title: AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

abstract: In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different subregions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous state of the art, boosting the best reported inception score by 14.14% on the CUB dataset and 170.25% on the more challenging COCO dataset. A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image.


title: Improved Variational Autoencoders for Text Modeling using Dilated Convolutions

abstract: Recent work on generative modeling of text has found that variational auto-encoders (VAE) incorporating LSTM decoders perform worse than simpler LSTM language models (Bowman et al., 2015). This negative result is so far poorly understood, but has been attributed to the propensity of LSTM decoders to ignore conditioning information from the encoder. In this paper, we experiment with a new type of decoder for VAE: a dilated CNN. By changing the decoder's dilation architecture, we control the effective context from previously generated words. In experiments, we find that there is a trade off between the contextual capacity of the decoder and the amount of encoding information used. We show that with the right decoder, VAE can outperform LSTM language models. We demonstrate perplexity gains on two datasets, representing the first positive experimental result on the use VAE for generative modeling of text. Further, we conduct an in-depth investigation of the use of VAE (with our new decoding architecture) for semi-supervised and unsupervised labeling tasks, demonstrating gains over several strong baselines.


Analysis and visualisation of movement: an interdisciplinary review


Learning A Second Language with a Socially Assistive Robot


title: Using plane + parallax for calibrating dense camera arrays

abstract: A light field consists of images of a scene taken from different viewpoints. Light fields are used in computer graphics for image-based rendering and synthetic aperture photography, and in vision for recovering shape. In this paper, we describe a simple procedure to calibrate camera arrays used to capture light fields using a plane + parallax framework. Specifically, for the case when the cameras lie on a plane, we show (i) how to estimate camera positions up to an affine ambiguity, and (ii) how to reproject light field images onto a family of planes using only knowledge of planar parallax for one point in the scene. While planar parallax does not completely describe the geometry of the light field, it is adequate for the first two applications which, it turns out, do not depend on having a metric calibration of the light field. Experiments on acquired light fields indicate that our method yields better results than full metric calibration.


title: Enhancing network efficiency lead time reduction in a three-level supply chain

abstract: The study illustrates the supply chain management (SCM) of Canadian companies in the furniture industry, including a reduction of lead times, communication, logistics, and supply chain integration. This paper uses a 2-year case study of a supply chain network composed of one large firm and several small- and medium-sized firms (SMEs). It also uses the Supply Chain Operations Reference (SCOR) model to analyze and evaluate the performance of the supply chain. The results, based on an evaluation of strengths and weaknesses, for the basis for a number of recommendations, which may subsequently be used by the companies concerned to increase their potential of becoming world-class performers. Process integration and standardization, inter-organizational information system (IOIS) implementation, manufacturing process improvement, and logistics are found to be the most critical factors for supporting the supply chain network. In light of these results, the study discusses the implications of the findings and suggests several avenues for future research


title: A Near-Optimal Solution to a Two-Dimensional Cutting Stock Problem

abstract: We present an asymptotic fully polynomial approximation scheme for strip-packing, or packing rectangles into a rectangle of fixed width and minimum height, a classicalNP-hard cutting-stock problem. The algorithm, based on a new linear-programming relaxation, finds a packing ofn rectangles whose total height is within a factor of (1 +e) of optimal (up to an additive term), and has running time polynomial both in n and in 1/ e.


title: Cytoscape Web: an interactive web-based network browser

abstract: UNLABELLED ::: Cytoscape Web is a web-based network visualization tool-modeled after Cytoscape-which is open source, interactive, customizable and easily integrated into web sites. Multiple file exchange formats can be used to load data into Cytoscape Web, including GraphML, XGMML and SIF. ::: ::: ::: AVAILABILITY AND IMPLEMENTATION ::: Cytoscape Web is implemented in Flex/ActionScript with a JavaScript API and is freely available at http://cytoscapeweb.cytoscape.org/.


title: The top-ten in journal impact factor manipulation

abstract: A considerable part of the scientific community is, at least to some degree, involved in the "impact factor game". Editors strive to increase their journals' impact factor (IF) in order to gain influence in the fields of basic and applied research and scientists seek to profit from the "added value" of publishing in top IF journals. In this article we point out the most common "tricks" of engineering and manipulating the IF undertaken by a portion of professionals of the scientific publishing industry. They attempt to increase the nominator or decrease the denominator of the IF equation by taking advantage of certain design flaws and disadvantages of the IF that permit a degree of artificial and arbitrary inflation. Some of these practices, if not scientifically unethical, are at least questionable and should be abandoned. Editors and publishers should strive for quality through fair and thoughtful selection of papers forwarded for peer review and editorial comments that enhance the quality and scientific accuracy of a manuscript.


Natural Language Explanations in Human-Collaborative Systems


title: Sensorless control of PMSM in high speed range with iterative sliding mode observer

abstract: This paper proposes two control algorithms for a sensorless speed control of a PMSM. One is a new low pass filter. This filter is designed to have the variable cutoff frequency according to the rotor speed. And the phase delay angle is so small as to be ignored not only in the low speed region but also in the high speed region including the field weakening region. Sensorless control of a PMSM can be guaranteed without any delay angle by using the proposed low pass filter. The other is a new iterative sliding mode observer (I-SMO). Generally the sliding mode observer (SMO) has the attractive features of the robustness to disturbances, and parameter variations. In the high speed region the switching gain of SMO must be large enough to operate the sliding mode stably. But the estimated currents and back EMF can not help having much ripple or chattering components especially in the high speed region including the flux weakening region. Using I-SMO can reduce chattering components of the estimated currents and back EMF in all speed regions without any help of the expensive hardware such as the high performance DSP and A/D converter. Experimental results show the usefulness of the proposed two algorithms for the sensorless drive system of a PMSM.


title: Automatic Question Generation for Educational Applications – The State of Art

abstract: Recently, researchers from multiple disciplines have been showing their common interest in automatic question generation for educational purposes. In this paper, we review the state of the art of approaches to developing educational applications of question generation. We conclude that although a great variety of techniques on automatic question generation exists, just a small amount of educational systems exploiting question generation has been developed and deployed in real classroom settings. We also propose research directions for deploying the question technology in computer-supported educational systems.


title: Dynamics of biodiversity and mechanisms of change: A plea for balanced attention to form creation and extinction

abstract: Abstract Species and other taxa below the species level are constantly changing. The environment is responsible for most of their variation. Intraspecific forms may appear and disappear within a few generations and even those that became more or less isolated as species are never permanent. In spite of that, species extinction is rarely viewed as opposite to species formation. Conversely, species extinction caused by human activities is hardly ever related to the possible acceleration of new form creation by similar causes. We must keep in mind that the maintenance of biodiversity is not a static phenomenon but a process in which creative forces are as important as destructive ones. The intra- and interspecific creation of altricial and precocial forms, under both natural and unnatural conditions, is demonstrated to be an ongoing process in some fish species. The vulnerability of specialized precocial forms, such as the aurora charr, and the relative resilience of generalized altricial forms, such as the brook charr, will be pointed out. Precocial specialization has been developed to avoid competition in saturated taxocenes in more stable environments whereas altricial generalists are able to cope with environmental extremes in species-poor taxocenes but are less able to withstand competition. In our conservation efforts we should not try to protect the status quo but merely maintain the rate of change at near natural levels—i.e. control human intervention.


title: Design, Analysis, and Prototyping of a New Wound-Rotor Axial Flux Brushless Resolver

abstract: Resolvers are electromagnetic position sensors that are widely used in industrial applications. In this study, a new axial flux brushless resolver (AFBR) with a wound rotor is introduced. In the proposed resolver, the core of the rotary transformer (RT) is omitted, and an innovative design is used to supply the excitation winding of the resolver. The proposed resolver can be built with smaller dimensions, and its thermal stability and mechanical strength are increased compared with conventional AFBRs. The performance of the proposed structure is simulated and optimized by using a three-dimensional time-stepping finite element method. The effect of the leakage flux of the RT on the excitation and signal windings is also discussed for both the proposed and conventional structures. A prototype based on the optimized topology is constructed and tested. Good agreement is obtained between the simulation and experimental results, validating the success of the proposed resolver.


title: Design of wideband single feed truncated corner microstrip patch antennas for circularly polarized applications

abstract: Several single feed truncated corner circularly polarized microstrip antennas with different substrate thickness are studied. It is found that the axial ratio bandwidth could be enhanced considerably when a thicker substrate is used, provided that a U-slot and/or L-probe are used to tune the impedance matching. One of the configurations attains an axial ratio bandwidth (Lt3dB) of about 14% within the impedance matching band when the substrate thickness is about 0.2lambdao.


title: Quantile regression for modelling distributions of profit and loss

abstract: Quantile regression is applied in two retail credit risk assessment exercises exemplifying the power of the technique to account for the diverse distributions that arise in the financial service industry. The first application is to predict loss given default for secured loans, in particular retail mortgages. This is an asymmetric process since where the security (such as a property) value exceeds the loan balance the banks cannot retain the profit, whereas when the security does not cover the value of the defaulting loan then the bank realises a loss. In the light of this asymmetry it becomes apparent that estimating the low tail of the house value is much more relevant for estimating likely losses than estimates of the average value where in most cases no loss is realised. In our application quantile regression is used to estimate the distribution of property values realised on repossession that is then used to calculate loss given default estimates. An illustration is given for a mortgage portfolio from a European mortgage lender. A second application is to revenue modelling. While credit issuing organisations have access to large databases, they also build models to assess the likely effects of new strategies for which, by definition, there is no existing data. Certain strategies are aimed at increasing the revenue stream or decreasing the risk in specific market segments. Using a simple artificial revenue model, quantile regression is applied to elucidate the details of subsets of accounts, such as the least profitable, as predicted from their covariates. The application uses standard linear and kernel smoothed quantile regression.


title: BEYOND ALIGNMENT : A COEVOLUTIONARY VIEW OF THE INFORMATION SYSTEMS STRATEGY PROCESS

abstract: How do organizations achieve and sustain the process of continuous adaptation and change that is necessary to realize strategic information systems alignment? While research has focused on developing deterministic alignment models and on identifying the factors that contribute to alignment, there is little understanding of the process as it evolves over time. In this paper, we propose that coevolution theory offers the opportunity to explore coevolving interactions, interrelationships, and effects as both IS and business strategies evolve. An initial model of this coevolution is presented that applies the key attributes and concepts of coevolution theory to strategic IS alignment. Future directions for advancing our work are highlighted.


title: Packing unequal circles into a strip of minimal length with a jump algorithm

abstract: The paper considers a problem of packing unequal circles into a rectangular strip with fixed width and minimal length. We develop the idea of increasing the dimension of the solution space by assuming radii of circles to be variables. A mathematical model of the problem is constructed and its characteristics are investigated. Taking into account the characteristics we offer a solution strategy of the problem including a number of non-linear programming subproblems of packing circles of variable radii. The solution strategy involves special ways of construction of starting points, calculation of local minima, jump from one local extremum to another, decrease of the problem dimension and rearrangement of pairs of circles. For calculating local extrema an interior point optimizer together with the concept of active inequalities are used. We compare 146 numerical benchmark examples and give seven new ones for 125, 150, 175, 225, 250, 275 and 300 circles.


title: A novel COL1A1 mutation in infantile cortical hyperostosis (Caffey disease) expands the spectrum of collagen-related disorders

abstract: Infantile cortical hyperostosis (Caffey disease) is characterized by spontaneous episodes of subperiosteal new bone formation along 1 or more bones commencing within the first 5 months of life. A genome-wide screen for genetic linkage in a large family with an autosomal dominant form of Caffey disease (ADC) revealed a locus on chromosome 17q21 (LOD score, 6.78). Affected individuals and obligate carriers were heterozygous for a missense mutation (3040Ctwo head right arrowT) in exon 41 of the gene encoding the alpha1(I) chain of type I collagen (COL1A1), altering residue 836 (R836C) in the triple-helical domain of this chain. The same mutation was identified in affected members of 2 unrelated, smaller families with ADC, but not in 2 prenatal cases and not in more than 300 chromosomes from healthy individuals. Fibroblast cultures from an affected individual produced abnormal disulfide-bonded dimeric alpha1(I) chains. Dermal collagen fibrils of the same individual were larger, more variable in shape and size, and less densely packed than those in control samples. Individuals bearing the mutation, whether they had experienced an episode of cortical hyperostosis or not, had joint hyperlaxity, hyperextensible skin, and inguinal hernias resembling symptoms of a mild form of Ehlers-Danlos syndrome type III. These findings extend the spectrum of COL1A1-related diseases to include a hyperostotic disorder.


title: A New Generation of Brain-Computer Interface Based on Riemannian Geometry

abstract: Based on the cumulated experience over the past 25 years in the field of Brain-Computer Interface (BCI) we can now envision a new generation of BCI. Such BCIs will not require training; instead they will be smartly initialized using remote massive databases and will adapt to the user fast and effectively in the first minute of use. They will be reliable, robust and will maintain good performances within and across sessions. A general classification framework based on recent advances in Riemannian geometry and possessing these characteristics is presented. It applies equally well to BCI based on event-related potentials (ERP), sensorimotor (mu) rhythms and steady-state evoked potential (SSEP). The framework is very simple, both algorithmically and computationally. Due to its simplicity, its ability to learn rapidly (with little training data) and its good across-subject and across-session generalization, this strategy a very good candidate for building a new generation of BCIs, thus we hereby propose it as a benchmark method for the field.


title: An effective hybridized classifier for breast cancer diagnosis

abstract: After lung cancer, breast cancer is known to be the greatest cause for death among females [20]. The improving effectiveness of machine learning approaches is being given a lot of importance by medical practitioners for breast cancer diagnosis. The paper proposes an effective hybridized classifier for breast cancer diagnosis. The classifier is made by combining an unsupervised artificial neural network (ANN) method named self organizing maps (SOM) with a supervised classifier called stochastic gradient descent (SGD). Also a comparative analysis is performed between the proposed approach and three supervised state of the art machine learning techniques decision tree (DTs), random forests (RF) and support vector machine (SVM). Initially SGD method is used in isolation for the classification task and then it is made to perform the classification after being hybridized with the unsupervised ANN technique on Wisconsin Breast Cancer Database (WBCD) [10]. The comparison is based up on classification accuracy that is produced by generating a confusion matrix. For verifying consistency of accuracy values, the classification task was repeated with Internet Advertisements Dataset [11]. The results of the classification experimentation using hybridization of SOM with SGD are much more superior to SGD in isolation. All the accuracy values have been computed after achieving a ten-fold cross validation on the both the datasets to further verify the classifier's performance.


title: 3D scanning deformable objects with a single RGBD sensor

abstract: We present a 3D scanning system for deformable objects that uses only a single Kinect sensor. Our work allows considerable amount of nonrigid deformations during scanning, and achieves high quality results without heavily constraining user or camera motion. We do not rely on any prior shape knowledge, enabling general object scanning with freeform deformations. To deal with the drift problem when nonrigidly aligning the input sequence, we automatically detect loop closures, distribute the alignment error over the loop, and finally use a bundle adjustment algorithm to optimize for the latent 3D shape and nonrigid deformation parameters simultaneously. We demonstrate high quality scanning results in some challenging sequences, comparing with state of art nonrigid techniques, as well as ground truth data.


title: Modified High-Power Nanosecond Marx Generator Prevents Destructive Current Filamentation

abstract: A traditional Marx circuit (TMC) based on avalanche transistors with a shortened emitter and a base was investigated numerically by using a two-dimensional (2-D) physics-based approach and experimentally, and compared with a special Marx circuit (SMC) suggested here, in which an intrinsic base triggering of all the stages protects the transistors, especially the second one, from thermal destruction due to current filamentation. This is because the entire emitter–base perimeter in the SMC participates in switching, whereas in a TMC the switching is initiated across the entire area of the emitter but then changes to current filamentation due to certain 3-D transient effects reported earlier. Very significant difference in local transient overheating in the transistors operating in TMC and SMC determines the difference in reliability of those two pulse generators. The results suggest a new circuit design for improving reliability and explain the difference in the operating mode of different transistors in the chain which makes the second transistor most prone to destructive thermal filamentation. This new understanding points additionally to ways of optimizing the design of the transistors to be used in a Marx circuit.


title: User acceptance of mobile internet based on the Unified Theory of Acceptance and Use of Technology: Investigating the determinants and gender differences

abstract: Based on the Unified Theory of Acceptance and Use of Technology Model (UTAUT; Venkatesh, Morris, Davis, & Davis, 2003), the purpose of this study was to investigate the determinants of mobile Internet (m-Internet) acceptance and to understand whether or not there are gender differences in the acceptance of m-Internet. Data collected online from 343 respondents in Taiwan were tested against the research model, using the structural equation modeling approach. The proposed model was mostly supported by the empirical data. The findings of this study provide several crucial implications for m-Internet service practitioners and researchers.


Sueviota tubicola, a new species of coral-reef goby(Teleostei: Gobiidae) from Papua New Guinea


title: Lowry, Romans & Curtis/Scientometric Study of IS Journals IS RESEARCH PERSPECTIVES ARTICLE Global Journal Prestige and Supporting Disciplines: A Scientometric Study of Information Systems Journals ∗ Abstract

abstract: Many argue that the Information Systems (IS) field is at a critical juncture in its evolving identity. In debating whether the IS field is in crisis, we agree with Hirschheim and Klein (2003) that reflective analysis will contribute to the field's continued prosperity. Indeed, reflective analysis is needed to evaluate the journals of the field as well as IS journal rankings, which evaluate the effectiveness and productivity of researchers and the effectiveness and productivity of journals in communicating research results. After all, where and how we publish are fundamental aspects of the identity of the IS field - reflecting our value systems, paradigms, cultural practices, reward systems, political hierarchy, and aspirations.This article reviews the results of the largest global, scientometric survey to date of IS journal rankings that targeted 8741 faculty from 414 IS departments world-wide, and resulted in 2559 responses, or a 32% response rate. Rather than using predetermined journal lists, the study required respondents to freely recall their top-four research journals.This research improves on the usual scientometric journal ranking studies by providing a foundation for further reflection and self-analysis. For instance, it first examines the global structure of the IS field and investigates perceptions among global IS academics concerning current research outlets. Specific results then illustrate the values and cultural norms in the global IS community that affect the evaluation of research and publication outlets. Finally, in addition to rankings of scholarly journals by the entire world-wide sample of IS academics, rankings are provided for top IS practitioner journals, most frequently read IS journals, top journals for the major IS supporting disciplines, and top journals by world region.


title: Angr - The Next Generation of Binary Analysis

abstract: Software is becoming increasingly more complex, and vul-nerabilities more subtle Better approaches are required to effectively analyze modern binaries, efficiently identify deeply buried defects, and intelligently assist human analysts with specific software reversing tasks Tons of good techniques and approaches regarding binary analysis have recently emerged from both academia and industry, many of which are fairly applicable to real-world binary research tasks However, due to the lack of a flexible and approachable binary analysis platform, testing and applying these techniques becomes a difficult job. angr is the next-generation binary analysis platform devel-oped by the SecLab of University of California, Santa Barbara It is flexible, easy to work with, cross-platform and cross-architecture, and has many techniques from academia already implemented and embedded inside In this course, we will start about the fundamental underpinnings of angr, dynamic symbolic execution, and static binary analysis We will then demonstrate best practices in doing symbolic execution and data dependence tracking in angr In the end, we will show how angr can assist in bug hunting All demos will be performed on CTF challenges and real-world programs.


title: Motor Development and Physical Activity: A Longitudinal Discordant Twin-Pair Study.

abstract: INTRODUCTION ::: Previous longitudinal research suggests that motor proficiency in early life predicts physical activity in adulthood. Familial effects including genetic and environmental factors could explain the association, but no long-term follow-up studies have taken into account potential confounding by genetic and social family background. The present twin study investigated whether childhood motor skill development is associated with leisure-time physical activity levels in adulthood independent of family background. ::: ::: ::: METHODS ::: Altogether, 1550 twin pairs from the FinnTwin12 study and 1752 twin pairs from the FinnTwin16 study were included in the analysis. Childhood motor development was assessed by the parents' report of whether one of the co-twins had been ahead of the other in different indicators of motor skill development in childhood. Leisure-time physical activity (MET·h·d) was self-reported by the twins in young adulthood and adulthood. Statistical analyses included conditional and ordinary linear regression models within twin pairs. ::: ::: ::: RESULTS ::: Using all activity-discordant twin pairs, the within-pair difference in a sum score of motor development in childhood predicted the within-pair difference in the leisure-time physical activity level in young adulthood (P < 0.001). Within specific motor development indicators, learning to stand unaided earlier in infancy predicted higher leisure-time MET values in young adulthood statistically significantly in both samples (FinnTwin12, P = 0.02; and FinnTwin16, P = 0.001) and also in the pooled data set of the FinnTwin12 and FinnTwin16 studies (P < 0.001). Having been more agile than the co-twin as a child predicted higher leisure-time MET values up to adulthood (P = 0.03). ::: ::: ::: CONCLUSIONS ::: More advanced childhood motor development is associated with higher leisure-time MET values in young adulthood at least partly independent of family background in both men and women.


title: Bridgeless half-bridge AC-DC converter with series-connected two transformers

abstract: This paper proposes a bridgeless half-bridge ac-dc converter with series-connected two transformers. The proposed converter integrates the operation of the bridgeless power factor correction (PFC) boost rectifier and the asymmetrical pulse-width modulation half-bridge dc-dc converter. The proposed converter provides an isolated dc output voltage without any full-bridge diode rectifier. Conduction losses are lowered by eliminating the full-bridge diode rectifier. Switching losses are reduced by achieving zero-voltage switching of the power switches. To improve the power efficiency of the bridgeless half-bridge ac-dc converter, this paper presents the study results about the bridgeless half-bridge ac-dc converter with the series-connected two transformers. By using series-connected two transformers, the proposed convert has a simpler power circuit. The proposed converter gives a high efficiency, high power factor, and low cost. The proposed converter achieves a high efficiency of 93.5 % for 250 W (50 V / 5 A) output power at 90 Vrms line voltage.


title: Guillain-Barré Syndrome in India: population-based validation of the Brighton criteria.

abstract: OBJECTIVE ::: Case definitions of GBS were recently developed in response to the 2009 H1N1 vaccination programme but have undergone limited field testing. We validate the sensitivity of the Brighton Working Group case definitions for Guillain-Barré Syndrome (GBS) using a population-based cohort in India. ::: ::: ::: METHODS ::: The National Polio Surveillance Unit of India actively collects all cases of acute flaccid paralysis (AFP) in children <15 years old, including cases of GBS. Cases of GBS with available cerebrospinal fluid (CSF) and nerve conduction studies (NCS) results, neurological examination, clinical history, and exclusion of related diagnoses were selected (2002-2003). Relevant data were abstracted and entered into a central database. Sensitivity of the Brighton GBS criteria for level 3 of diagnostic certainty which requires no clinical laboratory testing, level 2 which employs CSF or NCS, and level 1 which employs both, were calculated. ::: ::: ::: RESULTS ::: 79 cases of GBS (mean age 6.5 years, range 4.0-14.5; 39% female) met the case definition. GBS cases were ascending (79%), symmetrical (85%), and bilateral (100%); involving lower extremity hypotonia (86%) and weakness (100%), upper extremity hypotonia (62%) and weakness (80%), areflexia/hyporeflexia (88%), respiratory muscles (22%), bulbar muscles (22%), and cranial nerves (13%). Four limbs were involved in 80% of cases. Mean time to maximal weakness was 5.2 days (range 0.5-30 days) with nadir GBS disability scores of 3 (7%), 4 (67%), 5 (15%), 6 (10%), or unclear (1%). CSF (mean time to lumbar puncture 29 days) was normal in 29% with cytoalbuminologic dissociation in 65% (mean protein 105 mg/dL, range 10-1000; mean cell count 11/μL, range 0-220, n=4 with >50 cells/μL). Significant improvement occurred in 73% whereas death (9%) occurred 6-29 days after sensorimotor symptom onset. The majority of cases (86%) fulfilled Brighton level 3, level 2 (84%), and level 1 (62%) of diagnostic certainty. ::: ::: ::: CONCLUSION ::: The diagnosis of GBS can be made using Brighton Working Group criteria in India with moderate to high sensitivity. Brighton Working Group case definitions are a plausible standard for capturing a majority of cases of GBS in field operations in low income settings during AFP surveillance.


Evaluation of Biodiesel Production from Babassu Oil and Ethanol Applying Alkaline Transesterification Under Ultrasonic Technology


title: Anti-steganalysis for image on convolutional neural networks

abstract: Nowadays, convolutional neural network (CNN) based steganalysis methods achieved great performance. While those methods are also facing security problems. In this paper, we proposed an attack scheme aiming at CNN based steganalyzer including two different attack methods 1) the LSB-Jstego Gradient Based Attack; 2) LSB-Jstego Evolutionary Algorithms Based Attack. The experiment results show that the attack strategies could achieve 96.02% and 90.25% success ratio separately on the target CNN. The proposed attack scheme is an effective way to fool the CNN based steganalyzer and in addition demonstrates the vulnerability of the neural networks in steganalysis.


title: HAPPY PEOPLE BECOME HAPPIER THROUGH KINDNESS: A COUNTING KINDNESSES INTERVENTION

abstract: We examined the relationship between the character strength of kindness and subjective happiness (Study 1), and the effects of a counting kindnesses intervention on subjective happiness (Study 2). In Study 1, participants were 175 Japanese undergraduate students and in Study 2, participants were 119 Japanese women (71 in the intervention group and 48 in the control group). Results showed that: (a) Happy people scored higher on their motivation to perform, and their recognition and enactment of kind behaviors. (b) Happy people have more happy memories in daily life in terms of both quantity and quality. (c) Subjective happiness was increased simply by counting one’s own acts of kindness for one week. (d) Happy people became more kind and grateful through the counting kindnesses intervention. Discussion centers on the importance of kindness in producing subjective happiness.


title: Training IBM Watson using Automatically Generated Question-Answer Pairs

abstract: IBM Watson is a cognitive computing system capable of question answering in natural languages. It is believed that IBM Watson can understand large corpora and answer relevant questions more effectively than any other question-answering system currently available. To unleash the full power of Watson, however, we need to train its instance with a large number of well-prepared question-answer pairs. Obviously, manually generating such pairs in a large quantity is prohibitively time consuming and significantly limits the efficiency of Watson's training. Recently, a large-scale dataset of over 30 million question-answer pairs was reported. Under the assumption that using such an automatically generated dataset could relieve the burden of manual question-answer generation, we tried to use this dataset to train an instance of Watson and checked the training efficiency and accuracy. According to our experiments, using this auto-generated dataset was effective for training Watson, complementing manually crafted question-answer pairs. To the best of the authors' knowledge, this work is the first attempt to use a large-scale dataset of automatically generated question-answer pairs for training IBM Watson. We anticipate that the insights and lessons obtained from our experiments will be useful for researchers who want to expedite Watson training leveraged by automatically generated question-answer pairs.


title: Online Learning in Markov Decision Processes with Changing Cost Sequences

abstract: In this paper we consider online learning in finite Markov decision processes (MDPs) with changing cost sequences under full and bandit-information. We propose to view this problem as an instance of online linear optimization. We propose two methods for this problem: MD2 (mirror descent with approximate projections) and the continuous exponential weights algorithm with Dikin walks. We provide a rigorous complexity analysis of these techniques, while providing near-optimal regret-bounds (in particular, we take into account the computational costs of performing approximate projections in MD2). In the case of full-information feedback, our results complement existing ones. In the case of bandit-information feedback we consider the online stochastic shortest path problem, a special case of the above MDP problems, and manage to improve the existing results by removing the previous restrictive assumption that the state-visitation probabilities are uniformly bounded away from zero under all policies.


title: Cloud Password Manager Using Privacy-Preserved Biometrics

abstract: Using one password for all web services is not secure because the leakage of the password compromises all the web services accounts, while using independent passwords for different web services is inconvenient for the identity claimant to memorize. A password manager is used to address this security-convenience dilemma by storing and retrieving multiple existing passwords using one master password. On the other hand, a password manager liberates human brain by enabling people to generate strong passwords without worry about memorizing them. While a password manager provides a convenient and secure way to managing multiple passwords, it centralizes the passwords storage and shifts the risk of passwords leakage from distributed service providers to a software or token authenticated by a single master password. Concerned about this one master password based security, biometrics could be used as a second factor for authentication by verifying the ownership of the master password. However, biometrics based authentication is more privacy concerned than a non-biometric password manager. In this paper we propose a cloud password manager scheme exploiting privacy enhanced biometrics, which achieves both security and convenience in a privacy-enhanced way. The proposed password manager scheme relies on a cloud service to synchronize all local password manager clients in an encrypted form, which is efficient to deploy the updates and secure against untrusted cloud service providers.


title: Decentralized Coordination Control for Parallel Bidirectional Power Converters in a Grid-Connected DC Microgrid

abstract: In the grid-connected mode, the dc microgrid is connected to the utility by parallel bidirectional power converters (BPCs), which provide voltage support for the dc microgrid. The coordination control for these parallel BPCs is very challenging. First, for the parallel BPCs, there is circulating current among them, which limits the total system capacity and may damage switching devices. Second, taking the constant power load into consideration, the performance especially the stability of the dc microgrid is degraded if the conventional ${\mathbf i}_{\boldsymbol {dc}}{\mathbf -}{\mathbf v}_{\boldsymbol {dc}}$ droop is adopted to share the power. Third, the voltage restoration should be faster for the better quality of dc voltage, and the restoration strategy should be scalable to the changes of the dc microgrid topology for the better flexibility. For these problems, this paper proposes a decentralized control method for the parallel BPCs, which can make the BPCs operate in coordination and provide voltage support for the dc microgrid well. Furthermore, the reliability of the proposed control method is illustrated by the stability analysis. All the conclusions are verified by the real-time hardware-in-loop tests.


title: Parallel Stream Processing Against Workload Skewness and Variance

abstract: Key-based workload partitioning is a common strategy used in parallel stream processing engines, enabling effective key-value tuple distribution over worker threads in a logical operator. It is likely to generate poor balancing performance when workload variance occurs on the incoming data stream. This paper presents a new key-based workload partitioning framework, with practical algorithms to support dynamic workload assignment for stateful operators. The framework combines hash-based and explicit key-based routing strategies for workload distribution, which specifies the destination worker threads for a handful of keys and assigns the other keys with the hash function. When short-term distribution fluctuations occur to the incoming data stream, the system adaptively updates the routing table containing the chosen keys, in order to rebalance the workload with minimal migration overhead within the stateful operator. We formulate the rebalance operation as an optimization problem, with multiple objectives on minimizing state migration costs, controlling the size of the routing table and breaking workload imbalance among worker threads. Despite of the NP-hardness nature behind the optimization formulation, we carefully investigate and justify the heuristics behind key (re)routing and state migration, to facilitate fast response to workload variance with ignorable cost to the normal processing in the distributed system. Empirical studies on synthetic data and real-world stream applications validate the usefulness of our proposals.


title: A Low-Cost Wideband 77-GHz Planar Butler Matrix in SIW Technology

abstract: This communication presents a low-cost 77-GHz switched-beam slot antenna array driven by a Butler matrix. In the proposed configuration, four broadband couplers are combined and crossovers are effectively avoided. In this case, the overall circuit and beamforming network losses are considerably reduced. A 4 × 4 planar SIW Butler matrix is designed and demonstrated for integrated beamforming network applications, which exhibits about 7 degrees phase error and ±0.75 dB coupling imbalance over 11% bandwidth. This experimentally prototyped matrix is integrated with a four-array slot antenna on the same substrate. An alternating displacement of the slots in subsequent radiating waveguides is proposed to save the arrangement of the input ports and to achieve broadband performances. Measured radiation patterns are in good agreement with simulated counterparts over the proposed 77-GHz frequency range.


title: Permanent Magnet Vernier Machines: A Review

abstract: Permanent-magnet Vernier machines (PMVMs) gained a lot of interest over the past couple of decades. This is mainly due to their high torque density enabled by magnetic gearing effect. This paper will provide a thorough review of recent advances in PMVMs. This review will cover principle of operation and nature of magnetic gearing in PMVMs, and better understanding of novel PMVMs topologies using different winding configuration as well as different modulation poles and rotor structures. Detailed discussions on choice of gear ratio/slot-pole combinations, design optimization and role of advanced materials in PMVMs will be presented. This will provide an update on the current state of the art as well as future areas of research. Furthermore, design issues, fault tolerance as well as cost reduction will be discussed highlighting the gap between the current state of the art and what is needed in practical applications.


title: Re-architecting datacenter networks and stacks for low latency and high performance

abstract: Modern datacenter networks provide very high capacity via redundant Clos topologies and low switch latency, but transport protocols rarely deliver matching performance. We present NDP, a novel data-center transport architecture that achieves near-optimal completion times for short transfers and high flow throughput in a wide range of scenarios, including incast. NDP switch buffers are very shallow and when they fill the switches trim packets to headers and priority forward the headers. This gives receivers a full view of instantaneous demand from all senders, and is the basis for our novel, high-performance, multipath-aware transport protocol that can deal gracefully with massive incast events and prioritize traffic from different senders on RTT timescales. We implemented NDP in Linux hosts with DPDK, in a software switch, in a NetFPGA-based hardware switch, and in P4. We evaluate NDP's performance in our implementations and in large-scale simulations, simultaneously demonstrating support for very low-latency and high throughput.


title: Spatial computation

abstract: This paper describes a computer architecture, Spatial Computation (SC), which is based on the translation of high-level language programs directly into hardware structures. SC program implementations are completely distributed, with no centralized control. SC circuits are optimized for wires at the expense of computation units.In this paper we investigate a particular implementation of SC: ASH (Application-Specific Hardware). Under the assumption that computation is cheaper than communication, ASH replicates computation units to simplify interconnect, building a system which uses very simple, completely dedicated communication channels. As a consequence, communication on the datapath never requires arbitration; the only arbitration required is for accessing memory. ASH relies on very simple hardware primitives, using no associative structures, no multiported register files, no scheduling logic, no broadcast, and no clocks. As a consequence, ASH hardware is fast and extremely power efficient.In this work we demonstrate three features of ASH: (1) that such architectures can be built by automatic compilation of C programs; (2) that distributed computation is in some respects fundamentally different from monolithic superscalar processors; and (3) that ASIC implementations of ASH use three orders of magnitude less energy compared to high-end superscalar processors, while being on average only 33% slower in performance (3.5x worst-case).


title: From Retinex to Automatic Color Equalization : issues in developing a new algorithm for unsupervised color equalization

abstract: We present a comparison between two color equalization algorithms: Retinex, the famous model due to Land and McCann, and Automatic Color Equalization (ACE), a new algorithm recently presented by the authors. These two algorithms share a common approach to color equalization, but different computational models. We introduce the two models focusing on differences and common points. An analysis of their computational characteristics illustrates the way the Retinex approach has influenced ACE structure, and which aspects of the first algorithm have been modified in the second one and how. Their interesting equalization properties, like lightness and color constancy, image dynamic stretching, global and local filtering, and data driven dequantization, are qualitatively and quantitatively presented and compared, together with their ability to mimic the human visual system. © 2004 SPIE and IS&T.


title: An emerging trend of big data analytics with health insurance in India

abstract: For the last century, the healthcare services and delivery including financing in India has been shrouded by life insurance challenges and importantly, shares key landmarks with general insurance. Many experts, practitioners and policy makers including researchers identified that, there is a big gap in the knowledge about innovations in public and private health financing and delivery. Apparent entails the necessity to alter the prevailing structure of the present health care services by applying “Big Data Analytics”. In this paper we'll discuss the various assorted implications, characteristics beside decrease in healthcare costs and how this new era of advanced and improved Data management is helping in Insurance Sector, more even we have to pay specific attention to the use cases which leads and drive new technology and eventually lead towards economic progress.


title: Data-driven contextual modeling for 3D scene understanding

abstract: The recent development of fast depth map fusion technique enables the realtime, detailed scene reconstruction using commodity depth camera, making the indoor scene understanding more possible than ever. To address the specific challenges in object analysis at subscene level, this work proposes a data-driven approach to modeling contextual information covering both intra-object part relations and inter-object object layouts. Our method combines the detection of individual objects and object groups within the same framework, enabling contextual analysis without knowing the objects in the scene a priori. The key idea is that while contextual information could benefit the detection of either individual objects or object groups, both can contribute to object extraction when objects are unknown.Our method starts with a robust segmentation and partitions a subscene into segments, each of which represents either an independent object or a part of some object. A set of classifiers are trained for both individual objects and object groups, using a database of 3D scene models. We employ the multiple kernel learning (MKL) to learn per-category optimized classifiers for objects and object groups. Finally, we perform a graph matching to extract objects using the classifiers, thus grouping the segments into either an object or an object group. The output is an object-level labeled segmentation of the input subscene. Experiments demonstrate that the unified contextual analysis framework achieves robust object detection and recognition over cluttered subscenes. Graphical abstractDisplay Omitted HighlightsTo address the specific challenges in object analysis at subscene level, this work proposes a data-driven approach to modeling contextual information covering both intra-object part relations and inter-object object layouts.our method combines the detection of individual objects and object groups within the same framework, enabling contextual analysis without knowing objects.The key idea is that while contextual information could benefit the detection of either individual objects or object groups, when object are unknown, both can contribute to object extraction.


title: Reducing Internet Latency: A Survey of Techniques and Their Merits

abstract: Latency is increasingly becoming a performance bottleneck for Internet Protocol (IP) networks, but historically, networks have been designed with aims of maximizing throughput and utilization. This paper offers a broad survey of techniques aimed at tackling latency in the literature up to August 2014, as well as their merits. A goal of this work is to be able to quantify and compare the merits of the different Internet latency reducing techniques, contrasting their gains in delay reduction versus the pain required to implement and deploy them. We found that classifying techniques according to the sources of delay they alleviate provided the best insight into the following issues: 1) The structural arrangement of a network, such as placement of servers and suboptimal routes, can contribute significantly to latency; 2) each interaction between communicating endpoints adds a Round Trip Time (RTT) to latency, particularly significant for short flows; 3) in addition to base propagation delay, several sources of delay accumulate along transmission paths, today intermittently dominated by queuing delays; 4) it takes time to sense and use available capacity, with overuse inflicting latency on other flows sharing the capacity; and 5) within end systems, delay sources include operating system buffering, head-of-line blocking, and hardware interaction. No single source of delay dominates in all cases, and many of these sources are spasmodic and highly variable. Solutions addressing these sources often both reduce the overall latency and make it more predictable.


title: 5G uplink performance of filter bank multi-carrier

abstract: One of 5G communication typical scenarios is machine-to-machine communication with large number of devices, which proposes new requirements for uplink transmission. Filter bank multi-carrier with offset quadrature amplitude modulation (FBMC/OQAM) is one of the popular nonorthogonal waveform candidates for 5G transmission. In this paper, FBMC/OQAM and cyclic prefix OFDM with single antenna settings are comprehensively evaluated and compared in uplink transmission form four aspects: power spectral density, timing and frequency misalignments, guard bands between adjacent users and time efficiency. At last, our comparison results show that FBMC is more suitable for 5G uplink multi-user asynchronous transmissions than CP-OFDM.


title: SDN/NFV-enabled satellite communications networks

abstract: In the context of next generation 5G networks, the satellite industry is clearly committed to revisit and revamp the role of satellite communications. As major drivers in the evolution of (terrestrial) fixed and mobile networks, Software Defined Networking (SDN) and Network Function Virtualisation (NFV) technologies are also being positioned as central technology enablers towards improved and more flexible integration of satellite and terrestrial segments, providing satellite network further service innovation and business agility by advanced network resources management techniques. Through the analysis of scenarios and use cases, this paper provides a description of the benefits that SDN/NFV technologies can bring into satellite communications towards 5G. Three scenarios are presented and analysed to delineate different potential improvement areas pursued through the introduction of SDN/NFV technologies in the satellite ground segment domain. Within each scenario, a number of use cases are developed to gain further insight into specific capabilities and to identify the technical challenges stemming from them.


title: Violence on the Brain: A Critique of Neuroscience in Criminal Law

abstract: Is there such a thing as a criminally “violent brain”? Does it make sense to speak of “the neurobiology of violence” or the “psychopathology of crime”? Is it possible to answer on a physiological level what makes one person engage in criminal violence and another not, under similar circumstances?Current research in law and neuroscience is promising to answer these questions with a “yes.” Some legal scholars working in this area claim that we are close to realizing the “early criminologists' dream of identifying the biological roots of criminality.” These hopes for a neuroscientific transformation of the criminal law, although based in the newest research, are continuous with a long-standing history. Criminal law and neuroscience have been engaged in an ill-fated and sometimes tragic affair for over two hundred years. Three issues have recurred that track those that bedeviled earlier efforts to ground criminal law in brain sciences. First is the claim that the brain is often the most relevant or fundamental level at which to understand criminal conduct. Second is that the various phenomena we call “criminal violence” arise causally from dysfunction within specific locations in the brain (“localization”). Third is the related claim that, because much violent criminality arises from brain dysfunction, people who commit such acts are biologically different from typical people (“alterity” or “otherizing”).This Article first demonstrates parallels between certain current claims about the neurobiology of criminal violence and past movements that were concerned with the law and neuroscience of violence: phrenology, Lombrosian biological criminology, and lobotomy. It then engages in a substantive review and critique of several current claims about the neurological bases of criminal violence. Drawing on research and interviews with neuroscientists, this Article shows that causally localizing what we call “criminal violence” to bits of the brain is scientifically contestable and epistemologically untenable. In viewing the criminal law-neuroscience relationship through the lens of history of science, this Article hopes to offer a constructive portrait of how current neuroscience might inform criminal law discourse about regulating violence.


title: Digital image processing techniques for detecting, quantifying and classifying plant diseases

abstract: ABSTRACT ::: This paper presents a survey on methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum. Although disease symptoms can manifest in any part of the plant, only methods that explore visible symptoms in leaves and stems were considered. This was done for two main reasons: to limit the length of the paper and because methods dealing with roots, seeds and fruits have some peculiarities that would warrant a specific survey. The selected proposals are divided into three classes according to their objective: detection, severity quantification, and classification. Each of those classes, in turn, are subdivided according to the main technical solution used in the algorithm. This paper is expected to be useful to researchers working both on vegetable pathology and pattern recognition, providing a comprehensive and accessible overview of this important field of research.


Research Problems Associated with Big Data Utilization in Logistics and Supply Chains Design and Management.


title: CallRank: Combating SPIT using call duration, social networks and global reputation

abstract: The growing popularity of IP telephony systems has made them attractive targets for spammers. Voice call spam, also known as Spam over Internet Telephony (SPIT), is potentially a more serious problem than email spam because of the real time processing requirements of voice packets. We explore a novel mechanism that uses duration of calls between users to combat SPIT. CallRank, the scheme proposed by us, uses call duration to establish social network linkages and global reputations for callers, based on which call recipients can decide whether the caller is legitimate or not. CallRank has been implemented within a VoIP system simulation and our results show that we are able to achieve a false negative rate of 10% and a false positive rate of 3% even in the presence of a significant fraction of spammers.


title: High speed locomotion for a quadrupedal microrobot

abstract: Research over the past several decades has elucidated some of the mechanisms behind high speed, highly efficient, and robust locomotion in insects such as cockroaches. Roboticists have used this information to create biologically inspired machines capable of running, jumping, and climbing robustly over a variety of terrains. To date, little work has been done to develop an at-scale insect-inspired robot capable of similar feats due to challenges in fabrication, actuation, and electronics integration for a centimeter-scale device. This paper addresses these challenges through the design, fabrication, and control of a 1.27 g walking robot, the Harvard Ambulatory MicroRobot (HAMR). The current design is manufactured using a method inspired by pop-up books that enables fast and repeatable assembly of the miniature walking robot. Methods to drive HAMR at low and high speeds are presented, resulting in speeds up to 0.44 m/s (10.1 body lengths per second) and the ability to maneuver and control the robot along desired trajectories.


title: Design of a slot antenna for future 5G wireless communication systems

abstract: In this study, a square-shaped slot antenna is designed for the future fifth generation (5G) wireless applications. The antenna has a compact size of 0.64λ g × 0.64λ g at 38 GHz, which consists of ellipse shaped radiating patch fed by a 50 Q micro-strip line on the Rogers RT5880 substrates. A rectangle shaped slot is etched in the ground plane to enhance the antenna bandwidth. In order to obtain better impedance matching bandwidth of the antennas, some small circular radiating patches are added to the square-shaped slot. Simulations show that the measured impedance bandwidth of the proposed antenna ranges from 20 to 42 GHz for a reflection coefficient of Su less than −10dB which is cover 5G bands (28/38GHz). The proposed antenna provides almost omni-directional patterns, relatively flat gain, and high radiation efficiency through the frequency band.


title: Culture Matters: Factors Affecting the Adoption of Telemedicine

abstract: The present research investigates the influence of culture on telemedicine adoption and patient information privacy, security, and policy. The results, based on the SEM analysis of the data collected in the United States, demonstrate that culture plays a significant role in telemedicine adoption. The results further show that culture also indirectly influences telemedicine adoption through information security, information privacy, and information policy. Our empirical results further indicate that information security, privacy, and policy impact telemedicine adoption.


title: Determinants of successful knowledge management programs

abstract: The main objective of this paper is to investigate and identify the main determinants of successful knowledge management (KM) programs. We draw upon the institutional theory and the theory of technology assimilation to develop an integrative model of KM success that clarifies the role of information technology (IT) in relation to other important KM infrastructural capabilities and to KM process capabilities. We argue that the role of IT cannot be studied in isolation and that the effect of IT on KM success is fully mediated by KM process capabilities. The research model is tested with a survey study involving 191 KM practitioners. The empirical results provided strong support for the model. In addition to its theoretical contributions, this study also presents important practical implications through the identification of specific infrastructural capabilities leading to KM success.


title: Characterising Lithium-Ion Battery Degradation through the Identification and Tracking of Electrochemical Battery Model Parameters

abstract: Lithium-ion (Li-ion) batteries undergo complex electrochemical and mechanical degradation. This complexity is pronounced in applications such as electric vehicles, where highly demanding cycles of operation and varying environmental conditions lead to non-trivial interactions of ageing stress factors. This work presents the framework for an ageing diagnostic tool based on identifying and then tracking the evolution of model parameters of a fundamental electrochemistry-based battery model from non-invasive voltage/current cycling tests. In addition to understanding the underlying mechanisms for degradation, the optimisation algorithm developed in this work allows for rapid parametrisation of the pseudo-two dimensional (P2D), Doyle-Fuller-Newman, battery model. This is achieved through exploiting the embedded symbolic manipulation capabilities and global optimisation methods within MapleSim. Results are presented that highlight the significant reductions in the computational resources required for solving systems of coupled non-linear partial differential equations.


title: Tocilizumab for induction and maintenance of remission in giant cell arteritis: a phase 2, randomised, double-blind, placebo-controlled trial

abstract: BACKGROUND ::: Giant cell arteritis is an immune-mediated disease of medium and large-sized arteries that affects mostly people older than 50 years of age. Treatment with glucocorticoids is the gold-standard and prevents severe vascular complications but is associated with substantial morbidity and mortality. Tocilizumab, a humanised monoclonal antibody against the interleukin-6 receptor, has been associated with rapid induction and maintenance of remission in patients with giant cell arteritis. We therefore aimed to study the efficacy and safety of tocilizumab in the first randomised clinical trial in patients with newly diagnosed or recurrent giant cell arteritis. ::: ::: ::: METHODS ::: In this single centre, phase 2, randomised, double-blind, placebo-controlled trial, we recruited patients aged 50 years and older from University Hospital Bern, Switzerland, who met the 1990 American College of Rheumatology criteria for giant cell arteritis. Patients with new-onset or relapsing disease were randomly assigned (2:1) to receive either tocilizumab (8 mg/kg) or placebo intravenously. 13 infusions were given in 4 week intervals until week 52. Both groups received oral prednisolone, starting at 1 mg/kg per day and tapered down to 0 mg according to a standard reduction scheme defined in the study protocol. Allocation to treatment groups was done using a central computerised randomisation procedure with a permuted block design and a block size of three, and concealed using central randomisation generated by the clinical trials unit. Patients, investigators, and study personnel were masked to treatment assignment. The primary outcome was the proportion of patients who achieved complete remission of disease at a prednisolone dose of 0·1 mg/kg per day at week 12. All analyses were intention to treat. This trial is registered with ClinicalTrials.gov, number NCT01450137. ::: ::: ::: RESULTS ::: Between March 3, 2012, and Sept 9, 2014, 20 patients were randomly assigned to receive tocilizumab and prednisolone, and ten patients to receive placebo and glucocorticoid; 16 (80%) and seven (70%) patients, respectively, had new-onset giant cell arteritis. 17 (85%) of 20 patients given tocilizumab and four (40%) of ten patients given placebo reached complete remission by week 12 (risk difference 45%, 95% CI 11-79; p=0·0301). Relapse-free survival was achieved in 17 (85%) patients in the tocilizumab group and two (20%) in the placebo group by week 52 (risk difference 65%, 95% CI 36-94; p=0·0010). The mean survival-time difference to stop glucocorticoids was 12 weeks in favour of tocilizumab (95% CI 7-17; p<0·0001), leading to a cumulative prednisolone dose of 43 mg/kg in the tocilizumab group versus 110 mg/kg in the placebo group (p=0·0005) after 52 weeks. Seven (35%) patients in the tocilizumab group and five (50%) in the placebo group had serious adverse events. ::: ::: ::: INTERPRETATION ::: Our findings show, for the first time in a trial setting, the efficacy of tocilizumab in the induction and maintenance of remission in patients with giant cell arteritis. ::: ::: ::: FUNDING ::: Roche and the University of Bern.


title: True Inline Cross-Coupled Coaxial Cavity Filters

abstract: In this paper, a novel true inline configuration for cross-coupled coaxial cavity filters is presented, which is characterized by a simple and compact structure, improved performance, and good tunability. Instead of using folded structures, dedicated coupling probes, or extra cavities, as required by conventional techniques, cross coupling is realized by changing the orientation of selected resonators. Sequential coupling between adjacent resonators and cross coupling between nonadjacent resonators are effectively controlled by introducing small metal plates at different locations. A six-pole bandpass filter with two transmission zeros was built and tested. The measurement and simulation results agree very well, demonstrating feasibility of the inline filter configuration. It enables compact design with improved resonator Q compared to conventional combline filters. Furthermore, cross coupling can be readily adjusted using tuning elements.


title: Levodopa enhances synaptic plasticity in the substantia nigra pars reticulata of Parkinson's disease patients.

abstract: Parkinson's disease, caused by the loss of dopaminergic nigrostriatal projections, is a debilitating neurodegenerative disease characterized by bradykinesia, rigidity, tremor and postural instability. The dopamine precursor levodopa (L-dopa) is the most effective treatment for the amelioration of Parkinson's disease signs and symptoms, but long-term administration can lead to disabling motor fluctuations and L-dopa -induced dyskinesias (LIDs). Studies in rat striatal slices have shown dopamine to be an essential component of activity-dependent synaptic plasticity at the input to the basal ganglia, but dopamine is also released from ventrally projecting dendrites of the substantia nigra pars compacta (SNc) on the substantia nigra pars reticulata (SNr), a major output structure of the basal ganglia. We characterized synaptic plasticity in the SNr using field potentials evoked with a nearby microelectrode (fEPs), in 18 Parkinson's disease patients undergoing implantation of deep brain stimulating (DBS) electrodes in the subthalamic nucleus (STN). High frequency stimulation (HFS--four trains of 2 s at 100 Hz) in the SNr failed to induce a lasting change in test fEPs (1 Hz) amplitudes in patients OFF medication (decayed to baseline by 160 s). Following oral L-dopa administration, HFS induced a potentiation of the fEP amplitudes (+29.3% of baseline at 160 s following a plateau). Our findings suggest that extrastriatal dopamine modulates activity-dependent synaptic plasticity at basal ganglia output neurons. Dopamine medication state clearly impacts fEP amplitude, and the lasting nature of the increase is reminiscent of LTP-like changes, indicating that aberrant synaptic plasticity may play a role in the pathophysiology of Parkinson's disease.


title: Simgrid: a toolkit for the simulation of application scheduling

abstract: Advances in hardware and software technologies have made it possible to deploy parallel applications over increasingly large sets of distributed resources. Consequently, the study of scheduling algorithms for such applications has been an active area of research. Given the nature of most scheduling problems one must resort to simulation to effectively evaluate and compare their efficacy over a wide range of scenarios. It has thus become necessary to simulate those algorithms for increasingly complex distributed dynamic, heterogeneous environments. We present Simgrid a simulation toolkit for the study of scheduling algorithms for distributed application. We give the main concepts and models behind Simgrid, describe its API and highlight current implementation issues. We also give some experimental results and describe work that builds on Simgrid's functionalities.


title: WSCD2013: workshop on web search click data 2013

abstract: WSCD 2013 is the third workshop on Web Search Click Data, following WSCD 2009 and WSCD 2012. It is a forum for new research relating to Web search usage logs and for discussing desirable properties of publicly released search log datasets. Research relating to search logs has been hampered by the limited availability of click datasets. This workshop comes with a new dataset based on logged user search behaviour and an accompanying challenge to predict switches between search engines within a given search session.


title: DemographicVis: Analyzing demographic information based on user generated content

abstract: The wide-spread of social media provides unprecedented sources of written language that can be used to model and infer online demographics. In this paper, we introduce a novel visual text analytics system, DemographicVis, to aid interactive analysis of such demographic information based on user-generated content. Our approach connects categorical data (demographic information) with textual data, allowing users to understand the characteristics of different demographic groups in a transparent and exploratory manner. The modeling and visualization are based on ground truth demographic information collected via a survey conducted on Reddit.com. Detailed user information is taken into our modeling process that connects the demographic groups with features that best describe the distinguishing characteristics of each group. Features including topical and linguistic are generated from the user-generated contents. Such features are then analyzed and ranked based on their ability to predict the users' demographic information. To enable interactive demographic analysis, we introduce a web-based visual interface that presents the relationship of the demographic groups, their topic interests, as well as the predictive power of various features. We present multiple case studies to showcase the utility of our visual analytics approach in exploring and understanding the interests of different demographic groups. We also report results from a comparative evaluation, showing that the DemographicVis is quantitatively superior or competitive and subjectively preferred when compared to a commercial text analysis tool.


title: Inside Intel® Core microarchitecture

abstract: This article consists of a collection of slides from the author's conference presentation on Intel's Core product line's microarchitecture, a new foundation for Intel architecture-based mobile, desktop, and server processors that incorporates advanced innovations which optimize performance over a range of market segments. Some of the specific topics discussed include: the special features and system specifications of Intel Core; memory management and prefetching capabilities; system performance and flexibility; multithreading capabilities; and a summary of key features and processing facilities.


title: Deformable Kernel Networks for Joint Image Filtering

abstract: Joint image filters are used to transfer structural details from a guidance picture used as a prior to a target image, in tasks such as enhancing spatial resolution and suppressing noise. Previous methods based on convolutional neural networks (CNNs) combine nonlinear activations of spatially-invariant kernels to estimate structural details and regress the filtering result. In this paper, we instead learn explicitly sparse and spatially-variant kernels. We propose a CNN architecture and its efficient implementation, called the deformable kernel network (DKN), that outputs sets of neighbors and the corresponding weights adaptively for each pixel. The filtering result is then computed as a weighted average. We also propose a fast version of DKN that runs about four times faster for an image of size 640 × 480. We demonstrate the effectiveness and flexibility of our models on the tasks of depth map upsampling, saliency map upsampling, cross-modality image restoration, texture removal, and semantic segmentation. In particular, we show that the weighted averaging process with sparsely sampled 3 × 3 kernels outperforms the state of the art by a significant margin.


title: Deep Learning Modeling for Top-N Recommendation With Interests Exploring

abstract: Recommender systems (RS) currently play a crucial role in information filtering and retrieval, and have been ubiquitously applied in many domains, although suffering from such data sparsity and cold start problems. There are plenty of studies that try to make efforts to improve the performance of RS through different aspects, such as traditional matrix factorization technique and deep learning methods in recent years, however, it’s still a challenging issue under research. In this paper, motivated by this, a two-stage deep learning-based model for top-N recommendation with interests exploring (DLMR) is proposed: 1) DLMR explores latent interests for each user, captures factors from reviews and contextual information via convolutional neural network, and performs convolutional matrix factorization to generate the candidates list; 2) In order to enhance the recommendation performance, DLMR further conducts candidates ranking through a three-layer denoising autoencoder, with taking account of heterogeneous side information. The DLMR provides a flexible scheme to leverage the available resources for recommendation, which is able to explore user’s latent interests, capture the intricate interactions between users and items, and provide accurate and personalized recommendations. Experimental analysis over real world data sets demonstrates that DLMR could provide high performance top-N recommendation in sparse settings and outperform state-of-the-art recommender approaches significantly.


title: Helicobacter pylori infection in a pediatric population: in vitro susceptibilities to omeprazole and eight antimicrobial agents.

abstract: The in vitro activities of omeprazole and eight antimicrobial agents against 18 clinical strains of Helicobacter pylori isolated from a pediatric population were determined by an agar dilution method. Ampicillin and erythromycin were the most active agents in vitro. All strains were susceptible to azithromycin, ciprofloxacin, doxycycline, metronidazole, and tinidazole. One isolate demonstrated resistance to cefixime (MIC, greater than or equal to 4 micrograms/ml). H. pylori was inhibited by the proton pump inhibitor omeprazole.


title: Thermal properties of graphene and nanostructured carbon materials

abstract: The thermal properties of nanostructures have become a fundamental topic owing to the necessity of heat removal in increasingly smaller electronic devices. Carbon allotropes present a range of intriguing thermal features, with the thermal conductivity spanning five orders of magnitude at room temperature. The topic is reviewed here with particular emphasis on graphene, which exhibits the highest thermal conductivity observed.


title: A flexible capacitive tactile sensor array with high scanning speed for distributed contact force measurements

abstract: This paper presents an 8×8 (=64) capacitive tactile sensor array with integrated four capacitance-digital-converter (CDC) scanning circuit for three-axis force measurement. For the structural design of the sensor array, the upper and lower electrodes generate four capacitors in one sensing unit and decompose the contact force into three-axis force components. To enhance the flexibility, a thinner PDMS bump (0.5 mm) and truncated pyramid dielectric layer with finer dimensions are utilized. To increase the scanning speed, we divide the sensor array into four regions and using four CDC circuits to simultaneously measure the capacitance changes of these regions. The sensor array is mounted onto a hand finger for grasping of a paper cup. Results showed that the sensor array can detect the distributed contact force at high scanning speed, thus could be utilized in robotic grasping applications.


title: Do Confessions Taint Perceptions of Handwriting Evidence? An Empirical Test of the Forensic Confirmation Bias

abstract: Citing classic psychological research and a smattering of recent studies, Kassin, Dror, and Kukucka (2013) proposed the operation of a forensic confirmation bias, whereby preexisting expectations guide the evaluation of forensic evidence in a self-verifying manner. In a series of studies, we tested the hypothesis that knowing that a defendant had confessed would taint people’s evaluations of handwriting evidence relative to those not so informed. In Study 1, participants who read a case summary in which the defendant had previously confessed were more likely to erroneously conclude that handwriting samples from the defendant and perpetrator were authored by the same person, and were more likely to judge the defendant guilty, compared with those in a no-confession control group. Study 2 replicated and extended these findings using a within-subjects design in which participants rated the same samples both before and after reading a case summary. These findings underscore recent critiques of the forensic sciences as subject to bias, and suggest the value of insulating forensic examiners from contextual information.


title: VizQL: a language for query, analysis and visualization

abstract: Conventional query languages such as SQL and MDX have limited formatting and visualization capabilities. Thus, although powerful queries can be composed, another layer of software is needed to report or present the results in a useful form to the analyst. VizQL™ is designed to fill that gap. VizQL evolved from the Polaris system at Stanford, which combined query, analysis and visualization into a single framework [1].VizQL is a formal language for describing tables, charts, graphs, maps, time series and tables of visualizations. These different types of visual representations are unified into one framework, making it easy to switch from one visual representation to another (e.g. from a list view to a cross-tab to a chart). Unlike current charting packages and like query languages, VizQL permits an unlimited number of picture expressions. Visualizations can thus be easily customized and controlled. VizQL is a declarative language. The desired picture is described; the low-level operations needed to retrieve the results, to perform analytical calculations, to map the results to a visual representation, and to render the image are generated automatically by the query analyzer. The query analyzer compiles VizQL expressions to SQL and MDX and thus VizQL can be used with relational databases and datacubes. The current implementation supports Hyperion Essbase, Microsoft SQL Server, Microsoft Analysis Services, MySQL, Oracle, as well as desktop data sources such as CSV and Excel files. This analysis phase includes many optimizations that allow large databases to be browsed interactively. VizQL enables a new generation of visual analysis tools that closely couple query, analysis and visualization.


title: Reference Architecture for SMAC solutions

abstract: Web and internet computing is evolving into a combination of social media, mobile, analytics and cloud (SMAC) solutions. There is a need for an integrated approach when developing solutions that address web scale requirements with technologies that enable SMAC solutions. This paper presents an architecture model for the integrated approach that can form the basis for solutions and result in reuse, integration and agility for the business and IT in an enterprise.


title: Home blood glucose biosensors: a commercial perspective

abstract: Twenty years on from a review in the first issue of this journal, this contribution revisits glucose sensing for diabetes with an emphasis on commercial developments in the home blood glucose testing market. Following a brief introduction to the needs of people with diabetes, the review considers defining technologies that have enabled the introduction of commercial products and then reviews the products themselves. Drawing heavily on the performance of actual instruments and publicly available information from the companies themselves, this work is designed to complement more conventional reviews based on papers published in scholarly journals. It focuses on the commercial reality today and the products that we are likely to see in the near future.


title: Input impedance of rectangular substrate integrated waveguide (SIW) cavity backed slot antennas

abstract: In this paper, modeling of the rectangular substrate integrated waveguide (SIW)cavity backed narrow slot antenna for input impedance is developed by using the transmission-line model and lumped-element circuit. The model of a transverse slot in broad wall of the waveguide is used in the transmission-line model. The results based on transmission-line and circuit modeling are presented and compared with those obtained by full wave simulation. The proposed model gives, for the first time, some fundamental characteristics of the rectangular SIW cavity backed narrow slot antenna.


title: Analysis of LLC Resonant Converter considering effects of parasitic components

abstract: Nowadays the trend of power supply market is more inclined to high switching frequency, high efficiency and high power density. To meet this trend, resonant power supply holds more attraction, because it can be operated in high switching frequency with high efficiency. There are many resonant power supplies such as Series-Resonant Converter (SRC), Parallel-Resonant Converter (PRC) and Series-Parallel Resonant Converter (SPRC). Among them, LLC Resonant Converter has a lot of advantages over the conventional SRC and PRC considering relatively narrow switching frequency variation over wide input and load variation and Zero-Voltage-Switching for entire load range. Therefore, the LLC Resonant Converter has been widely used and discussed. However, the conventional analysis of LLC Resonant Converter with Fundamental Harmonic Approximation (FHA) can not explain the practical operation of LLC Resonant Converter. To overcome this limitation, in this paper, analysis and design of the LLC Resonant Converter including parasitic components which are affecting converter operation are proposed using a traditional analysis based on FHA. The effect of each parasitic component is analyzed with simulation results and the design guideline standing on this analysis will be described. Moreover, the experimental results of prototype designing on the basis of the analysis are shown to demonstrate the proposed analysis and design guideline.


title: Practical clinical trials: increasing the value of clinical research for decision making in clinical and health policy.

abstract: Decision makers in health care are increasingly interested in using high-quality scientific evidence to support clinical and health policy choices; however, the quality of available scientific evidence is often found to be inadequate. Reliable evidence is essential to improve health care quality and to support efficient use of limited resources. The widespread gaps in evidence-based knowledge suggest that systematic flaws exist in the production of scientific evidence, in part because there is no consistent effort to conduct clinical trials designed to meet the needs of decision makers. Clinical trials for which the hypothesis and study design are developed specifically to answer the questions faced by decision makers are called pragmatic or practical clinical trials (PCTs). The characteristic features of PCTs are that they (1) select clinically relevant alternative interventions to compare, (2) include a diverse population of study participants, (3) recruit participants from heterogeneous practice settings, and (4) collect data on a broad range of health outcomes. The supply of PCTs is limited primarily because the major funders of clinical research, the National Institutes of Health and the medical products industry, do not focus on supporting such trials. Increasing the supply of PCTs will depend on the development of a mechanism to establish priorities for these studies, significant expansion of an infrastructure to conduct clinical research within the health care delivery system, more reliance on high-quality evidence by health care decision makers, and a substantial increase in public and private funding for these studies. For these changes to occur, clinical and health policy decision makers will need to become more involved in all aspects of clinical research, including priority setting, infrastructure development, and funding.


title: Learning Simpler Language Models with the Delta Recurrent Neural Network Framework.

abstract: Learning useful information across long time lags is a critical and difficult problem for temporal neural models in tasks like language modeling. Existing architectures that address the issue are often complex and costly to train. The Delta Recurrent Neural Network (Delta-RNN) framework is a simple and high-performing design that unifies previously proposed gated neural models. The Delta-RNN models maintain longer-term memory by learning to interpolate between a fast-changing data-driven representation and a slowly changing, implicitly stable state. This requires hardly any more parameters than a classical simple recurrent network. The models outperform popular complex architectures, such as the Long Short Term Memory (LSTM) and the Gated Recurrent Unit (GRU) and achieve state-of-the art performance in language modeling at character and word levels and yield comparable performance at the subword level.


title: Preservation of Fertility in Females Treated for Cancer

abstract: Advancements of diagnosis and treatment have substantially improved cancer survival rates in the last few decades. The increasing number of survivors focuses attention on long-term effects caused by cancer treatment and its impact on quality of life. Ovarian failure is one of the major sequelae of cytotoxic chemotherapy and/or radiotherapy in female children and reproductive-age women. Oncologists should address the patients about fertility preservation options before therapy. Embryo cryopreservation is the only well-established method for females in preserving fertility; however other strategies including ovarian suppression, ovarian transposition and cryopreservation of oocytes and ovarian tissue are still experimental. Patients need advice and to know which are the most practical options for them. This article reviews the available fertility preservation methods in women, and the related issues including normal physiology of the ovary, effect of anticancer therapy on fertility, role of the oncologist and ethics. We performed a MEDLINE search from 1971 to 2011 in a similar way as Jensen et al. 2011, using the following MeSH terms: antineoplastic agents; ovarian failure; premature; infertility, female; fertility preservation; child and cancer; reproductive technologies, assisted.


title: Building real-time groupware with GroupKit, a groupware toolkit

abstract: This article presents an overview of GroupKit, a groupware toolkit that lets developers build applications for synchronous and distributed computer-based conferencing. GroupKit was constructed from our belief that programming groupware should be only slightly harder than building functionally similar single-user systems. We have been able to significantly reduce the implementation complexity of groupware through the key features that comprise GroupKit. A runtime infrastructure automatically manages the creation, interconnection, and communications of the distributed processes that comprise conference sessions. A set of groupware programming abstractions allows developers to control the behavior of distributed processes, to take action on state changes, and to share relevant data. Groupware widgets let interface features of value to conference participants to be easily added to groupware applications. Session managers —interfaces that let people create and manage their meetings—are decoupled from groupware applications and are built by developers to accommodate the group's working style. Example GroupKit applications in a variety of domains have been implemented with only modest effort.


title: 1-Gb/s Transmission Over a Phosphorescent White LED by Using Rate-Adaptive Discrete Multitone Modulation

abstract: Light-emitting diodes (LEDs), which will be increasingly used in lighting technology, will also allow for distribution of broadband optical wireless signals. Visible-light communication (VLC) using white LEDs offers several advantages over the RF-based wireless systems, i.e., license-free spectrum, low power consumption, and higher privacy. Mostly, optical wireless can provide much higher data rates. In this paper, we demonstrate a VLC system based on a white LED for indoor broadband wireless access. After investigating the nonlinear effects of the LED and the power amplifier, a data rate of 1 Gb/s has been achieved at the standard illuminance level, by using an optimized discrete multitone modulation technique and adaptive bit- and power-loading algorithms. The bit-error ratio of the received data was $1.5\cdot 10^{-3}$ , which is within the limit of common forward error correction (FEC) coding. These results twice the highest capacity that had been previously obtained.


title: The Secure Remote Password Protocol

abstract: This paper presents a new password authentication and key-exchange protocol suitable for authenticating users and exchanging keys over an untrusted network. The new protocol resists dictionary attacks mounted by either passive or active network intruders, allowing, in principle, even weak passphrases to be used safely. It also o ers perfect forward secrecy, which protects past sessions and passwords against future compromises. Finally, user passwords are stored in a form that is not plaintext-equivalent to the password itself, so an attacker who captures the password database cannot use it directly to compromise security and gain immediate access to the host. This new protocol combines techniques of zero-knowledge proofs with asymmetric key exchange protocols and o ers signi cantly improved performance over comparably strong extended methods that resist stolen-veri er attacks such as Augmented EKE or B-SPEKE.


title: Machine learning classifiers and fmri: A tutorial overview

abstract: Abstract Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviours and other variables of interest from fMRI data and thereby show the data contain information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of ‘is there information about a variable of interest’ (pattern discrimination), classifiers can be used to tackle other classes of question, namely ‘where is the information’ (pattern localization) and ‘how is that information encoded’ (pattern characterization).


title: Statisticum: Data Statistics Management in SAP HANA

abstract: We introduce a new concept of leveraging traditional data statistics as dynamic data integrity constraints. These data statistics produce transient database constraints, which are valid as long as they can be proven to be consistent with the current data. We denote this type of data statistics by constraint data statistics, their properties needed for consistency checking by consistency metadata, and their implied integrity constraints by implied data statistics constraints (implied constraints for short). Implied constraints are valid integrity constraints which are powerful query optimization tools employed, just as traditional database constraints, in semantic query transformation (aka query reformulation), partition pruning, runtime optimization, and semi-join reduction, to name a few. To our knowledge, this is the first work introducing this novel and powerful concept of deriving implied integrity constraints from data statistics. We discuss theoretical aspects of the constraint data statistics concept and their integration into query processing. We present the current architecture of data statistics management in SAP HANA and detail how constraint data statistics are designed and integrated into this architecture. As an instantiation of this framework, we consider dynamic partition pruning for data aging scenarios. We discuss our current implementation for constraint data statistics objects in SAP HANA which can be used for dynamic partition pruning. We enumerate their properties and show how consistency checking for implied integrity constraints is supported in the data statistics architecture. Our experimental evaluations on the TPC-H benchmark and a real customer application confirm the effectiveness of the implied integrity constraints; (1) for 59% of TPC-H queries, constraint data statistics utilization results in pruning cold partitions and reducing memory consumption, and (2) we observe up to 3 orders of magnitude speed-up in query processing time, for a real customer running an S/4HANA application.


title: Genetic epidemiology in Germany--from biobanking to genetic statistics.

abstract: OBJECTIVES ::: Genetic epidemiology investigates the role of genetic factors and their interaction with environmental factors (in a broad meaning) for the occurrence of diseases in human populations. Its aim is to undestand the influence of genetics on the development of diseases, their course and the clinical implications, with the final goal to improve prevention, diagnostics and therapy. ::: ::: ::: METHODS ::: Originally genetic epidemiology was understood as a specialized discipline with the main focus on family-based studies. The extraordinary development of genetics in the last decades--with respect of the understanding of the meaning of genes for human health, as well as by the availability of cost-effective high throughput methods in the lab, has opened enormous opportunities to study genetic factors. Now, genetic epidemiology and genetic statistics have a much broader application. In addition, access to large samples of patients or from the population is needed. This can be realized via biobanks. ::: ::: ::: RESULTS ::: Large biobanks with 500,000 or more patients or participants from the general population are being established or planned in the UK, Japan or the US. However, in Germany only two smaller activities are ongoing, KORA-gen in the south and POPGEN in the north. Possibilities to reach larger numbers, based on existing cohorts or disease networks are discussed. Ethical boundary conditions have to be taken into account, which seem to improve due to the Opinion of the German National Ethics Council on Biobanks for Research. Furthermore, the activities of the German centers for Genetic Epidemiological Methods (GEMs) as research and support units for genetic statistics and epidemiological methodology are described. ::: ::: ::: CONCLUSIONS ::: Genetic epidemiology is based strongly on interdisciplinary collaboration and includes basics of genetics, elements of molecular biology to identify genes, population genetics, clinical medicine, and methodological disciplines as epidemiology, biostatistics and bioinformatics. In Germany the situation for this type of patient-based research has recently improved due to the National Genome Research Network (NGFN).