title: Lower Health Literacy is Associated with Poorer Health Status and Outcomes in Chronic Obstructive Pulmonary Disease

abstract: BACKGROUND ::: Limited health literacy is associated with poor outcomes in many chronic diseases, but little is known about health literacy in chronic obstructive pulmonary disease (COPD). ::: ::: ::: OBJECTIVE ::: To examine the associations between health literacy and both outcomes and health status in COPD. PARTICIPANTS, DESIGN AND MAIN MEASURES: Structured interviews were administered to 277 subjects with self-report of physician-diagnosed COPD, recruited through US random-digit telephone dialing. Health literacy was measured with a validated three-item battery. Multivariable linear regression, controlling for sociodemographics including income and education, determined the cross-sectional associations between health literacy and COPD-related health status: COPD Severity Score, COPD Helplessness Index, and Airways Questionnaire-20R [measuring respiratory-specific health-related quality of life (HRQoL)]. Multivariable logistic regression estimated associations between health literacy and COPD-related hospitalizations and emergency department (ED) visits. ::: ::: ::: KEY RESULTS ::: Taking socioeconomic status into account, poorer health literacy (lowest tertile compared to highest tertile) was associated with: worse COPD severity (+2.3 points; 95 % CI 0.3-4.4); greater COPD helplessness (+3.7 points; 95 % CI 1.6-5.8); and worse respiratory-specific HRQoL (+3.5 points; 95 % CI 1.8-4.9). Poorer health literacy, also controlling for the same covariates, was associated with higher likelihood of COPD-related hospitalizations (OR = 6.6; 95 % CI 1.3-33) and COPD-related ED visits (OR = 4.7; 95 % CI 1.5-15). Analyses for trend across health literacy tertiles were statistically significant (p < 0.05) for all above outcomes. ::: ::: ::: CONCLUSIONS ::: Independent of socioeconomic status, poor health literacy is associated with greater COPD severity, greater COPD helplessness, worse respiratory-specific HRQoL, and higher odds of COPD-related emergency health-care utilization. These results underscore that COPD patients with poor health literacy may be at particular risk for poor health-related outcomes.


title: Plasticity during stroke recovery: from synapse to behaviour

abstract: Reductions in blood flow to the brain of sufficient duration and extent lead to stroke, which results in damage to neuronal networks and the impairment of sensation, movement or cognition. Evidence from animal models suggests that a time-limited window of neuroplasticity opens following a stroke, during which the greatest gains in recovery occur. Plasticity mechanisms include activity-dependent rewiring and synapse strengthening. The challenge for improving stroke recovery is to understand how to optimally engage and modify surviving neuronal networks, to provide new response strategies that compensate for tissue lost to injury.


title: Combinatorial auctions with tractable winner determination

abstract: Some recent advances in the identification of tractable classes of combinatorial auctions are discussed. In particular, the work of [Gottlob and Greco 2007] is illustrated, where a research question raised in [Conitzer et al. 2004] is solved by showing that the class of structured item graphs is not efficiently recognizable (i.e., deciding the membership of instances is NP-hard), and where this difficulty is overcome trough a different approach based on the notion of hypertree decomposition.


title: Removal of boron from aqueous solutions by continuous polymer-enhanced ultrafiltration with polyvinyl alcohol

abstract: Boron is a highly contaminating metal due to its toxic effects for plants even at very low concentrations. Continuous polymer-enhanced ultrafiltration (PEUF) was applied for removal of boron from aqueous solutions. The effects of operating parameters on the performance of PEUF were investigated. A commercial polymer, polyvinyl alcohol (PVA) was used as the boron-complexing agent. The methodology consists of two steps: complexing boron with PVA following separation of boron and polymer complexes by ultrafiltration process. The pilot scale system utilized for the PEUF process accommodates a spiral-wound cellulose cartridge with 10,000 Da MWCO. The experimental parameters studied are metal/polymer ratio (loading) (0.01–0.5), pH (7–10), and the polymer characteristics such as molecular weight (M n) of the polymer and degree of hydrolysis (DoH). The results showed that PEUF could be a successful alternative method for removal of boron. The permeate flux remained constant at around 19 L/m2 hr throughout the run...


title: Improving Business Process Interoperability by Shared Ledgers

abstract: External business processes involve collaboration with different participants. An integration between systems is usually case by case. We propose a system connection paradigm that the connection across systems are based on a mutual agreed shared ledger, and together with business processes modelling capability on the ledger. Such a paradigm reduces the need to manually settlement; a common base ledger makes connection solution reusable so that the overall development cost is reduced.


title: An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

abstract: Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation. However, training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory. In this work, we address the task of performing semantic segmentation on large data sets, such as three-dimensional medical images. We propose an adaptive sampling scheme that uses a-posterior error maps, generated throughout training, to focus sampling on difficult regions, resulting in improved learning. Our contribution is threefold: 1) We give a detailed description of the proposed sampling algorithm to speed up and improve learning performance on large images. We propose a deep dual path CNN that captures information at fine and coarse scales, resulting in a network with a large field of view and high resolution outputs. We show that our method is able to attain new state-of-the-art results on the VISCERAL Anatomy benchmark.


title: Adjuvant trastuzumab in the treatment of her-2-positive early breast cancer: a meta-analysis of published randomized trials

abstract: BackgroundBreast cancer is the most common cancer in women in the U.S. and Western Europe. Amplification of the her-2/neu gene occurs in approximately 25% of invasive ductal carcinomas of the breast. The first HER-2/neu-targeted approach to reach the clinic was trastuzumab, a humanized monoclonal antibody directed against the extracellular domain of the HER-2/neu protein. Trastuzumab therapy prolongs the survival of patients with metastático HER-2/neu-overexpressing breast cancer when combined with chemotherapy and has recently been demonstrated to lead to dramatic improvements in disease-free survival when used in the adjuvant therapy setting in combination with or following chemotherapy. Here, we performed a meta-analysis of completed clinical trials of adjuvant trastuzumab in the adjuvant setting. Survival, recurrence, brain metastases, cardiotoxicity and directions for future research are discussed.MethodsA meta-analysis of randomized controlled trials (RCT) was performed comparing adjuvant trastuzumab treatment for HER2-positive early breast cancer (EBC) to observation. The MEDLINE, EMBASE, CANCERLIT and Cochrane Library databases, and abstracts published in the annual proceedings were systematically searched for evidence. Relevant reports were reviewed by two reviewers independently and the references from these reports were searched for additional trials, using guidelines set by QUOROM statement criteria.ResultsPooled results from that five randomized trials of adjuvant Trastuzumab showed a significant reduction of mortality (p < 0.00001), recurrence (p < 0.00001), metastases rates (p < 0.00001) and second tumors other than breast cancer (p = 0.007) as compared to no adjuvant Trastuzumab patients. There were more grade III or IV cardiac toxicity after trastuzumab (203/4555 = 4.5%) versus no trastuzumab (86/4562 = 1.8%). The likelihood of cardiac toxicity was 2.45-fold higher (95% CI 1.89 – 3.16) in trastuzumab arms, however that result was associated with heterogeneity. The likelihood of brain metastases was 1.82-fold higher (95% CI 1.16 – 2.85) in patients who received trastuzumab.ConclusionThe results from this meta-analysis are sufficiently compelling to consider 1 year of adjuvant trastuzumab treatment for women with HER-2-positive EBC based on the risk: benefit ratio demonstrated in these studies. Adequate assessment of HER-2/neu status is critical, and careful cardiac monitoring is warranted because of cardiac toxicity. Clinical trials should be designed to answer unsolved questions.


title: Lessons learned from building and deploying a code review analytics platform

abstract: Tool-based code review is growing in popularity and has become a standard part of the development process at Mi-crosoft. Adoption of these tools makes it possible to mine data from code reviews and provide access to it. In this paper, we pre-sent an experience report for CodeFlow Analytics, a system that collects code review data, generates metrics from this data, and provides a number of ways for development teams to access the metrics and data. We discuss the design, design decisions and chal-lenges that we encountered when building CodeFlow Analytics. We contacted teams that used CodeFlow Analytics over the past two years and discuss what prompted them to use CodeFlow Ana-lytics, how they have used it, and what the impact has been. Fur-ther, we survey research that has been enabled by using the Code-Flow Analytics platform. We provide a series of lessons learned from this experience to help others embarking on a task of building an analytics platform in an enterprise setting.


title: The Science of Resilience: Implications for the Prevention and Treatment of Depression

abstract: Human responses to stress and trauma vary widely. Some people develop trauma-related psychological disorders, such as posttraumatic stress disorder (PTSD) and depression; others develop mild to moderate psychological symptoms that resolve rapidly; still others report no new psychological symptoms in response to traumatic stress. Individual variability in how animals and humans respond to stress and trauma depends on numerous genetic, developmental, cognitive, psychological, and neurobiological risk and protective factors.


title: Predicting Personality Traits using Multimodal Information

abstract: Measuring personality traits has a long story in psychology where analysis has been done by asking sets of questions. These question sets (inventories) have been designed by investigating lexical terms that we use in our daily communications or by analyzing biological phenomena. Whether consciously or unconsciously we express our thoughts and behaviors when communicating with others, either verbally, non-verbally or using visual expressions. Recently, research in behavioral signal processing has focused on automatically measuring personality traits using different behavioral cues that appear in our daily communication. In this study, we present an approach to automatically recognize personality traits using a video-blog (vlog) corpus, consisting of transcription and extracted audio-visual features. We analyzed linguistic, psycholinguistic and emotional features in addition to the audio-visual features provided with the dataset. We also studied whether we can better predict a trait by identifying other traits. Using our best models we obtained very promising results compared to the official baseline.


title: Complexation of 3,4-bis(cyanamido)cyclobutane-1,2-dione dianion with copper. Crystal structures and spectroscopic data of copper-(I) and -(II) complexes

abstract: Three complexes [Cu2(PPh3)4(MeCN)2(cis-(NCN)2sq)]·MeCN 1, {[Cu2(dien)2(cis-(NCN)2sq)2]·5H2O}n2 and [Cu(tren)(cis-(NCN)2sq)] 3 where [cis-(NCN)2sq]2− = 3,4-bis(cyanamido)cyclobutane-1,2-dione dianion, have been synthesized and characterized by electronic absorption and IR spectroscopies. The structures of the three complexes have been determined by X-ray crystallography. Complex 1 is present as a dinuclear copper(I) compound. Complex 2 is made of infinite chains with copper(II) atoms bridged by the bis monodentate [cis-(NCN)2sq]2− ion. In the mononuclear complex 3 the co-ordination of the cyanamido group unusually occurs via the amido nitrogen and not by the nitrile group; this is accompanied by an important distortion of the co-ordinated cyanamido group. The co-ordination mode of the cyanamido moiety is discussed in regard to IR data and crystallographic results.


European Conference on Underwater Acoustics DOPPLER EFFECT COMPENSATION FOR CYCLIC-PREFIX-FREE OFDM SIGNALS IN FAST-VARYING UNDERWATER ACOUSTIC CHANNEL


title: A self-adaptive decentralized multichannel MAC protocol for the cognitive wireless network

abstract: In this paper, we propose a multichannel MAC protocol used in the distributed cognitive radio wireless network. The protocol not only carries out the channel, rate and power allocation algorithm according to the QoS requirements and environments that different secondary users have, but also solves the hidden stations and exposed stations problems. Through the simulation results based on the OPNET, we can easily get the conclusion that the protocol can efficiently limit the harmful interference to the primary users and enhance the throughput of the cognitive radio network and spectrum efficiency greatly.


title: Reliable Classification of Vehicle Types Based on Cascade Classifier Ensembles

abstract: Vehicle-type recognition based on images is a challenging task. This paper comparatively studied two feature extraction methods for image description, i.e., the Gabor wavelet transform and the Pyramid Histogram of Oriented Gradients (PHOG). The Gabor transform has been widely adopted to extract image features for various vision tasks. PHOG has the superiority in its description of more discriminating information. A highly reliable classification scheme was proposed by cascade classifier ensembles with reject option to accommodate the situations where no decision should be made if there exists adequate ambiguity. The first ensemble is heterogeneous, consisting of several classifiers, including k-nearest neighbors (kNNs), multiple-layer perceptrons (MLPs), support vector machines (SVMs), and random forest. The classification reliability is further enhanced by a second classifier ensemble, which is composed of a set of base MLPs coordinated by an ensemble metalearning method called rotation forest (RF). For both of the ensembles, rejection option is accomplished by relating the consensus degree from majority voting to a confidence measure and by abstaining to classify ambiguous samples if the consensus degree is lower than a threshold. The final class label is assigned by dual majority voting from the two ensembles. Experimental results using more than 600 images from a variety of 21 makes of cars and vans demonstrated the effectiveness of the proposed approach. The cascade ensembles produce consistently reliable results. With a moderate ensemble size of 25 in the second ensemble, the two-stage classification scheme offers 98.65% accuracy with a rejection rate of 2.5%, exhibiting promising potential for real-world applications.


title: Multivariable PID control design for wastewater systems

abstract: This paper investigates the application of multivariable PID controllers to a wastewater treatment process. Four multivariable PID control schemes are investigated. The methods considered are those suggested by Davison, Penttinen-Koivo, Maciejowski and a new method proposed in this paper. All of the methods are suitable for multi-input multi-output (MIMO) control loops that experience loop interaction. The methods, furthermore, requires only simplistic plant models. The performance of the methods is assessed using a nonlinear benchmark model, and the optimal tuning values are determined using an optimisation method. The simulation results show the significance of the study and lead to the conclusion that the proposed method yields somewhat better results than other methods with respect to decoupling capabilities and closed-loop performance.


title: A positive level shifter for high speed symmetric switching in flash memories

abstract: In this paper, a novel positive level shifter is designed for high speed interfacing between the digital circuits operating at 1.2V and the memory core circuits operating at high voltages in flash memories. The proposed design is also validated for a wide range of output voltage levels and has been optimized to perform efficiently across the different process corners and temperatures. For the targeted design of 1.2V and 33 MHz input signal to 4V output signal, the level shifter has a symmetric switching speed with a rise time and fall time of 2ns and 1.89ns respectively while the average power consumption of 0.056mW is obtained in the typical condition. Simulation results are compared with various other conventional positive level shifters at different process corners and temperatures.


title: Improving similarity measures for short segments of text

abstract: In this paper we improve previous work on measuring the similarity of short segments of text in two ways. First, we introduce a Web-relevance similarity measure and demonstrate its effectiveness. This measure extends the Web-kernel similarity function introduced by Sahami and Heilman (2006) by using relevance weighted inner-product of term occurrences rather than TF×IDF. Second, we show that one can further improve the accuracy of similarity measures by using a machine learning approach. Our methods outperform other state-of-the-art methods in a general query suggestion task for multiple evaluation metrics.


title: Negation and the temporal structure of narrative discourse

abstract: This paper develops an analysis of the temporal role of negative sentences in narrative discourse in Etigfati and French. The analysis focuses on differences in the aspectual systems of English and French, and their consequences for the interpretation of negation and quantification. A recursive rule for the introduction of discourse referents characterizes both quantificational and negated sequences as complex states. The notion of coercion explains why states (including complex states) can behave as events at the level of narrative discourse. The analysis is implemented in the framework of Discourse Representation Theory (DKI) developed by Kamp & Reyle (1993).


title: Game theory in cyber security defence

abstract: Cyber threats and the field of computer cyber defense are gaining more and more an increased importance in our lives. Starting from our regular personal computers and ending with thin clients such as netbooks or smartphones we find ourselves bombarded with constant malware attacks. In this paper we will present a new and novel way in which we can detect these kind of attacks by using elements of modern game theory. We will present the effects and benefits of game theory and we will talk about a defense exercise model that can be used to train cyber response specialists.


title: A new class of wideband multisection 180/spl deg/ hybrid rings using vertically installed planar couplers

abstract: A new class of wideband multisection 180deg hybrid rings using the vertically installed planar (VIP) coupler is proposed. On the basis of the reconfigured ideal single-section 180deg hybrid ring (i.e., the 180deg hybrid ring with an ideal phase inverter), the multisection 180deg hybrid rings can be realized by properly cascading of single-section 180deg hybrid rings. Compared with the conventional hybrid ring, the two-section hybrid rings exhibit wide bandwidth, size reduction, and easily achievable high power-division ratios. Design equations based on the equal-ripple functions are derived. Design curves for the equal and unequal power-division ratio are also described. In addition, a cascade of single-section cascadable hybrid rings with a unit element at each I/O port can be used for bandwidth enhancement. Good agreement is obtained between the experimental and simulated results


2-hop Blockchain : Combining Proof-of-Work and Proof-of-Stake Securely


title: Perfectionism and Workaholism in Employees: The Role of Work Motivation

abstract: Abstract Even though it has been over 20 years since Spence and Robbins (1992) first showed perfectionism and workaholism to be closely related, the relationship between perfectionism and workaholism is still under-researched. In particular, it has remained unclear why perfectionism is linked to workaholism. Using data from 131 employees, this study—examining self-oriented and socially prescribed perfectionism—investigated whether intrinsic–extrinsic work motivation could explain the positive relationship between perfectionism and workaholism. Whereas socially prescribed perfectionism was unrelated to workaholism, self-oriented perfectionism showed a positive correlation with workaholism. Furthermore autonomous (integrated and identified regulation) and controlled (introjected and external regulation) work motivation showed positive correlations. However, when all predictors were entered in a regression analysis, only self-oriented perfectionism, identified regulation, and introjected regulation positively predicted workaholism. In addition, a mediation analysis showed that identified and introjected regulation fully mediated the effect of self-oriented perfectionism on workaholism. The findings suggest that high levels of work motivation explain why many self-oriented perfectionists are workaholic.


title: Web Mining for Web Personalization 1

abstract: The Web has become a huge repository of information and keeps growing exponentially under no editorial control, while the human capability to find, read and understand content remains constant. Providing people with access to information is not the problem; the problem is that people with varying needs and preferences navigate through large Web structures, missing the goal of their inquiry. Web personalization is one of the most promising approaches for alleviating this information overload, providing tailored Web experiences. This chapter explores the different faces of personalization, traces back its roots and follows its progress. It describes the modules typically comprising a personalization process, demonstrates its close relation to Web mining, depicts the technical issues that arise, recommends solutions when possible, and discusses the effectiveness of personalization and the related concerns. Moreover, the chapter illustrates current trends in the field suggesting directions that may lead to new scientific results. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com IDEA GROUP PUBLISHING This chapter appears in the book, Web Mining: Applications a d Techniques, dited by Anthony Scime. Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 28 Markellou, Rigou & Sirmakessis Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. INTRODUCTION Technological innovation has led to an explosive growth of recorded information, with the Web being a huge repository under no editorial control. More and more people everyday browse through it in an effort to satisfy their “primitive” need for information, as humans might properly be characterized as a species of Informavores who “have gained an adaptive advantage because they are hungry for further information about the world they inhabit (and about themselves)” (Dennett, 1991, p. 181). Based on the observation that humans actively seek, gather, share and consume information to a degree unapproached by other organisms, Pirolli and Card (1999, p. 3) took the informavores approach one step further and introduced the Information Foraging Theory according to which, “when feasible, natural information systems evolve towards stable states that maximize gains of valuable information per unit cost” (see also Resnikoff, 1989). Under the information foraging assumption, people need information and the Web today provides open access to a large volume. Thus providing people with access to more information is not the problem; the problem is that more and more people with varying needs and preferences navigate through large and complicated Web structures, missing -in many casesthe goal of their inquiry. The challenge today is how to concentrate human attention on information that is useful (maximizing gains of valuable information per unit cost), a point eloquently made by H.A. Simon (as quoted by Hal Varian in 1995, p. 200), “...what information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.” Personalization can be the solution to this information overload problem, as its objective is to provide users with what they want or need, without having to ask (or search) for it explicitly (Mulvenna et al., 2000). It is a multidiscipline area deploying techniques from various scientific fields for putting together data and producing personalized output for individual users or groups of users. These fields comprise information retrieval, user modeling, artificial intelligence, databases, and more. Personalization on the Web covers a broad area, ranging from check-box customization to recommender systems and adaptive Websites. The spectrum from customizable Websites (in which users are allowed, usually manually, to configure the site in order to better suit their preferences) to adaptive ones (the site undertakes to automatically produce all adaptations according to the user profile, recorded history, etc.) is wide and personalization nowadays has moved towards the latter end. We meet cases of personalization in use in e-commerce applications (product recommendations for cross-selling and upselling, one-to-one marketing, personalized pricing, storefront page customization, etc.), in information portals (home page customization such as my.yahoo.com, etc.), in search engines (in which returned results are filtered and/or sorted according to the profile of the specific user or group of users), and e-learning applications (topic recommendations, student/teacher/administrator views, content adaptations based on student level and skills, etc.). And while recently there has been a lot of talking about personalization, one has to wonder whether it is hype or an actual opportunity. Doug Riecken, in his editorial article in the Communications of the ACM Special Issue on Personalization (2000), claims that it should be considered as an opportunity, but it must be defined clearly and it must be designed to be useful and usable, conditions that in the traditional HCI field 21 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the publisher's webpage: www.igi-global.com/chapter/mining-webpersonalization/31132


Habit Neuroses as True Functional Diseases


title: Long Short Memory Process: Modeling Growth Dynamics of Microscopic Social Connectivity

abstract: How do people make friends dynamically in social networks? What are the temporal patterns for an individual increasing its social connectivity? What are the basic mechanisms governing the formation of these temporal patterns? No matter cyber or physical social systems, their structure and dynamics are mainly driven by the connectivity dynamics of each individual. However, due to the lack of empirical data, little is known about the empirical dynamic patterns of social connectivity at microscopic level, let alone the regularities or models governing these microscopic dynamics. We examine the detailed growth process of "WeChat", the largest online social network in China, with 300 million users and 4.75 billion links spanning two years. We uncover a wide range of long-term power law growth and short-term bursty growth for the social connectivity of different users. We propose three key ingredients, namely average-effect, multiscale-effect and correlation-effect, which govern the observed growth patterns at microscopic level. As a result, we propose the long short memory process incorporating these ingredients, demonstrating that it successfully reproduces the complex growth patterns observed in the empirical data. By analyzing modeling parameters, we discover statistical regularities underlying the empirical growth dynamics. Our model and discoveries provide a foundation for the microscopic mechanisms of network growth dynamics, potentially leading to implications for prediction, clustering and outlier detection on human dynamics.


title: The Flow Engine Framework: A Cognitive Model of Optimal Human Experience

abstract: Flow is a well-known concept in the fields of positive and applied psychology. Examination of a large body of flow literature suggests there is a need for a conceptual model rooted in a cognitive approach to explain how this psychological phenomenon works. In this paper, we propose the Flow Engine Framework, a theoretical model explaining dynamic interactions between rearranged flow components and fundamental cognitive processes. Using an IPO framework (Inputs – Processes – Outputs) including a feedback process, we organize flow characteristics into three logically related categories: inputs (requirements for flow), mediating and moderating cognitive processes (attentional and motivational mechanisms) and outputs (subjective and objective outcomes), describing the process of the flow. Comparing flow with an engine, inputs are depicted as flow-fuel, core processes cylinder strokes and outputs as power created to provide motion.


title: Abstraction Learning

abstract: There has been a gap between artificial intelligence and human intelligence. In this paper, we identify three key elements forming human intelligence, and suggest that abstraction learning combines these elements and is thus a way to bridge the gap. Prior researches in artificial intelligence either specify abstraction by human experts, or take abstraction as a qualitative explanation for the model. This paper aims to learn abstraction directly. We tackle three main challenges: representation, objective function, and learning algorithm. Specifically, we propose a partition structure that contains pre-allocated abstraction neurons; we formulate abstraction learning as a constrained optimization problem, which integrates abstraction properties; we develop a network evolution algorithm to solve this problem. This complete framework is named ONE (Optimization via Network Evolution). In our experiments on MNIST, ONE shows elementary human-like intelligence, including low energy consumption, knowledge sharing, and lifelong learning.


Speech Emotion Recognition Using Spectrogram & Phoneme Embedding


title: Deep visual analogy-making

abstract: In addition to identifying the content within a single image, relating images and generating related images are critical tasks for image understanding. Recently, deep convolutional networks have yielded breakthroughs in predicting image labels, annotations and captions, but have only just begun to be used for generating high-quality images. In this paper we develop a novel deep network trained end-to-end to perform visual analogy making, which is the task of transforming a query image according to an example pair of related images. Solving this problem requires both accurately recognizing a visual relationship and generating a transformed query image accordingly. Inspired by recent advances in language modeling, we propose to solve visual analogies by learning to map images to a neural embedding in which analogical reasoning is simple, such as by vector subtraction and addition. In experiments, our model effectively models visual analogies on several datasets: 2D shapes, animated video game sprites, and 3D car models.


title: A Compact L-Band Orthomode Transducer for Radio Astronomical Receivers at Cryogenic Temperature

abstract: We describe the design, construction, and performance of a compact orthomode transducer (OMT) for the L-band receiver (1.3–1.8 GHz) of the Sardinia Radio Telescope (SRT). The complete OMT consists of a cylindrical orthomode junction (OMJ), which is presented in this paper, and two identical 180 $^{\circ}$ hybrid power combiners in a double-ridged waveguide, which have been proposed in a previous work. The OMT operates at the cryogenic temperature of 20 K to reduce its thermal noise contribution to the noise of the receiving chain. Therefore, particular care has been taken in the design of the OMJ to minimize its dimension and insure a good thermalization. The proposed OMT has been designed and optimized by using CST Microwave Studio, and then manufactured, tested at room temperature, and installed on the L-band receiver of the SRT. The measured results fully comply with the design specifications. In particular, the isolation between the OMT output ports is more than 40 dB, and the cross polarization is less than $-{\hbox{35 dB}}$ for both polarization channels.


title: Smallworld networks and functional connectivity in Alzheimer’s disease. Cereb Cortex 17(1):92–9

abstract: We investigated whether functional brain networks are abnormally organized in Alzheimer’s disease (AD). To this end, graph theoretical analysis was applied to matrices of functional connectivity of beta band--filtered electroencephalography (EEG) channels, in 15 Alzheimer patients and 13 control subjects. Correlations between all pairwise combinations of EEG channels were determined with the synchronization likelihood. The resulting synchronization matrices were converted to graphs by applying a threshold, and cluster coefficients and path lengths were computed as a function of threshold or as a function of degree K. For a wide range of thresholds, the characteristic path length L was significantly longer in the Alzheimer patients, whereas the cluster coefficient C showed no significant changes. This pattern was still present when L and C were computed as a function of K. A longer path length with a relatively preserved cluster coefficient suggests a loss of complexity and a less optimal organization. The present study provides further support for the presence of ‘‘small-world’’ features in functional brain networks and demonstrates that AD is characterized by a loss of small-world network characteristics. Graph theoretical analysis may be a useful approach to study the complexity of patterns of interrelations between EEG channels.


title: Formation of highly reliable Cu/low-k interconnects by using CVD Co barrier in dual damascene structures

abstract: CVD Co film was investigated as an alternative barrier layer to the conventional PVD TaN\Ta in V1\M2 structure for 32nm node. We improved via filling performance and upstream (V1ƒM2) electromigration (EM) lifetime by more than three times. Excellent step coverage of CVD barrier makes it possible to reduce the thickness of the barrier metal by 30% and to increase the volume of Cu in metal lines. RC delay also reduced with decrease in resistance. Since adhesion at the interface between the barrier-Co and Cu also is strong, migration of Cu atoms is dramatically slowed down. EM in the via is finally deterred due to absence of pre-existing voids, consequently lifetime increases. This CVD Co process is expected to be beneficial for the next technology generation beyond 20nm node.


title: Privacy issues on the Internet

abstract: An increasing number of people are using the Internet, in many instances unaware of the information being collected about them. In contrast, other people concerned about the privacy and security issues are limiting their use of the Internet, abstaining from purchasing products online. Businesses should be aware that consumers are looking for privacy protection and a privacy statement can help to ease consumers' concerns. New Zealand based Web sites are expected to have privacy statements on their Web sites under the New Zealand Privacy Act 1993. The incidence of the information gathered from New Zealand Web sites and their use of privacy statements is examined. In particular, Web sites utilizing cookies and statements about them are scanned. Global consistency on Internet privacy protection is important to boost the growth of electronic commerce. To protect consumers in a globally consistent manner, legislation, self-regulation, technical solutions and combination solutions are different techniques that can be implemented.


title: A Survey of Parallelization Techniques for Multigrid Solvers.

abstract: This paper surveys the techniques that are necessary for constructing computationally efficient parallel multigrid solvers. Both geometric and algebraic methods are considered. We first cover the sources of parallelism, including traditional spatial partitioning and more novel additive multilevel methods. We then cover the parallelism issues that must be addressed: parallel smoothing and coarsening, operator complexity, and parallelization of the coarsest grid solve.


Anonymous Alone ? Measuring Bitcoin ’ s Second-Generation Anonymization Techniques


title: Conflict resolution and mediation

abstract: Two approaches are introduced to model and analyze third party intervention, an important element in conflict resolution. These approaches are based on the GMCR framework. Although many studies discuss conflict resolution and conflict modeling, little work has been done on modeling third party interventions. The inverse approach to GMCR is introduced and investigated to find an optimal way to resolve a conflict. An historical conflict is analyzed using the introduced approach. Stability predictions based on different solution concepts are calculated, and the factors influencing third party intervention in a conflict are identified.


title: Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience

abstract: A fundamental challenge for systems neuroscience is to quantitatively relate its three major branches of research: brain-activity measurement, behavioral measurement, and computational modeling. Using measured brain-activity patterns to evaluate computational network models is complicated by the need to define the correspondency between the units of the model and the channels of the brain-activity data, e.g. single-cell recordings or voxels from functional magnetic resonance imaging (fMRI). Similar correspondency problems complicate relating activity patterns between different modalities of brain-activity measurement, and between subjects and species. In order to bridge these divides, we suggest abstracting from the activity patterns themselves and computing representational dissimilarity matrices, which characterize the information carried by a given representation in a brain or model. We propose a new experimental and data-analytical framework called representational similarity analysis (RSA), in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing representational dissimilarity matrices. We demonstrate RSA by relating representations of visual objects as measured with fMRI to computational models spanning a wide range of complexities. We argue that these ideas, which have deep roots in psychology and neuroscience, will allow the integrated quantitative analysis of data from all three branches, thus contributing to a more unified systems neuroscience.


title: Hardware Covert Attacks and Countermeasures

abstract: Computing platforms deployed in many critical infrastructures, such as smart grid, financial systems, governmental organizations etc., are subjected to security attacks and potentially devastating consequences. Computing platforms often get attacked 'physically' by an intruder accessing stored information, studying the internal structure of the hardware or injecting a fault. Even if the attackers fail to gain sensitive information stored in hardware, they may be able to disrupt the hardware or deny service leading to other kinds of security failures in the system. Hardware attacks could be covert or overt, based on the awareness of the intended system. This work classifies existing hardware attacks. Focusing mainly on covert attacks, they are quantified using a proposed schema. Different countermeasure techniques are proposed to prevent such attacks.


title: Predicting Users’ Future Interests on Twitter

abstract: In this paper, we address the problem of predicting future interests of users with regards to a set of unobserved topics in microblogging services which enables forward planning based on potential future interests. Existing works in the literature that operate based on a known interest space cannot be directly applied to solve this problem. Such methods require at least a minimum user interaction with the topic to perform prediction. To tackle this problem, we integrate the semantic information derived from the Wikipedia category structure and the temporal evolution of user’s interests into our prediction model. More specifically, to capture the temporal behaviour of the topics and user’s interests, we consider discrete intervals and build user’s topic profile in each time interval separately. Then, we generalize users’ interests that have been observed over several time intervals by transferring them over the Wikipedia category structure. Our approach not only allows us to generalize users’ interests but also enables us to transfer users’ interests across different time intervals that do not necessarily have the same set of topics. Our experiments illustrate the superiority of our model compared to the state of the art.


title: Overview of automotive sensors

abstract: An up-to-date review paper on automotive sensors is presented. Attention is focused on sensors used in production automotive systems. The primary sensor technologies in use today are reviewed and are classified according to their three major areas of automotive systems application-powertrain, chassis, and body. This subject is extensive. As described in this paper, for use in automotive systems, there are six types of rotational motion sensors, four types of pressure sensors, five types of position sensors, and three types of temperature sensors. Additionally, two types of mass air flow sensors, five types of exhaust gas oxygen sensors, one type of engine knock sensor, four types of linear acceleration sensors, four types of angular-rate sensors, four types of occupant comfort/convenience sensors, two types of near-distance obstacle detection sensors, four types of far-distance obstacle detection sensors, and and ten types of emerging, state-of the-art, sensor technologies are identified.


title: High performance drives for hybrid electric vehicles through voltage stabilization

abstract: This paper deals with the approach of integrating a bidirectional boost-converter into the drivetrain of a (hybrid) electric vehicle in order to exploit the full potential of the electric drives and the battery. Currently, the automotive norms and standards are defined based on the characteristics of the voltage source. The current technologies of batteries for automotive applications have voltage which depends on the load and the state-of charge. The aim of this paper is to provide better system performance by stabilizing the voltage without the need of redesigning any of the current components in the system. To show the added-value of the proposed electrical topology, loss estimation is developed and proved based on actual components measurements and design. The component and its modelling is then implemented in a global system simulation environment of the electric architecture to show how it contributes enhancing the performance of the system.


title: GenPerm: A Unified Method for Detecting Non-overlapping and Overlapping Communities

abstract: Detection of non-overlapping and overlapping communities are essentially the same problem. However, current algorithms focus either on finding overlapping or non-overlapping communities. We present a generalized framework that can identify both non-overlapping and overlapping communities, without any prior input about the network or its community distribution. To do so, we introduce a vertex-based metric, GenPerm , that quantifies by how much a vertex belongs to each of its constituent communities. Our community detection algorithm is based on maximizing the GenPerm over all the vertices in the network. We demonstrate, through experiments over synthetic and real-world networks, that GenPerm is more effective than other metrics in evaluating community structure. Further, we show that due to its vertex-centric property, GenPerm can be used to unfold several inferences beyond community detection, such as core-periphery analysis and message spreading. Our algorithm for maximizing GenPerm outperforms six state-of-the-art algorithms in accurately predicting the ground-truth labels. Finally, we discuss the problem of resolution limit in overlapping communities and demonstrate that maximizing GenPerm can mitigate this problem.


title: Simulation modeling of alternatives to avoid interruptions of the x-ray screening operation at security checkpoints

abstract: A simulation model of a standard security checkpoint of the Transportation Security Administration (TSA) is compared to two other simulation models, which represent two different alternatives designed to alleviate the congestion of passengers at the exit roller area of a generic representation of a security checkpoint. Both alternatives consist of separately processing passengers who travel for business from those who travel for leisure. However, for the second alternative, a non-stop circulating conveyor is modeled in place of the exit roller of the checkpoint line. The results show that the non-stop circulating conveyor decreases the system time for both business and leisure passengers, and it significantly improves the hourly throughput of passengers.


Research problems in data provenance


title: How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions

abstract: How much is 131 million US dollars? To help readers put such numbers in context, we propose a new task of automatically generating short descriptions known as perspectives, e.g. "$131 million is about the cost to employ everyone in Texas over a lunch period". First, we collect a dataset of numeric mentions in news articles, where each mention is labeled with a set of rated perspectives. We then propose a system to generate these descriptions consisting of two steps: formula construction and description generation. In construction, we compose formulae from numeric facts in a knowledge base and rank the resulting formulas based on familiarity, numeric proximity and semantic compatibility. In generation, we convert a formula into natural language using a sequence-to-sequence recurrent neural network. Our system obtains a 15.2% F1 improvement over a non-compositional baseline at formula construction and a 12.5 BLEU point improvement over a baseline description generation.


title: Computationalism, The Church–Turing Thesis, and the Church–Turing Fallacy

abstract: The Church–Turing Thesis (CTT) is often employed in arguments for computationalism. I scrutinize the most prominent of such arguments in light of recent work on CTT and argue that they are unsound. Although CTT does nothing to support computationalism, it is not irrelevant to it. By eliminating misunderstandings about the relationship between CTT and computationalism, we deepen our appreciation of computationalism as an empirical hypothesis.


title: Convolutional Experts Constrained Local Model for Facial Landmark Detection

abstract: Constrained Local Models (CLMs) are a well-established family of methods for facial landmark detection. However, they have recently fallen out of favor to cascaded regression-based approaches. This is in part due to the inability of existing CLM local detectors to model the very complex individual landmark appearance that is affected by expression, illumination, facial hair, makeup, and accessories. In our work, we present a novel local detector -- Convolutional Experts Network (CEN) -- that brings together the advantages of neural architectures and mixtures of experts in an end-to-end framework. We further propose a Convolutional Experts Constrained Local Model (CE-CLM) algorithm that uses CEN as local detectors. We demonstrate that our proposed CE-CLM algorithm outperforms competitive state-of-the-art baselines for facial landmark detection by a large margin on four publicly-available datasets. Our approach is especially accurate and robust on challenging profile images.


title: Penoscrotal approach for IPP: still up-to-date after more than 40 years?

abstract: Inflatable penile prosthesis is the definitive operative intervention utilized for treatment of erectile dysfunction. There are a handful of surgical approaches to perform this operation in a safe and efficient manner. The aim of this review article is to discuss the surgical approaches described in the literature, and describe the preferred approach of authors. Ultimately, the surgical approach utilized by the prosthetic surgeon depends upon their training, patient factors, and surgeon preference.


title: Jointly Aligning and Segmenting Multiple Web Photo Streams for the Inference of Collective Photo Storylines

abstract: With an explosion of popularity of online photo sharing, we can trivially collect a huge number of photo streams for any interesting topics such as scuba diving as an outdoor recreational activity class. Obviously, the retrieved photo streams are neither aligned nor calibrated since they are taken in different temporal, spatial, and personal perspectives. However, at the same time, they are likely to share common storylines that consist of sequences of events and activities frequently recurred within the topic. In this paper, as a first technical step to detect such collective storylines, we propose an approach to jointly aligning and segmenting uncalibrated multiple photo streams. The alignment task discovers the matched images between different photo streams, and the image segmentation task parses each image into multiple meaningful regions to facilitate the image understanding. We close a loop between the two tasks so that solving one task helps enhance the performance of the other in a mutually rewarding way. To this end, we design a scalable message-passing based optimization framework to jointly achieve both tasks for the whole input image set at once. With evaluation on the new Flickr dataset of 15 outdoor activities that consist of 1.5 millions of images of 13 thousands of photo streams, our empirical results show that the proposed algorithms are more successful than other candidate methods for both tasks.


title: Automated regression-based statistical downscaling tool

abstract: Many impact studies require climate change information at a finer resolution than that provided by Global Climate Models (GCMs). In the last 10 years, downscaling techniques, both dynamical (i.e. Regional Climate Model) and statistical methods, have been developed to obtain fine resolution climate change scenarios. In this study, an automated statistical downscaling (ASD) regression-based approach inspired by the SDSM method (statistical downscaling model) developed by Wilby, R.L., Dawson, C.W., Barrow, E.M. [2002. SDSM - a decision support tool for the assessment of regional climate change impacts, Environmental Modelling and Software 17, 147-159] is presented and assessed to reconstruct the observed climate in eastern Canada based extremes as well as mean state. In the ASD model, automatic predictor selection methods are based on backward stepwise regression and partial correlation coefficients. The ASD model also gives the possibility to use ridge regression to alleviate the effect of the non-orthogonality of predictor vectors. Outputs from the first generation Canadian Coupled Global Climate Model (CGCM1) and the third version of the coupled global Hadley Centre Climate Model (HadCM3) are used to test this approach over the current period (i.e. 1961-1990), and compare results with observed temperature and precipitation from 10 meteorological stations of Environment Canada located in eastern Canada. All ASD and SDSM models, as these two models are evaluated and inter-compared, are calibrated using NCEP (National Center for Environmental Prediction) reanalysis data before the use of GCMs atmospheric fields as input variables. The results underline certain limitations to downscale the precipitation regime and its strength to downscale the temperature regime. When modeling precipitation, the most commonly combination of predictor variables were relative and specific humidity at 500hPa, surface airflow strength, 850hPa zonal velocity and 500hPa geopotential height. For modeling temperature, mean sea level pressure, surface vorticity and 850hPa geopotential height were the most dominant variables. To evaluate the performance of the statistical downscaling approach, several climatic and statistical indices were developed. Results indicate that the agreement of simulations with observations depends on the GCMs atmospheric variables used as ''predictors'' in the regression-based approach, and the performance of the statistical downscaling model varies for different stations and seasons. The comparison of SDSM and ASD models indicated that neither could perform well for all seasons and months. However, using different statistical downscaling models and multi-sources GCMs data can provide a better range of uncertainty for climatic and statistical indices.


title: Data integration: a theoretical perspective

abstract: Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.


Hand Gesture Recognition with Batch and Reinforcement Learning


title: Problem-based learning: where are we now?

abstract: The definition of problem-based learning (PBL) as an educational concept is as elusive in 2008 as it has been since the concept was first expressed over forty years ago. A definitive guide to the practice of PBL is equally elusive. Like all worthwhile educational ideas, PBL has proved attractive to those teachers who seek improvements for their courses. Its appeal has transcended the traditional boundaries in formal education so that there are examples of PBL from primary to tertiary education, and across many disciplines within these. Dissemination, however, has wrought confusion in understanding and practice, and consequent difficulties for researchers in evaluating its efficacy, and lack of clear advice for those who would like to adopt PBL. Rather than attempting to be definitive, this Guide explores the various interpretations and practices that claim the label PBL, and critiques these against the original concept and practice. The primary aim is to provide insight into the causes of the confusion about PBL in 2008. The second aim is to point a feasible way forward so that, where appropriate, the potential of PBL as a whole-of-curriculum concept may be realised; and, where it is not possible to implement the whole concept, worthwhile educational principles that have been associated more or less with PBL may be recognised as such and given value in their own right.


title: L2-Nonexpansive Neural Networks

abstract: This paper proposes a class of well-conditioned neural networks in which a unit amount of change in the inputs causes at most a unit amount of change in the outputs or any of the internal layers. We develop the known methodology of controlling Lipschitz constants to realize its full potential in maximizing robustness, with a new regularization scheme for linear layers, new ways to adapt nonlinearities and a new loss function. With MNIST and CIFAR-10 classifiers, we demonstrate a number of advantages. Without needing any adversarial training, the proposed classifiers exceed the state of the art in robustness against white-box L2-bounded adversarial attacks. They generalize better than ordinary networks from noisy data with partially random labels. Their outputs are quantitatively meaningful and indicate levels of confidence and generalization, among other desirable properties.


title: Example-based super-resolution

abstract: We call methods for achieving high-resolution enlargements of pixel-based images super-resolution algorithms. Many applications in graphics or image processing could benefit from such resolution independence, including image-based rendering (IBR), texture mapping, enlarging consumer photographs, and converting NTSC video content to high-definition television. We built on another training-based super-resolution algorithm and developed a faster and simpler algorithm for one-pass super-resolution. Our algorithm requires only a nearest-neighbor search in the training set for a vector derived from each patch of local image data. This one-pass super-resolution algorithm is a step toward achieving resolution independence in image-based representations. We don't expect perfect resolution independence-even the polygon representation doesn't have that-but increasing the resolution independence of pixel-based representations is an important task for IBR.


title: Distributions of C-Reactive Protein and its Association With Metabolic Syndrome in Middle-Aged and Older Chinese People

abstract: Objectives We evaluated the distributions of C-reactive protein (CRP) and its association with metabolic syndrome (MetS) in middle-aged and older Chinese people. Background Several studies have suggested that CRP is a risk factor of MetS. However, it remains unclear how CRP levels are distributed and whether they are associated with MetS in Chinese people. Methods We conducted a population-based cross-sectional survey in 2005 in Beijing and Shanghai, with a total of 1,458 men and 1,831 women age 50 to 70 years. Metabolic syndrome was defined according to the updated National Cholesterol Education Program Adult Treatment Panel III criteria for Asian Americans. Results The median CRP level was 0.68 mg/l among the study population. The CRP levels were significantly higher among participants from Beijing or from urban areas than those in participants from Shanghai or from rural areas (p 1.50 mg/l), the risk for MetS was substantially higher (odds ratio 5.97; 95% confidence interval 4.75 to 7.51) compared with that in the lowest quartile of CRP levels (≤0.33 mg/l) after adjustment for age, gender, geographic location, lifestyle factors, educational attainment, and family history of chronic diseases. This association was observed in both obese and nonobese participants. Conclusions The overall plasma level of CRP is low but highly associated with the MetS among the middle-aged and elderly Chinese population. Prospective studies are needed to investigate the role of CRP in the development of MetS and related chronic diseases among Chinese people.


title: Explaining Deep Convolutional Neural Networks on Music Classification

abstract: Deep convolutional neural networks (CNNs) have been actively adopted in the field of music information retrieval, e.g. genre classification, mood detection, and chord recognition. However, the process of learning and prediction is little understood, particularly when it is applied to spectrograms. We introduce auralisation of a CNN to understand its underlying mechanism, which is based on a deconvolution procedure introduced in [2]. Auralisation of a CNN is converting the learned convolutional features that are obtained from deconvolution into audio signals. In the experiments and discussions, we explain trained features of a 5-layer CNN based on the deconvolved spectrograms and auralised signals. The pairwise correlations per layers with varying different musical attributes are also investigated to understand the evolution of the learnt features. It is shown that in the deep layers, the features are learnt to capture textures, the patterns of continuous distributions, rather than shapes of lines.


title: Survey on Credit Card Fraud Detection Methods

abstract: Due to a rapid advancement in the electronic commerce technology, the use of credit cards has increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of credit card fraud also rising. Financial fraud is increasing significantly with the development of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. The fraudulent transactions are scattered with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Neural Network, Bayesian Network, Fuzzy logic, Artificial Immune System, K- nearest neighbor algorithm, Support Vector Machine, Decision Tree, Fuzzy Logic Based System, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. This paper presents a survey of various techniques used in credit card fraud detection mechanisms.


title: 3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review

abstract: This paper presents a systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging. This analysis covers articles published in the range 2006-March 2018 found in four scientific databases (Science Direct, IEEEXplore, ACM, and PubMed), using the methodology for systematic review proposed by Kitchenham. We present the analyzed segmentation methods categorized according to its application, algorithmic strategy, validation, and use of prior knowledge, as well as its general conceptual description. Additionally, we present a general overview, discussions, and further prospects for the 3D segmentation methods applied for tomographic images.


title: A Theory of Market Efficiency

abstract: We introduce a mathematical theory called market connectivity that gives concrete ways to both measure the efficiency of markets and find inefficiencies in large markets. The theory leads to new methods for testing the famous efficient markets hypothesis that do not suffer from the joint-hypothesis problem that has plagued past work. Our theory suggests metrics that can be used to compare the efficiency of one market with another, to find inefficiencies that may be profitable to exploit, and to evaluate the impact of policy and regulations on market efficiency. A market's efficiency is tied to its ability to communicate information relevant to market participants. Market connectivity calculates the speed and reliability with which this communication is carried out via trade in the market. We model the market by a network called the trade network, which can be computed by recording transactions in the market over a fixed interval of time. The nodes of the network correspond to participants in the market. Every pair of nodes that trades in the market is connected by an edge that is weighted by the rate of trade, and associated with a vector that represents the type of item that is bought or sold. We evaluate the ability of the market to communicate by considering how it deals with shocks. A shock is a change in the beliefs of market participants about the value of the products that they trade. We compute the effect of every potential significant shock on trade in the market. We give mathematical definitions for a few concepts that measure the ability of the market to effectively dissipate shocks.


title: Detecting and Localizing Wireless Spoofing Attacks

abstract: Wireless networks are vulnerable to spoofing attacks, which allows for many other forms of attacks on the networks. Although the identity of a node can be verified through cryptographic authentication, authentication is not always possible because it requires key management and additional infrastructural overhead. In this paper we propose a method for both detecting spoofing attacks, as well as locating the positions of adversaries performing the attacks. We first propose an attack detector for wireless spoofing that utilizes K-means cluster analysis. Next, we describe how we integrated our attack detector into a realtime indoor localization system, which is also capable of localizing the positions of the attackers. We then show that the positions of the attackers can be localized using either area-based or point-based localization algorithms with the same relative errors as in the normal case. We have evaluated our methods through experimentation using both an 802.11 (WiFi) network as well as an 802.15.4 (ZigBee) network. Our results show that it is possible to detect wireless spoofing with both a high detection rate and a low false positive rate, thereby providing strong evidence of the effectiveness of the K-means spoofing detector as well as the attack localizer.


Metacognition and Theory of Mind


title: Large-Scale Weakly Supervised Audio Classification Using Gated Convolutional Neural Network

abstract: In this paper, we present a gated convolutional neural network and a temporal attention-based localization method for audio classification, which won the 1st place in the large-scale weakly supervised sound event detection task of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 challenge. The audio clips in this task, which are extracted from YouTube videos, are manually labeled with one or a few audio tags but without timestamps of the audio events, which is called as weakly labeled data. Two sub-tasks are defined in this challenge including audio tagging and sound event detection using this weakly labeled data. A convolutional recurrent neural network (CRNN) with learnable gated linear units (GLUs) non-linearity applied on the log Mel spectrogram is proposed. In addition, a temporal attention method is proposed along the frames to predicate the locations of each audio event in a chunk from the weakly labeled data. We ranked the 1st and the 2nd as a team in these two sub-tasks of DCASE 2017 challenge with F value 55.6\% and Equal error 0.73, respectively.


title: Knowledge graph completion with adaptive sparse transfer matrix

abstract: We model knowledge graphs for their completion by encoding each entity and relation into a numerical space. All previous work including Trans(E, H, R, and D) ignore the heterogeneity (some relations link many entity pairs and others do not) and the imbalance (the number of head entities and that of tail entities in a relation could be different) of knowledge graphs. In this paper, we propose a novel approach TranSparse to deal with the two issues. In TranSparse, transfer matrices are replaced by adaptive sparse matrices, whose sparse degrees are determined by the number of entities (or entity pairs) linked by relations. In experiments, we design structured and unstructured sparse patterns for transfer matrices and analyze their advantages and disadvantages. We evaluate our approach on triplet classification and link prediction tasks. Experimental results show that TranSparse outperforms Trans(E, H, R, and D) significantly, and achieves state-of-the-art performance.


title: Umbilical Endometriosis: A Rare Diagnosis in Plastic and Reconstructive Surgery

abstract: Umbilical endometriosis infrequently presents to the plastic surgeon. As such, the diagnosis is difficult to make because it is often overlooked. The current report presents a 35-year-old nulligravid woman with a six-month history of a firm, cyclically swelling lesion in her umbilical region. None of the signs characteristic of pelvic endometriosis except dysmenorrhea and a one-year history of infertility were present. Biopsy of the lesion revealed umbilical endometriosis (grade IV), and laparoscopy uncovered extensive disease. Monopolar cautery with coagulating current umbilical excision and reconstruction with a purse-string suture was used. The results underscore the importance of a broad differential diagnosis for an umbilical lesion in a middle-age woman. They also highlight the importance of early recognition and appropriate surgical intervention to minimize morbidity and mortality associated with umbilical endometriosis.


title: IT ADOPTION : A PERSUASIVE PERSPECTIVE

abstract: Based on Fogg’s Functional Triad, we develop a model to explain how a feature of persuasive technologies can influence usage intention by shaping an individual’s beliefs in regard to the Functional Triad. Specifically, we operationalize the Functional Triad by adopting the constructs of perceived usefulness, perceived medium function, and perceived social presence, respectively. We investigate the trade-off transparency of an online decision aid as a persuasive feature of a computing technology. Through an experimental study, the results show that 1) perceived tool function, perceived medium function, and perceived social actor function all significantly influence intention to use a computing technology; 2) trade-off transparency influences the perceptions of these three functions; and 3) the three functions fully mediate the effect of trade-off transparency on usage intention. Implications for researchers and technology designers are also discussed.


title: Fraud detection in big data using supervised and semi-supervised learning techniques

abstract: This paper addresses the credit card fraud detection problem in the context of Big Data, based on machine learning techniques. In the fraud detection task, typically the available datasets for ML training present some peculiarities, such as the unavoidable condition of a strong class imbalance, the existence of unlabeled transactions, and the large number of records that must be processed. The present paper aims to propose a methodology for automatic detection of fraudulent transactions, that tackle all these problems. The methodology is based on a Balanced Random Forest, that can be used in supervised and semi-supervised scenarios through a co-training approach. Two different schemes for the co-training approach are tested, in order to overcome the class imbalance problem. Moreover, a Spark platform and Hadoop file system support our solution, in order to enable the scalability of the proposed solution. The proposed approach achieves an absolute improvement of around 24% in terms of geometric mean in comparison to a standard random forest learning strategy.


title: A dexterous gripper for in-hand manipulation

abstract: During the last few decades, robotic grippers are developed by research community to solve grasping complexities of several objects as their primary objective. Due to the increasing demands of industries, many issues are rising and remain unsolved such as in-hand manipulation, placing object with appropriate posture. Operations like twisting, altering orientation of object, in a hand, requires significant dexterity of the gripper that must be achieved from a compact mechanical design at the first place. In this paper, a newly designed gripper is proposed whose primary goal is to solve in-hand manipulation. The gripper is derived from four identical finger modules; each module has four DOFs, consists of a five bar linkage to grasp and additional two DOFs mechanism similar to ball spline is conceived to manipulate object. Hence the easily constructible gripper is capable to grasp and manipulate plurality of object. As a preliminary inspection, an optimized platform that represents the central concept of the proposed gripper is developed, to the aim of evaluating kinematic feasibilities of manipulation of different objects.


title: [Vulvar inflammatory dermatoses].

abstract: Vulvar skin disease is a common reason for consultation. The vulva, like the rest of the skin, can be affected by numerous diseases of various etiologies, but its particular anatomic and physiologic characteristics create additional diagnostic and therapeutic difficulties. The study of vulvar disease is emerging as a new branch of dermatology. In this article, we examine the characteristics of the normal vulva, and perform a brief, structured review of vulvar inflammatory dermatoses, which comprise a heterogeneous group of diseases in which a broad, multidisciplinary approach is essential.


title: A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction

abstract: Numerous studies on wind power forecasting show that random errors found in the prediction results cause uncertainty in wind power prediction and cannot be solved effectively using conventional point prediction methods. In contrast, interval prediction is gaining increasing attention as an effective approach as it can describe the uncertainty of wind power. A wind power interval forecasting approach is proposed in this article. First, the original wind power series is decomposed into a series of subseries using variational mode decomposition (VMD); second, the prediction model is established through kernel extreme learning machine (KELM). Three indices are taken into account in a novel objective function, and the improved artificial bee colony algorithm (IABC) is used to search for the best wind power intervals. Finally, when compared with other competitive methods, the simulation results show that the proposed approach has much better performance.


Synthesis , Characterization and Antibacterial Activity of 4-( Phenyl Sulfonyl )


title: Brain storm optimization algorithm: a review

abstract: For swarm intelligence algorithms, each individual in the swarm represents a solution in the search space, and it also can be seen as a data sample from the search space. Based on the analyses of these data, more effective algorithms and search strategies could be proposed. Brain storm optimization (BSO) algorithm is a new and promising swarm intelligence algorithm, which simulates the human brainstorming process. Through the convergent operation and divergent operation, individuals in BSO are grouped and diverged in the search space/objective space. In this paper, the history development, and the state-of-the-art of the BSO algorithm are reviewed. In addition, the convergent operation and divergent operation in the BSO algorithm are also discussed from the data analysis perspective. Every individual in the BSO algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscape of the problem. Swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone.


title: Force Optimization of a Double-Sided Tubular Linear Induction Motor

abstract: An innovative double-sided tubular linear induction motor is presented, and its optimal design in terms of thrust force is discussed. A dedicated semi-analytical model of the device is developed allowing for fast and accurate evaluation of all the electromechanic quantities in the device, including the thrust force, back-electromotive force, distribution of the induced current, and average magnetic flux density in the teeth. This provides a basis for the design optimization, which has been performed by a novel evolutionary algorithm based on the self-organizing maps. Using the semi-analytical formulation, the characterization of the machine is greatly facilitated, thus allowing a fast evaluation of the cost function and design constraints. Finally, the obtained optimal design is validated by comparison with finite elements method analysis.


title: Waste gasification vs. conventional Waste-to-Energy: a comparative evaluation of two commercial technologies.

abstract: A number of waste gasification technologies are currently proposed as an alternative to conventional Waste-to-Energy (WtE) plants. Assessing their potential is made difficult by the scarce operating experience and the fragmentary data available. After defining a conceptual framework to classify and assess waste gasification technologies, this paper compares two of the proposed technologies with conventional WtE plants. Performances are evaluated by proprietary software developed at Politecnico di Milano and compared on the basis of a coherent set of assumptions. Since the two gasification technologies are configured as "two-step oxidation" processes, their energy performances are very similar to those of conventional plants. The potential benefits that may justify their adoption relate to material recovery and operation/emission control: recovery of metals in non-oxidized form; collection of ashes in inert, vitrified form; combustion control; lower generation of some pollutants.


title: Multiple Decision Aided Successive Interference Cancellation Receiver for NOMA Systems

abstract: Non-orthogonal multiple access (NOMA) is one of the critical technologies in fifth generation systems. Successive interference cancellation (SIC) is a typical solution of multiple users detection in NOMA systems. However, conventional SIC suffers from error propagation. To alleviate this problem, in this letter, a multiple decision SIC (MD-SIC) strategy is introduced. Instead of choosing one codeword for each user in traditional SIC detection, the proposed algorithm introduces multiple codewords as the candidates to combat the error propagation. Furthermore, a threshold is designed to control the size of candidate set for each user. It helps the MD-SIC to achieve a better tradeoff between performance and complexity. The simulation results indicate that the MD-SIC algorithm obviously outperforms the conventional SIC with a little additional computational complexity.


title: Forensic analysis of Kik messenger on iOS devices

abstract: Instant messaging applications continue to grow in popularity as a means of communicating and sharing multimedia files. The information contained within these applications can prove invaluable to law enforcement in the investigation of crimes.Kik messenger is a recently introduced instant messaging application that has become very popular in a short period of time, especially among young users. The novelty of Kik means that there has been little forensic examination conducted on this application.This study addresses this issue by investigating Kik messenger on Apple iOS devices. The goal was to locate and document artefacts created or modified by Kik messenger on devices installed with the latest version of iOS, as well as in iTunes backup files. Once achieved, the secondary goal was to analyse the artefacts to decode and interpret their meaning and by doing so, be able to answer the typical questions faced by forensic investigators.A detailed description of artefacts created or modified by Kik messenger is provided. Results from experiments showed that deleted images are not only recoverable from the device, but can also be located and downloaded from Kik servers. A process to link data from multiple database tables producing accurate chat histories is explained. These outcomes can be used by law enforcement to investigate crimes and by software developers to create tools to recover evidence.


title: Enabling Efficient and Geometric Range Query With Access Control Over Encrypted Spatial Data

abstract: As a basic query function, range query has been exploited in many scenarios such as SQL retrieves, location-based services, and computational geometry. Meanwhile, with explosive growth of data volume, users are increasingly inclining to store data on the cloud for saving local storage and computational cost. However, a long-standing problem is that the user’s data may be completely revealed to the cloud server because it has full data access right. To cope with this problem, a frequently-used method is to encrypt raw data before outsourcing them, but the availability and operability of data will be reduced significantly. In this paper, we propose an efficient and geometric range query scheme (EGRQ) supporting searching and data access control over encrypted spatial data. We employ secure KNN computation, polynomial fitting technique, and order-preserving encryption to achieve secure, efficient, and accurate geometric range query over cloud data. Then, we propose a novel spatial data access control strategy to refine user’s rights in our EGRQ. To improve the efficiency, R-tree is adopted to reduce the searching space and matching times in whole search process. Finally, we theoretically prove the security of our proposed scheme in terms of confidentiality of spatial data, privacy protection of index and trapdoor, and the unlinkability of trapdoors. In addition, extensive experiments demonstrate the high efficiency of our proposed model compared with existing schemes.


title: Challenges in the manufacturing of hairpin windings and application opportunities of infrared lasers for the contacting process

abstract: Accelerated by electric mobility and new requirements regarding quantities, quality and innovative motor designs, production technologies for windings of electrical drives gain in importance. Especially the demand for increasing slot fill ratios and a product design allowing manufacturers of electric drives to produce big quantities in a good quality impels innovations in the design of windings. The hairpin winding is a result of this development combining high slot fill ratios with new potentials for an economical production of the winding for electric drives. This is achieved by a new method for the production of the winding: The winding is assembled by mounting preformed elements of insulated copper wire to the stator simplifying the elaborate winding process on the one hand. On the other hand it becomes necessary to join these elements mechanically and electrically to manufacture the winding. Due to this, contacting technologies gain in importance regarding the production of hairpin windings. The new challenge consists of the high number of contact points that have to be produced in a small amount of space. On account of its process stability, high process velocities and the possibility of realizing a remote joining process, the laser welding shows big potentials for the realization of a contacting process for hairpin windings that is capable of series production. This paper describes challenges and possibilities for the application of infrared lasers in the field of hairpin winding production.


title: Simulating water and smoke with an octree data structure

abstract: We present a method for simulating water and smoke on an unrestricted octree data structure exploiting mesh refinement techniques to capture the small scale visual detail. We propose a new technique for discretizing the Poisson equation on this octree grid. The resulting linear system is symmetric positive definite enabling the use of fast solution methods such as preconditioned conjugate gradients, whereas the standard approximation to the Poisson equation on an octree grid results in a non-symmetric linear system which is more computationally challenging to invert. The semi-Lagrangian characteristic tracing technique is used to advect the velocity, smoke density, and even the level set making implementation on an octree straightforward. In the case of smoke, we have multiple refinement criteria including object boundaries, optical depth, and vorticity concentration. In the case of water, we refine near the interface as determined by the zero isocontour of the level set function.


title: Optimal sizing of Hybrid Energy Storage Systems to mitigate wind power fluctuations

abstract: Wind energy penetration level may be adversely impacted by frequency deviations as a result of wind power fluctuations. Energy Storage System (ESS) can be a viable candidate for mitigating wind power fluctuations. However, economic considerations for ESS may cause significant limits in implementation. Therefore, effectiveness and optimal size of ESS are important to study. This paper proposes a method to find an optimal size of sodium sulfur battery energy storage and pumped storage hydro to accommodate high penetrations of wind energy. The proposed method decomposes the imbalance power by using Discrete Fourier Transform (DFT) to fast and slow components. Hence, different ESS technologies can be dispatched based on their effectiveness in various time spans. For instance, the slower pumped storage hydro can compensate slow cycles, and the fast-acting batteries can be dispatched to balance smaller and more frequent cycles. This method can be used in the planning of different ESS technologies. Comparative studies on real wind data from Bonneville Power Administration (BPA) shows the efficacy of the proposed method.


title: Neural Discrete Representation Learning

abstract: Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Using the VQ method allows the model to circumvent issues of "posterior collapse" -- where the latents are ignored when they are paired with a powerful autoregressive decoder -- typically observed in the VAE framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations.


Pulse Width Modulation for Current Source Converters – A Detailed Concept


title: The Evolution to Modern Phased Array Architectures

abstract: Phased array technology has been evolving steadily with advances in solid-state microwave integrated circuits, analysis and design tools, and reliable fabrication practices. With significant government investments, the technologies have matured to a point where phased arrays are widely used in military systems. Next-generation phased arrays will employ high levels of digitization, which enables a wide range of improvements in capability and performance. Digital arrays leverage the rapid commercial evolution of digital processor technology. The cost of phased arrays can be minimized by utilizing high-volume commercial microwave manufacturing and packaging techniques. Dramatic cost reductions are achieved by employing a tile array architecture, which greatly reduces the number of printed circuit boards and connectors in the array.


title: A neural theory of speech acquisition and production

abstract: Abstract This article describes a computational model, called DIVA, that provides a quantitative framework for understanding the roles of various brain regions involved in speech acquisition and production. An overview of the DIVA model is first provided, along with descriptions of the computations performed in the different brain regions represented in the model. Particular focus is given to the model's speech sound map , which provides a link between the sensory representation of a speech sound and the motor program for that sound. Neurons in this map share with “mirror neurons” described in monkey ventral premotor cortex the key property of being active during both production and perception of specific motor actions. As the DIVA model is defined both computationally and anatomically, it is ideal for generating precise predictions concerning speech-related brain activation patterns observed during functional imaging experiments. The DIVA model thus provides a well-defined framework for guiding the interpretation of experimental results related to the putative human speech mirror system.


title: 3D image reconstruction and human body tracking using stereo vision and Kinect technology

abstract: Kinect is a recent technology used for motion detection and human body tracking designed for a video game console. In this study, we explore two different types of 3D image reconstruction methods to achieve a new method for faster and higher quality 3D images. Generating depth perception information using high quality stereo image textures is computationally heavy and inefficient. On the other hand, depth information can be obtained very fast using Kinect but the overall 3D image quality is not refined and it is low resolution. Thus, in this study we explore the combination of higher quality images on a webcam and faster computation of depth information on Kinect in order to create an efficient and enhanced 3D image reconstruction system. This high resolution system has a broad range of applications including 3D motion sensing of human body, hands tracking and finger gestures.


title: Rapid word learning under uncertainty via cross-situational statistics

abstract: There are an infinite number of possible word- to-word pairings in naturalistic learning environments. Previous proposals to solve this mapping problem have focused on linguistic, social, representational, and at- tentional constraints at a single moment. This article dis- cusses a cross-situational learning strategy based on computing distributional statistics across words, across referents, and, most important, across the co-occurrences of words and referents at multiple moments. We briefly exposed adults to a set of trials that each contained mul- tiple spoken words and multiple pictures of individual objects; no information about word-picture correspon- dences was given within a trial. Nonetheless, over trials, subjects learned the word-picture mappings through cross-trial statistical relations. Different learning condi- tions varied the degree of within-trial reference uncer- tainty, the number of trials, and the length of trials. Overall, the remarkable performance of learners in vari- ous learning conditions suggests that they calculate cross- trial statistics with sufficient fidelity and by doing so rapidly learn word-referent pairs even in highly ambigu- ous learning contexts. Quine (1960) famously presented the core problem for learning word meanings from their co-occurrence with perceived events in the world. He imagined an anthropologist who observes a speaker saying ''gavagai'' while pointing in the general direction of a field. The intended referent (rabbit, grass, the field, or rabbit ears, etc.) is indeterminate from this experience. The solution to this indeterminacy problem requires that the learning system be somehow constrained. Research on children's word learning has concentrated on how this learning might be constrained in a single trial, such that the word is correctly mapped to the referent on that trial. This literature suggests that attentional (Smith, 2000), social (Bald- win, 1993; Tomasello, 2000), linguistic (Gleitman, 1990), and representational (Markman, 1990) constraints enable learners to ''fast map'' words to referents in a single encounter. However, the indeterminacy problem may also be solved cross-situation- ally, not in a single encounter with a word and potential referent but across multiple encounters and learning trials. A learner who is unable to unambiguously decide the referent of a word on any single learning trial might nonetheless store possible word-referent pairings across trials, evaluate the statistical evidence, and ultimately map individual words to the right


title: Three-Phase Transformerless Shunt Active Power Filter With Reduced Switch Count for Harmonic Compensation in Grid-Connected Applications

abstract: Shunt active power filter (SAPF) is the preeminent solution against nonlinear loads, current harmonics, and power quality problems. APF topologies for harmonic compensation use numerous high-power rating components and are therefore disadvantageous. Hybrid topologies combining low-power rating APF with passive filters are used to reduce the power rating of voltage source inverter (VSI). Hybrid APF topologies for high-power rating systems use a transformer with large numbers of passive components. In this paper, a novel four-switch two-leg VSI topology for a three-phase SAPF is proposed for reducing the system cost and size. The proposed topology comprises a two-arm bridge structure, four switches, coupling inductors, and sets of LC PFs. The third leg of the three-phase VSI is removed by eliminating the set of power switching devices, thereby directly connecting the phase with the negative terminals of the dc-link capacitor. The proposed topology enhances the harmonic compensation capability and provides complete reactive power compensation compared with conventional APF topologies. The new experimental prototype is tested in the laboratory to verify the results in terms of total harmonic distortion, balanced supply current, and harmonic compensation, following the IEEE-519 standard.


title: Simultaneous localization and classification of acute lymphoblastic leukemic cells in peripheral blood smears using a deep convolutional network with average pooling layer

abstract: It is important to analyze and classify the blood cells for the evaluation and diagnosis of many diseases. Acute Lymphoblastic Leukemia (ALL) is a blood cancer mostly found in children below the age of 7–8 years. It can be fatal if left untreated. ALL cells are abnormal lymphocytes that have a condensed appearance to their chromatin. ALL can be detected through the analysis of white blood cells (WBCs) also called as leukocytes. Presently the morphological analysis of blood cells is performed manually by skilled operators, which makes it a time-taking and non-standardized process. This paper presents a novel deep learning approach to automate the process of detecting ALL from whole-slide blood smear images. Previous work in this domain deal with the isolation and classification of WBCs based on certain morphological image features. However, this work uses a deep network for simultaneous localization and classification of WBCs. The network makes use of Average Pooling layers to figure out the hot-spots of WBC locations in whole-slide images. Although this workflow fails to successfully figure out all the ALL lymphocytes in a whole-slide image, but it does performs very well in the task of predicting whether the blood smear image belongs to an ALL patient or not.


title: Children Who are Anxious in Silence: A Review on Selective Mutism, the New Anxiety Disorder in DSM-5

abstract: Selective mutism (SM) is a relatively rare childhood disorder characterized by a consistent failure to speak in specific settings (e.g., school, social situations) despite speaking normally in other settings (e.g., at home). The latest edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) lists SM among the anxiety disorders. This makes sense as the current review of the literature confirms that anxiety is a prominent symptom in many children suffering from this condition. Further, research on the etiology and treatment of SM also corroborates the conceptualization of SM as an anxiety disorder. At the same time, critical points can be raised regarding the classification of SM as an anxiety disorder. We explore a number of such issues in this review. Recommendations for dealing with this diagnostic conundrum are made for psychologists, psychiatrists, and other mental health workers who face children with SM in clinical practice, and directions for future research are highlighted.


Toward an ethics of persuasive technology


title: Molecular profiling of high-risk neuroblastoma by cDNA array.

abstract: The aim of this study was to investigate gene expression changes in disseminated neuroblastoma by cDNA array technique. Three stage IV neuroblastomas and one neuroblastoma cell line (SK-N-FI) were analyzed. Expression profiles were confirmed by semiquantitative RT-PCR and in some instances by Northern blotting. Comparison of expression profiles identified several genes which were highly expressed as well as some which were down-regulated in tumor samples relatively to SK-N-FI cells. The tumors studied lacked N-myc overexpression, while showing up-regulation of basic transcription factors, growth factors and receptors such as NSEP, c-myc binding protein MM1, thymosin beta10 (TMSB10), TNF-R superfamily member 10A (TNFRSF10A) and FZ9. These results suggest that cDNA array technology is a powerful tool to identify a set of genes involved in neuroblastoma genesis and progression. Thus, the sensitive cDNA array may provide new insight into many aspects of pediatric tumors and play crucial role in the administration of adjuvant therapy of patients with neuroblastoma.