title: Eigenvector method for maximum-likelihood estimation of phase errors in synthetic-aperture-radar imagery

abstract: We develop a maximum-likelihood (ML) algorithm for estimation and correction (autofocus) of phase errors induced in synthetic-aperture-radar (SAR) imagery. Here, M pulse vectors in the range-compressed domain are used as input for simultaneously estimating M − 1 phase values across the aperture. The solution involves an eigenvector of the sample covariance matrix of the range-compressed data. The estimator is then used within the basic structure of the phase gradient autofocus (PGA) algorithm, replacing the original phase-estimation kernel. We show that, in practice, the new algorithm provides excellent restorations to defocused SAR imagery, typically in only one or two iterations. The performance of the new phase estimator is demonstrated essentially to achieve the Cramer–Rao lower bound on estimation-error variance for all but small values of target-toclutter ratio. We also show that for the case in which M is equal to 2, the ML estimator is similar to that of the original PGA method but achieves better results in practice, owing to a bias inherent in the original PGA phase estimation kernel. Finally, we discuss the relationship of these algorithms to the shear-averaging and spatial correlation methods, two other phase-correction techniques that utilize the same phase-estimation kernel but that produce substantially poorer performance because they do not employ several fundamental signal-processing steps that are critical to the algorithms of the PGA class.


title: Interactivity in Distance Learning: The Digital Divide and Student Satisfaction

abstract: Like any other complex communications infrastructure, the network connecting different locations of virtual classes in university systems has developed over time with differing combinations of synchronous and asynchronous communication technologies at the various university locations. This research focuses on graduate nursing students’ satisfaction with the learning experience in seven distinct virtual classes during the 1998-99 academic year of a midwestern state university system. Each virtual class included course offerings from multiple campuses at eight locations, two urban and six non-urban. Differences between student perceptions of satisfaction for host and remote sites are analyzed, as well as differences between students at urban sites and non-urban sites whether remote or host. When geographic location is controlled, separating the data into urban and non-urban settings, students attending classes with a web requirement in urban settings were more likely to express positive degrees of satisfaction than students attending classes with a web requirement in non-urban settings. The relationship holds for both host and remote classes.


title: Pilot-scale data provide enhanced estimates of the life cycle energy and emissions profile of algae biofuels produced via hydrothermal liquefaction.

abstract: Abstract Life cycle assessment (LCA) has been used widely to estimate the environmental implications of deploying algae-to-energy systems even though no full-scale facilities have yet to be built. Here, data from a pilot-scale facility using hydrothermal liquefaction (HTL) is used to estimate the life cycle profiles at full scale. Three scenarios (lab-, pilot-, and full-scale) were defined to understand how development in the industry could impact its life cycle burdens. HTL-derived algae fuels were found to have lower greenhouse gas (GHG) emissions than petroleum fuels. Algae-derived gasoline had significantly lower GHG emissions than corn ethanol. Most algae-based fuels have an energy return on investment between 1 and 3, which is lower than petroleum biofuels. Sensitivity analyses reveal several areas in which improvements by algae bioenergy companies (e.g., biocrude yields, nutrient recycle) and by supporting industries (e.g., CO 2 supply chains) could reduce the burdens of the industry.


Hash-based Digital Signature Schemes


title: Evidence from neuroimaging for the role of the menstrual cycle in the interplay of emotion and cognition

abstract: Women show increased predisposition for certain psychiatric disorders, such as depression, that are associated with disturbances in the integration of emotion and cognition. While this suggests that sex hormones need to be considered as modulating factors in the regulation of emotion, we still lack a sound understanding of how the menstrual cycle impacts emotional states and cognitive function. Though signals for the influence of the menstrual cycle on the integration of emotion and cognition have appeared as secondary findings in numerous behavioral and neuroimaging studies, this has only very rarely been the primary research goal. This review summarizes evidence: (1) that the menstrual cycle modulates the integration of emotional and cognitive processing on a behavioral level, and (2) that this change in behavior can be associated with functional, molecular and structural changes in the brain during a specific menstrual cycle phase. The growing evidence for menstrual cycle-specific differences suggests a modulating role for sex hormones on the neural networks supporting the integration of emotional and cognitive information. It will further be discussed what methodological aspects need to be considered to capture the role of the menstrual cycle in the emotion-cognition interplay more systematically.


title: Pick and place mobile robot for the disabled through voice commands

abstract: A mobile robot is designed to pick and place the objects through voice commands. This work would be practically useful to wheelchair bound persons. The pick and place robot is designed in a way that it is able to help the user to pick up an item that is placed at two different levels using an extendable arm. The robot would move around to pick up an item and then pass it back to the user or to a desired location as told by the user. The robot control is achieved through voice commands such as left, right, straight, etc. in order to help the robot to navigate around. Raspberry Pi 2 controls the overall design with 5 DOF servo motor arm. The webcam is used to navigate around which provides live streaming using a mobile application for the user to look into. Results show the ability of the robot to pick and place the objects up to a height of 23.5cm through proper voice commands.


title: Development of a measure of the motives underlying the selection of food: the food choice questionnaire

abstract: Abstract A number of factors are thought to influence people's dietary choices, including health, cost, convenience and taste, but there are no measures that address health-related and non-health-related factors in a systematic fashion. This paper describes the development of a multidimensional measure of motives related to food choice. The Food Choice Questionnaire (FCQ) was developed through factor analysis of responses from a sample of 358 adults ranging in age from 18 to 87 years. Nine factors emerged, and were labelledhealth,mood,convenience,sensory appeal,natural content,price,weight control,familiarityandethical concern. The questionnaire structure was verified using confirmatory factor analysis in a second sample (n=358), and test–retest reliability over a 2- to 3-week period was satisfactory. Convergent validity was investigated by testing associations between FCQ scales and measures of dietary restraint, eating style, the value of health, health locus of control and personality factors. Differences in motives for food choice associated with sex, age and income were found. The potential uses of this measure in health psychology and other areas are discussed.


title: CrashScope: A Practical Tool for Automated Testing of Android Applications

abstract: Unique challenges arise when testing mobile applications due to their prevailing event-driven nature and complex contextual features (e.g. sensors, notifications). Current automated input generation approaches for Android apps are typically not practical for developers to use due to required instrumentation or platform dependence and generally do not effectively exercise contextual features. To better support developers in mobile testing tasks, in this demo we present a novel, automated tool called C rash S cope . This tool explores a given Android app using systematic input generation, according to several strategies informed by static and dynamic analyses, with the intrinsic goal of triggering crashes. When a crash is detected, C rash S cope generates an augmented crash report containing screenshots, detailed crash reproduction steps, the captured exception stack trace, and a fully replayable script that automatically reproduces the crash on a target device(s). Results of preliminary studies show that C rash S cope is able to uncover about as many crashes as other state of the art tools, while providing detailed useful crash reports and test scripts to developers. Website: www.android-dev-tools.com/crashscope-home Video url: https://youtu.be/ii6S1JF6xDw


title: ICDAR2017 Competition on Recognition of Documents with Complex Layouts - RDCL2017

abstract: This paper presents an objective comparative evaluation of page segmentation and region classification methods for documents with complex layouts. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2017, presenting the results of the evaluation of seven methods – five submitted, two state-of-the-art systems (commercial and open-source). Three scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions and two evaluating both segmentation and region classification (one focusing only on text regions). For the first time, nested region content (table cells, chart labels etc.) are evaluated in addition to the top-level page content. Text recognition was a bonus challenge and was not taken up by all participants. The results indicate that an innovative approach has a clear advantage but there is still a considerable need to develop robust methods that deal with layout challenges, especially with the non-textual content.


title: Optimization of sleep apnea detection using SpO2 and ANN

abstract: Repetitive respiratory disturbance during sleep is called Sleep Apnea Hypopnea Syndrome and causes various diseases. Different features and classifiers have been used by different researchers to detect sleep apnea. This study is undertaken to identify the better performing blood oxygen saturation features subset using an Artificial Neural Network classifier for sleep Apnea detection. A database of 8 subjects with one-minute annotation is used to test the proposed system. The optimized system has seven features chosen from a total set of sixty-one features presenting a high accuracy rate using a genetic algorithm. Artificial Neural Network was able to achieve 97.7 percentage of accuracy with only seven features chosen by the Genetic algorithm.


title: Global Convergence of ADMM in Nonconvex Nonsmooth Optimization

abstract: In this paper, we analyze the convergence of the alternating direction method of multipliers (ADMM) for minimizing a nonconvex and possibly nonsmooth objective function, $$\phi (x_0,\ldots ,x_p,y)$$ ::: , subject to coupled linear equality constraints. Our ADMM updates each of the primal variables $$x_0,\ldots ,x_p,y$$ ::: , followed by updating the dual variable. We separate the variable y from $$x_i$$ ::: ’s as it has a special role in our analysis. The developed convergence guarantee covers a variety of nonconvex functions such as piecewise linear functions, $$\ell _q$$ ::: quasi-norm, Schatten-q quasi-norm ( ::: $$0<q<1$$ ::: ), minimax concave penalty (MCP), and smoothly clipped absolute deviation penalty. It also allows nonconvex constraints such as compact manifolds (e.g., spherical, Stiefel, and Grassman manifolds) and linear complementarity constraints. Also, the $$x_0$$ ::: -block can be almost any lower semi-continuous function. By applying our analysis, we show, for the first time, that several ADMM algorithms applied to solve nonconvex models in statistical learning, optimization on manifold, and matrix decomposition are guaranteed to converge. Our results provide sufficient conditions for ADMM to converge on (convex or nonconvex) monotropic programs with three or more blocks, as they are special cases of our model. ADMM has been regarded as a variant to the augmented Lagrangian method (ALM). We present a simple example to illustrate how ADMM converges but ALM diverges with bounded penalty parameter $$\beta $$ ::: . Indicated by this example and other analysis in this paper, ADMM might be a better choice than ALM for some nonconvex nonsmooth problems, because ADMM is not only easier to implement, it is also more likely to converge for the concerned scenarios.


title: A small microstrip patch antenna for future 5G applications

abstract: This paper presents a low profile microstrip patch antenna for next generation 5G devices. The proposed patch antenna has a compact structure of 20mm × 20mm × 1.6mm including the ground plane, which is suitable to be used in handheld devices. The antenna resonates at 10.15 GHz covering 5G frequency band. The proposed design provides a gain of 4.46dBi and the radiation pattern is omni-directional. In this paper geometry of the antenna and various parameters such as return loss plot, gain plot, radiation pattern plot and VSWR plot are presented & discussed. Measured results are also presented and they are in good match with simulated results.


title: A Comparison of Commercial and MilitarY computer Security Policies

abstract: Most discussions of computer security focus on control of disclosure. In Particular, the U.S. Department of Defense has developed a set of criteria for computer mechanisms to provide control of classified information. However, for that core of data processing concerned with business operation and control of assets, the primary security concern is data integrity. This paper presents a policy for data integrity based on commercial data processing practices, and compares the mechanisms needed for this policy with the mechanisms needed to enforce the lattice model for information security. We argue that a lattice model is not sufficient to characterize integrity policies, and that distinct mechanisms are needed to Control disclosure and to provide integrity.


title: A Virtual Reprise of the Stanley Milgram Obedience Experiments

abstract: BACKGROUND ::: Stanley Milgram's 1960s experimental findings that people would administer apparently lethal electric shocks to a stranger at the behest of an authority figure remain critical for understanding obedience. Yet, due to the ethical controversy that his experiments ignited, it is nowadays impossible to carry out direct experimental studies in this area. In the study reported in this paper, we have used a similar paradigm to the one used by Milgram within an immersive virtual environment. Our objective has not been the study of obedience in itself, but of the extent to which participants would respond to such an extreme social situation as if it were real in spite of their knowledge that no real events were taking place. ::: ::: ::: METHODOLOGY ::: Following the style of the original experiments, the participants were invited to administer a series of word association memory tests to the (female) virtual human representing the stranger. When she gave an incorrect answer, the participants were instructed to administer an 'electric shock' to her, increasing the voltage each time. She responded with increasing discomfort and protests, eventually demanding termination of the experiment. Of the 34 participants, 23 saw and heard the virtual human, and 11 communicated with her only through a text interface. ::: ::: ::: CONCLUSIONS ::: Our results show that in spite of the fact that all participants knew for sure that neither the stranger nor the shocks were real, the participants who saw and heard her tended to respond to the situation at the subjective, behavioural and physiological levels as if it were real. This result reopens the door to direct empirical studies of obedience and related extreme social situations, an area of research that is otherwise not open to experimental study for ethical reasons, through the employment of virtual environments.


title: Optimally splitting cases for training and testing high dimensional classifiers

abstract: BackgroundWe consider the problem of designing a study to develop a predictive classifier from high dimensional data. A common study design is to split the sample into a training set and an independent test set, where the former is used to develop the classifier and the latter to evaluate its performance. In this paper we address the question of what proportion of the samples should be devoted to the training set. How does this proportion impact the mean squared error (MSE) of the prediction accuracy estimate?ResultsWe develop a non-parametric algorithm for determining an optimal splitting proportion that can be applied with a specific dataset and classifier algorithm. We also perform a broad simulation study for the purpose of better understanding the factors that determine the best split proportions and to evaluate commonly used splitting strategies (1/2 training or 2/3 training) under a wide variety of conditions. These methods are based on a decomposition of the MSE into three intuitive component parts.ConclusionsBy applying these approaches to a number of synthetic and real microarray datasets we show that for linear classifiers the optimal proportion depends on the overall number of samples available and the degree of differential expression between the classes. The optimal proportion was found to depend on the full dataset size (n) and classification accuracy - with higher accuracy and smaller n resulting in more assigned to the training set. The commonly used strategy of allocating 2/3rd of cases for training was close to optimal for reasonable sized datasets (n ≥ 100) with strong signals (i.e. 85% or greater full dataset accuracy). In general, we recommend use of our nonparametric resampling approach for determing the optimal split. This approach can be applied to any dataset, using any predictor development method, to determine the best split.


title: Facial Expression Recognition Using Facial Landmarks and Random Forest Classifier

abstract: Human emotions are the universally common mode of interaction. Automated human facial expression identification has its own advantages. In this paper, the author has proposed and developed a methodology to identify facial emotions using facial landmarks and random forest classifier. Firstly, faces are identified in each image using a histogram of oriented gradients with a linear classifier, image pyramid, and sliding window detection scheme. Then facial landmarks are identified using a model trained with the iBUG 300-W dataset. A feature vector is calculated using a proposed method which uses identified facial landmarks and it is normalized using a proposed method in order to remove facial size variations. The same feature vector is calculated for the neutral pose and vector difference is used to identify emotions using random forest classifier. Famous Extended Cohn-Kanade database has been used to train random forest and to test the accuracy of the system.


title: Propagation Models and Performance Evaluation for 5G Millimeter-Wave Bands

abstract: Fifth-generation (5G) wireless networks are expected to operate at both microwave and millimeter-wave (mmWave) frequency bands, including frequencies in the range of 24 to 86 GHz. Radio propagation models are used to help engineers design, deploy, and compare candidate wireless technologies, and have a profound impact on the decisions of almost every aspect of wireless communications. This paper provides a comprehensive overview of the channel models that will likely be used in the design of 5G radio systems. We start with a discussion on the framework of channel models, which consists of classical models of path loss versus distance, large-scale, and small-scale fading models, and multiple-input multiple-output channel models. Then, key differences between mmWave and microwave channel models are presented, and two popular mmWave channel models are discussed: the 3rd Generation Partnership Project model, which is adopted by the International Telecommunication Union, and the NYUSIM model, which was developed from several years of field measurements in New York City. Examples on how to apply the channel models are then given for several diverse applications demonstrating the wide impact of the models and their parameter values, where the performance comparisons of the channel models are done with promising hybrid beamforming approaches, including leveraging coordinated multipoint transmission. These results show that the answers to channel performance metrics, such as spectrum efficiency, coverage, hardware/signal processing requirements, etc., are extremely sensitive to the choice of channel models.


title: Enabling Technology for Knowledge Sharing

abstract: Building new knowledge-based systems today usually entails constructing new knowledge bases from scratch. It could instead be done by assembling reusable components. System developers would then only need to worry about creating the specialized knowledge and reasoners new to the specific task of their system. This new system would interoperate with existing systems, using them to perform some of its reasoning. In this way, declarative knowledge, problem- solving techniques, and reasoning services could all be shared among systems. This approach would facilitate building bigger and better systems cheaply. The infrastructure to support such sharing and reuse would lead to greater ubiquity of these systems, potentially transforming the knowledge industry. This article presents a vision of the future in which knowledge-based system development and operation is facilitated by infrastructure and technology for knowledge sharing. It describes an initiative currently under way to develop these ideas and suggests steps that must be taken in the future to try to realize this vision.


title: Distance-based Outlier Detection in Data Streams

abstract: Continuous outlier detection in data streams has important applications in fraud detection, network security, and public health. The arrival and departure of data objects in a streaming manner impose new challenges for outlier detection algorithms, especially in time and space efficiency. In the past decade, several studies have been performed to address the problem of distance-based outlier detection in data streams (DODDS), which adopts an unsupervised definition and does not have any distributional assumptions on data values. Our work is motivated by the lack of comparative evaluation among the state-of-the-art algorithms using the same datasets on the same platform. We systematically evaluate the most recent algorithms for DODDS under various stream settings and outlier rates. Our extensive results show that in most settings, the MCOD algorithm offers the superior performance among all the algorithms, including the most recent algorithm Thresh_LEAP.


title: A fuzzy logic based autonomous vehicle control system design in the TORCS environment

abstract: In this study, a fuzzy logic based autonomous vehicle control system is designed and tested in The Open Racing Car Simulator (TORCS) environment. The aim of this study is that vehicle complete the race without to get any damage and to get out of the way. In this context, an intelligent control system composed of fuzzy logic and conventional control structures has been developed such that the racing car is able to compete the race autonomously. In this proposed structure, once the vehicle's gearshifts have been automated, a fuzzy logic based throttle/brake control system has been designed such that the racing car is capable to accelerate/decelerate in a realistic manner as well as to drive at desired velocity. The steering control problem is also handled to end up with a racing car that is capable to travel on the road even in the presence of sharp curves. In this context, we have designed a fuzzy logic based positioning system that uses the knowledge of the curvature ahead to determine an appropriate position. The game performance of the developed fuzzy logic systems can be observed from https://youtu.be/qOvEz3-PzRo.


title: Agile Project Management: A Case Study of a Virtual Research Environment Development Project

abstract: In this paper we use a case study of a project to create a Web 2.0-based, Virtual Research Environment (VRE) for researchers to share digital resources in order to reflect on the principles and practices for embedding eResearch applications within user communities. In particular, we focus on the software development methodologies and project management techniques adopted by the project team in order to ensure that the project remained responsive to changing user requirements without compromising their capacity to keep the project `on track', i.e. meeting the goals declared in the project proposal within budget and on time. Drawing on ethnographic fieldwork, we describe how the project team, whose members are distributed across multiple sites (and often mobile), exploit a repertoire of coordination mechanisms, communication modes and tools, artefacts and structuring devices as they seek to establish the orderly running of the project while following an agile, user-centred development approach.


title: What Is the Chin-down Posture? A Questionnaire Survey of Speech Language Pathologists in Japan and the United States

abstract: The "chin-down" or "chin-tuck" maneuver is a postural technique widely used in dysphagia treatment. The posture, however, does not have a precise anatomical definition. We studied the current practice of 42 speech-language pathologists (SLPs) in Japan and the U.S. with a questionnaire survey regarding the chin-down posture. The main findings were that (1) three of five of the pictures were selected by respondents both in Japan and in the U.S. as depicting the chin-down posture; (2) 23% of Japanese and 58% of the U.S. SLPs made a distinction between chin down and chin tuck; and (3) the use of anatomical terminology by SLPs differed between the two countries. This study showed that there is poor agreement among SLPs about the meaning of the chin-down and chin-tuck postures. Developing a precise definition is important because various postures may have differing physiologic effects.


title: Automatic Mass Balancing of Air-Bearing based Three-Axis Rotational Spacecraft Simulator

abstract: Abstract : Air-bearing-based spacecraft simulators are widely used to develop and verify spacecraft control techniques required by modern spacecraft applications. To create a spacelike environment with ground spacecraft simulators, the effects of gravity should be minimized. For a spherical air-bearing system with 3 rotational degrees of freedom, the center of rotation of the spacecraft simulator should be exactly aligned with the center of gravity. This paper presents the automatic mass balancing method, which compensates for the center of gravity offset from the center of rotation by actuating three proof masses on linear motion stages. Adaptive control of the automatic mass balancing system is used while the balancing masses are actuated in real time. The proposed techniques are implemented on the ground-based three-axis spacecraft simulator for the bifocal relay mirror spacecraft.


title: Programming your network at run-time for big data applications

abstract: Recent advances of software defined networking and optical switching technology make it possible to program the network stack all the way from physical topology to flow level traffic control. In this paper, we leverage the combination of SDN controller with optical switching to explore the tight integration of application and network control. We particularly study the run-time network configuration for big data applications to jointly optimize application performance and network utilization. We use Hadoop as an example to discuss the integrated network control architecture, job scheduling, topology and routing configuration mechanisms for Hadoop jobs. Our analysis suggests that such an integrated control has great potential to improve application performance with relatively small configuration overhead. We believe our study shows early promise of achieving the long-term goal of tight network and application integration using SDN.


title: Classification of Breast Cancer Histology using Deep Learning

abstract: Breast cancer is a major cause of death among women worldwide. Hematoxylin and eosin (H&E) stained breast tissue samples from biopsies are observed under microscopes for primary diagnosis of breast cancer. In this paper, we propose a deep learning-based method for classification of H&E stained breast tissue images released for BACH challenge [1] by fine-tuning Inception-v3 convolutional neural network (CNN) [9]. These images are to be classified into four classes – (i) normal tissue, (ii) benign lesion, (iii) in situ carcinoma and (iv) invasive carcinoma. Our strategy is to extract patches based on nuclear density and rejecting patches that are not rich in nuclei, e.g. from non-epithelial regions. This allowed us to discard uninformative regions of the images as compared to random or grid sampling, because visual signs of tumors are most evident in the epithelium. Every patch with high nuclear density in an image is classified in one of the four above mentioned categories. The class of the entire image is determined using majority voting over the nuclear classes. We obtained an average accuracy of 85% over the four classes and 93% for non-cancer (i.e. normal or benign) vs. malignant (in situ or invasive carcinoma), which significantly improves upon a previous benchmark [2].


title: Cluster analysis in retail segmentation for credit scoring

abstract: The aim of this paper is to segment retail clients by using adaptive Mahalanobis clustering in a way that each segment can be suitable for separate credit scoring development such that a better risk assessment of retail clients could be accomplished. A real data set on retail clients from a Croatian bank was used in the paper. Grouping of the data point set is carried out by using the adaptive Mahalanobis partitioning algorithm (see, e.g., [20]). It is an incremental algorithm, which recognizes ellipsoidal clusters with the main axes in the directions of eigenvectors of the corresponding covariance matrix of the data set. On the basis of the given data set, by using the well-known DIRECT algorithm for global optimization it is possible to search successively for an optimal partition with k=2,3,... clusters. After that, a partition with the most appropriate number of clusters is determined by using various validity indexes. Based on the description of each cluster, banks could decide to develop a separate credit scoring model for each cluster as well as to create a business strategy customized to each cluster.


title: A Case Study in Hybrid Multi-threading and Hierarchical Reinforcement Learning Approach for Cooperative Multi-agent Systems

abstract: This paper describes a case study about a multi-agent system for cooperative tasks, i.e. a mixing color task given three different sources of color. A reinforcement learning approach was performed by the agents, however, this type of learning exploits exponentially when the number of states in the environment is very large. In that sense, the paper proposes to use the MaxQ-Q hierarchical reinforcement learning algorithm to obtain a suitable policy for agents in order to minimize the time process to achieve the goal, and to reduce the state space. In addition, since the multi-agent system runs in a software application, a multi-threading paradigm was proposed to use. Experimental results show that this multi-agent system can reduce the time process and still maintain independence of agents.


title: Design of low cost linearly-polarized microstrip grid array antenna for 24 GHz doppler sensors

abstract: A microstrip grid array antenna for 24 GHz Doppler sensor is proposed for the first time. It is designed on 0.787 mm thick Rogers Duroid 5880 (e r = 2.2 and tan δ = 0.0009) with 0.017 mm copper cladding. Wet etch PCB process is used to fabricate the grid array antenna for cost consideration. Dimensions of the antenna are 40 mm × 40 mm × 0.787 mm. Simulated results show that impedance bandwidth of the antenna is from 22.7 GHz to 25.4 GHz, the highest gain is 17.5 dBi at 24 GHz, and the radiation bandwidth from 22.15 GHz to 25.5 GHz. The simulated beamwidth is 26° and 22° on E- and H-plane respectively. Measured results show that the antenna has achieved impedance bandwidth 20.5 GHz – 25.7 GHz, the highest gain is 18.4 dBi at 24.15 GHz, and radiation bandwidth 22.7 GHz – 24.7 GHz. The measured beamwidth is 20° and 22° on E- and H-plane respectively. The simulated and measured results agree well with each other. It is a capable candidate for 24 GHz Doppler sensor application.


title: Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud

abstract: Using service-oriented decision support systems (DSS in cloud) is one of the major trends for many organizations in hopes of becoming more agile. In this paper, after defining a list of requirements for service-oriented DSS, we propose a conceptual framework for DSS in cloud, and discus about research directions. A unique contribution of this paper is its perspective on how to servitize the product oriented DSS environment, and demonstrate the opportunities and challenges of engineering service oriented DSS in cloud. When we define data, information and analytics as services, we see that traditional measurement mechanisms, which are mainly time and cost driven, do not work well. Organizations need to consider value of service level and quality in addition to the cost and duration of delivered services. DSS in CLOUD enables scale, scope and speed economies. This article contributes new knowledge in service science by tying the information technology strategy perspectives to the database and design science perspectives for a broader audience.


title: Cloud Computing: Distributed Internet Computing for IT and Scientific Research

abstract: Cloud computing is a disruptive technology with profound implications not only for Internet services but also for the IT sector as a whole. Its emergence promises to streamline the on-demand provisioning of software, hardware, and data as a service, achieving economies of scale in IT solutions' deployment and operation. This issue's articles tackle topics including architecture and management of cloud computing infrastructures, SaaS and IaaS applications, discovery of services and data in cloud computing infrastructures, and cross-platform interoperability. Still, several outstanding issues exist, particularly related to SLAs, security and privacy, and power efficiency. Other open issues include ownership, data transfer bottlenecks, performance unpredictability, reliability, and software licensing issues. Finally, hosted applications' business models must show a clear pathway to monetizing cloud computing. Several companies have already built Internet consumer services such as search, social networking, Web email, and online commerce that use cloud computing infrastructure. Above all, cloud computing's still unknown "killer application" will determine many of the challenges and the solutions we must develop to make this technology work in practice.


title: Perspectives for Evaluating Conversational AI

abstract: Conversational AI systems are becoming famous in day to day lives. In this paper, we are trying to address the following key question: To identify whether design, as well as development efforts for search oriented conversational AI are successful or not.It is tricky to define 'success' in the case of conversational AI and equally tricky part is to use appropriate metrics for the evaluation of conversational AI. We propose four different perspectives namely user experience, information retrieval, linguistic and artificial intelligence for the evaluation of conversational AI systems. Additionally, background details of conversational AI systems are provided including desirable characteristics of personal assistants, differences between chatbot and an AI based personal assistant. An importance of personalization and how it can be achieved is explained in detail. Current challenges in the development of an ideal conversational AI (personal assistant) are also highlighted along with guidelines for achieving personalized experience for users.


title: Planar graphs, negative weight edges, shortest paths, and near linear time

abstract: In this paper, we present an O(n log3 n) time algorithm for finding shortest paths in an n-node planar graph with real weights. This can be compared to the best previous strongly polynomial time algorithm developed by Lipton, Rose, and Tarjan in 1978 which runs in O(n3/2) time, and the best polynomial time algorithm developed by Henzinger, Klein, Subramanian, and Rao in 1994 which runs in O(n 4/3) time. We also present significantly improved data structures for reporting distances between pairs of nodes and algorithms for updating the data structures when edge weights change.


title: Development of an Axial Gap Motor With Amorphous Metal Cores

abstract: This paper presents a novel motor design concept that utilizes amorphous cut cores in the stator and ferrite magnets in the rotor to obtain a high-efficiency motor. This motor employs a six-pole nine-slot axial gap construction and aims to provide 90% efficiency with decreased size and low cogging torque for fan motor applications. Amorphous cut cores are applied to the stator to decrease iron loss and save motor space. Skewed magnets are employed to decrease cogging torque. Three-dimensional finite element analysis was used to analyze motor performance. In order to verify the accuracy of the design, a trial motor was constructed, and a large range of experiments was conducted to measure the motor characteristics. The results of the trial motor meet the design goals.


title: How effective is tailored print communication

abstract: This article reviews the “frist generation” of tailored print communications studies in the published literature, describing the purpose, theoretical framework, sample, research design, message type and source, outcomes measured, and findings of each. Eight studies compared tailored versus similar nontailored print; one compared tailored print versus an alternate intervention, and three included tailored print as one of several intervention components. Although studies varied by behavioral topic, type of tailoring, and measurement of behavioral outcomes, several themes persist.


title: Orodispersible drug formulations for children and elderly.

abstract: Various orodispersible drug formulations have been recently introduced into the market. Oral lyophilisates and orodispersible granules, tablets or films have enriched the therapeutic options. In particular, the paediatric and geriatric population may profit from the advantages like convenient administration, lack of swallowing, ease of use. Until now, only a few novel products made it to the market as the development and production usually is more expensive than for conventional oral drug dosage forms like tablets or capsules. The review reports the recent advances, existing and upcoming products, and the significance of formulating patient-friendly oral dosage forms. The preparation of the medicines can be performed both in pharmaceutical industry and in community pharmacies. Recent advances, e.g. drug printing technologies, may facilitate this process for community or hospital pharmacies. Still, regulatory guidelines and pharmacopoeial monographs lack appropriate methods, specifications and global harmonization to foster the development of innovative orodispersible drug dosage forms.


title: News Across Languages - Cross-Lingual Document Similarity and Event Tracking

abstract: In today's world, we follow news which is distributed globally. Significant events are reported by different sources and in different languages. In this work, we address the problem of tracking of events in a large multilingual stream. Within a recently developed system Event Registry we examine two aspects of this problem: how to compare articles in different languages and how to link collections of articles in different languages which refer to the same event. Taking a multilingual stream and clusters of articles from each language, we compare different cross-lingual document similarity measures based on Wikipedia. This allows us to compute the similarity of any two articles regardless of language. Building on previous work, we show there are methods which scale well and can compute a meaningful similarity between articles from languages with little or no direct overlap in the training data. Using this capability, we then propose an approach to link clusters of articles across languages which represent the same event. We provide an extensive evaluation of the system as a whole, as well as an evaluation of the quality and robustness of the similarity measure and the linking algorithm.


title: Framewise phoneme classification with bidirectional LSTM and other neural network architectures

abstract: In this paper, we present bidirectional Long Short Term Memory (LSTM) networks, and a modified, full gradient version of the LSTM learning algorithm. We evaluate Bidirectional LSTM (BLSTM) and several other network architectures on the benchmark task of framewise phoneme classification, using the TIMIT database. Our main findings are that bidirectional networks outperform unidirectional ones, and Long Short Term Memory (LSTM) is much faster and also more accurate than both standard Recurrent Neural Nets (RNNs) and time-windowed Multilayer Perceptrons (MLPs). Our results support the view that contextual information is crucial to speech processing, and suggest that BLSTM is an effective architecture with which to exploit it'.


title: Improved adaptive sparse channel estimation based on the least mean square algorithm

abstract: Least mean square (LMS) based adaptive algorithms have been attracted much attention since their low computational complexity and robust recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods, e.g., zero-attracting LMS (ZA-LMS), reweighted zero-attracting LMS (RZA-LMS) and L p - norm sparse LMS (LP-LMS), have also been proposed. To take full advantage of channel sparsity, in this paper, we propose several improved adaptive sparse channel estimation methods using L p -norm normalized LMS (LP-NLMS) and L 0 -norm normalized LMS (L0-NLMS). Comparing with previous methods, effectiveness of the proposed methods is confirmed by computer simulations.


Big Data in the physical sciences: challenges and opportunities


title: FPGA-accelerated deep convolutional neural networks for high throughput and energy efficiency‡

abstract: Summary ::: Recent breakthroughs in the deep convolutional neural networks (CNNs) have led to great improvements in the accuracy of both vision and auditory systems. Characterized by their deep structures and large numbers of parameters, deep CNNs challenge the computational performance of today. Hardware specialization in the form of field-programmable gate array offers a promising path towards major leaps in computational performance while achieving high-energy efficiency. ::: ::: In this paper, we focus on accelerating deep CNNs using the Xilinx Zynq-zq7045 FPGA SoC. As most of the computational workload can be converted to matrix multiplications, we adopt a matrix multiplier-based accelerator architecture. Dedicated units are designed to eliminate the conversion overhead. We also design a customized memory system according to the memory access pattern of CNNs. To make the accelerator easily usable by application developers, our accelerator supports Caffe, which is a widely used software framework of deep CNN. Different CNN models can be adopted by our accelerator, with good performance portability. The experimental results show that for a typical application of CNN, image classification, an average throughout of 77.8 GFLOPS is achieved, while the energy efficiency is 4.7× better than an Nvidia K20 GPGPU. © 2016 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons Ltd


title: Content of children's loss of control eating episodes assessed by self-report and laboratory test meal.

abstract: Pediatric loss of control (LOC) eating heightens risk for excessive weight gain and further disordered eating. Assessment of LOC typically involves self-report interview or laboratory test meal, although no study has concurrently examined data from both methods. We gathered eating episode data via interview (Child Eating Disorder Examination; ChEDE) and a laboratory test meal, among 22 overweight girls (aged 7–12 years) reporting LOC eating. Children consumed more energy during ChEDE episodes, although ChEDE and test meal episodes did not differ in macronutrient content. Episodes' correlation for amount consumed (grams) did not reach significance, p = .076. In exploratory analyses among the seven children reporting LOC during the test meal, episodes were significantly correlated for grams consumed. Findings provide preliminary data to suggest that semi-structured interviews accurately capture children's LOC episode quantity. Episodes did not qualitatively differ, although children reported consuming more energy during self-reported episodes. Replication is warranted in larger studies. Copyright © 2013 John Wiley & Sons, Ltd and Eating Disorders Association.


title: Choice is good, but relevance is excellent: Autonomy-enhancing and suppressing teacher behaviours predicting students' engagement in schoolwork

abstract: Background and aims: This article examines two questions concerning teacher-behaviours that are characterised in Self-Determination Theory (Ryan & Deci, 2000) as autonomy-supportive or suppressive: (1) Can children differentiate among various types of autonomy-enhancing and suppressing teacher behaviours? (2) Which of those types of behaviour are particularly important in predicting feelings toward and engagement in schoolwork? It was hypothesised that teacher behaviours that help students to understand the relevance of schoolwork for their personal interests and goals are particularly important predictors of engagement in schoolwork. ::: ::: Samples and methods: Israeli students in grades 3-5 (N = 498) and in grades 6-8 (N = 364) completed questionnaires assessing the variables of interest. ::: ::: Results: Smallest Space Analyses indicated that both children and early adolescents can differentiate among three types of autonomy enhancing teacher behaviours — fostering relevance, allowing criticism, and providing choice — and three types of autonomy suppressing teacher behaviours — suppressing criticism, intruding, and forcing unmeaningful acts. Regression analyses supported the hypothesis concerning the importance of teacher behaviours that clarify the personal relevance of schoolwork. Among the autonomy-suppressing behaviours, ‘Criticism-suppression’ was the best predictor of feelings and engagement. ::: ::: Conclusions: The findings underscore the active and empathic nature of teachers' role in supporting students' autonomy, and suggest that autonomy-support is important not only for early adolescents but also for children. Discussion of potential determinants of the relative importance of various autonomy-affecting teacher actions suggests that provision of choice should not always be viewed as a major indicator of autonomy support.


title: Margin based feature selection - theory and algorithms

abstract: Feature selection is the task of choosing a small set out of a given set of features that capture the relevant properties of the data. In the context of supervised classification problems the relevance is determined by the given labels on the training data. A good choice of features is a key for building compact and accurate classifiers. In this paper we introduce a margin based feature selection criterion and apply it to measure the quality of sets of features. Using margins we devise novel selection algorithms for multi-class classification problems and provide theoretical generalization bound. We also study the well known Relief algorithm and show that it resembles a gradient ascent over our margin criterion. We apply our new algorithm to various datasets and show that our new Simba algorithm, which directly optimizes the margin, outperforms Relief.


title: Nimrod/G: An Architecture of a Resource Management and Scheduling System in a Global Computational Grid

abstract: The availability of powerful microprocessors and high-speed networks as commodity components has enabled high-performance computing on distributed systems (wide-area cluster computing). In this environment, as the resources are usually distributed geographically at various levels (department, enterprise or worldwide), there is a great challenge in integrating, coordinating and presenting them as a single resource to the user, thus forming a computational grid. Another challenge comes from the distributed ownership of resources, with each resource having its own access policy, cost and mechanism. The proposed Nimrod/G grid-enabled resource management and scheduling system builds on our earlier work on Nimrod (D. Abramson et al., 1994, 1995, 1997, 2000) and follows a modular and component-based architecture enabling extensibility, portability, ease of development, and interoperability of independently developed components. It uses the GUSTO (GlobUS TOolkit) services and can be easily extended to operate with any other emerging grid middleware services. It focuses on the management and scheduling of computations over dynamic resources scattered geographically across the Internet at department, enterprise or global levels, with particular emphasis on developing scheduling schemes based on the concept of computational economy for a real testbed, namely the Globus testbed (GUSTO).


A Web Service Discovery Scheme Based on Structural and Semantic Similarity.


title: Contrastive Analysis of P2SP Network and P2P Network

abstract: The performance of P2SP (peer-to-servers& peer) network and Peer to Peer (P2P) network is studied and compared in this paper. Two important key parameters are used to judge the performance: the time of file downloading and the speed of chunk propagation. The difference of the first parameter between P2SP and P2P has been studied first. P2SP has higher performance than P2P in two different assumptions has also been proved. A systematical model is built to study the second parameter. The result also shows that P2SP system has better performance than pure p2p system.


title: I Send, Therefore I Leak: Information Leakage in Low-Power Wide Area Networks

abstract: Low-power wide area networks (LPWANs), such as LoRa, are fast emerging as the preferred networking technology for large-scale Internet of Things deployments (e.g., smart cities). Due to long communication range and ultra low power consumption, LPWAN-enabled sensors are today being deployed in a variety of application scenarios where sensitive information is wirelessly transmitted. In this work, we study the privacy guarantees of LPWANs, in particular LoRa. We show that, although the event-based duty cycling of radio communication, i.e., transmission of radio signals only when an event occurs, saves power, it inherently leaks information. This information leakage is independent of the implemented crypto primitives. We identify two types of information leakage and show that it is hard to completely prevent leakage without incurring significant additional communication and computation costs.


title: Random Positional Deviations Correction for Each LED via ePIE in Fourier Ptychographic Microscopy

abstract: Fourier ptychography microscopy (FPM) is a lately developed technique, which achieves wide field, high resolution, and phase imaging, simultaneously. FPM stitches together the captured low-resolution images corresponding to angular varying illuminations in Fourier domain utilizing the concept of synthetic aperture and phase retrieval algorithms, which can surpass the space-bandwidth product limit of the objective lens and reconstruct a high-resolution complex image. In general FPM system, the LED source is important for the reconstructed quality and it is sensitive to the positions of each LED element. We find that the random positional deviations of each LED element can bring errors in reconstructed results, which is relative to a feedback parameter. To improve the reconstruction rate and correct random deviations, we combine an initial phase guess and a feedback parameter based on differential phase contrast and extended ptychographical iterative engine to propose an optimized iteration process for FPM. The simulated and experimental results indicate that the proposed method shows the reliability and validity towards the random deviations yet accelerates the convergence. More importantly, it is verified that this method can accelerate the convergence, reduce the requirement of LED array accuracy, and improve the quality of the reconstructed results.


title: Separating agreement from execution for byzantine fault tolerant services

abstract: We describe a new architecture for Byzantine fault tolerant state machine replication that separates agreement that orders requests from execution that processes requests. This separation yields two fundamental and practically significant advantages over previous architectures. First, it reduces replication costs because the new architecture can tolerate faults in up to half of the state machine replicas that execute requests. Previous systems can tolerate faults in at most a third of the combined agreement/state machine replicas. Second, separating agreement from execution allows a general privacy firewall architecture to protect confidentiality through replication. In contrast, replication in previous systems hurts confidentiality because exploiting the weakest replica can be sufficient to compromise the system. We have constructed a prototype and evaluated it running both microbenchmarks and an NFS server. Overall, we find that the architecture adds modest latencies to unreplicated systems and that its performance is competitive with existing Byzantine fault tolerant systems.


title: Symbolic Execution for Checking the Accuracy of Floating-Point Programs

abstract: Programs with oating-point calculations tend to give rise to hard-to-predict behavior. Such uncertainty cannot be ignored: oating-point errors can have catastrophic consequences, as it happened with the Patriot missile accident in 1991. The likelihood of such incidents can be decreased by using automated technology to reliably analyze numerical code. We present a symbolic execution approach to checking the accuracy of numerical programs, investigating how much a oating-point computation deviates from the "ideal" computation on real values. Our method is implemented in the Symbolic PathFinder tool and leverages and extends the floating-point decision procedure Realizer to check symbolic path constraints and to perform the accuracy checks. We further illustrate the possibility of using our tools to enhance abstract interpretation-based analyses to obtain tighter bounds on the numerical error introduced by oating-point computations. Initial experiments show the promise of our approach.


title: Caring for a Child with Autism Spectrum Disorder and Parents’ Quality of Life: Application of the CarerQol

abstract: This study describes the impact of caregiving on parents of children with autism spectrum disorders (ASDs). Secondly, we investigate construct validation of the care-related quality of life instrument (CarerQol) measuring impact of caregiving. Primary caregivers of children with ASDs were included. Many parents experienced considerable problems combining daily activities with care, had financial problems or suffered from depressive mood. Validity tests showed that a higher impact of caring on the CarerQol was positively associated with higher subjective burden and lower family quality of life. Most of the associations between CarerQol scores and background characteristics confirmed previous research. The CarerQol validly measures the impact of caregiving for children with ASDs on caregivers in our sample. The CarerQol may therefore be useful for including parent outcomes in research on ASDs.


title: Literature review on wearable systems in upper extremity rehabilitation

abstract: This paper reports a structured literature survey of research in wearable technology for upper-extremity rehabilitation, e.g., after stroke, spinal cord injury, for multiple sclerosis patients or even children with cerebral palsy. A keyword based search returned 61 papers relating to this topic. Examination of the abstracts of these papers identified 19 articles describing distinct wearable systems aimed at upper extremity rehabilitation. These are classified in three categories depending on their functionality: movement and posture monitoring; monitoring and feedback systems that support rehabilitation exercises, serious games for rehabilitation training. We characterize the state of the art considering respectively the reported performance of these technologies, availability of clinical evidence, or known clinical applications.


title: The Onboarding Effect: Leveraging User Engagement and Retention in Crowdsourcing Platforms

abstract: Onboarding is essential for any online service or platform interested in acquiring more users, members or customers. The beginning of a relationship between the user and a platform is the moment where motivations take part and the overall strategy? the flow, messages, interactions and UI design elements -- should be carefully planned, so the experience as a whole can support new users' engagement. Such an important strategy still lacks systematic investigation. Most of what is employed by the industry result from empirical testing that turns out to serve a particular product in a certain instance. This research aims to provide scientific evidence regarding the effect of each onboarding element on user engagement and experience, in addition to solid guidelines that support decision making for future designs.


title: A Microactuation and Sensing Platform With Active Lockdown for In Situ Calibration of Scale Factor Drifts in Dual-Axis Gyroscopes

abstract: This paper presents the design and experimental results of a microvibratory actuation and sensing platform to provide on-chip physical stimulus for in situ calibration of long-term scale factor drifts in multiaxis microelectromechanical systems (MEMS) inertial sensors. The platform consists of a three degrees-of-freedom micromotion stage that can provide piezoelectric actuation for X / Y -tilting reference stimuli, compensation of undesired off-axis motion, integrated sensing of applied periodic stimulus, and electrostatic position lock-down for shock protection. A dual-axis MEMS gyroscope is mounted on top of the microplatform, and its electrical interconnects are provided through microfabricated highly flexible parylene cables with virtually zero-loading. The piezoelectric stage is measured to provide up to 280°/s angular ac excitation to a 25-mg inertial sensor payload at an expense of <100 μW, while providing an analog sensing signal (11 mV/°/s) to determine the applied rate with a precision of 1.2 °/s. The estimated scale factor has < 0.8% deviation from rate-table characterized values on the same-model gyroscope samples. With further improvements in control precision and angular velocity estimation, the introduced platform is expected enable on-chip self-calibration of long-term scale-factor drifts to < 100 ppm.


title: Thermal and catalytic degradation of polyethylene wastes in the presence of silica gel, 5A molecular sieve and activated carbon.

abstract: A comparative study of thermal and catalytic degradation of polyethylene wastes has been carried out with the aim of obtaining chemical compounds with potential use in the chemical industry and the energy production. Polyethylene wastes were obtained from polyethylene bags used in supermarkets. Catalysts utilized in the study were silica gel, 5A molecular sieve and activated carbon. The pyrolysis was performed in a batch reactor at 450, 500 and 700 °C during 2h for each catalyst. The ratio catalyst/PE was 10% w/w and the solid and gaseous products were analyzed by gas chromatography and mass spectrometry. The optimum operation temperature and the influence of the three catalysts are discussed with regards to the products formed. The best temperature for degradation with silica gel and activated carbon as catalysts was 450 °C and with 5A molecular sieve was 700 °C. Degradation products of PE (solid fraction and gas fraction) are depending on temperature and catalyst used. External surface and structure of catalysts were visualized by Scanning Electron Microscopy (SEM) and the contribution on product distribution is commented. All products from different degradations could be used as feed stocks in chemical industry or in energy production based on the value of heat of combustion for solid fraction (45000 J/g), similar to the heat of combustion of commercial fuels.


title: Theoretical analysis of non-invasive oscillometric maximum amplitude algorithm for estimating mean blood pressure

abstract: A theoretical analysis is performed to evaluate the effect of arterial mechanical and blood pressure pulse properties on the accuracy of non-invasive oscillometric maximum amplitude algorithm (MAA) estimates of the mean blood pressure obtained using air-filled occlusive cuffs. Invasively recorded blood pressure pulses, selected for their varied shapes, are scaled to simulate a wide range of blood pulse pressures (diastolic blood pressure minus systolic blood pressure). Each scaled blood pressure pulse is transformed through an exponential model of an artery to create a series of blood volume pulses from which a simulated oscillometric waveform is created and the corresponding MAA estimate of the mean blood pressure and error (mean blood pressure minus MAA estimate) are determined. The MAA estimates are found to depend on the arterial blood pressure. The errors are found to depend on the arterial mechanical properties, blood pressure pulse shape and blood pulse pressure. These results suggest that there is no direct relationship between the mean blood pressure and MAA estimate, and that multiple variables may affect the accuracy of MAA estimates of the mean blood pressure obtained using air-filled occlusive cuffs.


title: Cutting corners and working overtime: Quality erosion in the service industry

abstract: The erosion of service quality throughout the economy is a frequent concern in the popular press. The American Customer Satisfaction Index for services fell in 2000 to 69.4%, down 5 percentage points from 1994. We hypothesize that the characteristics of services--inseparability, intangibility, and labor intensity--interact with management practices to bias service providers toward reducing the level of service they deliver, often locking entire industries into a vicious cycle of eroding service standards. To explore this proposition we develop a formal model that integrates the structural elements of service delivery. We use econometric estimation, interviews, observations, and archival data to calibrate the model for a consumer-lending service center in a major bank in the United Kingdom. We find that temporary imbalances between service capacity and demand interact with decision rules for effort allocation, capacity management, overtime, and quality aspirations to yield permanent erosion of the service standards and loss of revenue. We explore policies to improve performance and implications for organizational design in the service sector.


title: Security analysis of the constrained application protocol in the Internet of Things

abstract: The concept of Internet of Things involves huge number of constrained devices such as wireless sensors to communicate in a machine-to-machine pattern. Based on the implementation scenario, such communication might take place over a public network such as the Internet, which is based on the TCP/IP stack. However, different research working groups argue that some of these stack protocols such as the Hyper Text Transfer Protocol (HTTP) might not be suitable for constrained devices. Therefore, the IETF Constrained RESTful Environments (CoRE) WG has proposed the Constrained Application Protocol (CoAP); an application layer protocol for constrained devices in the IoTs. The CoRE WG proposed using IPSec or DTLS to secure the CoAP communication at different levels of the protocol stack. However, to investigate the feasibility of such a proposal, we use the X.805 security standard to analyze the security aspects of such implementation. The analysis highlights the main security drawbacks and hence argues of the need for a new integrated security solution.


title: Selecting Valuable Stock Using Genetic Algorithm

abstract: In this study, we utilize the genetic algorithm (GA) to select high quality stocks with investment value. Given the fundamental financial and price information of stocks trading, we attempt to use GA to identify stocks that are likely to outperform the market by having excess returns. To evaluate the efficiency of the GA for stock selection, the return of equally weighted portfolio formed by the stocks selected by GA is used as evaluation criterion. Experiment results reveal that the proposed GA for stock selection provides a very flexible and useful tool to assist the investors in selecting valuable stocks.


title: Riemannian Gaussian Distributions on the Space of Symmetric Positive Definite Matrices

abstract: Data which lie in the space $\mathcal{P}_{m\,}$, of $m \times m$ symmetric positive definite matrices, (sometimes called tensor data), play a fundamental role in applications including medical imaging, computer vision, and radar signal processing. An open challenge, for these applications, is to find a class of probability distributions, which is able to capture the statistical properties of data in $\mathcal{P}_{m\,}$, as they arise in real-world situations. The present paper meets this challenge by introducing Riemannian Gaussian distributions on $\mathcal{P}_{m\,}$. Distributions of this kind were first considered by Pennec in $2006$. However, the present paper gives an exact expression of their probability density function for the first time in existing literature. This leads to two original contributions. First, a detailed study of statistical inference for Riemannian Gaussian distributions, uncovering the connection between maximum likelihood estimation and the concept of Riemannian centre of mass, widely used in applications. Second, the derivation and implementation of an expectation-maximisation algorithm, for the estimation of mixtures of Riemannian Gaussian distributions. The paper applies this new algorithm, to the classification of data in $\mathcal{P}_{m\,}$, (concretely, to the problem of texture classification, in computer vision), showing that it yields significantly better performance, in comparison to recent approaches.


title: A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

abstract: Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e. deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs). We start with "AlexNet'' as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.


SMEs ’ adoption of enterprise applications A technology-organisation-environment model


DNA damage and repair in the initiation phase of carcinogenesis


title: Vision-Guided Aerial Manipulation Using a Multirotor With a Robotic Arm

abstract: This paper presents a vision guidance approach using an image-based visual servo (IBVS) for an aerial manipulator combining a multirotor with a multidegree of freedom robotic arm. To take into account the dynamic characteristics of the combined manipulation platform, the kinematic and dynamic models of the combined system are derived. Based on the combined model, a passivity-based adaptive controller which can be applied on both position and velocity control is designed. The position control is utilized for waypoint tracking such as taking off and landing, and the velocity control is engaged when the platform is guided by visual information. In addition, a guidance law utilizing IBVS is employed with modifications. To secure the view of an object with an eye-in-hand camera, IBVS is utilized with images taken from a fisheye camera. Also, to compensate underactuation of the multirotor, an image adjustment method is developed. With the proposed control and guidance laws, autonomous flight experiments involving grabbing and transporting an object are carried out. Successful experimental results demonstrate that the proposed approaches can be applied in various types of manipulation missions.


Robust Hand Motion Tracking through Data Fusion of 5DT Data Glove and Nimble VR Kinect Camera Measurements


title: Usefulness of the Powerplex ® Fusion System as typing methodology for CODIS and ESS genetic markers in an immigrant population from Cabo Verde in Lisboa

abstract: Abstract Cabo Verde is located on the West African coast, was discovered uninhabited by Portuguese explorers, colonized by European and African slaves and is culturally characterized by a mixture of their settlers. Cabo Verde is one of the countries with the highest number of emigrants living in Portugal. In order to validate the genetic markers included in the Combined DNA Index System and in the European Standard Set, samples from 500 unrelated individuals from Cabo Verde, currently living in Lisboa, were studied with the PowerPlex ® Fusion System. To implement the genotyping method at the Servico de Genetica e Biologia Forenses (SGBF) we determined the minimum final volume of the PCR reaction, the proper number of PCR cycles and the minimum DNA concentration to obtain a complete genetic profile. Stutter identification and its proportion and allelic imbalance within markers were also determined. Results were compared with genetic profiles obtained by the current PCR kits used in SGBF routine testing verifying concordance between common loci. There were no off-ladder alleles detected and it was possible to type microvariants which were not present in the allelic ladders of other PCR kits. PowerPlex ® Fusion System allows for the simultaneous characterization of Combined DNA Index System and in the European Standard Set genetic markers, in an economically way, with greater sensitivity and solid results.


Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit


title: Religiosity, Religious Schools, and Their Relationship with the Achievement Gap: A Research Synthesis and Meta-Analysis.

abstract: A research synthesis was conducted including three meta-analyses, a review of the relevant literature, and supplemental analyses examining the relationship between personal faith and the reduction of the achievement gap. Personal faith included belief and adherence to any religion. The results of the three meta-analyses indicated that: (a) personal religious faith was one the two largest factors that consistently reduced the achievement gap; (b) personal religious commitment reduces the achievement gap by 50%; and (c) attending a religious school reduced the achievement gap by 25%. Supplemental analyses using a nationwide dataset added further significance to the findings. These analyses indicated that if an African American student was a person of faith and came from an intact family, the achievement gap disappeared entirely. Keywords: Achievement gap, achievement, African American, meta-analysis, religiosity, religious schools, faith Over the last four decades, one of the most persistent debates in education has been on how to close the achievement gap between White students on the one hand and African American students on the other (Green, 2001; Simpson, 1981). This achievement gap exists in virtually every measure of educational progress, including standardized tests, GPA, the dropout rate, and the extent to which students are left back a grade (Green, Blasik, Hartshorn, & Shatter-Jones, 2000; Jeynes, 2008b, 2009; Olneck, 2005). Olneck (2005) noted that by the "eighth grade" the achievement gap is usually "about two years" (p. 95. The United States was founded on the principle of equaUty. As a result, Americans tend to feel uncomfortable when unequal results emerge, and American educators have frequently tried to reduce those inequalities (Evans, 2005; Haycock, 2001). The intractable nature of the difference in academic outcomes that exists between students of certain races of color and White students, as well as those of low versus high socioeconomic status (SES), has been of considerable concern to educators and the American public (Rippeyoung, 2009; Roscigno, 1998). Ronald Roach (2001) asserted that "in the academic and think tank world, pondering achievement gap remedies takes center stage" (p. 377). Moreover, Kozol (1991) and other researchers assert that low-SES youth and children of color are the most crucial for the U. S. educational system to reach (Michael, Farquhar, Wiggins, & Green, 2008). Given the persistence of this gap, the government has launched a plethora of initiatives designed to eradicate it (Evans, 2005; Green et al., 2000; Jeynes, 2008a). These initiatives include Head Start, the school lunch program, President Clinton's national standards program, a host of affirmative action programs, No Child Left Behind, and various other programs. Moreover, academics, research mstitutes, foundations, and other organizations have initiated copious private programs (Cohen, Garcia, Apfel, & Master, 2006; Lee, 2009; Ross, Smith, & Casey, 1999; Slavin & Madden, 2001). These efforts have focused on multicultural teaching, attempting to raise students' self-esteem; parental involvement; requiring school uniforms; encouraging community partnerships, and so forth (Jeynes, 2008a; McFadden, 2009; Slavin & Madden, 2006). Of all the inequalities that exist in the American education system, researchers have probably tried to address racial inequality more than any other (Haycock, 2001; Jeynes, 1999, 2010a, 2010b; Orfield et al., 2000). And while there was a period during the 1980s when the achievement gap did show some reduction, (Green, 2001; Jeynes, 2007), it remains stubbornly wide even to this day (Dunham & Wilson, 2007; Haycock, 2001; Jeynes, 1999, 2003, 2010a) Although researchers and educators acknowledge that an achievement gap exists, social scientists differ widely in their suggestions about how to bridge the gap. GENERAL APPROACHES TO REDUCING THE ACHIEVEMENT GAP Social scientists, politicians, educators, and community leaders have undertaken a number of approaches to addressing the achievement gap. …


title: Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines

abstract: Here, large-scale genome-wide association studies were carried out with the naturally occurring inbred lines of Arabidopsis thaliana, which can be genotyped once and phenotyped repeatedly. The results range from significant associations, usually corresponding to single genes, to findings that are more difficult to interpret, because confounding by complex genetics and population structure makes it hard to distinguish true associations from false.


title: Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps

abstract: Recent developments in the field of robot grasping have shown great improvements in the grasp success rates when dealing with unknown objects. In this work we improve on one of the most promising approaches, the Grasp Quality Convolutional Neural Network (GQ-CNN) trained on the DexNet 2.0 dataset. We propose a new architecture for the GQ-CNN and describe practical improvements that increase the model validation accuracy from 92.2% to 95.8% and from 85.9% to 88.0% on respectively image-wise and object-wise training and validation splits.


title: High-performance robotic muscles from conductive nylon sewing thread

abstract: Natural muscles exhibit high power-to-weight ratios, inherent compliance and damping, fast actuation and high dynamic ranges. Unfortunately, traditional robotic actuators have been unable to attain similar properties, especially in a slender muscle-like form factor. Recently, super-coiled polymer (SCP) actuators have rejuvenated the promise of an artificial muscle. Constructed from commercial nylon fishing line or sewing thread and twisted until coils form, these lightweight actuators have been shown to produce significant mechanical power when thermally cycled. In this paper, we develop a thermomechanical and thermoelectric model of SCP actuators, and examine their controllability. With off-the-shelf conductive sewing thread, we show the ability to produce controlled forces in under 30 ms, exceeding human muscle performance. Finally, we use SCP actuators in a robotic hand to demonstrate their applicability as a low-cost, high performance robotic muscle.


title: General Autonomic Components of Motion Sickness

abstract: This report refers to a body of investigations directed toward the examination of autonomic nervous system responses to motion sickness. Heart rate, respiration rate, finger pulse volume, and basal skin resistance were measured on 127 men and women before, during, and after exposure to a nauseogenic rotating chair test. Significant changes in all autonomic responses were observed across the tests (p<.05). Significant differences in autonomic responses among groups divided according to motion sickness susceptibility were also observed (p<.05). Results suggest that the examination of autonomic responses as an objective indicator of motion sickness malaise is warranted and may contribute to the overall understanding of the syndrome.


title: Optimization of beam parameters for dual-energy digital subtraction angiography.

abstract: Dual‐energy digital subtraction angiography(DSA) is immune to the misregistration artifacts which plague conventional temporal subtraction DSA. However, since the signal‐to‐noise ratio (SNR) of dual‐energy DSA is lower than temporal subtraction it is important to optimize the dual‐energy technique with respect to the SNR. This study investigates the optimization of x‐ray exposure parameters for the low‐ and high‐energy x‐ray beams, drawing upon our experience with dual‐energy DSA studies in 20 cardiac patients. It is shown that the SNR is optimized, with respect to patient exposure, when the image intensifier (II) exposure for the high‐energy beam is approximately 5 times higher than the low‐energy beam. The unequal distribution of exposure between the low‐ and high‐energy beams is achieved by selecting different video apertures for the two beams, and is therefore referred to as the ‘‘dual‐aperture’’ technique. The dual‐aperture technique achieves the same SNR as the single‐aperture technique (which uses equal II exposures for the two beams) with 45% less patient exposure. In addition, the dual‐aperture technique enhances the ability to maintain an optimum low‐energy kVp, which increases the dual‐energy signal amplitude. The optimum high‐beam filter thickness for use with the dual‐aperture approach is determined. The optimum filter thickness, which is dependent upon the desired tube loading, is found to range from 1.3 to 2.0 mm Cu for tube loads ranging from 188 to 688 J/beam pair.


Development of a Larvicidal Nanoemulsion with Pterodon emarginatus Vogel Oil


Gas exchange and leaf anatomy of a C3–CAM hybrid, Yucca gloriosa (Asparagaceae)


title: is sponsored by USENIXEffective Inter-Component Communication Mapping in Android with Epicc: An Essential Step Towards Holistic Security Analysis

abstract: Many threats present in smartphones are the result of interactions between application components, not just artifacts of single components. However, current techniques for identifying inter-application communication are ad hoc and do not scale to large numbers of applications. In this paper, we reduce the discovery of inter-component communication (ICC) in smartphones to an instance of the Interprocedural Distributive Environment (IDE) problem, and develop a sound static analysis technique targeted to the Android platform. We apply this analysis to 1,200 applications selected from the Play store and characterize the locations and substance of their ICC. Experiments show that full specifications for ICC can be identified for over 93% of ICC locations for the applications studied. Further the analysis scales well; analysis of each application took on average 113 seconds to complete. Epicc, the resulting tool, finds ICC vulnerabilities with far fewer false positives than the next best tool. In this way, we develop a scalable vehicle to extend current security analysis to entire collections of applications as well as the interfaces they export.


title: Characteristics of workloads used in high performance and technical computing

abstract: This paper provides a systematic comparison of various characteristics of computationally-intensive workloads. Our analysis focuses on standard HPC benchmarks and representative applications. For the selected workloads we provide a wide range of characterizations based on instruction tracing and hardware counter measurements. Each workload is analyzed at the instruction level by comparing the dynamic distribution of executed instructions. We also analyze memory access patterns including various aspects of cache utilization and locality properties of address distributions. Since prefetching plays an important role in the performance of computational workloads, we explore the prefetching potential and for parallel workloads we study the sharing properties of memory accesses. For the purpose of completeness, HPC workloads are compared to two commonly used commercial computing benchmarks. The results of this work show that the HPC application space is surprisingly diverse, with some codes showing similar data sharing and locality properties with commercial applications. The wide range of studies presented in this paper are instrumental in uncovering the diversity of this application space.


title: Sorting improves word-aligned bitmap indexes

abstract: Bitmap indexes must be compressed to reduce input/output costs and minimize CPU usage. To accelerate logical operations (AND, OR, XOR) over bitmaps, we use techniques based on run-length encoding (RLE), such as Word-Aligned Hybrid (WAH) compression. These techniques are sensitive to the order of the rows: a simple lexicographical sort can divide the index size by 9 and make indexes several times faster. We investigate row-reordering heuristics. Simply permuting the columns of the table can increase the sorting efficiency by 40%. Secondary contributions include efficient algorithms to construct and aggregate bitmaps. The effect of word length is also reviewed by constructing 16-bit, 32-bit and 64-bit indexes. Using 64-bit CPUs, we find that 64-bit indexes are slightly faster than 32-bit indexes despite being nearly twice as large.


title: Design issues for monolithic DC-DC converters

abstract: This paper presents various ideas for integrating different components of dc-dc converter on to a silicon chip. These converters are intended to process power levels up to 0.5W. Techniques for integrating capacitors and design issues for MOS transistors are discussed. The most complicated design issue involves inductors. Expressions for trace resistance and inductance estimation of on-chip planar spiral inductor on top metal layer of CMOS process are compared. These inductors have high series resistance due to low metal trace thickness, capacitive coupling with substrate and other metal traces, and eddy current loss. As an alternative, a CMOS compatible three-dimensional (3-D) surface micromachining technology known as plastic deformation magnetic assembly (PDMA) is used to fabricate high quality inductors with small footprints. Experimental results from a monolithic buck converter using this PDMA inductor are presented. A major conclusion of this work is that the 3-D "post-process" technology is more viable than traditional integrated circuit assembly methods for realizing of micro-power converters.


title: Event-based applications and enabling technologies

abstract: Event processing has become the paradigm of choice in many monitoring and reactive applications. However, the understanding of events, their composition and level of abstraction, the style of processing and the quality of service requirements vary drastically across application domains. We introduce the basic notions of event processing to create a common understanding, present the enabling technologies that are used for the implementation of event-based systems, survey a wide range of applications identifying their main features, and discuss open research issues.


title: Drift-free motion estimation from video images using phase correlation and linear optimization

abstract: Motion reconstruction from video images is known as an effective way in the autonomous robot guidance or teaching for a particular task. The authors' goal is to reconstruct motion from a video with the image-based visual servoing, and the main topic of this paper is to estimate camera motion on the image plane to make a reference. This paper focuses on a drift-free robust camera motion estimation by utilizing all relationships between every two frames. The proposed method in this paper does not use feature points like well-known bundle adjustment. It enables us to use a linear equation to solve its optimization. Then, brand new solution by using distance matrix is proposed so that it can save computation time and memory against a common pseudo inverse matrix based method. Finally, both qualitative and quantitative evaluations based on image mosaicing technique were achieved to confirm the effectiveness of proposed method.


title: Stress Test Evaluation for Natural Language Inference

abstract: Natural language inference (NLI) is the task of determining if a natural language hypothesis can be inferred from a given premise in a justifiable manner. NLI was proposed as a benchmark task for natural language understanding. Existing models perform well at standard datasets for NLI, achieving impressive results across different genres of text. However, the extent to which these models understand the semantic content of sentences is unclear. In this work, we propose an evaluation methodology consisting of automatically constructed"stress tests"that allow us to examine whether systems have the ability to make real inferential decisions. Our evaluation of six sentence-encoder models on these stress tests reveals strengths and weaknesses of these models with respect to challenging linguistic phenomena, and suggests important directions for future work in this area.


title: Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation

abstract: Domain adaptation manages to build an effective target classifier or regression model for unlabeled target data by utilizing the well-labeled source data but lying different distributions. Intuitively, to address domain shift problem, it is crucial to learn domain invariant features across domains, and most existing approaches have concentrated on it. However, they often do not directly constrain the learned features to be class discriminative for both source and target data, which is of vital importance for the final classification. Therefore, in this paper, we put forward a novel feature learning method for domain adaptation to construct both domain invariant and class discriminative representations, referred to as DICD. Specifically, DICD is to learn a latent feature space with important data properties preserved, which reduces the domain difference by jointly matching the marginal and class-conditional distributions of both domains, and simultaneously maximizes the inter-class dispersion and minimizes the intra-class scatter as much as possible. Experiments in this paper have demonstrated that the class discriminative properties will dramatically alleviate the cross-domain distribution inconsistency, which further boosts the classification performance. Moreover, we show that exploring both domain invariance and class discriminativeness of the learned representations can be integrated into one optimization framework, and the optimal solution can be derived effectively by solving a generalized eigen-decomposition problem. Comprehensive experiments on several visual cross-domain classification tasks verify that DICD can outperform the competitors significantly.


title: Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder

abstract: We present Tweet2Vec, a novel method for generating general-purpose vector representation of tweets. The model learns tweet embeddings using character-level CNN-LSTM encoder-decoder. We trained our model on 3 million, randomly selected English-language tweets. The model was evaluated using two methods: tweet semantic similarity and tweet sentiment categorization, outperforming the previous state-of-the-art in both tasks. The evaluations demonstrate the power of the tweet embeddings generated by our model for various tweet categorization tasks. The vector representations generated by our model are generic, and hence can be applied to a variety of tasks. Though the model presented in this paper is trained on English-language tweets, the method presented can be used to learn tweet embeddings for different languages.


title: A Linear Programming Approach to the Cutting-Stock Problem

abstract: The cutting-stock problem is the problem of filling an order at minimum cost for specified numbers of lengths of material to be cut from given stock lengths of given cost. When expressed as an integer programming problem the large number of variables involved generally makes computation infeasible. This same difficulty persists when only an approximate solution is being sought by linear programming. In this paper, a technique is described for overcoming the difficulty in the linear programming formulation of the problem. The technique enables one to compute always with a matrix which has no more columns than it has rows.


title: Motion Control of Wheeled Mobile Robots

abstract: The paper deals with the modeling and control strategies of the motion of wheeled mobile robots. The model of the vehicle has two driving wheels and the angular velocities of the two wheels are independently controlled. First, the vehicle kinematics model and the control strategies using a feedforward compensator are analyzed. Second, fuzzy reactive control of a mobile robot motion in an unknown environment with obstacles are proposed. Finally, the mobile robot simulation is illustrated.


title: Cognitive processing, language typology, and variation

abstract: Linguistic typological preferences have often been linked to cognitive processing preferences but often without recourse to typologically relevant experiments on cognitive processing. This article reviews experimental work on the possible parallels between preferences in cognitive processing and language typology. I summarize the main theoretical accounts of the processing-typology connection and show that typological distributions arise diachronically from preferred paths of language change, which may be affected by the degree to which alternative structures are preferred (e.g., easier) in acquisition or usage. The surveyed experimental evidence shows that considerable support exists for many linguistic universals to reflect preferences in cognitive processing. Artificial language learning experiments emerge as a promising method for researching the processing-typology connection, as long as its limitations are taken into account. I further show that social and cultural differences in cognition may have an effect on typological distributions and that to account for this variation a multidisciplinary approach to the processing-typology connection has to be developed. Lastly, since the body of experimental research does not adequately represent the linguistic diversity of the world's languages, it remains as an urgent task for the field to better account for this diversity in future work. WIREs Cogn Sci 2014, 5:477-487. doi: 10.1002/wcs.1294 ::: ::: ::: ::: For further resources related to this article, please visit the WIREs website. ::: ::: ::: ::: Conflict of interest: The author has declared no conflicts of interest for this article.


title: Design of a microstrip bandpass filter for 3.1-10.6 GHz UWB systems

abstract: In this thesis, ultra-wideband (UWB) microwave filters and design challenges are studied and, a microstrip UWB filter prototype design is presented. The UWB bandpass filter operating in the 3.6 GHz to 10.6 GHz frequency band is targeted to comply with the FCC spectral mask for UWB systems. The prototype filter is composed of quarterwavelength spaced shunt stub transmission lines. The circuit is first simulated and optimized by using AWR DE simulation software tool. Then Sonnet EM Simulation and CST EM Simulation Tools are further utilized to obtain more accurate simulated results. The fabricated microstrip UWB bandpass filter is then measured using a vector network analyzer and results are presented. The prototype built can be used in UWB communications or localization systems. DESIGN OF A MICROSTRIP BANDPASS FILTER FOR 3.1-10.6 GHz UWB SYSTEMS


title: Chromosomal-array analysis reveals partial 11q duplication and partial 12p deletion in a mildly affected case

abstract: Partial trisomy 11q is a rare syndrome and may be observed due to an intra-chromosomal duplication or an inter-chromosomal insertion. The deletions of the short arm of chromosome 12 are also uncommon structural aberrations. Only a small fraction of structural chromosome anomalies are related to the unbalanced progeny of balanced translocation carrier parents. We here report on a 10-month-old baby boy who shows a very mild phenotype related to unique chromosomal abnormality, partial trisomy of 11q, and partial monosomy of 12p, due to the maternal balanced reciprocal translocation (11;12). The proband showed a 49.64 Mb duplication of 11q14.1–q25 and 0.44 Mb deletion of 12p13.33 in chromosomal array analysis. Since it is known that the duplications may cause a milder phenotype than deletions. Dysmorphic facial features, minor cardiac anomalies, respiratory distress, central nervous system anomalies, and psychomotor delay observed in the patient was similar to the reported pure 11q duplication cases, while behavioral problems observed in pure monosomy 12p cases could not be evaluated due to the young age of the patient. Phenotype–genotype correlation will be discussed in view of all the reported pure partial 11q trisomies and pure partial 12p deletion cases. © 2014 Wiley Periodicals, Inc.


title: A Maintainable Software Architecture for Fast and Modular Bioinformatics Sequence Search

abstract: Bioinformaticists use the Basic Local Alignment Search Tool (BLAST) to characterize an unknown sequence by comparing it against a database of known sequences, thus detecting evolutionary relationships and biological properties. mpiBLAST is a widely-used, high-performance, open-source parallelization of BLAST that runs on a computer cluster delivering super-linear speedups. However, the Achilles heel of mpiBLAST is its lack of modularity, thus adversely affecting maintainability and extensibility. Alleviating this shortcoming requires an architectural refactoring to improve maintenance and extensibility while preserving high performance. Toward that end, this paper evaluates five different software architectures and details how each satisfies our design objectives. In addition, we introduce a novel approach to using mixin layers to enable mixing-and-matching of modules in constructing sequence-search applications for a variety of high-performance computing systems. Our design, which we call "mixin layers with refined roles", utilizes mixin layers to separate functionality into complementary modules and the refined roles in each layer improve the inherently modular design by precipitating flexible and structured parallel development, a necessity for an open-source application. We believe that this new software architecture for mpiBLAST-2.0 will benefit both the users and developers of the package and that our evaluation of different software architectures will be of value to other software engineers faced with the challenges of creating maintainable and extensible, high-performance, bioinformatics software.


LatticeRnn: Recurrent Neural Networks Over Lattices