs and examine their relative effects on enjoyment. The validated model explains 51% of the variance in enjoyment, even without including needs usually studied in relation to enjoyment such as pleasure seeking. Results indicate the utility of defining enjoyment as need satisfaction. These results are discussed in terms of a broader conceptualization of enjoyment represented as the satisfaction of a comprehensive set of functional needs.


Understanding the Intention of Using Mobile Social Networking Apps Full Papers


title: Dual-space adaptive control of redundantly actuated cable-driven parallel robots

abstract: Cable-driven parallel robots (CDPR) are efficient manipulators able to carry heavy payloads across large workspaces. Therefore, the dynamic parameters such as the mobile platform mass and center of mass location may considerably vary. Without any adaption, the erroneous parametric estimate results in mismatch terms added to the closed-loop system, which may decrease the robot performances. In this paper, we introduce an adaptive dual-space motion control scheme for CDPR. The proposed method aims at increasing the robot tracking performances, while keeping all the cable tensed despite uncertainties and changes in the robot dynamic parameters. Reel-time experimental tests, performed on a large redundantly actuated CDPR prototype, validate the efficiency of the proposed control scheme. These results are compared to those obtained with a non-adaptive dual-space feedforward control scheme.


title: Supervised hashing with latent factor models

abstract: Due to its low storage cost and fast query speed, hashing has been widely adopted for approximate nearest neighbor search in large-scale datasets. Traditional hashing methods try to learn the hash codes in an unsupervised way where the metric (Euclidean) structure of the training data is preserved. Very recently, supervised hashing methods, which try to preserve the semantic structure constructed from the semantic labels of the training points, have exhibited higher accuracy than unsupervised methods. In this paper, we propose a novel supervised hashing method, called latent factor hashing(LFH), to learn similarity-preserving binary codes based on latent factor models. An algorithm with convergence guarantee is proposed to learn the parameters of LFH. Furthermore, a linear-time variant with stochastic learning is proposed for training LFH on large-scale datasets. Experimental results on two large datasets with semantic labels show that LFH can achieve superior accuracy than state-of-the-art methods with comparable training time.


title: TIARA: a visual exploratory text analytic system

abstract: In this paper, we present a novel exploratory visual analytic system called TIARA (Text Insight via Automated Responsive Analytics), which combines text analytics and interactive visualization to help users explore and analyze large collections of text. Given a collection of documents, TIARA first uses topic analysis techniques to summarize the documents into a set of topics, each of which is represented by a set of keywords. In addition to extracting topics, TIARA derives time-sensitive keywords to depict the content evolution of each topic over time. To help users understand the topic-based summarization results, TIARA employs several interactive text visualization techniques to explain the summarization results and seamlessly link such results to the original text. We have applied TIARA to several real-world applications, including email summarization and patient record analysis. To measure the effectiveness of TIARA, we have conducted several experiments. Our experimental results and initial user feedback suggest that TIARA is effective in aiding users in their exploratory text analytic tasks.


title: Emotion recognition from text using semantic labels and separable mixture models

abstract: This study presents a novel approach to automatic emotion recognition from text. First, emotion generation rules (EGRs) are manually deduced from psychology to represent the conditions for generating emotion. Based on the EGRs, the emotional state of each sentence can be represented as a sequence of semantic labels (SLs) and attributes (ATTs); SLs are defined as the domain-independent features, while ATTs are domain-dependent. The emotion association rules (EARs) represented by SLs and ATTs for each emotion are automatically derived from the sentences in an emotional text corpus using the a priori algorithm. Finally, a separable mixture model (SMM) is adopted to estimate the similarity between an input sentence and the EARs of each emotional state. Since some features defined in this approach are domain-dependent, a dialog system focusing on the students' daily expressions is constructed, and only three emotional states, happy, unhappy, and neutral, are considered for performance evaluation. According to the results of the experiments, given the domain corpus, the proposed approach is promising, and easily ported into other domains.


title: Feasibility of drug screening with panels of human tumor cell lines using a microculture tetrazolium assay.

abstract: For the past 30 years strategies for the preclinical discovery and development of potential anticancer agents have been based largely upon the testing of agents in mice bearing transplantable leukemias and solid tumors derived from a limited number of murine as well as human sources. The feasibility of implementing an alternate approach, namely combined in vitro/in vivo screening for selective cytotoxicity among panels of human tumor cell lines derived from a broad spectrum of human solid tumors is under investigation. A group of 30 cell lines acquired from a variety of sources and representing 8 lung cancer pathologies as well as 76 cell lines representing 10 other categories of human cancer (carcinomas of colon, breast, kidney, prostate, ovary, head and neck; glioma; leukemia; melanoma; and sarcoma) have exhibited acceptable growth characteristics and suitable colorimetric profiles in a single, standard culture medium. Measurements of in vitro growth in microculture wells by cell-mediated reduction of tetrazolium showed excellent correlation (0.89 less than r2 less than 0.98) with measurements of cellular protein in adherent cell line cultures as well as viable cell count in suspension cell line cultures (0.94 less than r2 less than 0.99). Since the microculture tetrazolium assay provides sensitive and reproducible indices of growth as well as drug sensitivity in individual cell lines over the course of multiple passages and several months' cultivation, it appears suitable for initial-stage in vitro drug screening.


title: Utterance selection model of language change

abstract: We present a mathematical formulation of a theory of language change. The theory is evolutionary in nature and has close analogies with theories of population genetics. The mathematical structure we construct similarly has correspondences with the Fisher-Wright model of population genetics, but there are significant differences. The continuous time formulation of the model is expressed in terms of a Fokker-Planck equation. This equation is exactly soluble in the case of a single speaker and can be investigated analytically in the case of multiple speakers who communicate equally with all other speakers and give their utterances equal weight. Whilst the stationary properties of this system have much in common with the single-speaker case, time-dependent properties are richer. In the particular case where linguistic forms can become extinct, we find that the presence of many speakers causes a two-stage relaxation, the first being a common marginal distribution that persists for a long time as a consequence of ultimate extinction being due to rare fluctuations.


MUSEGAN : DEMONSTRATION OF A CONVOLUTIONAL GAN BASED MODEL FOR GENERATING MULTI-TRACK PIANO-ROLLS


title: Best-Response Multiagent Learning in Non-Stationary Environments

abstract: This paper investigates a relatively new direction in Multiagent Reinforcement Learning. Most multiagent learning techniques focus on Nash equilibria as elements of both the learning algorithm and its evaluation criteria. In contrast, we propose a multiagent learning algorithm that is optimal in the sense of finding a best-response policy, rather than in reaching an equilibrium. We present the first learning algorithm that is provably optimal against restricted classes of non-stationary opponents. The algorithm infers an accurate model of the opponentýs non-stationary strategy, and simultaneously creates a best-response policy against that strategy. Our learning algorithm works within the very general framework of n-player, general-sum stochastic games, and learns both the game structure and its associated optimal policy.


title: Dynamic Flow Analysis for JavaScript

abstract: Static flow analyses compute a safe approximation of a program’s dataflow without executing it. Dynamic flow analyses compute a similar safe approximation by running the program on test data such that it achieves sufficient coverage.


title: Identifying Factors That Encourage and Hinder Knowledge Sharing in a Longstanding Online Community of Practice.

abstract: Despite the strong interests among practitioners, there is a knowledge gap with regard to online communities of practice. This study examines knowledge sharing among critical-care and advanced-practice nurses, who are engaged in a longstanding online community of practice. Data were collected about members’ online knowledge contribution as well as motivations for sharing or not sharing knowledge with others. In sum, 27 interviews with members and content analysis of approximately 400 messages were conducted. Data analysis showed that the most common types of knowledge shared were “Institutional Practice” and “Personal Opinion”. Five factors were found that helped motivate knowledge sharing: (a) self-selection type of membership, (b) desire to improve the nursing profession, (c) reciprocity, (d) a non-competitive environment, and (e) the role of the listserv moderator. Regarding barriers for knowledge sharing, four were found: (a) no new or additional knowledge to add, (b) unfamiliarity with subject, (c) lack of time, and (d) technology. These results will be informative to researchers and practitioners of online communities of practice.


title: Fetal phallic growth and penile standards for newborn male infants.

abstract: No appreciable difference in size of the fetal clitoris versus the fetal penis was noted until 14 weeks' gestation, which is after the period of masculine differentiation of the external genitals. However, significant differences in the rate of penile and clitoral growth were evident in second trimester fetuses. The majority of the prenatal growth of the penis occurs after 14 weeks gestation at an almost linear rate. The penile stretched length of the full-term infant was 3.5 cm plus or minus 0.7 cm and the diameter was 1.1 cm plus or minus 0.2 cm.


title: Computational and Collective Creativity: Who's Being Creative?

abstract: Creativity research has traditionally focused on human creativity, and even more specifically, on the psychology of individual creative people. In contrast, computational creativity research involves the development and evaluation of creativity in a computational system. As we study the effect of scaling up from the creativity of a computational system and individual people to large numbers of diverse computational agents and people, we have a new perspective: creativity can ascribed to a computational agent, an individual person, collectives of people and agents and/or their interaction. By asking “Who is being creative?” this paper examines the source of creativity in computational and collective creativity. A framework based on ideation and interaction provides a way of characterizing existing research in computational and collective creativity and identifying directions for future research.


title: Bolt: Anonymous Payment Channels for Decentralized Currencies

abstract: Bitcoin owes its success to the fact that transactions are transparently recorded in the blockchain, a global public ledger that removes the need for trusted parties. Unfortunately, recording every transaction in the blockchain causes privacy, latency, and scalability issues. Building on recent proposals for "micropayment channels" --- two party associations that use the ledger only for dispute resolution --- we introduce techniques for constructing anonymous payment channels. Our proposals allow for secure, instantaneous and private payments that substantially reduce the storage burden on the payment network. Specifically, we introduce three channel proposals, including a technique that allows payments via untrusted intermediaries. We build a concrete implementation of our scheme and show that it can be deployed via a soft fork to existing anonymous currencies such as ZCash.


title: A New Single-Phase Single-Stage Three-Level Power-Factor-Correction AC–DC Converter With Phase-Shift Modulation

abstract: A new three-level ac–dc single-stage converter that can operate with standard phase-shift pulsewidth modulation is proposed in this letter. In this letter, the operation of the converter is explained, and its feasibility is confirmed with experimental results obtained from a prototype converter. Finally, the efficiency of the new converter is compared with that of a previously proposed converter of the same type, and it is shown that the new converter has better efficiency, particularly for light-load operation.


title: Functional hand proportion is approximated by the Fibonacci series.

abstract: The debatable relationship of functional human hand proportion with the Fibonacci series has remained an obscure scientific enigma short of clinical interest. The main difficulty of proving such a relationship lies in defining what should constitute true functional proportion. In this study, we re-evaluate this unique relationship using hand flexion creases as anatomical surrogates for the functional axes of joint rotation. Standardised desktop photocopies of palmar views of both hands in full digital extension and abduction were obtained from 100 healthy male volunteers of Chinese ethnicity. The functional axes were represented by the distal digital crease (distal interphalangeal joint, DIPJ), proximal digital crease (proximal interphalangeal joint, PIPJ), as well as the midpoint between the palmar digital and transverse palmar creases (metacarpophalangeal joint, MCPJ). The ratio of DIPJ-Fingertip:PIPJ-DIPJ:MCPJ-PIPJ (p3:p2:p1) was measured by two independent observers and represented as standard deviation about the mean, and then compared to the theoretical ratio of 1:1:2. Our results showed that, for the 2nd to 5th digits, the p2:p3 ratios were 0.97 ± ± 0.09, 1.10 ± 0.10, 1.04 ± 0.12, and 0.80 ± 0.08, respectively; whilst the p1:p2 ratios were 1.91 ± 0.17, 1.98 ± 0.14, 1.89 ± 0.16, and 2.09 ± 0.24, respectively. When the data were analysed for all digits, they showed a combined p3:p2:p1 ratio of 1:0.98:2.01. In conclusion, our results suggest that functional human hand proportion, as defined by flexion creases, is approximated by the Fibonacci series.


title: vCacheShare: automated server flash cache space management in a virtualization environment

abstract: Server Flash Cache (SFC) is increasingly adopted in virtualization environments for IO acceleration. Deciding the optimal SFC allocation among VMs or VM disks is a major pain-point, dominantly handled manually by administrators. In this paper, we present vCacheShare, a dynamic, workload-aware, policy-driven framework for continuous and automated optimization of SFC space partitioning. Its decision-making is based on multiple IO access characteristics. In particular, vCacheShare adopts a cache utility model that captures both longer-term locality behavior and transient locality spikes. ::: ::: This paper validates the growing applicability of analytical programming techniques to solve real-time resource management problems, traditionally addressed using heuristics. We designed vCacheShare to coordinate with typical VM mobility events and implemented it within the widely used ESXi hypervisor. We performed extensive evaluation using 13 representative enterprise IO workloads, one IO benchmark, and two end-to-end deployment test cases targeting Virtual Desktop Infrastructure (VDI) and data warehousing scenarios respectively. Our results verified the advantage of vCacheShare over implicit management schemes such as global LRU, and confirmed its self-adaptive capability.


Chapter 19 Semantic Information Integration for Smart Grid Applications*


title: Vector Witsenhausen counterexample as assisted interference suppression,” Int

abstract: Motivated by the existence of an implicit channel in Witsenhausen's counterexample, we formulate a vector extension that can be viewed as a toy wireless communication problem "Assisted Interference Suppression" (AIS). Information-theoretic lower and upper bounds (based respectively on ideas from rate-distortion theory and dirty-paper coding) are then derived on the optimal cost and the asymptotic optimal cost is characterised to within a factor of 2 regardless of the problem parameters. Restricted to the scalar problem, it is shown that the new lower bound can be better than Witsenhausen's bound by an arbitrarily large factor.


title: All about that Base: Differing Player Experiences in Video Game Genres and the Unique Case of MOBA Games

abstract: Video games provide unique interactive player experiences (PX) often categorised into different genres. Prior research has looked at different game genres, but rarely through a PX lens. Especially, PX in the emerging area of massive online battle arena (MOBA) games is not well understood by researchers in the field. We address this knowledge gap by presenting a PX study of different game genres, which we followed up with a second semi-structured interview study about PX in MOBA games. Among the results of our analyses are that games that are likely played with other players, such as MOBA games, stimulate less immersion and presence for players. Additionally, while challenge and frustration are significantly higher in this genre, players get a sense of satisfaction from teamwork, competition and mastery of complex gameplay interactions. Our study is the first to contribute a comprehensive insight into key motivators of MOBA players and how PX in this genre is different from other genres.


An information age without technology?


title: The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures

abstract: This article presents GraphSLAM, a unifying algorithm for the offline SLAM problem. GraphSLAM is closely related to a recent sequence of research papers on applying optimization techniques to SLAM problems. It transforms the SLAM posterior into a graphical network, representing the log-likelihood of the data. It then reduces this graph using variable elimination techniques, arriving at a lower-dimensional problems that is then solved using conventional optimization techniques. As a result, GraphSLAM can generate maps with 108 or more features. The paper discusses a greedy algorithm for data association, and presents results for SLAM in urban environments with occasional GPS measurements.


title: BigBIRD: A large-scale 3D database of object instances

abstract: The state of the art in computer vision has rapidly advanced over the past decade largely aided by shared image datasets. However, most of these datasets tend to consist of assorted collections of images from the web that do not include 3D information or pose information. Furthermore, they target the problem of object category recognition—whereas solving the problem of object instance recognition might be sufficient for many robotic tasks. To address these issues, we present a highquality, large-scale dataset of 3D object instances, with accurate calibration information for every image. We anticipate that “solving” this dataset will effectively remove many perceptionrelated problems for mobile, sensing-based robots. The contributions of this work consist of: (1) BigBIRD, a dataset of 100 objects (and growing), composed of, for each object, 600 3D point clouds and 600 high-resolution (12 MP) images spanning all views, (2) a method for jointly calibrating a multi-camera system, (3) details of our data collection system, which collects all required data for a single object in under 6 minutes with minimal human effort, and (4) multiple software components (made available in open source), used to automate multi-sensor calibration and the data collection process. All code and data are available at http://rll.eecs.berkeley.edu/bigbird.


title: Compact dual-band bandpass filter using stub-loaded stepped impedance resonators with mixed electric and magnetic couplings

abstract: A compact dual-band bandpass filter (BPF) using two stub-loaded stepped impedance resonators (SLSIRs) is presented. The first and the second resonant modes of the SLSIR can be independently controlled by the length of the open stub and the impedance ratio of the SIR, respectively. Mixed electric and magnetic coupling between two SLSIRs of the dual-band BPF are utilized to produce multiple transmission zeros in the filter response. A dual-band BPF operating at 1.27 and 2.45 GHz is designed and fabricated, and it demonstrates high passband selectivity and large isolation between the two passbands. The measured minimum insertion losses are 0.90 and 0.92 dB, and the passband return losses are better than 13 and 18.5 dB, respectively.


title: Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI

abstract: Magnetic Resonance Angiography (MRA) has become an essential MR contrast for imaging and evaluation of vascular anatomy and related diseases. MRA acquisitions are typically ordered for vascular interventions, whereas in typical scenarios, MRA sequences can be absent in the patient scans. This motivates the need for a technique that generates inexistent MRA from existing MR multi-contrast, which could be a valuable tool in retrospective subject evaluations and imaging studies. In this paper, we present a generative adversarial network (GAN) based technique to generate MRA from T1-weighted and T2-weighted MRI images, for the first time to our knowledge. To better model the representation of vessels which the MRA inherently highlights, we design a loss term dedicated to a faithful reproduction of vascularities. To that end, we incorporate steerable filter responses of the generated and reference images inside a Huber function loss term. Extending the well- established generator-discriminator architecture based on the recent PatchGAN model with the addition of steerable filter loss, the proposed steerable GAN (sGAN) method is evaluated on the large public database IXI. Experimental results show that the sGAN outperforms the baseline GAN method in terms of an overlap score with similar PSNR values, while it leads to improved visual perceptual quality.