title: Mechanisms of Rapid Antidepressant Effects of Sleep Deprivation Therapy: Clock Genes and Circadian Rhythms

abstract: A significant subset of both major depressive disorder and bipolar disorder patients rapidly (within 24 hours) and robustly improves with the chronotherapeutic intervention of sleep deprivation therapy (SDT). Major mood disorder patients are reported to have abnormal circadian rhythms including temperature, hormonal secretion, mood, and particularly sleep. These rhythms are modulated by the clock gene machinery and its products. It is hypothesized that SDT resets abnormal clock gene machinery, that relapse of depressive symptoms during recovery night sleep reactivates abnormal clock gene machinery, and that supplemental chronotherapies and medications can block relapse and help stabilize circadian-related improvement. The central circadian clock genes, BMAL1/CLOCK (NPAS2), bind to Enhancer Boxes to initiate the transcription of circadian genes, including the period genes (per1, per2, per3). It is suggested that a defect in BMAL1/CLOCK (NPAS2) or in the Enhancer Box binding contributes to altered circadian function associated, in part, with the period genes. The fact that chronotherapies, including SDT and sleep phase advance, are dramatically effective suggests that altered clock gene machinery may represent a core pathophysiological defect in a subset of mood disorder patients.


title: Face and Speech Based Multi-Modal Biometric Authentication

abstract: Inthispaper,�theuseoffiniteGaussianmixturem odal�(GMM)�basedExpectation� Maximization�(EM)�estimatedalgorithmforscorelev eldatafusionisproposed.�Automated� biometricsystemsforhumanidentificationmeasurea� "signature"� ofthehumanbody,� comparetheresultingcharacteristictoadatabase, �andrenderanapplicationdependent� decision.�Thesebiometricsystemsforpersonalauth enticationandidentificationarebased� uponphysiologicalorbehavioralfeatureswhichare �typicallydistinctive,�Multi-biometric� systems,� whichconsolidateinformationfrommultipl ebiometricsources,� aregaining� popularitybecausetheyareabletoovercomelimita tionssuchasnon-universality,�noisy� sensordata,�largeintra-uservariationsandsuscep tibilitytospoofattacksthatarecommonly� encounteredinmonomodalbiometricsystems.�Simula tionshowthatfinitemixturemodal� (GMM)�isquiteeffectiveinmodellingthegenuinea ndimpostorscoredensities,�fusionbased� theresultingdensityestimatesachievesasignific antperformanceon��eNTERFACE�2005� multi-biometric��databasebasedonfaceandspeech� modalities.


title: Progress and application of through glass via (TGV) technology

abstract: Glass provides many opportunities for advanced packaging. The most obvious advantage is given by the material properties. As an insulator, glass has low electrical loss, particularly at high frequencies. The relatively high stiffness and ability to adjust the coefficient of thermal expansion gives advantages to manage warp in glass core substrates and bonded stacks for both through glass vias (TGV) and carrier applications. Glass also gives advantages for developing cost effective solutions. Glass forming processes allow the potential to form both in panel format as well as at thicknesses as low as 100 um, giving opportunities to optimize or eliminate current manufacturing methods. As the industry adopts glass solutions, significant advancements have been made in downstream processes such as glass handling and via/surface metallization. Of particular interest is the ability to leverage tool sets and processes for panel fabrication to enable cost structures desired by the industry. By utilizing the stiffness and adjustable CTE of glass substrates, as well as continuously reducing via size that can be made in a panel format, opportunities to manufacture glass TGV substrates in a panel format increase. We will provide an update on advancements in these areas as well as handling techniques to achieve desired process flows. We will also provide the latest demonstrations of electrical, thermal and mechanical reliability.


title: Design, modeling and control of an omni-directional aerial vehicle

abstract: In this paper we present the design and control of a novel six degrees-of-freedom aerial vehicle. Based on a static force and torque analysis for generic actuator configurations, we derive an eight-rotor configuration that maximizes the vehicle's agility in any direction. The proposed vehicle design possesses full force and torque authority in all three dimensions. A control strategy that allows for exploiting the vehicle's decoupled translational and rotational dynamics is introduced. A prototype of the proposed vehicle design is built using reversible motor-propeller actuators and capable of flying at any orientation. Preliminary experimental results demonstrate the feasibility of the novel design and the capabilities of the vehicle.


title: Collaborative networks: a new scientific discipline

abstract: Collaborative networks manifest in a large variety of forms, including virtual organizations, virtual enterprises, dynamic supply chains, professional virtual communities, collaborative virtual laboratories, etc. A large body of empiric knowledge related to collaborative networks is already available, but there is an urgent need to consolidate this knowledge and build the foundations for a more sustainable development of this area. The establishment of a scientific discipline for collaborative networks is a strong instrument in achieving this purpose. In this article the main characteristics of a “discipline” are analyzed in the context of collaborative networks, showing that the pre-conditions necessary for building this new discipline are available.


title: Suboptimal initiation of dialysis with and without early referral to a nephrologist.

abstract: BACKGROUND ::: Our objective was to examine patients who initiate renal replacement therapy (RRT) at 10 representative Canadian centers, characterize their initiation as inpatient or outpatient and describe their initial type of dialysis access, duration of pre-dialysis care and clinical status at the time of dialysis initiation. We also examined the impact of an optimal dialysis start (i.e. initiated as an outpatient with an arteriovenous fistula, arteriovenous graft or peritoneal dialysis catheter) on subsequent health outcomes. ::: ::: ::: METHODS ::: Charts of consecutive incident RRT patients were identified from 1 July to 31 December 2006. Information was collected until 6 months after the initiation or until death, transplant or transfer. ::: ::: ::: RESULTS ::: Three hundred and thirty-nine incident RRT patients were studied: 39.6% initiated as an inpatient; 54% started hemodialysis (HD) with a central venous catheter; 15.3% had <1 month predialysis care, while 64.6% had >1 year. Optimal starts occurred in 39.5% of patients. For HD patients, optimal starts occurred in 19.8%. Suboptimal starts were noted in patients referred <12 months prior to end-stage renal disease (44%) and in patients referred earlier (56%). The composite end point of death, transfusion or subsequent hospitalization was significantly reduced with an optimal start [hazard ratio 0.47 (95% confidence interval 0.32-0.68), P = 0.0001]. ::: ::: ::: CONCLUSIONS ::: Suboptimal initiation of dialysis is common in patients referred early or late. The benefits of early referral are lost if dialysis is initiated suboptimally. There is a need to identify factors that lead to suboptimal initiation despite early referral.


title: Deep Learning for Tomato Diseases: Classification and Symptoms Visualization

abstract: ABSTRACTSeveral studies have invested in machine learning classifiers to protect plants from diseases by processing leaf images. Most of the proposed classifiers are trained and evaluated with small datasets, focusing on the extraction of hand-crafted features from image to classify the leaves. In this study, we have used a large dataset compared to the state-of-the art. Here, the dataset contains 14,828 images of tomato leaves infected with nine diseases. To train our classifier, we have introduced the Convolutional Neural Network (CNN) as a learning algorithm. One of the biggest advantages of CNN is the automatic extraction of features by processing directly the raw images. To analyze the proposed deep model, we have used visualization methods to understand symptoms and to localize disease regions in leaf. The obtained results are encouraging, reaching 99.18% of accuracy, which ourperforms dramatically shallow models, and they can be used as a practical tool for farmers to protect tomato against disease.


title: Analysis of IPSec overheads for VPN servers

abstract: Internet protocol security (IPSec) is a widely deployed mechanism for implementing virtual private networks (VPNs). This paper evaluates the performance overheads associated with IPSec. We use Openswan, an open source implementation of IPSec, and measure the running times of individual security operations and also the speedup gained by replacing various IPSec components with no-ops. The main findings of this study include: VPN connection establishment and maintenance overheads for short sessions could be significantly higher than those incurred while transferring data, and cryptographic operations contribute 32 - 60% of the total IPSec overheads.


title: CaffePresso: an optimized library for deep learning on embedded accelerator-based platforms

abstract: Off-the-shelf accelerator-based embedded platforms offer a competitive energy-efficient solution for lightweight deep learning computations over CPU-based systems. Low-complexity classifiers used in power-constrained and performance-limited scenarios are characterized by operations on small image maps with 2-3 deep layers and few class labels. For these use cases, we consider a range of embedded systems with 5--20 W power budgets such as the Xilinx ZC706 board (with MXP soft vector processor), NVIDIA Jetson TX1 (GPU), TI Keystone II (DSP) as well as the Adapteva Parallella board (custom multi-core with NoC). Deep Learning computations push the capabilities of these platforms to the limit through compute-intensive evaluations of multiple 2D convolution filters per layer, and high communication requirements arising from the movement of intermediate maps across layers. We present CaffePresso, a Caffe-compatible framework for generating optimized mappings of user-supplied ConvNet specifications to target various accelerators such as FPGAs, DSPs, GPUs, RISC-multicores. We use an automated code generation and auto-tuning approach based on knowledge of the ConvNet requirements, as well as platform-specific constraints such as on-chip memory capacity, bandwidth and ALU potential. While one may expect the Jetson TX1 + cuDNN to deliver high performance for ConvNet configurations, (1) we observe a flipped result with slower GPU processing compared to most other systems for smaller embedded-friendly datasets such as MNIST and CIFAR10, and (2) faster and more energy efficient implementation on the older 28nm TI Keystone II DSP over the newer 20nm NVIDIA TX1 SoC in all cases.


title: Gaussian Process Optimization with Mutual Information

abstract: In this paper, we analyze a generic algorithm scheme for sequential global optimization using Gaussian processes. The upper bounds we derive on the cumulative regret for this generic algorithm improve by an exponential factor the previously known bounds for algorithms like GP-UCB. We also introduce the novel Gaussian Process Mutual Information algorithm (GP-MI), which significantly improves further these upper bounds for the cumulative regret. We confirm the efficiency of this algorithm on synthetic and real tasks against the natural competitor, GP-UCB, and also the Expected Improvement heuristic.


title: A fully automatic problem solver with human-style output

abstract: This paper describes a program that solves elementary mathematical problems, mostly in metric space theory, and presents solutions that are hard to distinguish from solutions that might be written by human mathematicians. The program is part of a more general project, which we also discuss.


title: ``A piece of yourself'': Ethical issues in biometric identification

abstract: Le recours aux technique d'identification biometriques souleve des questions d'ethique, liees essentiellement a la notion de vie privee. Non seulement elles sont exposees aux memes abus que les autres techniques de collecte et de stockage de donnees personnelles, mais de surcroit elles presentent des dangers specifiques, lies a la perte de controle sur des representations d'une partie de notre corps. Car le droit a la vie privee au sens large implique le droit a controler la creation et l'utilisation d'images biometriques de nous-memes. C'est pourquoi le recours a la biometrie doit etre rigoureusement limite et reglemente.


title: XPath: looking forward

abstract: The location path language XPath is of particular importance for XML applications since it is a core component of many XML processing standards such as XSLT or XQuery. In this paper, based on axis symmetry of XPath, equivalences of XPath 1.0 location paths involving reverse axes, such as anc and prec, are established. These equivalences are used as rewriting rules in an algorithm for transforming location paths with reverse axes into equivalent reverse-axis-free ones. Location paths without reverse axes, as generated by the presented rewriting algorithm, enable efficient SAX-like streamed data processing of XPath.


title: Experiences on automatically assessed algorithm simulation exercises with different resubmission policies

abstract: In this paper, we present our experiences in using two automatic assessment tools, TRAKLA and TRAKLA2, in a second course of programming. In this course, 500--700 students have been enrolled annually during the period 1993--2004. The tools are specifically designed for assessing algorithm simulation exercises in which students simulate the working of algorithms at a conceptual level. Both of these tools allow students to resubmit their solutions after getting feedback. However, the resubmission policy has changed considerably during the period. Those changes reflect the students performance in the exercises. We conclude that an encouraging grading policy, i.e., the more exercises they solve the better grades they achive, combined with an option to resubmit the solution is a very important factor promoting students' learning. However, in order to prevent aimless trial-and-error problem solving method, the number of resubmissions allowed per assignment should be carefully controlled.


title: A small signal model for average current mode control based on describing function approach

abstract: A small signal model for average current mode control based on describing function method is proposed. Modeling of average current mode control is more complicated than peak current mode control due to the additional current compensator. In this paper, two commonly used current compensators are considered as examples. The structure of average current mode control is compared with V2 control to find the similarity. Previous modeling result about V2 control is borrowed and modified for average current mode control. The proposed small signal model can be accurate up to switching frequency. The proposed model is verified using Simplis simulation and measurement.


title: OpenGRASP: a toolkit for robot grasping simulation

abstract: Simulation is essential for different robotic research fields such as mobile robotics, motion planning and grasp planning. For grasping in particular, there are no software simulation packages, which provide a holistic environment that can deal with the variety of aspects associated with this problem. These aspects include development and testing of new algorithms, modeling of the environments and robots, including the modeling of actuators, sensors and contacts. In this paper, we present a new simulation toolkit for grasping and dexterous manipulation called OpenGRASP addressing those aspects in addition to extensibility, interoperability and public availability. OpenGRASP is based on a modular architecture, that supports the creation and addition of new functionality and the integration of existing and widely-used technologies and standards. In addition, a designated editor has been created for the generation and migration of such models. We demonstrate the current state of OpenGRASP's development and its application in a grasp evaluation environment.


title: sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings

abstract: Neural word representations have proven useful in Natural Language Processing (NLP) tasks due to their ability to efficiently model complex semantic and syntactic word relationships. However, most techniques model only one representation per word, despite the fact that a single word can have multiple meanings or "senses". Some techniques model words by using multiple vectors that are clustered based on context. However, recent neural approaches rarely focus on the application to a consuming NLP algorithm. Furthermore, the training process of recent word-sense models is expensive relative to single-sense embedding processes. This paper presents a novel approach which addresses these concerns by modeling multiple embeddings for each word based on supervised disambiguation, which provides a fast and accurate way for a consuming NLP model to select a sense-disambiguated embedding. We demonstrate that these embeddings can disambiguate both contrastive senses such as nominal and verbal senses as well as nuanced senses such as sarcasm. We further evaluate Part-of-Speech disambiguated embeddings on neural dependency parsing, yielding a greater than 8% average error reduction in unlabeled attachment scores across 6 languages.


title: Phrasier: a system for interactive document retrieval using keyphrases

abstract: UsersO information needs are often too complex to be effectively expressed in standard query interfaces to full-text retrieval systems. A typical need is to find documents that are similar to a given source document, yet describing the content of a document in a few terms is a difficult task. We describe Phrasier, an interactive system for browsing, querying and relating documents within a digital library. Phrasier exploits keyphrases that have been automatically extracted from source documents to create links to similar documents and to suggest appropriate query phrases to users. PhrasierOs keyphrase-based retrieval engine returns ranked lists of documents that are similar to a given source text. Evaluation indicates that PhrasierOs keyphrase-based retrieval performs as well as full-text retrieval when recall and relevance scores assigned by human assessors are considered.


title: A Seven-degrees-of-freedom Robot-arm Driven by Pneumatic Artificial Muscles for Humanoid Robots

abstract: Braided pneumatic artificial muscles, and in particular the better known type with a double helical braid usually called the McKibben muscle, seem to be at present the best means for motorizing robot-arms with artificial muscles. Their ability to develop high maximum force associated with lightness and a compact cylindrical shape, as well as their analogical behavior with natural skeletal muscle were very well emphasized in the 1980s by the development of the Bridgestone “soft robot” actuated by “rubbertuators”. Recent publications have presented ways for modeling McKibben artificial muscle as well as controlling its highly non-linear dynamic behavior. However, fewer studies have concentrated on analyzing the integration of artificial muscles with robot-arm architectures since the first Bridgestone prototypes were designed. In this paper we present the design of a 7R anthropomorphic robot-arm entirely actuated by antagonistic McKibben artificial muscle pairs. The validation of the robot-arm architecture was performed in a teleoperation mode.


title: Explaining B2C e-commerce acceptance: An integrative model based on the framework by Gatignon and Robertson

abstract: This study attempts to analyze e-commerce adoption, proposing a global model that integrates the most relevant approaches in the literature. Gatignon and Robertson's Adoption Model is taken as a reference framework because of its overall nature and its agreement with the main theories used to explain e-commerce acceptance. Thus, the model proposed to explain e-commerce adoption by consumers includes the simultaneous influence of attitudes, social norms, perceived risk, personal innovativeness in the field of new technologies and attributes perceived in the technology. The results obtained show that attitudes toward the system and Subjective Norm are the main determinants of the intention to shop on the Net. On the contrary, perceived risk has no significant effect on adoption process, while the influence of personal innovativeness is only relevant in the first purchase on the Internet.


title: Advances in Silicon-On-Insulator Cellular Antenna Switch Technology

abstract: Silicon-on-insulator technology utilizing very high resistivity handle wafers demonstrates sufficient performance for many cellular handset requirements. Technologies with either thin or thick device layers show promise. Thick-SOI prototype single pole six throw switch (SP6T) P-0.1dB of about 40 dBm and -75 dBc harmonic at 35 dBm output have been demonstrated at 900 MHz. Thin-SOI Ron-Coff product similar to that of pHEMT has been demonstrated experimentally. Obstacles such as distortion caused by Si-SiO2 interfaces remain a fascinating engineering challenge to fully exploit the opportunity.


Trustworthiness Perceptions of Computer Code: A Heuristic-Systematic Processing Model


title: A Conceptual Framework for Addressing IoT Threats: Challenges in Meeting Challenges

abstract: The Internet of Things (IoT) is rapidly growing, and offers many economical and societal potentials and benefits. Nevertheless, the IoT also introduces new threats to our Security, Privacy and Safety (SPS). The existing work on mitigating these SPS threats often fails to address the fundamental challenges behind the mitigation measures proposed, and fails to make the relations between different mitigation measures explicit. This paper, therefore, offers a conceptual framework for understanding and approaching the challenges and obstacles that arise in addressing the SPS threats of the IoT. This contribution aims to help policymakers in adopting policies and strategies that stimulate others to develop, deploy and use IoT devices, applications and services in secure, privacy-friendly and safe ways.


title: Boosting Black Box Variational Inference

abstract: Approximating a probability density in a tractable manner is a central task in Bayesian statistics. Variational Inference (VI) is a popular technique that achieves tractability by choosing a relatively simple variational family. Borrowing ideas from the classic boosting framework, recent approaches attempt to \emph{boost} VI by replacing the selection of a single density with a greedily constructed mixture of densities. In order to guarantee convergence, previous works impose stringent assumptions that require significant effort for practitioners. Specifically, they require a custom implementation of the greedy step (called the LMO) for every probabilistic model with respect to an unnatural variational family of truncated distributions. Our work fixes these issues with novel theoretical and algorithmic insights. On the theoretical side, we show that boosting VI satisfies a relaxed smoothness assumption which is sufficient for the convergence of the functional Frank-Wolfe (FW) algorithm. Furthermore, we rephrase the LMO problem and propose to maximize the Residual ELBO (RELBO) which replaces the standard ELBO optimization in VI. These theoretical enhancements allow for black box implementation of the boosting subroutine. Finally, we present a stopping criterion drawn from the duality gap in the classic FW analyses and exhaustive experiments to illustrate the usefulness of our theoretical and algorithmic contributions.


title: SLL suppressed monopulse microstrip antenna design

abstract: It is aimed to design a X-band monopulse microstrip antenna array that can be used almost in all modern tracking radars and having superior properties in angle detection and angular accuracy than the classical ones. In order to create a monopulse antenna array, a rectangular microstrip antenna is designed and 16 of it gathered together using the nonlinear central feeding to suppress the side lobe level (SLL) of the antenna. The monopulse antenna is created by the combining 4 of these 4×4 array antennas with a microstrip comparator designed using four branch line coupler. Good agreement is noted between the simulation and measurement results.


title: Coverage path planning: The Boustrophedon Cellular Decomposition

abstract: Coverage path planning is the determination of a path that a robot must take in order to pass over each point in an environment. Applica tions include vacuuming, floor scrubbing, and inspection. We developed the boustrophedon cellular decomposition, which is an exact cel lular decomposition approach, for the purposes of coverage. Each cell in the boustrophedon is covered with simple back and forth motions. Once each cell is covered, then the entire envi ronment is covered. Therefore, coverage is re duced to finding an exhaustive path through a graph which represents the adjacency relation ships of the cells in the boustrophedon decom position. This approach is provably complete and Experiments on a mobile robot validate this approach.


title: Application of Association Rule Mining in Supplier Selection Criteria

abstract: In this paper the application of rule mining in order to review the effective factors on supplier selection is reviewed in the following three sections 1) criteria selecting and information gathering 2) performing association rule mining 3) validation and constituting rule base. Afterwards a few of applications of rule base is explained. Then, a numerical example is presented and analyzed by Clementine software. Some of extracted rules as well as the results are presented at the end. Keywords—Association rule mining, data mining, supplier selection criteria.


title: Kahneman D. Interpretations of utility and their implications for the valuation of health. The Economic Journal 2007

abstract: The term utility can be interpreted in terms of the hedonic experience of an outcome (experienced utility) or in terms of the preference or desire for that outcome (decision utility). It is this second interpretation that lies at the heart of the methods that economists have developed to value non-market goods, such as health. In this article, we argue that decision utility is unlikely to generate meaningful data on the utility associated with different experiences, and instead economists should look towards developing measures that focus more directly on experienced utility.


title: Monte Carlo simulation of residential electricity demand for forecasting maximum demand on distribution networks

abstract: The prevalent engineering practice (PEP) for maximum demand estimation in low-voltage (LV) electricity networks is based on an After Diversity Maximum Demand (ADMD) modified by a diversity factor. This method predicts the maximum likely voltage drop accounting for consumer diversity. However, this approach does not take into account the stochastic nature of the demand and is inconsistent with international power quality standards. We present a Monte Carlo simulation model of consumer demand taking into account the statistical spread of demand in each half hour using data sampled from a gamma distribution. The parameters of the gamma distribution are based on data metered at a number of residential properties fed by one transformer. The simulated demand is corrected for temperature and total consumption. The simulated profiles at the residential properties are aggregated and the simulated maximum demand is compared with actual maximum demand at a given transformer and an entire distribution network showing good agreement in both cases.


title: Real-time 3D human objects rendering based on multiple camera details

abstract: 3D model construction techniques using RGB-D information have been gaining a great attention of the researchers around the world in recent decades. The RGB-D sensor, Microsoft Kinect is widely used in many research fields, such as in computer vision, computer graphics, and human computer interaction, due to its capacity of providing color and depth information. This paper presents our research finding on calibrating information from several Kinects in order to construct a 3D model of a human object and to render texture captured from RGB camera. We used multiple Kinect sensors, which are interconnected in a network. High bit rate streams captured at each Kinect are first sent to a centralized PC for the processing. This even can be extended to a remote PC in the Internet. Main contributions of this work include calibration of the multiple Kinects, properly aligning point clouds generated from multiple Kinects, and generation of the 3D shape of the human objects. Experimental results demonstrate that the proposed method provides a better 3D model of the human object being captured.


title: Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples

abstract: Measures of RNA abundance are important for many areas of biology and often obtained from high-throughput RNA sequencing methods such as Illumina sequence data. These measures need to be normalized to remove technical biases inherent in the sequencing approach, most notably the length of the RNA species and the sequencing depth of a sample. These biases are corrected in the widely used reads per kilobase per million reads (RPKM) measure. Here, we argue that the intended meaning of RPKM is a measure of relative molar RNA concentration (rmc) and show that for each set of transcripts the average rmc is a constant, namely the inverse of the number of transcripts mapped. Further, we show that RPKM does not respect this invariance property and thus cannot be an accurate measure of rmc. We propose a slight modification of RPKM that eliminates this inconsistency and call it TPM for transcripts per million. TPM respects the average invariance and eliminates statistical biases inherent in the RPKM measure.


title: Learning scheduling algorithms for data processing clusters

abstract: Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems use simple, generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically. Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective, such as minimizing average job completion time. However, off-the-shelf RL techniques cannot handle the complexity and scale of the scheduling problem. To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals. Our prototype integration with Spark on a 25-node cluster shows that Decima improves average job completion time by at least 21% over hand-tuned scheduling heuristics, achieving up to 2x improvement during periods of high cluster load.


title: Physical exercise habit: on the conceptualization and formation of habitual health behaviours

abstract: An observation one can make when reviewing the literature on physical activity is that healthenhancing exercise habits tend to wear off as soon as individuals enter adolescence. Therefore, exercise habits should be promoted and preserved early in life. This article focuses on the formation of physical exercise habits. First, the literature on motivational determinants of habitual exercise and related behaviours is discussed, and the concept of habit is further explored. Based on this literature, a theoretical model of exercise habit formation is proposed. More specifically, expanding on the idea that habits are the result of automated cognitive processes, it is argued that physical exercise habits are capable of being automatically activated by the situational features that normally precede these behaviours. These habits may enhance health as a result of consistent performance over a long period of time. Subsequently, obstacles to the formation of exercise habits are discussed and interventions that may anticipate these obstacles are presented. Finally, implications for theory and practice are briefly discussed.


title: Optimizing ETL processes in data warehouses

abstract: Extraction-transformation-loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. Usually, these processes must be completed in a certain time window; thus, it is necessary to optimize their execution time. In this paper, we delve into the logical optimization of ETL processes, modeling it as a state-space search problem. We consider each ETL workflow as a state and fabricate the state space through a set of correct state transitions. Moreover, we provide algorithms towards the minimization of the execution cost of an ETL workflow.


title: In pursuit of proper BLDC motor design for electric bicycles

abstract: In this paper, an electric bicycle motor design study is presented according to the related standard. Fundamental dimensions and characteristics are kept within some certain boundaries by means of motor rated power and physical constraints arising from standard bicycle sizes available on the market. Slot/pole ratios and winding configurations are investigated comprehensively in order to obtain a proper design of electric motor due to the facts of pedal assisted electric bicycle application. After defining the motor parameters and the materials that are used the motor design an optimisation attempts are undertaken by using an analytical solver which is employing both numerical calculations and finite element method (FEM) analyses. Motor designs are assessed based on torque production, weigh, efficiency and ease-of-manufacture. A concise experimental study is conducted for prototype motor in order to evaluate validity of design study.


title: Design of a printed log-periodic dipole antenna (LPDA) for 0.8–2.5 GHz band applications

abstract: This paper presents the step-by-step design procedure and simulated performance investigation of a printed log-periodic dipole antenna (LPDA) that is able to operate cover wide frequency range (800-2500 MHz). The advantage of this antenna is compact and easy to integrate with planar circuits which is suitable for applications that need wide bandwidth and high gain antenna. The dimension of designed antenna has initially been calculated; then modeled using a commercial electromagnetic simulation to optimize the effect of dielectric properties of substrate on the width and length of dipole element conductors. To validated the designed antenna, it is fabricated and measured to carry out its S11 and radiation patterns. The measured results are in good agreement with the simulation ones.


Electrical Characterization of Lead-Acid Battery at Load for HEV Applications


title: Maturity assessment models: a design science research approach

abstract: In order to ascertain and measure dedicated aspects of social and technical systems ‘maturity’, a wide range of maturity assessment models have been developed by both, practitioners and academics over the past years. In spite of its broad proliferation, the concept has not been untroubled by criticism. Unnecessary bureaucracy, poor theoretical foundation, and the impression of a falsified certainty to achieve success are just a few examples for that. ::: ::: As there is still a significant lack of knowledge on how to design theoretically sound and widely accepted maturity assessment models, it is the aim of this paper to discuss the typical phases of maturity model development and application by taking a design science research perspective. ::: ::: Given that both, development and application are intimately connected, different decision parameters are identified that are relevant in respect to rigour and relevance of the maturity assessment model.


abstract: Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation Dataset, but these can be insensitive to topo-logical differences, such as gaps in boundaries. Furthermore, the Berkeley metrics have not been useful as cost functions for supervised learning. Using concepts from digital topology, we propose a new metric called the warping error that tolerates disagreements over boundary location, penalizes topological disagreements, and can be used directly as a cost function for learning boundary detection, in a method that we call Boundary Learning by Optimization with Topological Constraints (BLOTC). We trained boundary detectors on electron microscopic images of neurons, using both BLOTC and standard training. BLOTC produced substantially better performance on a 1.2 million pixel test set, as measured by both the warping error and the Rand index evaluated on segmentations generated from the boundary labelings. We also find our approach yields significantly better segmentation performance than either gPb-OWT-UCM or multiscale normalized cut, as well as Boosted Edge Learning trained directly on our data.


title: FERNN: An Algorithm for Fast Extraction of Rules from Neural Networks

abstract: Before symbolic rules are extracted from a trained neural network, the network is usually pruned so as to obtain more concise rules. Typical pruning algorithms require retraining the network which incurs additional cost. This paper presents FERNN, a fast method for extracting rules from trained neural networks without network retraining. Given a fully connected trained feedforward network with a single hidden layer, FERNN first identifies the relevant hidden units by computing their information gains. For each relevant hidden unit, its activation values is divided into two subintervals such that the information gain is maximized. FERNN finds the set of relevant network connections from the input units to this hidden unit by checking the magnitudes of their weights. The connections with large weights are identified as relevant. Finally, FERNN generates rules that distinguish the two subintervals of the hidden activation values in terms of the network inputs. Experimental results show that the size and the predictive accuracy of the tree generated are comparable to those extracted by another method which prunes and retrains the network.


title: Adjustable Autonomy for Human-Centered Autonomous Systems

abstract: ion is also critical to the success of planning and scheduling activities. In our scenarios, the crew will often have to deal with planning and scheduling at a very high level (e.g., what crops do I need to plant now so they can be harvested in six months) and planning and scheduling at a detailed level (e.g., what is my next task). The autonomous system must be able to move between various time scales and levels of abstraction, presenting the correct level of information to the user at the correct time. Model-based diagnosis and recovery When something goes wrong, a robust autonomous should figure out what went wrong and recover as best as it can. A model-based diagnosis and recovery system, such as Livingstone [Williams and Nayak, 96], does this. It is analogous to the autonomic and immune systems of a living creature. If the autonomous system has a model of the system it controls, it can use this to figure out what is the most likely cause that explains the observed symptoms as well as how can the system recover given this diagnosis so its mission can continue. For example, if the pressure of a tank is low, it could be because the tank has a leak, the pump blew a fuse, a valve is not open to fill the tank or not closed to keep the tank from draining. However, it could be that the tank pressure is not low and the pressure sensor is defective. By analyzing the system from other sensors, it may say the pressure is normal or suggest closing a valve, resetting the pump circuit breaker, or requesting a crewmember to check the tank for a leak.


title: Head Reconstruction from Internet Photos

abstract: 3D face reconstruction from Internet photos has recently produced exciting results. A person’s face, e.g., Tom Hanks, can be modeled and animated in 3D from a completely uncalibrated photo collection. Most methods, however, focus solely on face area and mask out the rest of the head. This paper proposes that head modeling from the Internet is a problem we can solve. We target reconstruction of the rough shape of the head. Our method is to gradually “grow” the head mesh starting from the frontal face and extending to the rest of views using photometric stereo constraints. We call our method boundary-value growing algorithm. Results on photos of celebrities downloaded from the Internet are presented.


title: Micro-grid autonomous operation during and subsequent to islanding process

abstract: This paper investigates (i) preplanned switching events and (ii) fault events that lead to islanding of a distribution subsystem and formation of a micro-grid. The micro-grid includes two distributed generation (DG) units. One unit is a conventional rotating synchronous machine and the other is interfaced through a power electronic converter. The interface converter of the latter unit is equipped with independent real and reactive power control to minimize islanding transients and maintain both angle stability and voltage quality within the micro-grid. The studies are performed based on a digital computer simulation approach using the PSCAD/EMTDC software package. The studies show that an appropriate control strategy for the power electronically interfaced DG unit can ensure stability of the micro-grid and maintain voltage quality at designated buses, even during islanding transients. This paper concludes that presence of an electronically-interfaced DG unit makes the concept of micro-grid a technically viable option for further investigations.


title: BOR$$^$$G: Building Optimal Regularised Reconstructions with GPUs (in Cubes)

abstract: This paper is about dense regularised mapping using a single camera as it moves through large work spaces. Our technique is, as many are, a depth-map fusion approach. However, our desire to work both at large scales and outdoors precludes the use of RGB-D cameras. Instead, we need to work with the notoriously noisy depth maps produced from small sets of sequential camera images with known inter-frame poses. This, in turn, requires the application of a regulariser over the 3D surface induced by the fusion of multiple (of order 100) depth maps. We accomplish this by building and managing a cube of voxels. The combination of issues arising from noisy depth maps and moving through our workspace/voxel cube, so it envelops us, rather than orbiting around it as is common in desktop reconstructions, forces the algorithmic contribution of our work. Namely, we propose a method to execute the optimisation and regularisation in a 3D volume which has been only partially observed and thereby avoiding inappropriate interpolation and extrapolation. We demonstrate our technique indoors and outdoors and offer empirical analysis of the precision of the reconstructions.


title: "They Are Happier and Having Better Lives than I Am": The Impact of Using Facebook on Perceptions of Others' Lives

abstract: Abstract Facebook, as one of the most popular social networking sites among college students, provides a platform for people to manage others' impressions of them. People tend to present themselves in a favorable way on their Facebook profile. This research examines the impact of using Facebook on people's perceptions of others' lives. It is argued that those with deeper involvement with Facebook will have different perceptions of others than those less involved due to two reasons. First, Facebook users tend to base judgment on examples easily recalled (the availability heuristic). Second, Facebook users tend to attribute the positive content presented on Facebook to others' personality, rather than situational factors (correspondence bias), especially for those they do not know personally. Questionnaires, including items measuring years of using Facebook, time spent on Facebook each week, number of people listed as their Facebook “friends,” and perceptions about others' lives, were completed by 425 under...


title: Sonographic findings in hepatic involvement of hereditary haemorrhagic telangiectasia.

abstract: AIM ::: Hereditary haemorrhagic telangiectasia, or Rendu-Osler-Weber disease, is an autosomal-dominant disorder characterised by angiodysplastic lesions (telangiectases and arteriovenous malformations) which affect many organs including the skin, lungs, gastrointestinal tract, and brain. A broad spectrum of vascular and structural changes have been reported. Our objective was to systematically examine the prevalence of sonographic findings in hepatic involvement in patients with hereditary haemorrhagic telangiectasia (HHT). ::: ::: ::: METHODS ::: We studied 22 consecutive patients with hereditary haemorrhagic telangiectasia by ultrasonography in combination with colour-Doppler and pulsed wave-Doppler for liver involvement. The clinical diagnosis of HHT was based on the Curaçon criteria. ::: ::: ::: RESULTS ::: Sixteen of the 22 patients had signs of hepatic involvement including prominent common hepatic artery (14 of 16), dilatation of the intrahepatic part of the hepatic arteries (15 of 16) and intrahepatic AV-shunts (16 of 16). Ectasia of the hepatic vein, fibrotic parenchymal changes, left accessory hepatic artery and focal hepatic lesions were found less frequently. ::: ::: ::: CONCLUSION ::: Diagnosis of liver involvement in HHT can be made by sonography with colour-Doppler. The main features of this involvement include prominent common hepatic artery, dilatation of the intrahepatic part of the hepatic arteries and intrahepatic AV-shunts.


title: TOWARDS IMPLEMENTATION AND ACHIEVEMENT OF CONSTRUCTION AND ENVIRONMENTAL QUALITY IN THE MALAYSIAN CONSTRUCTION INDUSTRY

abstract: The purpose of this paper is to identify mechanisms and approaches involved in the local construction individuals and companies to enhance environmental concerns and the adoption of interdisciplinary method of managing the environment and up keeping of the quality in Malaysian Construction Industry. This paper describes an innovative approach to understanding the role of internal and external influence through LEGO concept. This approach builds on theories of environmental issues, in which learning about new ideas, practices or technologies occurs through integration of efforts particularly from the government. The approach also analyses the challenges faced by construction stakeholders. It draws on findings from different studies including some other countries of sustainability in which the engagement of previous research has been incorporated to speed up the environmental sustainability in Malaysia. Environmental sustainable development construction requires a holistic thinking and decision making and more innovative solutions that enhance sustainability and result in mutually benefited outcomes for all stakeholders. A dedicated effort especially government and government link company is in strong demand. This integrated achievement boosts the awareness and action among stakeholders. The role of government and leaders has been cited as an important component towards of environmental sustainability. Development of the approach will contribute to the understanding of environmental sustainability through identifying gaps in the understanding and pursuit of sustainability in the Malaysian construction industry. This paper suggests the future prospect that integrates several dimensions towards sustainability practice in Malaysia.


Deeply-supervised Knowledge Synergy Supplementary Materials


title: Digital health: meeting the ethical and policy challenges.

abstract: Digital health encompasses a wide range of novel digital technologies related to health and medicine. Such technologies rely on recent advances in the collection and analysis of ever increasing amounts of data from both patients and healthy citizens. Along with new opportunities, however, come new ethical and policy challenges. These range from the need to adapt current evidence-based standards, to issues of privacy, oversight, accountability and public trust as well as national and international data governance and management. This review illustrates key issues and challenges facing the rapidly unfolding digital health paradigm and reflects on the impact of big data in medical research and clinical practice both internationally and in Switzerland. It concludes by emphasising five conditions that will be crucial to fulfil in order to foster innovation and fair benefit sharing in digital health.


title: 3D animation of Java program execution for teaching object oriented concepts

abstract: The successful teaching of the object oriented (OO) programming paradigm has been identified as a difficult task. Coupled with the difficulty of conveying abstract concepts in general to students, is the complexity of the OO paradigm itself. A common approach to conveying complex abstract notions (such as algorithms or data structures) is the use of visualisations. This paper describes the design and implementation of the JOOPA2 tool for animation of the execution of simple OO (Java) programs in a 3D environment. The tool was developed for the purpose of supporting the teaching of OO concepts in an introductory level programming course.


title: Landmark of Mandarin nasal codas and its application in pronunciation error detection

abstract: L2 learners of Mandarin have difficulty learning native-like pronunciation of nasal codas. In order to help them learn native-like pronunciation, we propose to develop targeted classifiers for automatic pronunciation error detection. In this paper, perceptual experiments with modified speech are designed to analyze the exact position of the landmark of a nasal coda. Based on perceptual results from isolated words, we propose that information about nasal coda place of articulation is most dense near a landmark at the center of the nasalized vowel. Landmarks detected in a database of Japanese learners of Mandarin, and classified as correct vs. incorrect using an SVM. The result shows that the detection performance of the SVM+Landmark system is similar to that of a DNN-HMM+MFCC system. When the two systems are combined, an FRR of 4.6% is achieved at DA of 83.9%. This performance is comparable to that of previously developed classifiers for 16 common Mandarin pronunciation errors.


title: PRIMATE ANTERIOR CINGULATE CORTEX: WHERE MOTOR CONTROL, DRIVE AND COGNITION INTERFACE

abstract: Controversy surrounds the function of the anterior cingulate cortex. Recent discussions about its role in behavioural control have centred on three main issues: its involvement in motor control, its proposed role in cognition and its relationship with the arousal/drive state of the organism. I argue that the overlap of these three domains is key to distinguishing the anterior cingulate cortex from other frontal regions, placing it in a unique position to translate intentions to actions.


title: SINGO: A single-end-operative and genderless connector for self-reconfiguration, self-assembly and self-healing

abstract: Flexible and reliable connection is critical for self-reconfiguration, self-assembly, or self-healing. However, most existing connection mechanisms suffer from a deficiency that a connection would seize itself if one end malfunctions or is out of service. To mitigate this limitation on self-healing, this paper presents a new SINGO connector that can establish or disengage a connection even if one end of the connection is not operational. We describe the design and the prototype of the connector and demonstrate its performance by both theoretical analysis and physical experimentations.


title: RefSeer: A citation recommendation system

abstract: Citations are important in academic dissemination. To help researchers check the completeness of citations while authoring a paper, we introduce a citation recommendation system called RefSeer. Researchers can use it to find related works to cited while authoring papers. It can also be used by reviewers to check the completeness of a paper's references. RefSeer presents both topic based global recommendation and also citation-context based local recommendation. By evaluating the quality of recommendation, we show that such recommendation system can recommend citations with good precision and recall. We also show that our recommendation system is very efficient and scalable.


title: An implementation of hierarchical service function chaining using OpenDaylight platform

abstract: In recent year, the rise of Network Function Virtualization (NFV) and Software-defined Networking (SDN) makes the service chain and application provisioning process much agile and simpler, promoting the development of Service Function Chaining (SFC). Service Function Chaining (SFC) provides flexibility and agility for deploying and managing network services. The most challenging problems in SFC are deploying and organizing in large scale, multi-region data center with multiple operational teams. One feasible solution for these issues is decomposing SFC into multiple nested lower-level domains, establishing a hierarchical structure. In this paper, we present a Hierarchical Service Function Chaining (H-SFC) architecture using SDN to enable multi-layered service function chains. We implement the architecture in the OpenDaylight SFC environment and prove that it could simplify SFC management and orchestration.


title: A new planar compatible power combiner based on radial waveguide

abstract: A new broadband power combiner structure based on radial waveguide and broadband microstrip probe is presented. This power combiner is excited by an N-type connector, and stepped-impedance technology is used at the bottom of the radial waveguide to realize the impedance match. An eight-way radial waveguide power combiner over the X-band is designed, fabricated and measured. The measured results show a good agreement with the simulated results. The measured return loss is lower than −15 dB and the insertion loss is better than 1.8 dB from 8 to 12 GHz.


title: Looking at people: sensing for ubiquitous and wearable computing

abstract: The research topic of looking at people, that is, giving machines the ability to detect, track, and identify people and more generally, to interpret human behavior, has become a central topic in machine vision research. Initially thought to be the research problem that would be hardest to solve, it has proven remarkably tractable and has even spawned several thriving commercial enterprises. The principle driving application for this technology is "fourth generation" embedded computing: "smart" environments and portable or wearable devices. The key technical goals are to determine the computer's context with respect to nearby humans (e.g., who, what, when, where, and why) so that the computer can act or respond appropriately without detailed instructions. The paper examines the mathematical tools that have proven successful, provides a taxonomy of the problem domain, and then examines the state of the art. Four areas receive particular attention: person identification, surveillance/monitoring, 3D methods, and smart rooms/perceptual user interfaces. Finally, the paper discusses some of the research challenges and opportunities.


title: Riemannian Manifold Learning

abstract: Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold learning (RML), based on the assumption that the input high-dimensional data lie on an intrinsically low-dimensional Riemannian manifold. The main idea is to formulate the dimensionality reduction problem as a classical problem in Riemannian geometry, that is, how to construct coordinate charts for a given Riemannian manifold? We implement the Riemannian normal coordinate chart, which has been the most widely used in Riemannian geometry, for a set of unorganized data points. First, two input parameters (the neighborhood size k and the intrinsic dimension d) are estimated based on an efficient simplicial reconstruction of the underlying manifold. Then, the normal coordinates are computed to map the input high-dimensional data into a low- dimensional space. Experiments on synthetic data, as well as real-world images, demonstrate that our algorithm can learn intrinsic geometric structures of the data, preserve radial geodesic distances, and yield regular embeddings.


title: Better than SIFT?

abstract: Independent evaluation of the performance of feature descriptors is an important part of the process of developing better computer vision systems. In this paper, we compare the performance of several state-of-the art image descriptors including several recent binary descriptors. We test the descriptors on an image recognition application and a feature matching application. Our study includes several recently proposed methods and, despite claims to the contrary, we find that SIFT is still the most accurate performer in both application settings. We also find that general purpose binary descriptors are not ideal for image recognition applications but perform adequately in a feature matching application.


title: PEPsal: A Performance Enhancing Proxy for TCP Satellite Connections [Internetworking and Resource Management in Satellite Systems Series]

abstract: Internet communications with paths that include satellite link face some peculiar challenges, due to the presence of a long propagation wireless channel. Herein, we propose a Performance Enhancing Proxy (PEP) solution, called PEPsaI, which is, to the best of the authors' knowledge, the first open source Transmission Control Protocol (TCP) splitting solution for the GNU/Linux operating systems. PEPsaI improves the performance of a TCP connection over a satellite channel making use of the TCP Hybla, a TCP enhancement for satellite networks developed by the authors. The objective of the paper is to present and evaluate the PEPsal architecture by comparing it with end-to-end TCP variants (NewReno, SACK, Hybla), considering both performance and reliability issues. Performance is evaluated by making use of a testbed set up at the University of Bologna, to study advanced transport protocols and architectures for internet satellite communications. At present, PEPsaI is adopted, with success, by a satellite Internet provider.


title: Note on Attacking Object Detectors with Adversarial Stickers

abstract: Deep learning has proven to be a powerful tool for computer vision and has seen widespread adoption for numerous tasks. However, deep learning algorithms are known to be vulnerable to adversarial examples. These adversarial inputs are created such that, when provided to a deep learning algorithm, they are very likely to be mislabeled. This can be problematic when deep learning is used to assist in safety critical decisions. Recent research has shown that classifiers can be attacked by physical adversarial examples under various physical conditions. Given the fact that state-of-the-art objection detection algorithms are harder to be fooled by the same set of adversarial examples, here we show that these detectors can also be attacked by physical adversarial examples. In this note, we briefly show both static and dynamic test results. We design an algorithm that produces physical adversarial inputs, which can fool the YOLO object detector and can also attack Faster-RCNN with relatively high success rate based on transferability. Furthermore, our algorithm can compress the size of the adversarial inputs to stickers that, when attached to the targeted object, result in the detector either mislabeling or not detecting the object a high percentage of the time. This note provides a small set of results. Our upcoming paper will contain a thorough evaluation on other object detectors, and will present the algorithm.


title: Multiphysics Modeling and Impact of Thermal Boundary Resistance in Phase Change Memory Devices

abstract: Among the many emerging non-volatile memory technologies, chalcogenide (i.e. GeSbTe/GST) based phase change random access memory (PRAM) has shown particular promise. While accurate simulations are required for reducing programming current and enabling higher integration density, many challenges remain for improved simulation of PRAM cell operation including nanoscale thermal conduction and phase change. This work simulates the fully coupled electrical and thermal transport and phase change in 2D PRAM geometries, with specific attention to the impact of thermal boundary resistance between the GST and surrounding materials. For GST layer thicknesses between 25 and 75nm, the interface resistance reduces the predicted programming current and power by 31% and 53%, respectively, for a typical reset transition. The calculations also show the large sensitivity of programming voltage to the GST thermal conductivity. These results show the importance of temperature-dependent thermal properties of materials and interfaces in PRAM cells


title: Guest Editors’ Introduction to the Special Issue of JPA on the Flynn Effect

abstract: When James Flynn (1984) first reported that American children and adolescents scored higher on IQ tests than did their peers from an earlier generation, that finding generated few ripples in the literature or in clinical practice. Indeed, how would any other result have made sense? The first sets of data compared children from the mid-1930s with the early 1970s on the Stanford-Binet Form L-M and from the late 1940s with the early 1970s on the Wechsler Intelligence Scale for Children (WISC). The advent of television, the mass media explosion, the increasing awareness of the importance of a stimulating early environment, and other societal changes during the intervening years provided a simple and handy explanation for the 3 points per decade gain that Flynn observed in the United States. Flynn’s (1987) next publication, however, started to rock the empirical and clinical foundations of the nature of intelligence and cognitive development and the psychometric measurement of IQ. The gains in IQ from one generation to the next (which would come to be known as the Flynn effect [FE]) occurred on a worldwide basis, according to data from 14 developed nations, and the 3-point-per-decade American gain paled in comparison with the larger gains in most other nations studied. In fact, gains about twice that large occurred in Japan and in European countries such as Belgium, Germany, and the Netherlands. These gains defied easy explanation (Neisser, 1998). So did the fact that the 3-point-per-decade gain in the United States was not limited to post–World War II societal advances, but continued to occur into the 1980s (Kaufman & Kaufman, 1983), the 1990s (Wechsler, 1991), and the 2000s (Roid, 2003; Wechsler, 2008). FE has apparently stopped, and even reversed, in some Scandinavian countries (Teasdale & Owen, 2008), but it has remained a virtual constant gain for children and adults in the United States for the past eight decades. Researchers differ on why the FE occurs. Some claim that genetics is the key variable (Rodgers & Wanstrom, 2007), though most stress environmental factors such as nutrition (Colom, LluisFont, & Andres-Pueyo, 2005), education (Teasdale & Owen, 2005), or improvement in public health (Steen, 2009). Flynn (2007, 2009), in What Is Intelligence?, explains the effect named after him in terms of a societal shift from concrete to abstract thinking, whereas other scholars and researchers maintain that the so-called effect is nothing more than an artifact of statistical or methodological origin (Beaujean & Osterlind, 2008; Rodgers, 1998). And, in fact, the dispute about the scientific validity of the FE has entered the U.S. courtrooms in a big way as a burgeoning array of law cases asks whether the FE should be considered when sentencing low-functioning criminals convicted of a capital crime. Ever since the Supreme Court’s decision in Atkins v. Virginia (2002), which stipulated that a criminal who is mentally retarded cannot be executed,


title: A Way out of the Odyssey: Analyzing and Combining Recent Insights for LSTMs

abstract: LSTMs have become a basic building block for many deep NLP models. In recent years, many improvements and variations have been proposed for deep sequence models in general, and LSTMs in particular. We propose and analyze a series of architectural modifications for LSTM networks resulting in improved performance for text classification datasets. We observe compounding improvements on traditional LSTMs using Monte Carlo test-time model averaging, deep vector averaging (DVA), and residual connections, along with four other suggested modifications. Our analysis provides a simple, reliable, and high quality baseline model.


Blobgects: Digital museum catalogs and diverse user communities


title: Energy Feed-Forward and Direct Feed-Forward Control for Solid-State Transformer

abstract: There is large voltage deviation on the dc bus of the three-stage solid-state transformer (SST) when the load suddenly changes. The feed-forward control can effectively reduce the voltage deviation and transition time. However, conventional power feed-forward scheme of SST cannot develop the feed-forward control to the full without extra current sensor. In this letter, an energy feed-forward scheme, which takes the energy changes of inductors into consideration, is proposed for the dual active bridge (DAB) controller. A direct feed-forward scheme, which directly passes the power of DAB converter to the rectifier stage, is proposed for the rectifier controller. They can further improve the dynamic performances of the two dc bus voltages, respectively. The experimental results in a 2-kW SST prototype are provided to verify the proposed feed-forward schemes and show the superior performances.


title: Nasal Tip Plasty Using Various Techniques in Rhinoplasty

abstract: Rhinoplasty is one of the most common aesthetic surgical procedures in Korea today. However, simple augmentation rhinoplasty results often failed to satisfy the high expectations of patients. As a result, many procedures have been developed to improve the appearance of the nasal tip and nasal projection. However, the characteristics of Korean nasal tips including the bulbous appearance (attributable to the thickness of the skin), flared nostrils, and restriction of the nasal tip attributable to an underdeveloped medical crus of the alar cartilage and a short columella have made such procedures difficult. Currently, most plastic surgeons perform rhinoplasty simultaneously with various nasal tip plasty techniques to improve the surgical results. An important part of an aesthetically pleasing result is to ensure an adequate nasal tip positioned slightly higher than the proper dorsum, with the two tip defining points in close proximity to each other, giving the nose a triangular shape from the caudal view. From June 2002 to November 2003, the authors performed rhinoplasty with simultaneous nasal tip plasty using various techniques according to the tip status of 55 patients (25 deviated noses, 9 broad noses, 15 low noses, and 6 secondary cleft lip and nose deformities). The surgery included realignment of alar cartilage by resection and suture, fibroareolar and subcutaneous tissue resection, tip graft, and columellar strut. The postoperative results over an average period of 10 months were entirely satisfactory. There were no patient complaints, nor complications resulting from the procedures. Good nasal tip projection, natural columellar appearance, and improvement of the nasolabial angle were achieved for most patients. In conclusion, rhinoplasty with simultaneous nasal tip plasty, achieved by a variety of techniques according to patients' tip status, is an effective method for improving the appearance of the nose and satisfying the desires of the patients.


title: A Scalable Laplace Approximation for Neural Networks

abstract: We leverage recent insights from second-order optimisation for neural networks to construct a Kronecker factored Laplace approximation to the posterior over the weights of a trained network. Our approximation requires no modification of the training procedure, enabling practitioners to estimate the uncertainty of their models currently used in production without having to retrain them. We extensively compare our method to using Dropout and a diagonal Laplace approximation for estimating the uncertainty of a network. We demonstrate that our Kronecker factored method leads to better uncertainty estimates on out-of-distribution data and is more robust to simple adversarial attacks. Our approach only requires calculating two square curvature factor matrices for each layer. Their size is equal to the respective square of the input and output size of the layer, making the method efficient both computationally and in terms of memory usage. We illustrate its scalability by applying it to a state-of-the-art convolutional network architecture.


title: Distributional Semantics: A Montagovian View

abstract: This paper describes the current status of research in Distributional Semantics looking at the results from the Montagovian tradition stand point. It considers the main aspects of the Montagovian view as binoculars to observe those results, in particular: compositionality, syntax-semantics interface, logical words and entailment. To this end, it reviews some work that aims to tackle those issues within the Distributional Semantics Models and tries to highlight some open questions formal and distributional semanticists could address together.


title: Very-Heavy Sled Training for Improving Horizontal-Force Output in Soccer Players

abstract: Background:Sprint running acceleration is a key feature of physical performance in team sports, and recent literature shows that the ability to generate large magnitudes of horizontal ground-reaction force and mechanical effectiveness of force application are paramount. The authors tested the hypothesis that very-heavy loaded sled sprint training would induce an improvement in horizontal-force production, via an increased effectiveness of application. Methods:Training-induced changes in sprint performance and mechanical outputs were computed using a field method based on velocity–time data, before and after an 8-wk protocol (16 sessions of 10- × 20-m sprints). Sixteen male amateur soccer players were assigned to either a very-heavy sled (80% body mass sled load) or a control group (unresisted sprints). Results:The main outcome of this pilot study is that very-heavy sled-resisted sprint training, using much greater loads than traditionally recommended, clearly increased maximal horizontal-force production ...


title: Is a positive clinical outcome after exercise therapy for chronic non-specific low back pain contingent upon a corresponding improvement in the targeted aspect(s) of performance? A systematic review

abstract: INTRODUCTION ::: The effect size for exercise therapy in the treatment of chronic non-specific low back pain (cLBP) is only modest. This review aims to analyse the specificity of the effect by examining the relationship between the changes in clinical outcome (pain, disability) and the changes in the targeted aspects of physical function (muscle strength, mobility, muscular endurance) after exercise therapy. ::: ::: ::: METHODS ::: We searched for exercise therapy trials for cLBP published up to 15 April 2010 in Medline, Embase, Cochrane Library, Cinahl, and PEDro. Two independent reviewers selected studies according to the inclusion criteria. ::: ::: ::: DATA EXTRACTION ::: one author extracted the data of the articles. ::: ::: ::: RESULTS ::: ::: ::: ::: DATA SYNTHESIS ::: 16 studies with a total of 1,476 participants met the inclusion criteria. There was little evidence supporting a relationship between the changes in pain or physical function and the changes in performance for the following measures: mobility (no correlation in 9 studies, weak correlation in 1 study), trunk extension strength (7 and 2, respectively), trunk flexion strength (4 and 1, respectively) and back muscle endurance (7 and 0, respectively). Changes in disability showed no correlation with changes in mobility in three studies and a weak correlation in two; for strength, the numbers were four (no correlation) and two (weak correlation), respectively. ::: ::: ::: CONCLUSIONS ::: The findings do not support the notion that the treatment effects of exercise therapy in cLBP are directly attributable to changes in the musculoskeletal system. Future research aimed at increasing the effectiveness of exercise therapy in cLBP should explore the coincidental factors influencing symptom improvement.


title: Self-Fulfilling Prophesies, Placebo Effects, and the Social-Psychological Creation of Reality

abstract: In this essay, we pull together foundational research from the psychological, sociological, and medical sciences to illuminate the undeniable influence of the psychosocial context in constructing objective reality. From psychology, we review the growing body of research on how beliefs and expectations about common experiences (e.g., nutrition, stress, and aging) can fundamentally alter the impact of those experiences. From sociology, we review the role of social influence in constructing the quality and impact of cultural products and experiences. And from medicine, we review the neurological and physiological underpinnings of the placebo effect, a powerful demonstration of expectation and social context to produce physiological changes in the body. As we align evidence from these related—although currently disconnected—fields, we uncover important limitations from within each field of study and portray how an integrative approach can offer a more rich and comprehensive understanding of the phenomena underlying the social–psychological creation of reality. Combining foundational research with the interdisciplinary findings from our laboratory, we explore how psychological and social contexts can fundamentally alter the psychological, behavioral, and physiological effects of one of the most common human experiences: drinking water. To conclude, we present a series of questions and suggestions to assist and inspire further interdisciplinary collaboration. We offer a pathway for researchers to more frequently acknowledge, more thoroughly understand, and more effectively utilize the power of psychosocial influence to effect positive change in a number of disciplines including marketing, medicine, and public health. ::: ::: ::: Keywords: ::: ::: mindset; ::: perception; ::: belief; ::: social influence; ::: self-fulfilling prophesy; ::: expectancy; ::: valuation; ::: placebo effect


Building a Knowledge Graph for Food, Energy, and Water Systems


Snow White: Provably Secure Proofs of Stake.


title: Anomaly Detection for Astronomical Data

abstract: Modern astronomical observatories can produce massive amount of data that are beyond the capability of the researchers to even take a glance. These scientific observations present both great opportunities and challenges for astronomers and machine learning researchers. In this project we address the problem of detecting anomalies/novelties in these large-scale astronomical data sets. Two types of anomalies, the point anomalies and the group anomalies, are considered. The point anomalies include individual anomalous objects, such as single stars or galaxies that present unique characteristics. The group anomalies include anomalous groups of objects, such as unusual clusters of the galaxies that are close together. They both have great values for astronomical studies, and our goal is to detect them automatically in un-supervised ways. For point anomalies, we adopt the subspace-based detection strategy and proposed a robust low-rank matrix decomposition algorithm for more reliable results. For group anomalies, we use hierarchical probabilistic models to capture the generative mechanism of the data, and then score the data groups using various probability measures. Experimental evaluation on both synthetic and real world data sets shows the effectiveness of the proposed methods. On a real astronomical data sets, we obtained several interesting anecdotal results. Initial inspections by the astronomers confirm the usefulness of these machine learning methods in astronomical research.


title: Brain hemorrhage after electrical burn injury: Case report and probable mechanism

abstract: BACKGROUND ::: High-voltage electric injury may induce lesion in different organs. In addition to the local tissue damage, electrical injuries may lead to neurological deficits, musculoskeletal damage, and cardiovascular injury. Severe vascular damage may occur making the blood vessels involved prone to thrombosis and spontaneous rupture. ::: ::: ::: CASE DESCRIPTION ::: Here, we present the case of a 39-year-old male who suffered an electrical burn with high tension wire causing intracranial bleeding. He presented with an electrical burn in the parietal area (entry zone) and the left forearm (exit zone). The head tomography scan revealed an intraparenchimatous bleeding in the left parietal area. In this case, the electric way was the scalp, cranial bone, blood vessels and brain, upper limb muscle, and skin. The damage was different according to the dielectric property in each tissue. The injury was in the scalp, cerebral blood vessel, skeletal muscle, and upper limb skin. The main damage was in brain's blood vessels because of the dielectric and geometric features that lead to bleeding, high temperature, and gas delivering. ::: ::: ::: CONCLUSION ::: This is a report of a patient with an electric brain injury that can be useful to elucidate the behavior of the high voltage electrical current flow into the nervous system.


title: A document-level sentiment analysis approach using artificial neural network and sentiment lexicons

abstract: The abundance of discussion forums, Weblogs, e-commerce portals, social networking, product review sites and content sharing sites has facilitated flow of ideas and expression of opinions. The user-generated text content on Internet and Web 2.0 social media can be a rich source of sentiments, opinions, evaluations, and reviews. Sentiment analysis or opinion mining has become an open research domain that involves classifying text documents based on the opinion expressed, about a given topic, being positive or negative. This paper proposes a sentiment classification model using back-propagation artificial neural network (BPANN). Information Gain, and three popular sentiment lexicons are used to extract sentiment representing features that are then used to train and test the BPANN. This novel approach combines the strength of BPANN in classification accuracy with intrinsic subjectivity knowledge available in the sentiment lexicons. The results obtained from experiments on the movie and hotel review corpora have shown that the proposed approach has been able to reduce dimensionality, while producing accurate results for sentiment based classification of text.


title: PhaseLink: A Deep Learning Approach to Seismic Phase Association

abstract: Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is widely employed to detect earthquakes on permanent and temporary seismic networks and underlies most seismicity catalogs produced around the world. This task can be challenging because the number of sources is unknown, events frequently overlap in time, or can occur simultaneously in different parts of a network. We present PhaseLink, a framework based on recent advances in deep learning for grid‐free earthquake phase association. Our approach learns to link phases together that share a common origin and is trained entirely on millions of synthetic sequences of P and S wave arrival times generated using a 1‐D velocity model. Our approach is simple to implement for any tectonic regime, suitable for real‐time processing, and can naturally incorporate errors in arrival time picks. Rather than tuning a set of ad hoc hyperparameters to improve performance, PhaseLink can be improved by simply adding examples of problematic cases to the training data set. We demonstrate the state‐of‐the‐art performance of PhaseLink on a challenging sequence from southern California and synthesized sequences from Japan designed to test the point at which the method fails. For the examined data sets, PhaseLink can precisely associate phases to events that occur only ∼12 s apart in origin time. This approach is expected to improve the resolution of seismicity catalogs, add stability to real‐time seismic monitoring, and streamline automated processing of large seismic data sets.


title: A Fractional-n subsampling PLL based on a digital-to-time converter

abstract: The paper presents a subsampling PLL which uses a 10-bit, 0.5 ps unit step Digital-to-Time Converter (DTC) in the phase-error comparison path for the fractional-N lock. The gain and nonlinearity of the DTC can be digitally calibrated in the background while the PLL operates normally. During fractional multiplication of a 40 MHz reference to frequencies around 10 GHz, the measured jitter is in the range from 176 to 198 fs. The worst measured fractional spur is −57 dBc and the in-band phase noise performance of the PLL is −108 dBc/Hz. The presented analog PLL in advanced 28 nm CMOS achieves a figure-of-merit (FOM) of −246.6 dB that compares well to the recent state-of-the-art.


title: A Framework for Multiperspective Rendering

abstract: We present a framework for the direct rendering of multiperspective images. We treat multiperspective imaging systems as devices for capturing smoothly varying set of rays, and we show that under an appropriate parametrization, multiperspective images can be characterized as continuous manifolds in ray space. We use a recently introduced class of General Linear Cameras (GLC), which describe all 2D linear subspaces of rays, as primitives for constructing multiperspective images. We show GLCs when constrained by an appropriate set of rules, can be laid out to tile the image plane and, hence, generate arbitrary multiperspective renderings. Our framework can easily render a broad class of multiperspective images, such as multiperspective panoramas, neocubist style renderings, and faux-animations from still-life scenes. We also show a method to minimize distortions in multiperspective images by uniformly sampling rays on a sampling plane even when they do not share a common origin.


title: Strong subthreshold current array PUF with 265 challenge-response pairs resilient to machine learning attacks in 130nm CMOS

abstract: This paper presents a strong silicon physically unclonable function (PUF) immune to machine learning (ML) attacks. The PUF, termed the subthreshold current array (SCA) PUF, is composed of a pair of two-dimensional transistor arrays and a low-offset comparator. The fabricated PUF chip allows 265 challenge-response pairs (CRPs) and achieves high reliability with average bit error rate (BER) of 5.8% for temperatures −20 to 80°C and Vdd + 10%. The calibration-based CRPs filtering method effectively improves BER to 2.6% with a 10% loss of CRPs. When subjected to ML attacks, the PUF shows resilience that is 100X higher than known alternatives, with negligible loss in PUF unpredictability.


title: A first simulation on optimizing EDS for cabin baggage screening regarding throughput

abstract: Airport security screening is vital for secure air transportation. Screening of cabin baggage heavily relies on human operators reviewing X-ray images. Explosive detection systems (EDS) developed for cabin baggage screening can be a very valuable addition security-wise. Depending on the EDS machine and settings, false alarm rates increase, which could reduce throughput. A discrete event simulation was used to investigate how different machine settings of EDS, different groups of X-ray screeners, and different durations of alarm resolution with explosives trace detection (ETD) influence throughput of a specific cabin baggage screening process. For the modelling of screening behavior in the context of EDS and for the estimation of model parameters, data was borrowed from a human-machine interaction experiment and a work analysis. In a second step, certain adaptations were tested for their potential to reduce the impact of EDS on throughput. The results imply that moderate increases in the false alarm rate by EDS can be buffered by employing more experienced and trained X-ray screeners. Larger increases of the false alarm rate require a fast alarm resolution and additional resources for the manual search task.


Authentication Algorithm for Intrusion Detection in Wireless Networks


You Are What You Eat : How Food Affects Your Mood


title: Insertion, evasion, and denial of service: Eluding network intrusion detection

abstract: Abstract : All currently available network intrusion detection (ID) systems rely upon a mechanism of data collection passive protocol analysis-which is fundamentally flawed. In passive protocol analysis, the intrusion detection system (IDS) unobtrusively watches all traffic on the network, and scrutinizes it for patterns of suspicious activity. We outline in this paper two basic problems with the reliability of passive protocol analysis: (1) there isn't enough information on the wire on which to base conclusions about what is actually happening on networked machines, and (2) the fact that the system is passive makes it inherently "fail-open," meaning that a compromise in the availability of the IDS doesn't compromise the availability of the network. We define three classes of attacks which exploit these fundamentally problems---insertion, evasion and denial of service attacks--and describe how to apply these three types of attacks to IP and TCP protocol analysis. We present the results of tests of the efficacy of our attacks against four of the most popular network intrusion detection systems on the market. All of the ID systems tested were found to be vulnerable to each of our attacks. This indicates that network ID systems cannot be fully trusted until they are fundamentally redesigned.


title: Distantly supervised Web relation extraction for knowledge base population

abstract: Extracting information from Web pages for populating large, cross-domain knowledge bases requires methods which are suitable across domains, do not require manual effort to adapt to new domains, are able to deal with noise, and integrate information extracted from different Web pages. Recent approaches have used existing knowledge bases to learn to extract information with promising results, one of those approaches being distant supervision. Distant supervision is an unsupervised method which uses background information from the Linking Open Data cloud to automatically label sentences with relations to create training data for relation classifiers. In this paper we propose the use of distant supervision for relation extraction from the Web. Although the method is promising, existing approaches are still not suitable for Web extraction as they suffer from three main issues: data sparsity, noise and lexical ambiguity. Our approach reduces the impact of data sparsity by making entity recognition tools more robust across domains and extracting relations across sentence boundaries using unsupervised co- reference resolution methods. We reduce the noise caused by lexical ambiguity by employing statistical methods to strategically select training data. To combine information extracted from multiple sources for populating knowledge bases we present and evaluate several information integration strategies and show that those benefit immensely from additional relation mentions extracted using co-reference resolution, increasing precision by 8%. We further show that strategically selecting training data can increase precision by a further 3%.


title: Toddler-Inspired Visual Object Learning

abstract: Real-world learning systems have practical limitations on the size of the training datasets that they can collect and consider. Given a fixed training size budget, how should a system go about choosing a subset of the possible training examples that still allows for learning accurate, generalizable models? To help address this question, we draw inspiration from a highly efficient practical learning system: the human child. Using head-mounted cameras, eye gaze trackers, and a model of foveated vision, we collected first-person (egocentric) imagery that represents a highly accurate approximation of the "training data" that toddlers' visual systems collect in everyday, naturalistic learning contexts. We used state-of-the-art computer vision learning models (convolutional neural networks) to help characterize the structure of this data, and found that it produces significantly better object models than egocentric data experienced by adults in exactly the same environment. By using the CNNs as a modeling tool to investigate the properties of the child data that may enable this rapid learning, we found that the data exhibits a unique combination of quality and diversity, with not only many similar large, high-quality object views but also a greater number and diversity of rare views. This novel methodology of analyzing the visual "training data" used by children may not only reveal insights to improve machine learning, but also may suggest new experimental tools to better understand infant learning in developmental psychology.


title: Building an Industry 4 . 0 Analytics Platform Practical Challenges , Approaches and Future Research Directions

abstract: The ecosystem of big data technologies and advanced analytics tools has evolved rapidly in the last years offering companies new possibilities for digital transformation and data-driven solutions. Industry 4.0 represents a major application domain for big data and advanced analytics in order to exploit the huge amounts of data generated across the industrial value chain. However, building and establishing an Industry 4.0 analytics platform involves far more than tools and technology. In this paper, we report on our practical experiences when building the Bosch Industry 4.0 Analytics Platform and discuss challenges, approaches and future research directions. The analytics platform is designed for more than 270 factories as part of Bosch’s worldwide manufacturing network. We describe use cases and requirements for the analytics platform and present its architecture. On this basis, we discuss practical challenges related to analytical solution development, employee enablement, i. e., citizen data science, as well as analytics governance and present initial solution approaches. Thereby, we highlight future research directions in order to leverage advanced analytics and big data in industrial enterprises.


title: Performance analysis and security dependence of on paper digital signature using random and critical content

abstract: The content of physical paper documents is a major concern, after the development of modern scanning and printing technologies. The tendency for the document fraud has been growing and the paper-based documents require authenticating the sender identity and ensuring the content integrity. This paper presents a reliable and simple method for authenticating the content of a paper document by implementing an on paper digital signature. This method randomly selects the words at random positions of the document along with critical content and creates on paper digital signature. This method employs an online document verification system and the unauthorized access is denied by the user authentication mechanism. Performance analysis and security dependence on the system is also studied in this paper.


APPLICATION OF IMAGE PROCESSING FOR DETECTION OF CORROSION USING GROUND PENETRATING RADAR


title: Fast 2D Complex Gabor Filter With Kernel Decomposition

abstract: 2D complex Gabor filtering has found numerous applications in the fields of computer vision and image processing. Especially, in some applications, it is often needed to compute 2D complex Gabor filter bank consisting of filtering outputs at multiple orientations and frequencies. Although several approaches for fast Gabor filtering have been proposed, they focus primarily on reducing the runtime for performing filtering once at specific orientation and frequency. To obtain the Gabor filter bank, the existing methods are repeatedly applied with respect to multiple orientations and frequencies. In this paper, we propose a novel approach that efficiently computes the 2D complex Gabor filter bank by reducing the computational redundancy that arises when performing filtering at multiple orientations and frequencies. The proposed method first decomposes the Gabor kernel to allow a fast convolution with the Gaussian kernel in a separable manner. This enables reducing the runtime of the Gabor filter bank by reusing intermediate results computed at a specific orientation. By extending this idea, we also propose a fast approach for 2D localized sliding discrete Fourier transform that uses the Gaussian kernel in order to lend spatial localization ability as in the Gabor filter. Experimental results demonstrate that the proposed method runs faster than the state-of-the-art methods, while maintaining similar filtering quality.


title: Image tamper detection based on demosaicing artifacts

abstract: In this paper, we introduce tamper detection techniques based on artifacts created by Color Filter Array (CFA) processing in most digital cameras. The techniques are based on computing a single feature and a simple threshold based classifier. The efficacy of the approach was tested over thousands of authentic, tampered, and computer generated images. Experimental results demonstrate reasonably low error rates.


A Planing Autonomous Surface Vehicle with Azimuth Rim-driven


title: Developing a database for pedestrians’ earthquake emergency evacuation in indoor scenarios

abstract: With the booming development of evacuation simulation software, developing an extensive database in indoor scenarios for evacuation models is imperative. In this paper, we conduct a qualitative and quantitative analysis of the collected videotapes and aim to provide a complete and unitary database of pedestrians' earthquake emergency response behaviors in indoor scenarios, including human-environment interactions. Using the qualitative analysis method, we extract keyword groups and keywords that code the response modes of pedestrians and construct a general decision flowchart using chronological organization. Using the quantitative analysis method, we analyze data on the delay time, evacuation speed, evacuation route and emergency exit choices. Furthermore, we study the effect of classroom layout on emergency evacuation. The database for indoor scenarios provides reliable input parameters and allows the construction of real and effective constraints for use in software and mathematical models. The database can also be used to validate the accuracy of evacuation models.


title: Design of Direct LED Backlighting Based on an Analytical Method of Uniform Illumination

abstract: A direct LED backlight unit (DLBLU) with extended sources based on an analytical design method of uniform illumination is investigated. We developed an analytical calculation method of the light spread function (LSF) for achieving highly uniform illumination into the diffuser plate (target plane) of a DLBLU. The effect of the rays reflected by the sides of the backlight box was also considered and calculated using the LSF. When comparing the results calculated using the LSF method at $y\; = \;x$ and $y\; = \;0$ with the results from the optical design software, the mean absolute percentage error (MAPE) were 0.74% and 0.46%, respectively. To demonstrate the effectiveness of the LSF method, a DLBLU model with 36 batwing sources was designed using the LSF method and assembled. The measurement result for DLBLU uniformity was 96.68%. When comparing the measurement result with the results of the LSF method at $y\; = \;x$ and $y\; = \;0$ , the MAPEs were 0.92% and 0.46%, respectively. Thus, the proposed design method is an effective method for designing a DLBLU.


Crowdsourcing Annotations for Visual Object Detection


title: GoFFish: A Sub-Graph Centric Framework for Large-Scale Graph Analytics

abstract: Vertex centric models for large scale graph processing are gaining traction due to their simple distributed programming abstraction. However, pure vertex centric algorithms under-perform due to large communication overheads and slow iterative convergence. We introduce GoFFish a scalable sub-graph centric framework co-designed with a distributed persistent graph storage for large scale graph analytics on commodity clusters, offering the added natural flexibility of shared memory sub-graph computation. We map Connected Components, SSSP and PageRank algorithms to this model and empirically analyze them for several real world graphs, demonstrating orders of magnitude improvements, in some cases, compared to Apache Giraph’s vertex centric framework.


The Use of Rubrics in Higher Education and Competencies Evaluation. Scientific Study about Characteristics of Rating Scales Used by Professors