title: On the Design and Scalability of Distributed Shared-Data Databases

abstract: Database scale-out is commonly implemented by partitioning data across several database instances. This approach, however, has several restrictions. In particular, partitioned databases are inflexible in large-scale deployments and assume a partition-friendly workload in order to scale. In this paper, we analyze an alternative architecture design for distributed relational databases that overcomes the limitations of partitioned databases. The architecture is based on two fundamental principles: We decouple query processing and transaction management from data storage, and we share data across query processing nodes. The combination of these design choices provides scalability, elasticity, and operational flexibility without making any assumptions on the workload. As a drawback, sharing data among multiple database nodes causes synchronization overhead. To address this limitation, we introduce techniques for scalable transaction processing in shared-data environments. Specifically, we describe mechanisms for efficient data access, concurrency control, and data buffering. In combination with new hardware trends, the techniques enable performance characteristics that top state-of-the-art partitioned databases.


A Deep Framework for Automatic Annotation with Application to Retail Warehouses


License Plate Recognition and Super-resolution from Low-Resolution Videos by Convolutional Neural Networks


title: Facilitating Model-Based Control Through Software-Hardware Co-Design

abstract: This paper exemplifies the design process for legged machines capable of dynamic behaviors. In order to achieve high performance robots, it is crucial to guarantee harmonious integration between software and hardware. Hence, the development of such capable robotic platforms must address design requirements that meet the assumptions of typical model-based controllers but also respect the physical limitations of a real system. First, we show that proper hardware design choices can greatly aid the control algorithm by approximating the physical robot to the template assumptions. We include actuation and sensing design examples that allows a simple model to capture a major portion of the natural dynamic behavior of the physical machine. Results are applied to a real robot (Figure 1) and we show that the adopted methodology is able to address typical problems in legged robots such as high bandwidth force control and robustness to impact. Finally, a simple model-based balance controller that takes advantage of the fidelity of the template model to the real machine is implemented. These are examples of software-hardware codesign processes that vastly facilitate robotic control.


title: Recurrent Neural Networks Hardware Implementation on FPGA

abstract: Recurrent Neural Networks (RNNs) have the ability to retain memory and learn data sequences. Due to the recurrent nature of RNNs, it is sometimes hard to parallelize all its computations on conventional hardware. CPUs do not currently offer large parallelism, while GPUs offer limited parallelism due to sequential components of RNN models. In this paper we present a hardware implementation of Long-Short Term Memory (LSTM) recurrent network on the programmable logic Zynq 7020 FPGA from Xilinx. We implemented a RNN with $2$ layers and $128$ hidden units in hardware and it has been tested using a character level language model. The implementation is more than $21\times$ faster than the ARM CPU embedded on the Zynq 7020 FPGA. This work can potentially evolve to a RNN co-processor for future mobile devices.


title: A context-aware proactive recommender system for tourist

abstract: With the fast growth in Information Communication Technology (ICT), the aspect of Recommender system have proved to be a great tool to /finding appropriate items/objects for users. Traditional user request response pattern are usually used to make these recommendations, adding context information and integrating proactivity to the process of recommending items/objects has improved recommender system so far. Context-Aware Proactive Recommender system can be of great assistance in giving appropriate recommendation in mobile devices. Therefore this study proposes a Context-aware proactive recommender system for tourists, it will also include contextual factors such as time, location and weather of the user and how a particular item satisfies user's need, using Multi-criteria Collaborative filtering to make suggestions more efficient and accurate. Experiment will be conducted and the performance evaluation of the proposed system will be analysed to measure the efficiency and efficacy of the proposed techniques.


title: Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study

abstract: Deep Neural Networks (DNNs) are very popular these days, and are the subject of a very intense investigation. A DNN is made by layers of internal units (or neurons), each of which computes an affine combination of the output of the units in the previous layer, applies a nonlinear operator, and outputs the corresponding value (also known as activation). A commonly-used nonlinear operator is the so-called rectified linear unit (ReLU), whose output is just the maximum between its input value and zero. In this (and other similar cases like max pooling, where the max operation involves more than one input value), one can model the DNN as a 0-1 Mixed Integer Linear Program (0-1 MILP) where the continuous variables correspond to the output values of each unit, and a binary variable is associated with each ReLU to model its yes/no nature. In this paper we discuss the peculiarity of this kind of 0-1 MILP models, and describe an effective bound-tightening technique intended to ease its solution. We also present possible applications of the 0-1 MILP model arising in feature visualization and in the construction of adversarial examples. Preliminary computational results are reported, aimed at investigating (on small DNNs) the computational performance of a state-of-the-art MILP solver when applied to a known test case, namely, hand-written digit recognition.


title: SETAP: Software engineering teamwork assessment and prediction using machine learning

abstract: Effective teaching of teamwork skills in local and globally distributed Software Engineering (SE) teams is recognized as an important part of the education of current and future software engineers. Effective methods for assessment and early prediction of learning effectiveness in SE teamwork are not only a critical part of teaching but also of value in industrial training and project management. This paper presents a novel analytical approach to the assessment and, most importantly, the prediction of learning outcomes in SE teamwork based on data from our joint software engineering class concurrently taught at San Francisco State University (SFSU), Florida Atlantic University (FAU) and Fulda University, Germany (Fulda). Our approach focuses on assessment and prediction of SE teamwork in terms of ability of student teams to apply best SE processes and develop SE products. It differs from existing work in the following aspects: a) it develops and uses only objective and quantitative measures of team activity from multiple sources, such as statistics of student time use, software engineering tool use, and instructor observations; b) it leverages powerful machine learning (ML) techniques applied to team activity measurements to identify quantitative and objective factors which can assess and predict learning of software engineering teamwork skills at the team level. In this paper we provide the following contributions: a) we present in detail for the first time the full team activity measurement data set we developed, consisting of over 40 objective and quantitative measures extracted from student teams working on class projects; b) we present a ML framework which applies the Random Forest (RF) algorithm to the team activity measurements and team outcomes, focusing on predicting teams that are likely to fail; c) we describe in detail our now fully implemented and operational data processing pipeline, consisting of data collection methods from multiple sources, ML training database creation, and ML analysis subsystems; and finally d) we present very preliminary results of ML analysis results based on the data from our joint software engineering classes in Fall 2012, and Spring 2013, with the data from 17 student teams. While our ML training database is currently small, it continuously grows. Our preliminary results, verified with two independent accuracy measures, show that RF is able to predict SE Process and SE Product team performance in intuitively explainable manner.


title: Novel deployable reflectarray antennas for CubeSat communications

abstract: Two novel high gain deployable reflectarray antennas to support CubeSat telecommunications are described and compared with other high gain CubeSat antenna technologies. The first reflectarray is the Integrated Solar Array and Reflectarray Antenna (ISARA), a K/Ka-band antenna that also incorporates a dense packing of solar cells used to provide electrical power for the spacecraft. The second is an X-band reflectarray designed to provide a bent pipe telecom link. These reflectarrays are ideal for CubeSat applications because they require negligible stowed volume and impose a modest mass increase. The antenna designs and measured performance results are presented.


title: Scalable hands-free transfer learning for online advertising

abstract: Internet display advertising is a critical revenue source for publishers and online content providers, and is supported by massive amounts of user and publisher data. Targeting display ads can be improved substantially with machine learning methods, but building many models on massive data becomes prohibitively expensive computationally. This paper presents a combination of strategies, deployed by the online advertising firm Dstillery, for learning many models from extremely high-dimensional data efficiently and without human intervention. This combination includes: (i)~A method for simple-yet-effective transfer learning where a model learned from data that is relatively abundant and cheap is taken as a prior for Bayesian logistic regression trained with stochastic gradient descent (SGD) from the more expensive target data. (ii)~A new update rule for automatic learning rate adaptation, to support learning from sparse, high-dimensional data, as well as the integration with adaptive regularization. We present an experimental analysis across 100 different ad campaigns, showing that the transfer learning indeed improves performance across a large number of them, especially at the start of the campaigns. The combined "hands-free" method needs no fiddling with the SGD learning rate, and we show that it is just as effective as using expensive grid search to set the regularization parameter for each campaign.


title: Improving Correlation with Human Judgments by Integrating Semantic Similarity with Second--Order Vectors

abstract: Vector space methods that measure semantic similarity and relatedness often rely on distributional information such as co--occurrence frequencies or statistical measures of association to weight the importance of particular co--occurrences. In this paper, we extend these methods by incorporating a measure of semantic similarity based on a human curated taxonomy into a second--order vector representation. This results in a measure of semantic relatedness that combines both the contextual information available in a corpus--based vector space representation with the semantic knowledge found in a biomedical ontology. Our results show that incorporating semantic similarity into a second order co--occurrence matrices improves correlation with human judgments for both similarity and relatedness, and that our method compares favorably to various different word embedding methods that have recently been evaluated on the same reference standards we have used.


title: Investigating Dependencies in Software Requirements for Change Propagation Analysis

abstract: Context: The dependencies between individual requirements have an important influence on software engineering activities e.g., project planning, architecture design, and change impact analysis. Although dozens of requirement dependency types were suggested in the literature from different points of interest, there still lacks an evaluation of the applicability of these dependency types in requirements engineering. Objective: Understanding the effect of these requirement dependencies to software engineering activities is useful but not trivial. In this study, we aimed to first investigate whether the existing dependency types are useful in practise, in particular for change propagation analysis, and then suggest improvements for dependency classification and definition. Method: We conducted a case study that evaluated the usefulness and applicability of two well-known generic dependency models covering 25 dependency types. The case study was conducted in a real-world industry project with three participants who offered different perspectives. Results: Our initial evaluation found that there exist a number of overlapping and/or ambiguous dependency types among the current models; five dependency types are particularly useful in change propagation analysis; and practitioners with different backgrounds possess various viewpoints on change propagation. To improve the state-of-the-art, a new dependency model is proposed to tackle the problems identified from the case study and the related literature. The new model classifies dependencies into intrinsic and additional dependencies on the top level, and suggests nine dependency types with precise definitions as its initial set. Conclusions: Our case study provides insights into requirement dependencies and their effects on change propagation analysis for both research and practise. The resulting new dependency model needs further evaluation and improvement.


title: The Hierarchy of Cyber War Definitions

abstract: With the advent of militaries declaring cyberspace as the fifth domain of military warfare, those modern societies that are heavily dependent on its reliable operation need to have a clear understanding of the actors and future activities brought about by this new doctrine. Knowing what is meant by the terms ‘cyber war’ and ‘cyber warfare’ is critical to navigating a path forward in preparing for and mitigating the effects caused by such activities. In this paper, the authors identified and analysed 159 documents containing the definitions for these terms in order to discern definitional origins, patterns of usage and the relative trends that emerge as a result. From this analysis, we construct a discourse hierarchy of cyber war and cyber warfare definitions, both as a representation of the findings as well as a basis for incorporating future works into the larger context of the domain.


Solving the Rubik's Cube with Approximate Policy Iteration


title: Habitat barriers limit gene flow and illuminate historical events in a wide-ranging carnivore, the American puma

abstract: We examined the effects of habitat discontinuities on gene flow among puma (Puma concolor) populations across the southwestern USA. Using 16 microsatellite loci, we genotyped 540 pumas sampled throughout the states of Utah, Colorado, Arizona, and New Mexico, where a high degree of habitat heterogeneity provides for a wide range of connective habitat configurations between subpopulations. We investigated genetic structuring using complementary individual- and population-based analyses, the latter employing a novel technique to geographically cluster individuals without introducing investigator bias. The analyses revealed genetic structuring at two distinct scales. First, strikingly strong differentiation between northern and southern regions within the study area suggests little migration between them. Second, within each region, gene flow appears to be strongly limited by distance, particularly in the presence of habitat barriers such as open desert and grasslands. Northern pumas showed both reduced genetic diversity and greater divergence from a hypothetical ancestral population based on Bayesian clustering analyses, possibly reflecting a post-Pleistocene range expansion. Bayesian clustering results were sensitive to sampling density, which may complicate inference of numbers of populations when using this method. The results presented here build on those of previous studies, and begin to complete a picture of how different habitat types facilitate or impede gene flow among puma populations.


title: Conceptualising user hedonic experience

abstract: For many years, the approach taken towards technology design focused on supporting the effective, efficient, and satisfying use of it. The way people use technology has shifted from merely using it to enjoying using it. This paper describes an early approach to understanding user experience in context of technologies (e.g. digital cameras, PDAs, and mobile phones), as well as in more general context such as physical activities e.g. exercising, orienteering, and walking, and in context of diaries. The focus of this paper is on hedonic user experience; that is pleasure, enjoyment, excitement, and fun in the context of technology. This study provides insights into factors contributing to and influencing such experiences and the relationships between them.


title: A novel ZCS interleaved bidirectional buck-boost DC-DC converter for energy storage applications

abstract: This paper proposes a new ZCS interleaved bidirectional buck-boost DC-DC converter for energy storage applications. The conventional interleaved converter has been improved with the auxiliary resonant cells in order to obtain the zero current turn-off of the main switching devices. The major advantage is the reduction of switching losses in four main switching devices of the main converter by utilizing a less number of auxiliary resonant cells. This paper mainly describes the operational analysis and its simulation for theoretical expectations. The obtained results show the improved performance and soft-switching capability with reduced auxiliary resonant components.


title: Unification of Cas protein families and a simple scenario for the origin and evolution of CRISPR-Cas systems

abstract: BackgroundThe CRISPR-Cas adaptive immunity systems that are present in most Archaea and many Bacteria function by incorporating fragments of alien genomes into specific genomic loci, transcribing the inserts and using the transcripts as guide RNAs to destroy the genome of the cognate virus or plasmid. This RNA interference-like immune response is mediated by numerous, diverse and rapidly evolving Cas (CRISPR-associated) proteins, several of which form the Cascade complex involved in the processing of CRISPR transcripts and cleavage of the target DNA. Comparative analysis of the Cas protein sequences and structures led to the classification of the CRISPR-Cas systems into three Types (I, II and III).ResultsA detailed comparison of the available sequences and structures of Cas proteins revealed several unnoticed homologous relationships. The Repeat-Associated Mysterious Proteins (RAMPs) containing a distinct form of the RNA Recognition Motif (RRM) domain, which are major components of the CRISPR-Cas systems, were classified into three large groups, Cas5, Cas6 and Cas7. Each of these groups includes many previously uncharacterized proteins now shown to adopt the RAMP structure. Evidence is presented that large subunits contained in most of the CRISPR-Cas systems could be homologous to Cas10 proteins which contain a polymerase-like Palm domain and are predicted to be enzymatically active in Type III CRISPR-Cas systems but inactivated in Type I systems. These findings, the fact that the CRISPR polymerases, RAMPs and Cas2 all contain core RRM domains, and distinct gene arrangements in the three types of CRISPR-Cas systems together provide for a simple scenario for origin and evolution of the CRISPR-Cas machinery. Under this scenario, the CRISPR-Cas system originated in thermophilic Archaea and subsequently spread horizontally among prokaryotes.ConclusionsBecause of the extreme diversity of CRISPR-Cas systems, in-depth sequence and structure comparison continue to reveal unexpected homologous relationship among Cas proteins. Unification of Cas protein families previously considered unrelated provides for improvement in the classification of CRISPR-Cas systems and a reconstruction of their evolution.Open peer reviewThis article was reviewed by Malcolm White (nominated by Purficacion Lopez-Garcia), Frank Eisenhaber and Igor Zhulin. For the full reviews, see the Reviewers' Comments section.


title: Real-Time Multi-Criteria Social Graph Partitioning: A Game Theoretic Approach

abstract: Graph partitioning has attracted considerable attention due to its high practicality for real-world applications. It is particularly relevant to social networks because it enables the grouping of users into communities for market analysis and advertising purposes. In this paper, we introduce RMGP, a type of real-time multi-criteria graph partitioning for social networks that groups the users based on their connectivity and their similarity to a set of input classes. We consider RMGP as an on-line task, which may be frequently performed for different query parameters (e.g., classes). In order to overcome the serious performance issues associated with the large social graphs found in practice, we develop solutions based on a game theoretic framework. Specifically, we consider each user as a player, whose goal is to find the class that optimizes his objective function. We propose algorithms based on best-response dynamics, analyze their properties, and show their efficiency and effectiveness on real datasets under centralized and decentralized scenarios.


title: Cerebro?fronto?facial syndrome (Dandy-Walker Variant and Frontofacial Dysmorphisms): report of the first case identified by increased nuchal translucency beyond 13+6�weeks

abstract: A 32-year-old gravida 2, para 1 woman, with a previous uneventful pregnancy, underwent first trimester ultrasound screening for Down syndrome at 13 weeks accord- ing to the Fetal Medicine Foundation guidelines (http://www. fetalmedicine.com/pdf/11-14/english/FMF-English.pdf). The ultrasound showed increased nuchal translucency (NT) of 8.9 mm with an estimated risk of Down syndrome of 1:8. Fetal karyotype was normal 46,XX by chorionic villus sampling. The patient underwent weekly ultrasound and at 19 weeks of gesta- tion, a dilatation of the 4th ventricle with partial agenesis of the cerebellar vermis and normal posterior fossa were observed by transvaginal transcerebellar section of the fetal head. This finding was consistent with a diagnosis of Dandy-Walker variant and the patient opted for termination of pregnancy after extensive counselling. Autoptic examination confirmed the prenatal ultrasonographic findings and revealed signs of an underlying cerebro-fronto-facial syndrome due to the presence of facial dysmorphisms consistent with horizontal eyelid, high nasal root, low set ears and a wide forehead. Increased NT is not only a common phenotypic expression of chromosomal abnor- malities, but is also associated with a wide range of fetal defects and genetic syndromes. Careful ultrasonographic follow-up is mandatory in all cases of increased first trimester nuchal trans- lucency with normal karyotype in order to identify associated anomalies.


title: Is SDN the solution for NDN-VANETs?

abstract: Named Data Networking (NDN) is a future Internet architecture suitable for Vehicular Ad Hoc Networks (VANETs), since it provides solutions to frequent topology changes in VANETs. In NDN messages are exchanged according to their content and not to the location of the hosts. SDN provides centralized network control. This paper presents use cases for vehicular NDN scenarios, where SDN is a suitable solution to solve arising problems in VANETs. In particular, SDN assists for setting up rules that are used by RSUs to dictate to the vehicles how to perform forwarding in the VANET. Moreover, SDN could optimize the allocation of network resources. Finally, we present a network communication approach between different geographical areas using wired communication between SDN controllers or wireless communication in vehicles that are used as data mules in these areas. The areas are defined based on the communication range of RSUs.


title: Transition-based Dependency Parsing Using Two Heterogeneous Gated Recursive Neural Networks

abstract: Recently, neural network based dependency parsing has attracted much interest, which can effectively alleviate the problems of data sparsity and feature engineering by using the dense features. However, it is still a challenge problem to sufficiently model the complicated syntactic and semantic compositions of the dense features in neural network based methods. In this paper, we propose two heterogeneous gated recursive neural networks: tree structured gated recursive neural network (Tree-GRNN) and directed acyclic graph structured gated recursive neural network (DAG-GRNN). Then we integrate them to automatically learn the compositions of the dense features for transition-based dependency parsing. Specifically, Tree-GRNN models the feature combinations for the trees in stack, which already have partial dependency structures. DAG-GRNN models the feature combinations of the nodes whose dependency relations have not been built yet. Experiment results on two prevalent benchmark datasets (PTB3 and CTB5) show the effectiveness of our proposed model.


title: Affinity labeling of bovine adrenal 3 beta-hydroxysteroid dehydrogenase/steroid isomerase by 5'-[p-(fluorosulfonyl)benzoyl]adenosine.

abstract: Incubation of bovine adrenal 3 beta-hydroxysteroid dehydrogenase/steroid isomerase with 5'-[p-(fluorosulfonyl)benzoyl]adenosine (5'-FSBA) results in the inactivation of the 3 beta-hydroxysteroid dehydrogenase enzyme activity following pseudo-first-order kinetics. A double-reciprocal plot of 1/kobs versus 1/[5'-FSBA] yields a straight line with a positive y intercept, indicative of reversible binding of the inhibitor prior to an irreversible inactivation reaction. The dissociation constant (Kd) for the initial reversible enzyme-inhibitor complex is estimated at 0.533 mM, with k2 = 0.22 min-1. The irreversible inactivation could be prevented by the presence of NAD+ during the incubation, indicating that 5'-FSBA inactivates the 3 beta-hydroxysteroid dehydrogenase activity by reacting at the NAD+ binding site. Although the enzyme was inactivated by incubation with 5'-FSBA, no incorporation of the inhibitor was found in labeling studies using 5'-[p-(fluorosulfonyl)benzoyl] [14C]adenosine. However, the inactivation of 3 beta-hydroxysteroid dehydrogenase activity caused by incubation with 5'-FSBA could be completely reversed by the addition of dithiothreitol. This indicates the presence of at least two cysteine residues at or in the vicinity of the NAD+ binding site, which may form a disulfide bond catalyzed by the presence of 5'-FSBA. The intramolecular cysteine disulfide bridge was found between the cysteine residues in the peptides 274EWGFCLDSR282 and 18IICLLVEEK26, by comparing the [14C]iodoacetic acid labeling before and after recovering the enzyme activity upon the addition of dithiothreitol.


title: An analysis of reinforcement learning with function approximation

abstract: We address the problem of computing the optimal Q-function in Markov decision problems with infinite state-space. We analyze the convergence properties of several variations of Q-learning when combined with function approximation, extending the analysis of TD-learning in (Tsitsiklis & Van Roy, 1996a) to stochastic control settings. We identify conditions under which such approximate methods converge with probability 1. We conclude with a brief discussion on the general applicability of our results and compare them with several related works.


title: The International Workshop on Cooperative Robots and Sensor Networks ( RoboSense 2012 ) Robust Indoor Localization on a Commercial Smart Phone

abstract: Low-cost localization solutions for indoor environments have a variety of real-world applications ranging from emergency evacuation to mobility aids for people with disabilities. In this paper, we introduce a methodology for indoor localization using a commercial smart-phone combining dead reckoning and Wifi signal strength fingerprinting. Additionally, we outline an automated procedure for collecting Wifi calibration data that uses a robot equipped with a laser rangefinder and fiber optic gyroscope. These measurements along with a generated robot map of the environment are combined using a particle filter towards robust pose estimation. The uniqueness of our approach lies in the implementation of the complementary nature of the solution as well as in the efficient adaptation to the smart-phone platform. The system was tested using multiple participants in two different indoor environments, and achieved localization accuracies on the order of 5 meters; sufficient for a variety of navigation and context-aware applications.


title: Detailed 3D reconstruction of large-scale heritage sites with integrated techniques

abstract: Many cultural heritage applications require 3D reconstruction of real-world objects and scenes. Over the past few years, it has become increasingly common to use 3D digitization and modeling for this purpose. This is mainly due to advances in laser-scanning techniques, 3D modeling software, image-based modeling techniques, computer power, and virtual reality. Our approach integrates several technologies based on our experience over more than a decade of trying to accurately and completely model large-scale heritage monuments and sites. Using both interactive and automatic techniques, we can model a highly detailed structure or site at various levels of detail. We use image-based modeling for basic shape and structural elements, and laser scanning for fine details and sculpted surfaces. To present the site in its proper context, we use image-based rendering for landscapes and surroundings. To apply this approach, we created hundreds of models from sites all over the world for documentation, walk-through movies, and interactive visualization. The results were compelling and encouraging.


title: A Proactive Workflow Model for Healthcare Operation and Management

abstract: Advances in real-time location systems have enabled us to collect massive amounts of fine-grained semantically rich location traces, which provide unparalleled opportunities for understanding human activities and generating useful knowledge. This, in turn, delivers intelligence for real-time decision making in various fields, such as workflow management. Indeed, it is a new paradigm to model workflows through knowledge discovery in location traces. To that end, in this paper, we provide a focused study of workflow modeling by integrated analysis of indoor location traces in the hospital environment. In particular, we develop a workflow modeling framework that automatically constructs the workflow states and estimates the parameters describing the workflow transition patterns. More specifically, we propose effective and efficient regularizations for modeling the indoor location traces as stochastic processes. First, to improve the interpretability of the workflow states, we use the geography relationship between the indoor rooms to define a prior of the workflow state distribution. This prior encourages each workflow state to be a contiguous region in the building. Second, to further improve the modeling performance, we show how to use the correlation between related types of medical devices to reinforce the parameter estimation for multiple workflow models. In comparison with our preliminary work [11] , we not only develop an integrated workflow modeling framework applicable to general indoor environments, but also improve the modeling accuracy significantly. We reduce the average log-loss by up to 11 percent.


title: Wavelet turbulence for fluid simulation

abstract: We present a novel wavelet method for the simulation of fluids at high spatial resolution. The algorithm enables large- and small-scale detail to be edited separately, allowing high-resolution detail to be added as a post-processing step. Instead of solving the Navier-Stokes equations over a highly refined mesh, we use the wavelet decomposition of a low-resolution simulation to determine the location and energy characteristics of missing high-frequency components. We then synthesize these missing components using a novel incompressible turbulence function, and provide a method to maintain the temporal coherence of the resulting structures. There is no linear system to solve, so the method parallelizes trivially and requires only a few auxiliary arrays. The method guarantees that the new frequencies will not interfere with existing frequencies, allowing animators to set up a low resolution simulation quickly and later add details without changing the overall fluid motion.


Academic Motivation and the Big Five Yaman KÖSEOĞLU


title: Toward De-Anonymizing Bitcoin by Mapping Users Location

abstract: The Bitcoin system (https://bitcoin.org) is a pseudo-anonymous currency that can dissociate a user from any real-world identity. In that context, a successful breach of the virtual and physical divide represents a signficant aw in the Bit-coin system [1]. In this project we demonstrate how to glean information about the real-world users behind Bitcoin transactions. We analyze publicly available data about the cryptocurrency. In particular, we focus on determining information about a Bitcoin user's physical location by examining that user's spending habits.


title: JADE: a FIPA2000 compliant agent development environment

abstract: JADE (Java Agent Development Framework) is a software environment to build agent systems for the management of networked information resources in compliance with the FIPA2000 specifications for interoperable intelligent multi- agent systems. The goal of JADE is to simplify development while ensuring standard compliance through a comprehensive set of system services and agents. JADE offers an agent runtime system on which implement efficient FIPA 2000 compliant multi- agent systems and supports their development through the availability of a predefined programmable agent model and of a set of management and testing tools. This paper describes the main features of the JADE system and introduces some of the most important projects based on JADE software.


title: On time-discretizations for generalized Newtonian fluids

abstract: This work improves and extends results of (35) on time-discretization ansatzes for power-law models (p ≤ 2). New analytical results and techniques from (9) lead to improved convergence rates for a broader range of admissible p's. Then, optimally converging stabilization strategies for the time-discretization are discussed and it is shown how the range of p's enlarges, for which strong solutions of the stabilized system exist.


title: Acceleration of Polarized Protons at RHIC

abstract: Relativistic Heavy Ion Collider (RHIC) ended its second year of operation in January 2002 with five weeks of polarized proton collisions. Polarized protons were successfully injected in both RHIC rings and maintained polarization during acceleration up to 100 GeV per ring using two Siberian snakes in each ring. This is the first time that polarized protons have been accelerated to 100 GeV. The machine performance and accomplishments during the polarized proton run will be reviewed. The plans for the next polarized proton run will be outlined.


title: School bullying among adolescents in the United States: physical, verbal, relational, and cyber.

abstract: Abstract Purpose Four forms of school bullying behaviors among US adolescents and their association with sociodemographic characteristics, parental support, and friends were examined. Methods Data were obtained from the Health Behavior in School-Aged Children (HBSC) 2005 Survey, a nationally representative sample of grades 6–10 ( N = 7,182). The revised Olweus Bully/Victim Questionnaire was used to measure physical, verbal, and relational forms of bullying. Two items were added using the same format to measure cyber bullying. For each form, four categories were created: bully, victim, bully-victim, and not involved. Multinomial logistic regressions were applied, with sociodemographic variables, parental support, and number of friends as predictors. Results Prevalence rates of having bullied others or having been bullied at school for at least once in the last 2 months were 20.8% physically, 53.6% verbally, 51.4% socially, or 13.6% electronically. Boys were more involved in physical or verbal bullying, whereas girls were more involved in relational bullying. Boys were more likely to be cyber bullies, whereas girls were more likely to be cyber victims. African-American adolescents were involved in more bullying (physical, verbal, or cyber) but less victimization (verbal or relational). Higher parental support was associated with less involvement across all forms and classifications of bullying. Having more friends was associated with more bullying and less victimization for physical, verbal, and relational forms but was not associated with cyber bullying. Conclusions Parental support may protect adolescents from all four forms of bullying. Friends associate differentially with traditional and cyber bullying. Results indicate that cyber bullying is a distinct nature from that of traditional bullying.


title: Flexible resource allocation for reliable virtual cluster computing systems

abstract: Virtualization and cloud computing technologies now make it possible to create scalable and reliable virtual high performance computing clusters. Integrating these technologies, however, is complicated by fundamental and inherent differences in the way in which these systems allocate resources to computational tasks. Cloud computing systems immediately allocate available resources or deny requests. In contrast, parallel computing systems route all requests through a queue for future resource allocation. This divergence of allocation policies hinders efforts to implement efficient, responsive, and reliable virtual clusters. In this paper, we present a continuum of four scheduling polices along with an analytical resource prediction model for each policy to estimate the level of resources needed to operate an efficient, responsive, and reliable virtual cluster system. We show that it is possible to estimate the size of the virtual cluster system needed to provide a predictable grade of service for a realistic high performance computing workload and estimate the queue wait time for a partial or full resource allocation. Moreover, we show that it is possible to provide a reliable virtual cluster system using a limited pool of spare resources. The models and results we present are useful for cloud computing providers seeking to operate efficient and cost-effective virtual cluster systems.


title: Incorporating technology acceptance into composite IT BSC for IT investment evaluation: An exploratory study in Egypt

abstract: This Paper proposes a theoretical framework to evaluate IT Investments based on Composite IT Balanced Scorecard (IT BSC) which takes into consideration Behavioral Dimension represented by Unified Theory of Acceptance and Use of Technology (UTAUT). Clearly, the proposed framework extends the IT BSC to account for technology acceptance as a behavioral dimension along with other dimensions of IT BSC. Furthermore, UTAUT is updated with relevant variables and beliefs that are not considered by the theory, rather than inclusion of emerging trends proposed by other models and theories and subsequent studies (e.g. proposed framework take Attitude into consideration as a multi-dimension construct). Contributions of this paper are not limited to theory development, it is extended to practice as well. Not only does the study examine the extent that decision makers account for Technology Acceptance in IT investment decisions in Egypt, but also it explores the extent that users accept IT based information systems in Egypt. Results are in general so far consistent with what is proposed by theory and literature in the field. However, with regard to IT investment decisions in Egypt, results reflect lack of awareness of decision makers of Technology Acceptance in such decisions contexts.


title: On-line Boosting and Vision

abstract: Boosting has become very popular in computer vision, showing impressive performance in detection and recognition tasks. Mainly off-line training methods have been used, which implies that all training data has to be a priori given; training and usage of the classifier are separate steps. Training the classifier on-line and incrementally as new data becomes available has several advantages and opens new areas of application for boosting in computer vision. In this paper we propose a novel on-line AdaBoost feature selection method. In conjunction with efficient feature extraction methods the method is real time capable. We demonstrate the multifariousness of the method on such diverse tasks as learning complex background models, visual tracking and object detection. All approaches benefit significantly by the on-line training.


title: Broadband multilayer in-phase power divider

abstract: A new broadband in-phase power divider based on multilayer technology is presented. A simple design procedure is developed for the proposed multilayer power divider. An S-band four-way multilayer power divider was designed and measured. The simulated results are compared with the measured data, and good agreement is reported. The measured 15 dB return loss bandwidth is demonstrated to be about 72%, and its phase difference between the output signals is less than 3deg.


title: Road Sign Detection from Edge Orientation Histograms

abstract: This paper presents a system for road sign detection based on edge orientation histograms. Edge orientation histograms are reliable, scale and contrast invariant features that can be extracted efficiently using integral images. A learning method is introduced that selects features based on the implicit transmission function of the designer's template to the object's appearance in the image. The system is able to detect 85% of the objects on from 12 pixels width and 95% for objects on from 24 pixels width at a low false alarm rate.


title: Reconstruction independent component analysis-based methods for intelligent fault diagnosis

abstract: Based on machine learning techniques, this paper presents a novel intelligent fault diagnosis method, which is an integrated framework concerning reconstruction independent component analysis (RICA) and multiclass relevance vector machine (MRVM). In this method, the RICA is first used to automatically extract features from raw vibration signals. Then, the learned features are used as the input data of MRVM for the classification of different health conditions of machines. The proposed method is applied to the fault diagnosis of locomotive rolling bearings. According to the diagnosis results, it is verified that the proposed method is able to reliably classify different health conditions. By comparing with diagnosis method based on time-domain statistical analysis and wavelet transformation, the proposed method shows its superiority in automatic features extraction from raw signals.


title: Control Strategy to Naturally Balance Hybrid Converter for Variable-Speed Medium-Voltage Drive Applications

abstract: In this paper, a novel control strategy to naturally balance a hybrid converter is presented for medium-voltage induction motor drive applications. Fundamental current of the motor is produced by a three-level quasi-square-wave converter. Several single-phase full-bridge converters are connected in series with this quasi-square-wave converter to eliminate harmonic voltages from the net output voltage of this hybrid converter. Various novel modulation strategies for these series cells are proposed. These lead to a naturally regulated dc-bus voltage of series cells. These also optimize the number of series cells required. Variable fundamental voltage and frequency control of induction motor is adopted with a constant dc-bus voltage of the three-level quasi-square-wave converter. Experimental results are also produced here to validate the proposed modulation strategies for induction motor drives using low-voltage protomodel. Based on the aforementioned, a 2.2-kV induction motor drive has been built, and results are also produced in this paper.


title: The Persuasive Effect of Source Credibility: Tests of Cognitive Response

abstract: Two experiments are reported identifying the circumstances in which high credibility either facilitates, inhibits, or has no effect on the communicator's persuasiveness in relation to a less credible source. These data provide support for the cognitive response view of information processing and suggest the importance of message recipient's initial opinion as a determinant of persuasion.


title: Diabetes Mellitus and Increased Tuberculosis Susceptibility: The Role of Short-Chain Fatty Acids

abstract: Type 2 diabetes mellitus confers a threefold increased risk for tuberculosis, but the underlying immunological mechanisms are still largely unknown. Possible mediators of this increased susceptibility are short-chain fatty acids, levels of which have been shown to be altered in individuals with diabetes. We examined the influence of physiological concentrations of butyrate on cytokine responses to Mycobacterium tuberculosis (Mtb) in human peripheral blood mononuclear cells (PBMCs). Butyrate decreased Mtb-induced proinflammatory cytokine responses, while it increased production of IL-10. This anti-inflammatory effect was independent of butyrate's well-characterised inhibition of HDAC activity and was not accompanied by changes in Toll-like receptor signalling pathways, the eicosanoid pathway, or cellular metabolism. In contrast blocking IL-10 activity reversed the effects of butyrate on Mtb-induced inflammation. Alteration of the gut microbiota, thereby increasing butyrate concentrations, can reduce insulin resistance and obesity, but further studies are needed to determine how this affects susceptibility to tuberculosis.


title: Large-Scale Stochastic Mixed-Integer Programming Algorithms for Power Generation Scheduling

abstract: This chapter presents a stochastic unit commitment model for power systems and revisits parallel decomposition algorithms for these types of models. The model is a two-stage stochastic programming problem with first-stage binary variables and second-stage mixed-binary variables. The here-and-now decision is to find day-ahead schedules for slow thermal power generators. The wait-and-see decision consists of dispatching power and scheduling fast-start generators. We discuss advantages and limitations of different decomposition methods and provide an overview of available software packages. A large-scale numerical example is presented using a modified IEEE 118-bus system with uncertain wind power generation.


title: Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison

abstract: Measures of an athlete’s heart rate variability (HRV) have shown potential to be of use in the prescription of training. However, little data exists on elite athletes who are regularly exposed to high training loads. This case study monitored daily HRV in two elite triathletes (one male: 22 year, \( \dot{V}\)O2max 72.5 ml kg min−1; one female: 20 year, \( \dot{V}\)O2max 68.2 ml kg min−1) training 23 ± 2 h per week, over a 77-day period. During this period, one athlete performed poorly in a key triathlon event, was diagnosed as non-functionally over-reached (NFOR) and subsequently reactivated the dormant virus herpes zoster (shingles). The 7-day rolling average of the log-transformed square root of the mean sum of the squared differences between R–R intervals (Ln rMSSD), declined towards the day of triathlon event (slope = −0.17 ms/week; r 2 = −0.88) in the NFOR athlete, remaining stable in the control (slope = 0.01 ms/week; r 2 = 0.12). Furthermore, in the NFOR athlete, coefficient of variation of HRV (CV of Ln rMSSD 7-day rolling average) revealed large linear reductions towards NFOR (i.e., linear regression of HRV variables versus day number towards NFOR: −0.65%/week and r 2 = −0.48), while these variables remained stable for the control athlete (slope = 0.04%/week). These data suggest that trends in both absolute HRV values and day-to-day variations may be useful measurements indicative of the progression towards mal-adaptation or non-functional over-reaching.


title: Multi-objective path finding in stochastic time-dependent road networks using non-dominated sorting genetic algorithm

abstract: We propose a multi-objective path finding model.We consider the stochastic and time-varying nature of travel time in our model.The model is solved by the non-dominated sorting genetic algorithm.The Taguchi method is used to tune the parameters of the genetic algorithm. In this paper, a new multi-objective path-finding model is proposed to find optimal paths in road networks with time-dependent stochastic travel times. This study is motivated by the fact that different travelers usually have different route-choice preferences, often involving multiple conflicting criteria such as expected path travel time, variance of path travel time and so forth. However, most of the existing studies have only considered the expected value of path travel time as the sole decision criterion. In order to solve the multi-objective model, the non-dominated sorting genetic algorithm is employed and its parameters are tuned by the Taguchi method. Moreover, a dynamic n-point crossover operator is developed to enhance the search capability of the genetic algorithm. Experimental results on a grid network demonstrate that the proposed approach is able to provide a set of non-dominated paths from which travelers can choose their paths based on their attitudes toward travel time uncertainty. Statistical analysis confirms that the dynamic n-point crossover operator outperforms the traditional one-point crossover operator.


title: Influence of a superactivated charcoal on the toxic effects of aflatoxin or T-2 toxin in growing broilers

abstract: To evaluate the effectiveness of a superactivated charcoal (SAC) in alleviating mycotoxicosis, two experiments were conducted in which 432 male broiler chicks (216 per experiment) were fed diets containing 4 mg aflatoxin (AF) or 6 mg T-2 toxin/kg of diet, with and without 0.5% SAC, from 1 to 21 d of age. Feeding AF and T-2 toxin significantly decreased BW gain over the 21-d experimental period. Inclusion of SAC in the diet containing AF resulted in BW gains that were intermediate between gains of chicks fed AF and those of controls. No benefits were seen in BW gain when SAC + T-2 toxin was fed. Feeding AF increased relative weights of liver, spleen, and kidney; however, only liver weight in Experiment 1 was similar to controls when SAC was included. Of the blood parameters altered by AF (decreased cholesterol, inorganic phosphorus, total protein, and urea nitrogen, and increased mean corpuscular volume and hematocrit in Experiment 1; decreased albumin and total protein, and increased creatine kinase in Experiment 2) only urea nitrogen, hematocrit, and inorganic phosphorus (Experiment 1) and hematocrit (Experiment 2) were comparable to controls when SAC was included in the diet. Feeding T-2 toxin decreased serum cholesterol, total protein, urea nitrogen, and mean corpuscular volume; however, only cholesterol and mean corpuscular volume were improved with the addition of SAC (Experiment 1). Oral lesions were observed in birds fed T-2 toxin with no difference in severity when SAC was added in Experiment 1, however in Experiment 2, birds fed SAC + T-2 had a significantly lower lesion scores than those fed T-2 alone. Mortality was noted in both experiments but was not influenced by SAC treatment. These findings suggest that the addition of dietary SAC is marginally effective in alleviating some of the toxic affects associated with AF, but was of little benefit when T-2 toxin was fed to growing broiler chicks.


title: SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

abstract: In this paper, we address semantic segmentation of road-objects from 3D LiDAR point clouds. In particular, we wish to detect and categorize instances of interest, such as cars, pedestrians and cyclists. We formulate this problem as a point- wise classification problem, and propose an end-to-end pipeline called SqueezeSeg based on convolutional neural networks (CNN): the CNN takes a transformed LiDAR point cloud as input and directly outputs a point-wise label map, which is then refined by a conditional random field (CRF) implemented as a recurrent layer. Instance-level labels are then obtained by conventional clustering algorithms. Our CNN model is trained on LiDAR point clouds from the KITTI dataset, and our point-wise segmentation labels are derived from 3D bounding boxes from KITTI. To obtain extra training data, we built a LiDAR simulator into Grand Theft Auto V (GTA-V), a popular video game, to synthesize large amounts of realistic training data. Our experiments show that SqueezeSeg achieves high accuracy with astonishingly fast and stable runtime (8.7 ms per frame), highly desirable for autonomous driving applications. Furthermore, additionally training on synthesized data boosts validation accuracy on real-world data. Our source code and synthesized data will be open-sourced.


title: Automatic license plate recognition with convolutional neural networks trained on synthetic data

abstract: We present an Automatic License Plate Recognition system designed around Convolutional Neural Networks (CNNs) and trained over synthetic plate images. We first design CNNs suitable for plate and character detection, sharing a common architecture and training procedure. Then, we generate synthetic images that account for the varying illumination and pose conditions encountered with real plate images and we use exclusively such synthetic images to train our CNNs. Experiments with real vehicle images captured in natural light with commodity imaging systems show precision and recall in excess of 93% despite our networks are trained exclusively on synthetic images.


title: Towards Open Data for Linguistics: Linguistic Linked Data

abstract: ‘Open Data’ has become very important in a wide range of fields. However for linguistics, much data is still published in proprietary, closed formats and is not made available on the web. We propose the use of linked data principles to enable language resources to be published and interlinked openly on the web, and we describe the application of this paradigm to the modeling of two resources, WordNet and the MASC corpus. Here, WordNet and the MASC corpus serve as representative examples for two major classes of linguistic resources, lexical-semantic resources and annotated corpora, respectively.Furthermore, we argue that modeling and publishing language resources as linked data offers crucial advantages as compared to existing formalisms. In particular, it is explained how this can enhance the interoperability and the integration of linguistic resources. Further benefits of this approach include unambiguous identifiability of elements of linguistic description, the creation of dynamic, but unambiguous links between different resources, the possibility to query across distributed resources, and the availability of a mature technological infrastructure. Finally, recent community activities are described.


title: Online Multi-object Tracking Based on Hierarchical Association Framework

abstract: Online multi-object tracking is one of the crucial tasks in time-critical computer vision applications. In this paper, the problem of online multi-object tracking in complex scenes from a single, static, un-calibrated camera is addressed. In complex scenes, it is still challenging due to frequent and prolonged occlusions, abrupt motion change of objects, unreliable detections, and so on. To handle these difficulties, this paper proposes a four-stage hierarchical association framework based on online tracking-bydetection strategy. For this framework, tracks and detections are divided into several groups depending on several cues obtained from association results with the proposed track confidence. In each association stage, different sets of tracks and detections are associated to handle the following problems simultaneously: track generation, progressive trajectory construction, track drift and fragmentation. The experimental results show the robustness and effectiveness of the proposed method compared with other state-of-the-art methods.


title: Interaction between prior knowledge and concept-map structure on hypertext comprehension, coherence of reading orders and disorientation

abstract: The study examined the interaction effects of prior knowledge and hypertexts structure (network vs. hierarchy) on comprehension. Comprehension was investigated analyzing jointly three dependent variables: comprehension outcomes, coherence of the reading sequences and feelings of disorientation. The results supported most of the assumptions showing an interaction effect on each measure. For low prior knowledge readers, a hierarchical structure improved comprehension performance, helped them to follow coherent reading sequences and reduced their feelings of disorientation. For high prior knowledge readers, comprehension performance and feelings of disorientation were not affected by the type of structure. Moreover, prior knowledge was a relevant resource to cope with the cognitive requirements of reading non-linear texts. In the network condition, prior knowledge supported better comprehension, led the readers to follow more coherent reading sequences and limited their feelings of disorientation. The discussion dealt with processes based on prior knowledge involved in hypertext comprehension, and stressed the need for conducting further investigations on the nature of the on-line inferences and on relations between performance, navigation and disorientation.


title: Classical vs. Crowdsourcing Surveys for Eliciting Geographic Relevance Criteria

abstract: Geographic relevance aims to assess the relevance of physical entities (e.g., shops and museums) in geographic space for a mobile user in a given context, thereby shifting the focus from the digital world (the realm of classical information retrieval) to the physical world. We study the elicitation of geographic relevance criteria by means of both a classical survey and an Amazon Mechanical Turk (a crowdsourcing platform) survey. This allows us to obtain three results: first, we gather a set of criteria and their relative importance; second, we gain a first insight on the differences between geographic relevance and classical relevance as commonly understoon in the IR field; and third we draw some considerations on the agreement, on the importance of specific criteria, among the participants to the classical and the crowdsourcing surveys.


title: Exploring generative 3D shapes using autoencoder networks

abstract: We propose a new algorithm for converting unstructured triangle meshes into ones with a consistent topology for machine learning applications. We combine the orthogonal depth map computation and the shrink wrapping approach to efficiently and robustly parameterize the triangle geometry regardless of imperfections such as inverted faces, holes, and self-intersections. The converted mesh is consistently and compactly parameterized and thus is suitable for machine learning. We use an autoencoder network to extract the manifold of shapes in the same category to explore and synthesize a variety of shapes. Furthermore, we introduce a direct manipulation interface to navigate the synthesis. We demonstrate our approach with over one thousand car shapes represented in unstructured triangle meshes.


title: Timeline Summarization for Event-relate Facts and Public Issues on a ChineseSocial Media Platform

abstract: In this work, I proposed an approach to automatically generate timeline summarization for sub-event discussions related to a query event withou tsupervised learning. In order to select event-relate ...


title: ATTS2S-VC: Sequence-to-sequence Voice Conversion with Attention and Context Preservation Mechanisms

abstract: This paper describes a method based on a sequence-to-sequence learning (Seq2Seq) with attention and context preservation mechanism for voice conversion (VC) tasks. Seq2Seq has been outstanding at numerous tasks involving sequence modeling such as speech synthesis and recognition, machine translation, and image captioning. In contrast to current VC techniques, our method 1) stabilizes and accelerates the training procedure by considering guided attention and proposed context preservation losses, 2) allows not only spectral envelopes but also fundamental frequency contours and durations of speech to be converted, 3) requires no context information such as phoneme labels, and 4) requires no time-aligned source and target speech data in advance. In our experiment, the proposed VC framework can be trained in only one day, using only one GPU of an NVIDIA Tesla K80, while the quality of the synthesized speech is higher than that of speech converted by Gaussian mixture model-based VC and is comparable to that of speech generated by recurrent neural network-based text-to-speech synthesis, which can be regarded as an upper limit on VC performance.


title: A Precedence Graph-Based Approach to Detect Message Injection Attacks in J1939 Based Networks

abstract: Vehicles now include Electronic Control Units (ECUs) that communicate with each other via broadcast networks. Cyber-security professionals have shown that such embedded communication networks can be compromised. Very recently, it has been shown that embedded devices connected to commercial vehicle networks can be manipulated to perform unintended actions by injecting spoofed messages. Such attacks can be hard to detect as they can mimic safety critical actions performed by ECUs. We present a precedence graph-based anomaly detection technique to detect malicious message injections. Our approach can detect malicious message injections and is able to distinguish them from safety critical actions like hard braking.


title: Evidence of red sensitive photoreceptors in Pygopleurus israelitus (Glaphyridae: Coleoptera) and its implications for beetle pollination in the southeast Mediterranean

abstract: A very well-documented case of flower-beetle interaction is the association in the Mediterranean region between red bowl-shaped flowers and beetles of the family Glaphyridae. The present study examines the visual mechanisms by which Pygopleurus israelitus (Glaphyridae: Scarabaeoidea: Coleoptera) would perceive the colors of flowers they visit by characterizing the spectral sensitivity of its photoreceptors. Our measurements revealed the presence of three types of photoreceptors, maximally sensitive in the UV, green and red areas of the spectrum. Using color vision space diagrams, we calculated the distribution of beetle-visited flower colors in the glaphyrid and honeybee color space and evaluated whether chromatic discrimination differs between the two types of pollinators. Respective color loci in the beetle color space are located on one side of the locus for green foliage background, whereas in the honeybee the flower color loci surround the locus occupied by green foliage. Our results represent the first evidence of a red sensitive photoreceptor in a flower-visiting coleopteran species, highlighting Glaphyridae as an interesting model group to study the role of pollinators in flower color evolution.


title: Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning

abstract: This article addresses open questions of the discussions in the first SIRTEL workshop at the EC-TEL conference 2007. It argues why personal recommender systems have to be adjusted to the specific characteristics of learning in Learning Networks. Personal recommender systems strongly depend on the context or domain they operate in, and it is often not possible to take one recommender system with a specific purpose from one context and transfer it to another context or domain. The article describes a number of distinctive differences for personalised recommendation to learners when compared to recommendations for consumers. Similarities and differences for informal and formal learning are discussed and used to define the recommendation goal that recommender systems in informal learning networks have to address. The article further suggests an evaluation approach for recommender systems in Learning Networks.


title: SECURE IMAGE ENCRYPTION USING AES

abstract: Security in transmission of digital images has its importance in today’s image communications, due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access, Image security has become a critical issue. The difficulties in ensuring individuals privacy become increasingly challenging. Various methods have been investigated and developed to protect data and personal privacy. Encryption is probably the most obvious one. In order to protect valuable information from undesirable readers, image encryption is essential. This paper presents an application of AES (Advanced Encryption Standard) operations in image encryption and decryption. The encrypted cipher images always display the uniformly distributed RGB pixels.


title: Innovating Collaborative Content Creation: The Role of Altruism and Wiki Technology

abstract: Wikipedia demonstrates the feasibility and success of an innovative form of content creation, namely openly shared, collaborative writing. This research sought to understand the success of Wikipedia as a collaborative model, considering both technology and participant motivations. The research finds that while participants have both individualistic and collaborative motives, collaborative (altruistic) motives dominate. The collaboration model differs from that of open source software development, which is less inclusive with respect to participation, and more "selfish" with respect to contributor motives. The success of the Wikipedia model appears to be related to wiki technology and the "wiki way" of collaboration


title: Datacenter Traffic Control: Understanding Techniques and Trade-offs

abstract: Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today’s cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control, including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters, which have been receiving increasing attention recently and pose interesting and novel research problems. To measure the performance of datacenter networks, different performance metrics have been used, such as flow completion times, deadline miss rate, throughput, and fairness. Depending on the application and user requirements, some metrics may need more attention. While investigating different traffic control techniques, we point out the tradeoffs involved in terms of costs, complexity, and performance. We find that a combination of different traffic control techniques may be necessary at particular entities and layers in the network to improve the variety of performance metrics. We also find that despite significant research efforts, there are still open problems that demand further attention from the research community.


Succeeding With Agile: Software Development Using Scrum, by Mike Cohn.


title: Enhanced machine learning scheme for energy efficient resource allocation in 5G heterogeneous cloud radio access networks

abstract: Heterogeneous cloud radio access networks (H-CRAN) is a new trend of SC that aims to leverage the heterogeneous and cloud radio access networks advantages. Low power remote radio heads (RRHs) are exploited to provide high data rates for users with high quality of service requirements (QoS), while high power macro base stations (BSs) are deployed for coverage maintenance and low QoS users support. However, the inter-tier interference between the macro BS and RRHs and energy efficiency are critical challenges that accompany resource allocation in H-CRAN. Therefore, we propose a centralized resource allocation scheme using online learning, which guarantees interference mitigation and maximizes energy efficiency while maintaining QoS requirements for all users. To foster the performance of such scheme with a model-free learning, we consider users' priority in resource blocks (RBs) allocation and compact state representation based learning methodology to enhance the learning process. Simulation results confirm that the proposed resource allocation solution can mitigate interference, increase energy and spectral efficiencies significantly, and maintain users' QoS requirements.


1 The Development of Empathy: How, When, and Why


title: Musical Training as a Framework for Brain Plasticity: Behavior, Function, and Structure

abstract: Musical training has emerged as a useful framework for the investigation of training-related plasticity in the human brain. Learning to play an instrument is a highly complex task that involves the interaction of several modalities and higher-order cognitive functions and that results in behavioral, structural, and functional changes on time scales ranging from days to years. While early work focused on comparison of musical experts and novices, more recently an increasing number of controlled training studies provide clear experimental evidence for training effects. Here, we review research investigating brain plasticity induced by musical training, highlight common patterns and possible underlying mechanisms of such plasticity, and integrate these studies with findings and models for mechanisms of plasticity in other domains.


title: Who Were the Early Adopters of Dabigatran?: An Application of Group-based Trajectory Models

abstract: Background:Variation in physician adoption of new medications is poorly understood. Traditional approaches (eg, measuring time to first prescription) may mask substantial heterogeneity in technology adoption. Objective:Apply group-based trajectory models to examine the physician adoption of dabigratran, a novel anticoagulant. Methods:A retrospective cohort study using prescribing data from IMS Xponent™ on all Pennsylvania physicians regularly prescribing anticoagulants (n=3911) and data on their characteristics from the American Medical Association Masterfile. We examined time to first dabigatran prescription and group-based trajectory models to identify adoption trajectories in the first 15 months. Factors associated with rapid adoption were examined using multivariate logistic regressions. Outcomes:Trajectories of monthly share of oral anticoagulant prescriptions for dabigatran. Results:We identified 5 distinct adoption trajectories: 3.7% rapidly and extensively adopted dabigatran (adopting in ⩽3 mo with 45% of prescriptions) and 13.4% were rapid and moderate adopters (⩽3 mo with 20% share). Two groups accounting for 21.6% and 16.1% of physicians, respectively, were slower to adopt (6–10 mo post-introduction) and dabigatran accounted for <10% share. Nearly half (45.2%) of anticoagulant prescribers did not adopt dabigatran. Cardiologists were much more likely than primary care physicians to rapidly adopt [odds ratio (OR)=12.2; 95% confidence interval (CI), 9.27–16.1] as were younger prescribers (age 36–45 y: OR=1.49, 95% CI, 1.13–1.95; age 46–55: OR=1.34, 95% CI, 1.07–1.69 vs. >55 y). Conclusions:Trajectories of physician adoption of dabigatran were highly variable with significant differences across specialties. Heterogeneity in physician adoption has potential implications for the cost and effectiveness of treatment.


title: Wearable technology for spine movement assessment: A systematic review

abstract: Abstract Continuous monitoring of spine movement function could enhance our understanding of low back pain development. Wearable technologies have gained popularity as promising alternative to laboratory systems in allowing ambulatory movement analysis. This paper aims to review the state of art of current use of wearable technology to assess spine kinematics and kinetics. Four electronic databases and reference lists of relevant articles were searched to find studies employing wearable technologies to assess the spine in adults performing dynamic movements. Two reviewers independently identified relevant papers. Customised data extraction and quality appraisal form were developed to extrapolate key details and identify risk of biases of each study. Twenty-two articles were retrieved that met the inclusion criteria: 12 were deemed of medium quality (score 33.4–66.7%), and 10 of high quality (score >66.8%). The majority of articles (19/22) reported validation type studies. Only 6 reported data collection in real-life environments. Multiple sensors type were used: electrogoniometers (3/22), strain gauges based sensors (3/22), textile piezoresistive sensor (1/22) and accelerometers often used with gyroscopes and magnetometers (15/22). Two sensors units were mainly used and placing was commonly reported on the spine lumbar and sacral regions. The sensors were often wired to data transmitter/logger resulting in cumbersome systems. Outcomes were mostly reported relative to the lumbar segment and in the sagittal plane, including angles, range of motion, angular velocity, joint moments and forces. This review demonstrates the applicability of wearable technology to assess the spine, although this technique is still at an early stage of development.


title: An efficient DHT-based elastic SDN controller

abstract: Elasticity in distributed SDN Controllers [1], [2], [3] improves fault tolerance, power efficiency, cost efficiency and scalability. We find that the Elasticon [1] algorithm has high time complexity since it balances the load equally on all the controllers for every event. In this paper, we propose an efficient algorithm for elastic controllers using Chord [4], a Distributed Hash Table (DHT). In our scheme, we do not balance the load on the controllers until a controller is overloaded. The switch ID space forms the ring in the DHT. The controller is assigned an ID based on the range of switch IDs it is responsible for which changes as the switches migrate from an overloaded controller to its adjacent controllers in the ring. If the average load on the controllers falls below a given threshold, we consolidate the switch allocation to controllers so that some of the controllers can be powered off. We compared the performance of Elasticon and the proposed algorithm in terms of the number of migrations needed, the average number of controllers that are powered on, the time taken to run the algorithm and the standard deviation of the load on the controllers. We find that the number of controllers is similar for our algorithm and Elasticon, the migrations are fewer in our algorithm while the time taken by our algorithm is orders of magnitude less than Elasticon.


title: Scalable Hashing-Based Network Discovery

abstract: Discovering and analyzing networks from non-network data is a task with applications in fields as diverse as neuroscience, genomics, energy, economics, and more. In these domains, networks are often constructed out of multiple time series by computing measures of association or similarity between pairs of series. The nodes in a discovered graph correspond to time series, which are linked via edges weighted by the association scores of their endpoints. After graph construction, the network may be thresholded such that only the edges with stronger weights remain and the desired sparsity level is achieved. While this approach is feasible for small datasets, its quadratic time complexity does not scale as the individual time series length and the number of compared series increase. Thus, to avoid the costly step of building a fully-connected graph before sparsification, we propose a fast network discovery approach based on probabilistic hashing of randomly selected time series subsequences. Evaluation on real data shows that our methods construct graphs nearly 15 times as fast as baseline methods, while achieving both network structure and accuracy comparable to baselines in task-based evaluation.


title: Design and analysis of a transport-level solution for content-centric VANETs

abstract: Vehicular networks, characterized by dynamic, short-lived and spotty connectivity, challenge the design of reliable and efficient content delivery protocols. By leveraging content names instead of IP addresses and by enabling in-network caching, Content-Centric Networking (CCN) reveals to be a promising paradigm for content retrieval and distribution on the road. The CCN architecture defines new operations to transfer named data objects, including naming, security, routing and transport schemes, that need proper design choices and deserve further investigation. In this paper, we focus on reliability issues addressed at the transport-level of a CCN architecture properly customized to be deployed on top of the IEEE 802.11p access technology in VANETs. After analysing the benefits of a segmentation/reassembly procedure to transfer named data objects, we design a scheme to manage retransmissions of lost content packets. We assess the effects of our proposed transport function on the content download performance and the usage of channel resources.


title: Design and implementation of a 2.45 GHz RF sensor for non-contacting monitoring vital signs

abstract: The design and implementation of a non-contact RF (radio frequency) sensor that can monitor vital signsrespiration rate and heat rate is proposed. The designed low-cost system operates at 2.45 GHz by using one single Yagi antenna system to transmit and receive RF signals, realizes a miniaturized design of PCB that integrates both microwave transceivers and receivers. The gain of the sensor is 8.96 dB. The return loss is −22.87 dB at 2.45 GHz and the directionality of the antenna is satisfactory for monitoring vital signs. In comparison with pulse sensor and respiration sensor, it has been validated that the sensor could detect the respiration and heartbeat with mean error for the respiration rate within 0.5 beat/min and the heart rate within 1.9 beat/min.


title: Combining deep reinforcement learning with prior knowledge and reasoning

abstract: Recent improvements in deep reinforcement learning have allowed to solve problems in many 2D domains such as Atari games. However, in complex 3D environments, numerous learning episodes are required which may be too time consuming or even impossible especially in real-world scenarios. We present a new architecture to combine external knowledge and deep reinforcement learning using only visual input. A key concept of our system is augmenting image input by adding environment feature information and combining two sources of decision. We evaluate the performances of our method in 3D partially-observable environments from the Microsoft Malmo platform. Experimental evaluation exhibits higher performance and faster learning compared to a single reinforcement learning model.


Structured Variationally Auto-encoded Optimization


Notes On Balangao Grammar


title: Mindfulness-based stress reduction: an intervention to enhance the effectiveness of nurses' coping with work-related stress.

abstract: PURPOSE ::: This critical literature review explored the current state of the science regarding mindfulness-based stress reduction (MBSR) as a potential intervention to improve the ability of nurses to effectively cope with stress. ::: ::: ::: METHODS ::: Literature sources include searches from EBSCOhost, Gale PowerSearch, ProQuest, PubMed Medline, Google Scholar, Online Journal of Issues in Nursing, and reference lists from relevant articles. ::: ::: ::: FINDINGS ::: Empirical evidence regarding utilizing MBSR with nurses and other healthcare professionals suggests several positive benefits including decreased stress, burnout, and anxiety; and increased empathy, focus, and mood. ::: ::: ::: CONCLUSIONS ::: Nurse use of MBSR may be a key intervention to help improve nurses' ability to cope with stress and ultimately improve the quality of patient care provided.


title: Simple, Accurate, and Robust Projector-Camera Calibration

abstract: Structured-light systems are simple and effective tools to acquire 3D models. Built with off-the-shelf components, a data projector and a camera, they are easy to deploy and compare in precision with expensive laser scanners. But such a high precision is only possible if camera and projector are both accurately calibrated. Robust calibration methods are well established for cameras but, while cameras and projectors can both be described with the same mathematical model, it is not clear how to adapt these methods to projectors. In consequence, many of the proposed projector calibration techniques make use of a simplified model, neglecting lens distortion, resulting in loss of precision. In this paper, we present a novel method to estimate the image coordinates of 3D points in the projector image plane. The method relies on an uncalibrated camera and makes use of local homographies to reach sub-pixel precision. As a result, any camera model can be used to describe the projector, including the extended pinhole model with radial and tangential distortion coefficients, or even those with more complex lens distortion models.


title: Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks

abstract: This paper proposes a new approach to automatically quantify the severity of knee osteoarthritis (OA) from radiographs using deep convolutional neural networks (CNN). Clinically, knee OA severity is assessed using Kellgren \& Lawrence (KL) grades, a five point scale. Previous work on automatically predicting KL grades from radiograph images were based on training shallow classifiers using a variety of hand engineered features. We demonstrate that classification accuracy can be significantly improved using deep convolutional neural network models pre-trained on ImageNet and fine-tuned on knee OA images. Furthermore, we argue that it is more appropriate to assess the accuracy of automatic knee OA severity predictions using a continuous distance-based evaluation metric like mean squared error than it is to use classification accuracy. This leads to the formulation of the prediction of KL grades as a regression problem and further improves accuracy. Results on a dataset of X-ray images and KL grades from the Osteoarthritis Initiative (OAI) show a sizable improvement over the current state-of-the-art.


Data mining: concepts and techniques by Jiawei Han and Micheline Kamber


title: Cooperative Localization in Wireless Networks

abstract: Location-aware technologies will revolutionize many aspects of commercial, public service, and military sectors, and are expected to spawn numerous unforeseen applications. A new era of highly accurate ubiquitous location-awareness is on the horizon, enabled by a paradigm of cooperation between nodes. In this paper, we give an overview of cooperative localization approaches and apply them to ultrawide bandwidth (UWB) wireless networks. UWB transmission technology is particularly attractive for short- to medium-range localization, especially in GPS-denied environments: wide transmission bandwidths enable robust communication in dense multipath scenarios, and the ability to resolve subnanosecond delays results in centimeter-level distance resolution. We will describe several cooperative localization algorithms and quantify their performance, based on realistic UWB ranging models developed through an extensive measurement campaign using FCC-compliant UWB radios. We will also present a powerful localization algorithm by mapping a graphical model for statistical inference onto the network topology, which results in a net-factor graph, and by developing a suitable net-message passing schedule. The resulting algorithm (SPAWN) is fully distributed, can cope with a wide variety of scenarios, and requires little communication overhead to achieve accurate and robust localization.


title: Smokeless tobacco use related to military deployment, cigarettes and mental health symptoms in a large, prospective cohort study among US service members

abstract: Aims To characterize smokeless tobacco initiation and persistence in relation to deployment, combat, occupation, smoking and mental health symptoms. ::: ::: ::: ::: Design Prospective cohort, utilizing self-reported survey data from the Millennium Cohort Study. ::: ::: ::: ::: Setting US military service members in all branches including active duty, reserve and National Guard. ::: ::: ::: ::: Participants Population-based sample of 45 272 participants completing both baseline (July 2001–June 2003; n = 77 047) and follow-up (June 2004–January 2006; n = 55 021) questionnaires (follow-up response rate = 71.4%). ::: ::: ::: ::: Measurements Self-reported smokeless tobacco initiation and persistence. ::: ::: ::: ::: Findings Over the study period, 72.4% did not deploy, 13.7% deployed without combat exposures and 13.9% deployed with combat exposures, while 1.9% were smokeless tobacco initiators and 8.9% were persistent users. The odds of initiation were greater for deployers with combat exposure [odds ratio (OR), 1.76; 95% confidence interval (CI), 1.49–2.09], deployers without combat exposure (OR, 1.31; 95% CI, 1.07–1.60) and those who deployed multiple times (OR, 1.67; 95% CI, 1.31–2.14), as well as in smoking recidivists/initiators (OR, 4.65; 95% CI, 3.82–5.66) and those reporting post-traumatic stress disorder symptoms (OR, 1.54; CI, 1.15–2.07). A similar pattern for higher odds of persistent use was observed for deployment and combat exposure, but not for smoking and mental health symptoms. Military occupation was not significantly associated with initiation or persistence. ::: ::: ::: ::: Conclusions Deployment and combat exposure in the US military are associated with increased risk of smokeless tobacco initiation and persistence while smoking and symptoms of post-traumatic stress disorder increase the odds for initiation. Research is needed on aspects of military service amenable to the reduction or prevention of tobacco consumption.


title: Mobile-D: An Agile Approach for Mobile Application Development

abstract: Mobile phones have been closed environments until recent years. The change brought by open platform technologies such as the Symbian operating system and Java technologies has opened up a significant business opportunity for anyone to develop application software such as games for mobile terminals. However, developing mobile applications is currently a challenging task due to the specific demands and technical constraints of mobile development. Furthermore, at the moment very little is known about the suitability of the different development processes for mobile application development. Due to these issues, we have developed an agile development approach called Mobile-D. The Mobile-D approach is briefly outlined here and the experiences gained from four case studies are discussed.


title: Anonymous Gossip: improving multicast reliability in mobile ad-hoc networks

abstract: In recent years, a number of applications of ad-hoc networks have been proposed. Many of them are based on the availability of a robust and reliable multicast protocol. We address the issue of reliability and propose a scalable method to improve packet delivery of multicast routing protocols and decrease the variation in the number of packets received by different nodes. The proposed protocol works in two phases. In the first phase, any suitable protocol is used to multicast a message to the group, while in the second concurrent phase, the gossip protocol tries to recover lost messages. Our proposed gossip protocol is called Anonymous Gossip (AG) since nodes need not know the other group members for gossip to be successful. This is extremely desirable for mobile nodes, that have limited resources, and where the knowledge of group membership is difficult to obtain. As a first step, anonymous gossip is implemented over MAODV without much overhead and its performance is studied. Simulations show that the packet delivery of MAODV is significantly improved and the variation in number of packets delivered is decreased.


title: A subgraph isomorphism algorithm and its application to biochemical data

abstract: BackgroundGraphs can represent biological networks at the molecular, protein, or species level. An important query is to find all matches of a pattern graph to a target graph. Accomplishing this is inherently difficult (NP-complete) and the efficiency of heuristic algorithms for the problem may depend upon the input graphs. The common aim of existing algorithms is to eliminate unsuccessful mappings as early as and as inexpensively as possible.ResultsWe propose a new subgraph isomorphism algorithm which applies a search strategy to significantly reduce the search space without using any complex pruning rules or domain reduction procedures. We compare our method with the most recent and efficient subgraph isomorphism algorithms (VFlib, LAD, and our C++ implementation of FocusSearch which was originally distributed in Modula2) on synthetic, molecules, and interaction networks data. We show a significant reduction in the running time of our approach compared with these other excellent methods and show that our algorithm scales well as memory demands increase.ConclusionsSubgraph isomorphism algorithms are intensively used by biochemical tools. Our analysis gives a comprehensive comparison of different software approaches to subgraph isomorphism highlighting their weaknesses and strengths. This will help researchers make a rational choice among methods depending on their application. We also distribute an open-source package including our system and our own C++ implementation of FocusSearch together with all the used datasets (http://ferrolab.dmi.unict.it/ri.html). In future work, our findings may be extended to approximate subgraph isomorphism algorithms.


title: Bilateral linear nevus comedonicus on eyelids – A rare presentation

abstract: We describe a female child with linear comedone-like lesions over both the eyelids which histologically confirmed Naevus Comedonicus. She only partially responded to topical tretinoin cream. There was no clinically evident overt systemic association in our case.


title: Effect of exogenous and endogenous nitric oxide on biofilm formation by Lactobacillus plantarum

abstract: Biofilms are a widespread form of occurrence of microorganisms in nature, and understanding the mechanism of regulation of their formation is of unquestionable practical significance for medicine and biotechnology. In the present work, the effect of nitric oxide (NO) on biofilm formation by Lactobacillus plantarum was investigated and the micromolar concentrations of exogenous NO were shown to have a negative effect on this process due to its toxic effect on the cells. However, the decrease in the level of endogenous NO in bacteria in the presence of a nitric oxide scavenger 2-(4-carboxyphenyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (cPTIO) impaired the characteristics of the forming biofilms, as was evident from the decrease in their size.


title: A New Proposed Technique to Improve Software Regression Testing Cost

abstract: In this article, we describe the regression test process to test and verify the changes made on software. A developed technique use the automation test based on decision tree and test selection process in order to reduce the testing cost is given. The developed technique is applied to a practical case and the result show its improvement.


title: The 'wired' universe of organic chemistry.

abstract: The millions of reactions performed and compounds synthesized by organic chemists over the past two centuries connect to form a network larger than the metabolic networks of higher organisms and rivalling the complexity of the World Wide Web. Despite its apparent randomness, the network of chemistry has a well-defined, modular architecture. The network evolves in time according to trends that have not changed since the inception of the discipline, and thus project into chemistry's future. Analysis of organic chemistry using the tools of network theory enables the identification of most 'central' organic molecules, and for the prediction of which and how many molecules will be made in the future. Statistical analyses based on network connectivity are useful in optimizing parallel syntheses, in estimating chemical reactivity, and more.


title: Effectiveness of mobile technologies delivering Ecological Momentary Interventions for stress and anxiety: a systematic review

abstract: Objectives Mobile technologies may be suitable for delivering Ecological Momentary Interventions (EMI) to treat anxiety in real-time. This review aims to synthesize evidence on the effectiveness of EMI for treating anxiety conditions. ::: ::: Materials and Methods Four databases and the reference lists of previous studies were searched. A total of 1949 abstracts were double screened for inclusion. Sufficient studies were available to undertake a quantitative meta-analysis on EMIs on generalized anxiety symptoms. ::: ::: Results The 15 randomized trials and randomized controlled trials examined anxiety ( n = 7), stress ( n = 3), anxiety and stress ( n = 2), panic disorder ( n = 2), and social phobia ( n = 1). Eight EMIs comprised self-monitoring integrated with therapy modules, seven comprised multimedia content, and three comprised self-monitoring only. The quality of studies presented high risk of biases. Meta-analysis ( n = 7) demonstrated that EMIs reduced generalized anxiety compared to control and/or comparison groups (Effect Size (ES) = 0.32, 95% CI, 0.12-0.53). Most EMIs targeting stress were reported effective relative to control as were the two EMIs targeting panic disorders. The EMI targeting social phobia was not effective. ::: ::: Discussion EMIs have potential in treating both anxiety and stress. However, few high-quality trials have been conducted for specific anxiety disorders. Further trials are needed to assess the value of EMI technologies for anxiety in enhancing existing treatments. ::: ::: Conclusion This study found a small significant effect of EMI studies on reducing generalized anxiety. Studies on stress demonstrated EMI was effective compared to control, with the small number of studies on panic and social phobia demonstrating mixed results.


title: CCDD: AN ENHANCED STANDARD ECG DATABASE WITH ITS MANAGEMENT AND ANNOTATION TOOLS

abstract: Standard Electrocardiogram (ECG) database is created for validating and comparing different algorithms on feature detection and disease classification. At present, there are four frequently used st...


title: Hot-carrier behaviour and ron-BV trade-off optimization for p-channel LDMOS transistors in a 180 nm HV-CMOS technology

abstract: This work reports the hot-carrier (HC) behavior and specific on-resistance (R on,sp ) optimization of 20∼60 V p-channel LDMOS transistors implemented in a 180 nm HV-CMOS technology. By precise control the implant dose and energy of a p-drift region, which is surrounded by n-type isolation well, one can efficiently optimize the on-resistance and breakdown voltage (BV) trade-off while keeping very low HC degradation. Both of the TCAD simulations and measurements are described to explain the proposed technology and the transistor behaviour. Reported p-channel LDMOS transistor (pLDMOS) shows a very low HC-induced degradation - percent change of linear region of drain current (Idlin) below 3 % till 1×105 sec stress), and it shows an excellent R on,sp -BV trade-off (pLDMOS with 20V GOX: BV = −85 V and R on,sp = 1.64 mΩ-cm2).