Query: Development of 50-kV 100-kW Three-Phase Resonant Converter for 95-GHz Gyrotron

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Design and Implementation of a 40-kV, 20-kJ/s Capacitor Charger for Pulsed-Power Application <=SEP=> This paper presents the design and implementation of a 40-kV, 20-kJ/s high-voltage capacitor charger based on a series/parallel resonant converter. The inclusion of the parallel resonant capacitor component in this circuit results in the production of a trapezoidal resonant current, which reduces conduction loss. This capacitor is practically realized as a part of the balancing network of the high-voltage rectifiers in the secondary side of the circuit. Particular attention in this paper is paid to the high-voltage transformer, which must be carefully designed to provide the required functionality without negative impact on the resonant circuit. A PSpice simulation has been used to prove that the proposed control method for the circuit is valid. This is supported with the experimental results, which verify that the operation of the circuit is as expected and that the converter is able to meet all the design criteria with an efficiency of up to 96%.

Industrial Applications of Pulsed Power Technology <=SEP=> A review of mainly the past two years is undertaken of the industrial applications of pulsed power. Repetitively operated pulsed power generators with a moderate peak power have been developed for industrial applications. These generators are reliable and have low maintenance. Development of the pulsed power generators helps promote industrial applications of pulsed power for such things as food processing, medical treatment, water treatment, exhaust gas treatment, ozone generation, engine ignition, ion implantation and others. Here, industrial applications of pulsed power are classified by application for biological effects, for pulsed streamer discharges in gases, for pulsed discharges in liquid or liquid- mixture, and for material processing.

A Hybrid Symmetrical Voltage Multiplier <=SEP=> Voltage multiplier circuits are widely used in many high-voltage/low-current applications. A conventional symmetrical voltage multiplier (SVM) has much better performance, when compared with its half-wave counterpart. However, it requires a high-voltage transformer (HVT) with center-tapped secondary to perform its push-pull kind of operation. The design of an HVT with center-tapped secondary is relatively complex. This paper proposes a hybrid SVM (HSVM) for dc high-voltage applications. The multiplier is formed by cascading a diode-bridge rectifier and an SVM with diode-bridge rectifier as the first stage of multiplier. The proposed topology saves two high-voltage capacitors and requires only one secondary winding of HVT. Besides, it has lesser voltage drop and faster transient response at start-up, when compared with conventional SVM. The feasibility of the proposed HSVM is validated both by simulation and experimental results of a laboratory scaled-down prototype.

Solid state Marx Generator using series-connected IGBTs <=SEP=> This paper describes a newly developed novel repetitive impulse voltage generator using a boost converter array. To solve problems such as short life time, low operating frequency, and the fixed pulse width of conventional generators, the proposed generator is designed with a boost converter array that employs series-connected capacitors and insulated gate bipolar transistors. The circuit can easily obtain a high-voltage pulse without any high-voltage direct current source and pulse transformer. Thus, the proposed circuit not only allows elimination of the expensive high-frequency transformer but also allows operation at a frequency up to several kilohertz with high reliability and longer life span. To validate the proposed circuit, two pulse generators rated at 1.8 kV, 40 A and 20 kV, 300 A are implemented and tested.

Compact and high repetitive pulsed power modulator based on semiconductor switches <=SEP=> This paper describes the design of a compact and highly repetitive pulsed power modulator based on semiconductor switches. Output specifications of the proposed modulator are as follows: variable output pulse voltage, 1-10 kV; width, 1-10 μs; pulse repetition rate (PRR), 1-50,000 pps; and average output power, 10 kW. The proposed solid-state pulsed power modulator has two main parts: an IGBT stack that hasa gate drive circuit for the pulse output with a variable pulse width and repetition rate, and a capacitor charger that has a controllable output voltage. To connect all the storage capacitors in a series for high voltage pulses, a simple and reliable IGBT stack structure was proposed based on the Marx generator. In addition, the gate driver circuit, which supplies power and signals to all the IGBTs simultaneously, was introduced. This providesa superior protection function against the arc and the short. To charge the storage capacitors, a novel 10 kW (10 kV, 1 A) high voltage capacitor charger with the combined advantage of a series-loaded resonant converter and a ZVS (zero-voltage-switching) full-bridge pulse width modulation (PWM) converter was proposed and designed especially for the solid-state pulsed power modulator. Theexperiment results verified that the proposed scheme and structure can be used effectively for a high voltage pulsed power modulator that requires variable voltage, repetition rate, and pulse width, depending on the process of the applications.

Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization. <=SEP=> This two-part article provides an overview of the global burden of atherothrombotic cardiovascular disease. Part I initially discusses the epidemiologic transition which has resulted in a decrease in deaths in childhood due to infections, with a concomitant increase in cardiovascular and other chronic diseases; and then provides estimates of the burden of cardiovascular (CV) diseases with specific focus on the developing countries. Next, we summarize key information on risk factors for cardiovascular disease (CVD) and indicate that their importance may have been underestimated. Then, we describe overarching factors influencing variations in CVD by ethnicity and region and the influence of urbanization. Part II of this article describes the burden of CV disease by specific region or ethnic group, the risk factors of importance, and possible strategies for prevention.

Does Barbie make girls want to be thin? The effect of experimental exposure to images of dolls on the body image of 5- to 8-year-old girls. <=SEP=> The ubiquitous Barbie doll was examined in the present study as a possible cause for young girls' body dissatisfaction. A total of 162 girls, from age 5 to age 8, were exposed to images of either Barbie dolls, Emme dolls (U.S. size 16), or no dolls (baseline control) and then completed assessments of body image. Girls exposed to Barbie reported lower body esteem and greater desire for a thinner body shape than girls in the other exposure conditions. However, this immediate negative impact of Barbie doll was no longer evident in the oldest girls. These findings imply that, even if dolls cease to function as aspirational role models for older girls, early exposure to dolls epitomizing an unrealistically thin body ideal may damage girls' body image, which would contribute to an increased risk of disordered eating and weight cycling.

Supporting requirements traceability through refactoring <=SEP=> Modern traceability tools employ information retrieval (IR) methods to generate candidate traceability links. These methods track textual signs embedded in the system to establish relationships between software artifacts. However, as software systems evolve, new and inconsistent terminology finds its way into the system's taxonomy, thus corrupting its lexical structure and distorting its traceability tracks. In this paper, we argue that the distorted lexical tracks of the system can be systematically re-established through refactoring, a set of behavior-preserving transformations for keeping the system quality under control during evolution. To test this novel hypothesis, we investigate the effect of integrating various types of refactoring on the performance of requirements-to-code automated tracing methods. In particular, we identify the problems of missing, misplaced, and duplicated signs in software artifacts, and then examine to what extent refactorings that restore, move, and remove textual information can overcome these problems respectively. We conduct our experimental analysis using three datasets from different application domains. Results show that restoring textual information in the system has a positive impact on tracing. In contrast, refactorings that remove redundant information impact tracing negatively. Refactorings that move information among the system modules are found to have no significant effect. Our findings address several issues related to code and requirements evolution, as well as refactoring as a mechanism to enhance the practicality of automated tracing tools.

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension <=SEP=> We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K questionanswer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers. We also present two baseline algorithms: a featurebased classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study.1

3D face landmark labelling <=SEP=> Most 3D face processing systems require feature detection and localisation, for example to crop, register, analyse or recognise faces. The three features often used in the literature are the tip of the nose, and the two inner corner of the eyes. Failure to localise these landmarks can cause the system to fail and they become very difficult to detect under large pose variations or when occlusion is present. In this paper, we present a proof-of-concept for a face labelling system capable of overcoming this problem, as a larger number of landmarks are employed. A set of points containing hand-placed landmarks is used as input data. The aim here is to retrieve the landmark's labels when some part of the face is missing. By using graph matching techniques to reduce the number of candidates, and translation and unit-quaternion clustering to determine a final correspondence, we evaluate the accuracy at which landmarks can be retrieved under changes in expression, orientation and in the presence of occlusions.

Converging on the Divergent: The History (and Future) of the International Joint Workshops in Computational Creativity <=SEP=> When heckled, professional comedians frequently lament that “everyone’s a comedian!” It’s easy to see why: professional comedians don’t possess different kinds of brains from others or engage in radically different kinds of behaviors from others, and moreover, the success of their acts is predicated on others’ shared ability to understand and reason about comic situations they describe. The difference between comedians and their audience is a matter not of kind, but of degree, a difference that is reflected in the vocational emphasis they place on humor. Researchers in the field of computational creativity find themselves in a similar situation. As a subdiscipline of artificial intelligence, computational creativity explores theories and practices that give rise to a phenomenon, creativity, that all intelligent systems, human or machine, can legitimately lay claim to. Who is to say that a given AI system is not creative, insofar as it solves nontrivial problems or generates useful outputs that are not hard wired into its programming? As with comedians’ being funny, the difference between studying computational creativity and studying artificial intelligence is one of emphasis rather than one of kind: the field of computational creativity, as typified by a long-running series of workshops at AI-related conferences, places a vocational emphasis on creativity and attempts to draw together the commonalities of what Articles

Instance-level Facial Attributes Transfer with Geometry-Aware Flow <=SEP=> We address the problem of instance-level facial attribute transfer without paired training data, e.g., faithfully transferring the exact mustache from a source face to a target face. This is a more challenging task than the conventional semantic-level attribute transfer, which only preserves the generic attribute style instead of instance-level traits. We propose the use of geometry-aware flow, which serves as a wellsuited representation for modeling the transformation between instance-level facial attributes. Specifically, we leverage the facial landmarks as the geometric guidance to learn the differentiable flows automatically, despite of the large pose gap existed. Geometry-aware flow is able to warp the source face attribute into the target face context and generate a warp-and-blend result. To compensate for the potential appearance gap between source and target faces, we propose a hallucination sub-network that produces an appearance residual to further refine the warp-and-blend result. Finally, a cycle-consistency framework consisting of both attribute transfer module and attribute removal module is designed, so that abundant unpaired face images can be used as training data. Extensive evaluations validate the capability of our approach in transferring instance-level facial attributes faithfully across large pose and appearance gaps. Thanks to the flow representation, our approach can readily be applied to generate realistic details on high-resolution images.

GPS spoofer countermeasure effectiveness based on signal strength, noise power, and C/N0 measurements <=SEP=> Spoofing sources can effectively disrupt a GPS receiver during the acquisition phase by generating multiple false correlation peaks and increasing the noise floor. Such deceptive correlation peaks can mislead the GPS receiver into acquiring the spoofer generated signals rather than the authentic signals. Also, the spoofer can increase the receiver noise floor to bury the authentic signals in the noise and at the same time generate correlation peaks with amplitudes commensurate with reasonable C/N0 expectations. The main focus of this paper is on assessment of the reduced effectiveness of the GPS spoofer countermeasure during acquisition where the GPS receiver utilizes C/N0 discrimination. As shown, whereas the C/N0 discrimination is of limited effectiveness, with a modest circuit modification, the receiver can measure the absolute power of the correlation peaks, which is an effective means of detecting and discriminating spoofer sources. It will be shown that employing absolute power monitoring technique considerably reduces the vulnerability region of the receiver compared with the C/N0 monitoring techniques. Copyright © 2012 John Wiley & Sons, Ltd.

Cancer statistics in China, 2015. <=SEP=> With increasing incidence and mortality, cancer is the leading cause of death in China and is a major public health problem. Because of China's massive population (1.37 billion), previous national incidence and mortality estimates have been limited to small samples of the population using data from the 1990s or based on a specific year. With high-quality data from an additional number of population-based registries now available through the National Central Cancer Registry of China, the authors analyzed data from 72 local, population-based cancer registries (2009-2011), representing 6.5% of the population, to estimate the number of new cases and cancer deaths for 2015. Data from 22 registries were used for trend analyses (2000-2011). The results indicated that an estimated 4292,000 new cancer cases and 2814,000 cancer deaths would occur in China in 2015, with lung cancer being the most common incident cancer and the leading cause of cancer death. Stomach, esophageal, and liver cancers were also commonly diagnosed and were identified as leading causes of cancer death. Residents of rural areas had significantly higher age-standardized (Segi population) incidence and mortality rates for all cancers combined than urban residents (213.6 per 100,000 vs 191.5 per 100,000 for incidence; 149.0 per 100,000 vs 109.5 per 100,000 for mortality, respectively). For all cancers combined, the incidence rates were stable during 2000 through 2011 for males (+0.2% per year; P = .1), whereas they increased significantly (+2.2% per year; P < .05) among females. In contrast, the mortality rates since 2006 have decreased significantly for both males (-1.4% per year; P < .05) and females (-1.1% per year; P < .05). Many of the estimated cancer cases and deaths can be prevented through reducing the prevalence of risk factors, while increasing the effectiveness of clinical care delivery, particularly for those living in rural areas and in disadvantaged populations.

THE ACTIVITY-BASED APPROACH <=SEP=> 

Spermatocele presenting as acute scrotum. <=SEP=> Corresponding Author: Atsuya Hikosaka, MD Department of Urology, Toyota Kosei Hospital, 500-1, Ibobara, Josui-cho, Toyota, Aichi 470-0396, Japan Tel: +81 565 43 5000 Fax: +81 565 43 5100 E-mail: uropatho@ybb.ne.jp INTRODUCTION Spermatocele, a retention cyst of the scrotum which is or has been in communication with the semen-carrying system,(1) is a relatively common clinical entity. It presents typically as an intrascrotal paratesticular mass, but it usually has few subjective symptoms. Here, we report a case of spermatocele manifested with acute scrotum due to its unique feature.

Reconstructing Indian Population History <=SEP=> India has been underrepresented in genome-wide surveys of human variation. We analyse 25 diverse groups in India to provide strong evidence for two ancient populations, genetically divergent, that are ancestral to most Indians today. One, the ‘Ancestral North Indians’ (ANI), is genetically close to Middle Easterners, Central Asians, and Europeans, whereas the other, the ‘Ancestral South Indians’ (ASI), is as distinct from ANI and East Asians as they are from each other. By introducing methods that can estimate ancestry without accurate ancestral populations, we show that ANI ancestry ranges from 39–71% in most Indian groups, and is higher in traditionally upper caste and Indo-European speakers. Groups with only ASI ancestry may no longer exist in mainland India. However, the indigenous Andaman Islanders are unique in being ASI-related groups without ANI ancestry. Allele frequency differences between groups in India are larger than in Europe, reflecting strong founder effects whose signatures have been maintained for thousands of years owing to endogamy. We therefore predict that there will be an excess of recessive diseases in India, which should be possible to screen and map genetically.

Maximum energy harvesting from medium voltage electric-field energy using power line insulators <=SEP=> One essential requirement for a modern Smart Grid is continuous monitoring of the grid variables. This task is efficiently achieved using self-powered electronic devices deployed throughout the grid network. Within the vicinity of the grid power lines, the high voltages of these lines can provide a source of energy for such loads using capacitive coupling. This is known as Electric-Field Energy Harvesting (EFEH). However, the reported strategies that use this principle have not explored how to maximise their power transfer, which is essential for practical systems since the available energy from EFEH is invariably quite small. In this paper, a novel approach for optimum energy harvesting is proposed using the parasitic capacitance of medium-voltage power line insulators. A follow-on power electronic conversion system is then used to maintain the load conditions that maximise the extracted energy. The system performance has been analysed using theoretical studies and computer simulations to identify the operating conditions that maximise the extracted energy. Results obtained so far suggest that up to 100 mW can be harvested from 22 kV grid feeders, using only the capacitive coupling that exists between the line insulator harvester and the feeder conductor.

Measuring People-Flow through Doorways Using Easy-to-Install IR Array Sensors <=SEP=> People counting has many applications in smart buildings. For example, adjusting HVAC systems based on the number of occupants in each room can save a significant amount of energy. In addition, security and safety of the building can be managed by determining the number and location of occupants. Different technologies and sensing platforms have proposed for accurate and efficient people counting. However, these solutions are expensive, hard to deploy, or privacy invasive. We investigate the possibility of placing an 8×8 IR array sensor at the doorways and counting the number of people inside rooms. Our solution is real-time, inexpensive, privacy preserving with much less deployment constraints compared to its competitors. The proposed solution deals with realistic and dynamic changes in the sensing environment by leveraging a combination of Otsus thresholding and modeling thermal noise distribution. We evaluated our solution via several controlled and uncontrolled real-world environments. The results show an average of 93% accuracy in estimating the number of occupants in rooms.

A “K Hypotheses + Other” Belief Updating Model <=SEP=> Spoken dialog systems typically rely on recognition confidence scores to guard against potential misundersta ndings. While confidence scores can provide an initial assessment for the reliability of the information obtained fro m the user, ideally systems should leverage information that is available in subsequent user responses to update and improve the accuracy of their beliefs. We present a machine-learning based solution for this problem. We use a compresse d representation of beliefs that tracks up to k hypotheses for each concept at any given time. We train a generalized l inear model to perform the updates. Experimental results show that the proposed approach significantly outperform s heuristic rules used for this task in current systems. Fu rthermore, a user study with a mixed-initiative spoken dialog sy stem shows that the approach leads to significant gains in task success and in the efficiency of the interaction, a cross a wide range of recognition error-rates.

An efficient selection of HOG feature for SVM classification of vehicle <=SEP=> Support Vector Machine (SVM) classifier with Histogram of Oriented Gradients (HOG) feature become one of the most popular techniques used for vehicle detection in recent years. And the computing time of SVM is a main obstacle to get real time implementation which is important for Advanced Driver Assistance Systems (ADAS) applications. One of the effective ways to reduce the computing complexity of SVM is to reduce the dimension of HOG feature. In this paper, we examine the effect of the number of HOG bins on the vehicle detection and the symmetric characteristics of HOG feature of vehicle. And we successfully demonstrate the speed-up of SVM classifier for vehicle detection by about three times while maintaining the detection performance.

Multi-task assignment for crowdsensing in mobile social networks <=SEP=> Mobile crowdsensing is a new paradigm in which a crowd of mobile users exploit their carried smart devices to conduct complex computation and sensing tasks in mobile social networks (MSNs). In this paper, we focus on the task assignment problem in mobile crowdsensing. Unlike traditional task scheduling problems, the task assignment in mobile crowdsensing must follow the mobility model of users in MSNs. To solve this problem, we propose an oFfline Task Assignment (FTA) algorithm and an oNline Task Assignment (NTA) algorithm. Both FTA and NTA adopt a greedy task assignment strategy. Moreover, we prove that the FTA algorithm is an optimal offline task assignment algorithm, and give a competitive ratio of the NTA algorithm. In addition, we demonstrate the significant performance of our algorithms through extensive simulations, based on four real MSN traces and a synthetic MSN trace.

Biomimetic design through natural language analysis to facilitate cross-domain information retrieval <=SEP=> Biomimetic, or biologically inspired, design uses analogous biological phenomena to develop solutions for engineering problems. Several instances of biomimetic design result from personal observations of biological phenomena. However, many engineers’ knowledge of biology may be limited, thus reducing the potential of biologically inspired solutions. Our approach to biomimetic design takes advantage of the large amount of biological knowledge already available in books, journals, and so forth, by performing keyword searches on these existing natural-language sources. Because of the ambiguity and imprecision of natural language, challenges inherent to natural language processing were encountered. One challenge of retrieving relevant cross-domain information involves differences in domain vocabularies, or lexicons. A keyword meaningful to biologists may not occur to engineers. For an example problem that involved cleaning, that is, removing dirt, a biochemist suggested the keyword “defend.” Defend is not an obvious keyword to most engineers for this problem, nor are the words defend and “clean0remove” directly related within lexical references. However, previous work showed that biological phenomena retrieved by the keyword defend provided useful stimuli and produced successful concepts for the clean0remove problem. In this paper, we describe a method to systematically bridge the disparate biology and engineering domains using natural language analysis. For the clean0remove example, we were able to algorithmically generate several biologically meaningful keywords, including defend, that are not obviously related to the engineering problem. We developed a method to organize and rank the set of biologically meaningful keywords identified, and confirmed that we could achieve similar results for two other examples in encapsulation and microassembly. Although we specifically address cross-domain information retrieval from biology, the bridging process presented in this paper is not limited to biology, and can be used for any other domain given the availability of appropriate domain-specific knowledge sources and references.

NuSMV 2: An OpenSource Tool for Symbolic Model Checking <=SEP=> Alessandro Cimatti 1, Edmund Clarke2, Enrico Giunchiglia3, Fausto Giunchiglia 4, Marco Pistore1, Marco Roveri 1, Roberto Sebastiani 4, and Armando Tacchella 3 1 ITC-IRST, Via Sommarive 18, 38050 Trento, Italy fcimatti,pistore,roverig@irst.itc.it 2 Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh (PA), USA emc@cs.cmu.edu 3 DIST – Università di Genova, Viale Causa 13, 16145 Genova, Italy fenrico,tacg@mrg.dist.unige.it 4 Università di Trento, Via Sommarive 14, 38050 Trento, Italy ffausto,rsebag@science.unitn.it

On the Robustness of a Neural Network <=SEP=> With the development of neural networks based machine learning and their usage in mission critical applications, voices are rising against the black box aspect of neural networks as it becomes crucial to understand their limits and capabilities. With the rise of neuromorphic hardware, it is even more critical to understand how a neural network, as a distributed system, tolerates the failures of its computing nodes, neurons, and its communication channels, synapses. Experimentally assessing the robustness of neural networks involves the quixotic venture of testing all the possible failures, on all the possible inputs, which ultimately hits a combinatorial explosion for the first, and the impossibility to gather all the possible inputs for the second.In this paper, we prove an upper bound on the expected error of the output when a subset of neurons crashes. This bound involves dependencies on the network parameters that can be seen as being too pessimistic in the average case. It involves a polynomial dependency on the Lipschitz coefficient of the neurons' activation function, and an exponential dependency on the depth of the layer where a failure occurs. We back up our theoretical results with experiments illustrating the extent to which our prediction matches the dependencies between the network parameters and robustness. Our results show that the robustness of neural networks to the average crash can be estimated without the need to neither test the network on all failure configurations, nor access the training set used to train the network, both of which are practically impossible requirements.

A framework for automatic question generation from text using deep reinforcement learning <=SEP=> Automatic question generation (QG) is a useful yet challenging task in NLP. Recent neural network-based approaches represent the state-of-the-art in this task, but they are not without shortcomings. Firstly, these models lack the ability to handle rare words and the word repetition problem. Moreover, all previous works optimize the cross-entropy loss, which can induce inconsistencies between training (objective) and testing (evaluation measure). In this paper, we present a novel deep reinforcement learning based framework for automatic question generation. The generator of the framework is a sequence-to-sequence model, enhanced with the copy mechanism to handle the rare-words problem and the coverage mechanism to solve the word repetition problem. The evaluator model of the framework evaluates and assigns a reward to each predicted question. The overall model is trained by learning the parameters of the generator network which maximizes the reward. Our framework allows us to directly optimize any task-specific score including evaluation measures such as BLEU, GLEU, ROUGE-L, etc., suitable for sequence to sequence tasks such as QG. Our comprehensive evaluation shows that our approach significantly outperforms state-of-the-art systems on the widely-used SQuAD benchmark in both automatic and human evaluation.

The Practical Use of BI Visualizations in Decision-making <=SEP=> Type of thesis: Master Degree Project in Accounting, 30 credits University: University of Gothenburg, School of Business, Economics and Law Semester: Spring 2017 Author: Kevin Larsson & Philip Kaber Supervisor: Elisabeth Frisk Title: The Practical Use of BI Visualizations in Decision-making Background and problem: Due to the constantly altering business environment, firms are trying to find ways of combining information technology (IT) and decision-making in order to grasp information of the complex and dynamic business environment. As a solution to this strenuousness; business intelligence (BI) systems serve to conduct more efficient and reliable decisions by the means of visualizations. One existing gap in the research of BI concerns the sociotechnical effects involved in the decision-making, in which knowledge of how BI visualizations are used and may be used in practice to support the decision-making have remained limited. Research aim: The research aim of the study is to investigate how BI visualizations are and may be used to support strategic and operational decisions. Further, the study addresses how BI visualizations are used in various decision contexts. Research questions: The following questions are addressed in order to achieve the research aim: (1) How are and may BI visualizations be used in strategic and operational decisions? (1a) Which visual elements are used depending on the task and decision category? (2) How are BI visualizations used in different decision contexts? Research design: A qualitative study was made through semi-structured interviews. In-depth interviews with six people from the purchasing company were conducted in order to understand how BI and visualizations are and may be used. Two people from the consultancy company were interviewed in order to understand how BI and visualizations may be used. Discussion and conclusion: The primary purpose of BI systems in the purchasing company is to support decision-making from a strategic perspective by providing the business with new insights and monitor business processes. However, the findings in this study also suggest that BI visualizations are used for operational errands. Strategic decisions are primary based on graphical representations, whereas operational decisions are based on data derived from tables and additional calculations in Excel. By creating more interactive, initiative, and humanizing visualizations, the end-users may discover new patterns of information and enhance the cognitive fit without being overwhelmed by data. BI visualizations may also be used in a larger extent to detect errors in data. The usage depends on the decision situation, in which decisions made in the fact-based contexts domains tend to be based on intuitions and BI visualizations, before responding to the business problem. This differs from the pattern-based management domains, in which actions are done prior to the analysis as there is lack of data. Further the findings indicate that several people are often involved in the decisions. The mental model of the respondents is not entirely fit in a cognitive aspect.

Shuffle Index: Efficient and Private Access to Outsourced Data <=SEP=> Data outsourcing and cloud computing have been emerging at an ever-growing rate as successful approaches for allowing users and companies to rely on external services for storing and managing data. As data and access to them are not under the control of the data owner, there is a clear need to provide proper confidentiality protection. Such requirements concern the confidentiality not only of the stored data (content) but also of the specific accesses (or patterns of them) that users make on such data.
 In this article, we address these issues and propose an approach for guaranteeing content, access, and pattern confidentiality in a data outsourcing scenario. The proposed solution is based on the definition of a shuffle index structure, which adapts traditional B +-trees and, by applying a combination of techniques (covers, caches, and shuffling), ensures confidentiality of the data and of queries over them, protecting each single access as well as sequences thereof. The proposed solution also supports update operations over the data, while making reads and writes not recognizable as such by the server. We show that the shuffle index exhibits a limited performance cost, thus resulting effectively usable in practice.

A deep learning framework for unsupervised affine and deformable image registration <=SEP=> Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for unsupervised affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.

Implementing Efficient, Portable Computations for Machine Learning <=SEP=> Implementing E cient, Portable Computations for Machine Learning