title: Dorsal and ventral pathways in language development

abstract: The dorsal and ventral information streams between inferior frontal and temporal language regions in the human brain are implemented by two fiber connections that consist of separable tracts. We compared the maturation of the two connections including their subcomponents in three different age groups: newborn infants, 7-year-old children, and adults. Our results reveal a maturational primacy of the ventral connection in the language network associating the temporal areas to the inferior frontal gyrus during early development, which is already in place at birth. Likewise, a dorsal pathway from the temporal cortex to the premotor cortex is observable at this early age. This is in contrast to the dorsal pathway to the inferior frontal gyrus which matures at later stages in development and might play a role in more complex language functions.


title: Scratch input: creating large, inexpensive, unpowered and mobile finger input surfaces

abstract: We present Scratch Input, an acoustic-based input technique that relies on the unique sound produced when a fingernail is dragged over the surface of a textured material, such as wood, fabric, or wall paint. We employ a simple sensor that can be easily coupled with existing surfaces, such as walls and tables, turning them into large, unpowered and ad hoc finger input surfaces. Our sensor is sufficiently small that it could be incorporated into a mobile device, allowing any suitable surface on which it rests to be appropriated as a gestural input surface. Several example applications were developed to demonstrate possible interactions. We conclude with a study that shows users can perform six Scratch Input gestures at about 90% accuracy with less than five minutes of training and on wide variety of surfaces.


title: THE IFC STANDARD- A REVIEW OF HISTORY, DEVELOPMENT, AND STANDARDIZATION

abstract: IFC (Industry Foundation Classes) is an open and standardized data model intended to enable interoperability between building information modeling software applications in the AEC/FM industry. IFC has been in development by an industry consortium since 1994, and since the start of the effort, the evolving industry context, standardization organization, resource availability, and technology development have exposed the standardization process to a dynamic environment. While the overarching mission of IFC standardization has always been to enable interoperability between AEC/FM software applications, the approach for how best to operationalize that mission has changed over the years. Through a literature review supported by the general theory on IT standardization, this study follows the development process of the IFC standard from its origins in the early 1990s to its latest activities in 2012. The end result is both a descriptive review of the history of IFC standardization and the establishment of an initial connection to IT standardization research for the IFC standard by profiling the effort in accordance with existing IT standardization theories and typologies. The review highlights the evolution of IFC standardization through several distinct phases, and its gradual movement from emphasizing technical architecture development towards growing involvement in specifying the processes facilitating its use. The organization behind the standard has also seen changes in its modus operandi, from initially being a closed and loosely coupled alliance to evolving into a consortium incorporating open hybrid standardization, where a formal standards body publishes the standards prepared by the consortium. The consortium has faced many challenges compiling an ambitious interoperability standard with few resources, and were it not for the growing demand for the standard provided by public actors, momentum and enthusiasm for the effort might have petered out due to slow market uptake and low use of the data standard in actual construction projects thus far. While this paper does not investigate the adoption phenomenon in-depth, the moderate uptake of the standard can perhaps be explained to be a symptom of the slow adoption of collaborative model-based construction processes and industry reluctance to switch over to new IT tools, which in turn are prerequisites for the existence of demand for an open interoperability standard.


title: Improving feature location using structural similarity and iterative graph mapping

abstract: Locating program element(s) relevant to a particular feature is an important step in efficient maintenance of a software system. The existing feature location techniques analyse each feature independently and perform a one-time analysis after being provided an initial input. As a result, these techniques are sensitive to the quality of the input. In this paper, we propose to address the above issues in feature location using an iterative context-aware approach. The underlying intuition is that features are not independent of each other, and the structure of source code resembles the structure of features. The distinguishing characteristics of the proposed approach are: (1) it takes into account the structural similarity between a feature and a program element to determine feature-element relevance and (2) it employs an iterative process to propagate the relevance of the established mappings between a feature and a program element to the neighbouring features and program elements. We evaluate our approach using two different systems, DirectBank, a small-scale industry financial system, and Linux kernel, a large-scale open-source operating system. Our evaluation suggests that the proposed approach is more robust and can significantly increase the recall of feature location with only a minor decrease of precision.


title: Embedded Bandits for Large-Scale Black-Box Optimization

abstract: Random embedding has been applied with empirical success to large-scale black-box optimization problems with low effective dimensions. This paper proposes the EmbeddedHunter algorithm, which incorporates the technique in a hierarchical stochastic bandit setting, following the optimism in the face of uncertainty principle and breaking away from the multiple-run framework in which random embedding has been conventionally applied similar to stochastic black-box optimization solvers. Our proposition is motivated by the bounded mean variation in the objective value for a low-dimensional point projected randomly into the decision space of Lipschitz-continuous problems. In essence, the EmbeddedHunter algorithm expands optimistically a partitioning tree over a low-dimensional---equal to the effective dimension of the problem---search space based on a bounded number of random embeddings of sampled points from the low-dimensional space. In contrast to the probabilistic theoretical guarantees of multiple-run random-embedding algorithms, the finite-time analysis of the proposed algorithm presents a theoretical upper bound on the regret as a function of the algorithm's number of iterations. Furthermore, numerical experiments were conducted to validate its performance. The results show a clear performance gain over recently proposed random embedding methods for large-scale problems, provided the intrinsic dimensionality is low.


title: Sampling algorithms in a stream operator

abstract: Complex queries over high speed data streams often need to rely on approximations to keep up with their input. The research community has developed a rich literature on approximate streaming algorithms for this application. Many of these algorithms produce samples of the input stream, providing better properties than conventional random sampling. In this paper, we abstract the stream sampling process and design a new stream sample operator. We show how it can be used to implement a wide variety of algorithms that perform sampling and sampling-based aggregations. Also, we show how to implement the operator in Gigascope - a high speed stream database specialized for IP network monitoring applications. As an example study, we apply the operator within such an enhanced Gigascope to perform subset-sum sampling which is of great interest for IP network management. We evaluate this implemention on a live, high speed internet traffic data stream and find that (a) the operator is a flexible, versatile addition to Gigascope suitable for tuning and algorithm engineering, and (b) the operator imposes only a small evaluation overhead. This is the first operational implementation we know of, for a wide variety of stream sampling algorithms at line speed within a data stream management system.


title: How Digital Nudges Influence Consumers – Experimental Investigation in the Context of Retargeting

abstract: Retargeting is an innovative online marketing technique in the modern age. Although this advertising form offers great opportunities of bringing back customers who have left an online store without a com-plete purchase, retargeting is risky because the necessary data collection leads to strong privacy con-cerns which, in turn, trigger consumer reactance and decreasing trust. Digital nudges – small design modifications in digital choice environments which guide peoples’ behaviour – present a promising concept to bypass these negative consequences of retargeting. In order to prove the positive effects of digital nudges, we aim to conduct an online experiment with a subsequent survey by testing the impacts of social nudges and information nudges in retargeting banners. Our expected contribution to theory includes an extension of existing research of nudging in context of retargeting by investigating the effects of different nudges in retargeting banners on consumers’ behaviour. In addition, we aim to provide practical contributions by the provision of design guidelines for practitioners to build more trustworthy IT artefacts and enhance retargeting strategy of marketing practitioners.


CHEMICAL WEATHERING OF BIOTITE-


title: The Neurobiology of Stress and Development

abstract: Stress is a part of every life to varying degrees, but individuals differ in their stress vulnerability. Stress is usefully viewed from a biological perspective; accordingly, it involves activation of neurobiological systems that preserve viability through change or allostasis. Although they are necessary for survival, frequent neurobiological stress responses increase the risk of physical and mental health problems, perhaps particularly when experienced during periods of rapid brain development. Recently, advances in noninvasive measurement techniques have resulted in a burgeoning of human developmental stress research. Here we review the anatomy and physiology of stress responding, discuss the relevant animal literature, and briefly outline what is currently known about the psychobiology of stress in human development, the critical role of social regulation of stress neurobiology, and the importance of individual differences as a lens through which to approach questions about stress experiences during development and child outcomes.


C V ] 2 2 Ju n 20 18 Constructing Fast Network through Deconstruction of Convolution


title: Genetic characterization of populations of the golden jackal and the red fox in Israel

abstract: The golden jackal and red fox are among the wildlife species protected by Israeli law as enforced by the Israel Nature and Parks Authority. In 1964, as a part of a management program to control rabies in Israel, a poison eradication campaign was launched to exterminate golden jackals, considered to be the main reservoir of the disease. The program resulted in the near-complete extermination of jackals in Israel, while foxes were only mildly affected. Jackals have since regained their original numbers and have recolonized southern Israel. We here examined the population structure of the golden jackal and red fox in Israel, 48 years after the poison eradication campaign. DNA from 88 golden jackals and 89 red foxes representing five different geographic regions was extracted and amplified at 13 microsatellite loci in order to characterize the populations on a genetic level. High genetic diversity was found among the jackal and fox populations. A possible migration route through the Jordan Rift Valley was suggested for both species by the genetic similarity of populations in northern and southern Israel. However, in both species, the animals from the center of Israel were distinctive from those north or south, indicating the relative isolation of central populations, likely due to fragmentation or a high abundance of food resources. Genetic profiles obtained for the golden jackal and the red fox in Israel may aid in their conservation management and in the study of zoonotic diseases.


title: Reed-Muller universal logic module networks

abstract: Describes Reed-Muller universal logic modules (RM-ULMs) and their use for the implementation of logic functions given in Reed-Muller (RM) form. A programmed algorithm is presented for the synthesis and optimisation of RM-ULM networks. The level-by-level minimisation procedure is based on the selection of control variables at different levels with the aim of maximising the number of discontinued branches and hence minimising the number of modules required to implement a given function. The algorithm is programmed in Fortran and can be used to realise fixed-polarity generalised Reed-Muller (GRM) expansions of any polarity and any number of variables.


title: ChaLearn Looking at People: Events and Resources.

abstract: This paper reviews the historic of ChaLearn Looking at People (LAP) events. We started in 2011 (with the release of the first Kinect device) to run challenges related to human action/activity and gesture recognition. Since then we have regularly organized events in a series of competitions covering all aspects of visual analysis of humans. So far we have organized more than 10 international challenges and events in this field. This paper reviews associated events, and introduces the ChaLearn LAP platform where public resources (including code, data and preprints of papers) related to the organized events are available. We also provide a discussion on perspectives of ChaLearn LAP activities.


title: Theoretical and methodological considerations for the study of imagery in animals

abstract: Abstract Transformation of a moving stimulus is an operational definition of imagery that is useful in animal research. This definition was developed from structural, functional, and interactive theories of imagery in humans. The historical roots of the definition are traced to behavioristic methodologies within cognitive psychology. An imagery procedure for animals was developed that tests this definition with extrapolated movement. Pigeons were reinforced for discriminating between a rectangle that rotated at constant velocity from a rectangle whose final location violated constant velocity. Imagery trials included a short-term memory component in which the task of the pigeon was to discriminate between extrapolated trajectories of rotation while the rectangle was absent. The evidence supports similarities between human and animal representations of movement. A visible transformation employed as a sample stimulus and followed by a retention interval provides a powerful procedure for measuring the interaction between perception and memory. An advantage of this paradigm is that it could be employed to investigate imagery at the neural level or to obtain comparative behavioral data from different species.


title: System safety and ISO 26262 compliance for automotive lithium-ion batteries

abstract: Lithium ion batteries pave the way for today's plug-in hybrid and electric vehicles. However, these batteries contain the potential of thermal runaway, posing a higher safety risk from thermal incidences than NiMH batteries. Thermal runaway can be mitigated using electronic control systems, which are intended to maintain a safe state of the battery under all operating conditions. When safety depends on these control systems, any malfunction of the system or its elements (such as sensors, microcontrollers, contactors, software, etc.) may lead to a dangerous state. The newly published ISO 26262 standard provides processes and methods for the proper design, development and manufacturing of E/E automotive systems to ensure functional safety. In this paper, the ISO 26262 standard is applied to several example scenarios involving lithium-ion batteries for plug-in vehicles. Key concepts are explored and conclusions drawn regarding several of the standard's required processes, including hazard analysis and risk assessment, functional safety concept, functional safety and technical safety requirements, and related topics.


title: Red and blue lights induced oxidative stress tolerance promote cadmium rhizocomplexation in Oryza sativa

abstract: Cadmium (Cd) accumulation and related stress responses have been investigated in red, blue and white lights exposed Oryza sativa L. cv MTU 7029. Cd translocation was reduced significantly by red and blue lights. Increase in amount of organic acids, thiols, and nutrients in the roots that cause Cd rhizocomplexation was the reason for reduction in Cd translocation. These effects were due to higher efficiency to perform photosynthesis and transpiration under red or blue lights compare with white light during Cd stress. Increased photosynthetic assimilate turnover was witnessed as a function of sugar content. Amount of redox regulators such as glutathione and ascorbate were also increased under red and blue light exposure. Together with up regulation of antioxidant enzyme activities, these metabolites ensured redox balance in presence of reactive oxygen species produced due to Cd toxicity. Protection of photosynthesis from Cd inducible oxidative stress ensured supplies of sugar intermediates essential for the synthesis of metal chelators in roots. Therefore, it was inferred that red and blue lights promote Cd rhizocomplexation and ameliorated Cd stress in rice seedlings.


title: On effect size.

abstract: The call for researchers to report and interpret effect sizes and their corresponding confidence intervals has never been stronger. However, there is confusion in the literature on the definition of effect size, and consequently the term is used inconsistently. We propose a definition for effect size, discuss 3 facets of effect size (dimension, measure/index, and value), outline 10 corollaries that follow from our definition, and review ideal qualities of effect sizes. Our definition of effect size is general and subsumes many existing definitions of effect size. We define effect size as a quantitative reflection of the magnitude of some phenomenon that is used for the purpose of addressing a question of interest. Our definition of effect size is purposely more inclusive than the way many have defined and conceptualized effect size, and it is unique with regard to linking effect size to a question of interest. Additionally, we review some important developments in the effect size literature and discuss the importance of accompanying an effect size with an interval estimate that acknowledges the uncertainty with which the population value of the effect size has been estimated. We hope that this article will facilitate discussion and improve the practice of reporting and interpreting effect sizes.


title: Merge or Not? Learning to Group Faces via Imitation Learning

abstract: Given a large number of unlabeled face images, face grouping aims at clustering the images into individual identities present in the data. This task remains a challenging problem despite the remarkable capability of deep learning approaches in learning face representation. In particular, grouping results can still be egregious given profile faces and a large number of uninteresting faces and noisy detections. Often, a user needs to correct the erroneous grouping manually. In this study, we formulate a novel face grouping framework that learns clustering strategy from ground-truth simulated behavior. This is achieved through imitation learning (a.k.a apprenticeship learning or learning by watching) via inverse reinforcement learning (IRL). In contrast to existing clustering approaches that group instances by similarity, our framework makes sequential decision to dynamically decide when to merge two face instances/groups driven by short- and long-term rewards. Extensive experiments on three benchmark datasets show that our framework outperforms unsupervised and supervised baselines.


title: SETD5 gene variant associated with mild intellectual disability - a case report.

abstract: The recent advent of exome sequencing has allowed for the identification of pathogenic gene variants responsible for a variety of diseases that were previously clinically diagnosed, with no underlying molecular etiology. Among these conditions, intellectual disability is a prevalent heterogeneous condition, presenting itself in a large spectrum of intensity, in some cases associated with congenital malformations, behavioral and various other intellectual development alterations. Here we report on a 36-year-old male patient, with a mild intellectual disability that remained undiagnosed at the molecular level for all his life. Using Nextera Exome Sequencing, a Chr3:9.517.294 A>AC (c.3848_3849insC) SETD5 gene insertion was found. This rare variant was classified as likely pathogenic due to its frameshift nature in the gene, in which loss-of-function mutations have been previously reported to cause intellectual disability, as well as a 3p25.3 microdeletion phenotype. It is possible that this variant shows partial activity, due to its gene localization, which would explain the patient's mild phenotype when compared with other reports.


title: Predicting Personality from Book Preferences with User-Generated Content Labels

abstract: Psychological studies have shown that personality traits are associated with preferences of book content. However, past findings are based on questionnaires focusing on conventional book genres and are unrepresentative of niche content. For a more comprehensive measure of book content, this study harnesses a massive archive of content labels, also known as ‘tags’, created by users of an online book catalogue, Goodreads.com. Combined with data on preferences and personality scores collected from Facebook users, the tag labels achieve high accuracy in personality prediction by psychological standards. We also group tags into broader genres, to check their validity against past findings. Our results are robust across both tag and genre levels of analyses, and consistent with existing literature. Moreover, user-generated tag labels reveal unexpected insights, such as cultural differences, book reading behaviors, and other non-content factors affecting preferences. To our knowledge, this is currently the largest study that explores the relationship between personality and book content preferences.


title: Modified PSO based PID sliding mode control for inverted pendulum

abstract: Trajectory tracking and stabilization of a nonlinear single stage inverted pendulum is a complicated control problem, as nonlinearity is present inherently and external factors affect the equilibrium position. Sliding mode control (SMC) is a robust control technique, most popular for nonlinear system's controlling. In this paper, a new model based robust control law, called PID (Proportional-Integral-Derivative) sliding mode control is designed using modified particle swarm intelligent optimization (MPSO) for second order nonlinear inverted pendulum plant. This paper throws light on the sliding surface design and highlights the important features of sliding mode, such as robustness and higher convergence, through extensive mathematical modelling. Siding mode control law is derived using Lyapunov stability approach and its asymptotic stability is proved mathematically and simulations shows its validity. MPSO PID-SMC control will stabilize the highly nonlinear Inverted Pendulum system, will compensate disturbance and uncertainty and reduces tracking error. Simulations and experimental application is done on the system and are presented to make a quantitative comparison with PID, traditional SMC and MPSO PID-SMC.


title: A Novel Classification Method for Diagnosis of Diabetes Mellitus Using Artificial Neural Networks

abstract: Many real world problems can be solved with Artificial Neural Networks in the areas of pattern recognition, signal processing and medical diagnosis. Most of the medical data set is seldom complete. Artificial Neural Networks require complete set of data for an accurate classification. This paper dwells on the various missing value techniques to improve the classification accuracy. The proposed system also investigates the impact on preprocessing during the classification. A classifier was applied to Pima Indian Diabetes Dataset and the results were improved tremendously when using certain combination of preprocessing techniques. The experimental system achieves an excellent classification accuracy of 99% which is best than before.


title: Introduction to the Theory of Computation

abstract: From the Publisher: ::: Michael Sipser's philosophy in writing this book is simple: make the subject interesting and relevant, and the students will learn. His emphasis on unifying computer science theory - rather than offering a collection of low-level details - sets the book apart, as do his intuitive explanations. Throughout the book, Sipser - a noted authority on the theory of computation - builds students' knowledge of conceptual tools used in computer science, the aesthetic sense they need to create elegant systems, and the ability to think through problems on their own. INTRODUCTION TO THE THEORY OF COMPUTATION provides a mathematical treatment of computation theory grounded in theorems and proofs. Proofs are presented with a "proof idea" component to reveal the concepts underpinning the formalism. Algorithms are presented using prose instead of pseudocode to focus attention on the algorithms themselves, rather than on specific computational models. Topic coverage, terminology, and order of presentation are traditional for an upper-level course in computer science theory. Users of the Preliminary Edition (now out of print) will be interested to note several new chapters on complexity theory: Chapter 8 on space complexity; Chapter 9 on provable intractability, and Chapter 10 on advanced topics, including approximation algorithms, alternation, interactive proof systems, cryptography, and parallel computing.


title: Design of applying FlexRay-bus to federated archiectecture for triple redundant reliable UAV flight control system

abstract: For an Unmanned Aerial Vehicle (UAV) performing a mission, any failure of the flight control system can result in fatal disaster. Therefore, reliability requirement for UAV flight control system becomes increasingly important and then the redundancy technology is considered as a solution to enhance system reliability. This paper studies the designs of reliability, system architecture, and redundancy management logic for UAV flight control system. Therefore, a reliable federated architecture design for triple redundant UAV flight control system is introduced, in which the distributed hardware design combined with centralized software framework was adopted as well. The number of subsystems in the case study is more than that of centralized architecture and less for distributed architecture. Next, the fault detection and fault tolerant design can be achieved, and simultaneously the design problems associated with distributed software framework was reduced. Furthermore, by using the distributed hardware, system's reliability requirement of single hardware module was decreased. Consequently, the goal of high reliability for the entire system reached. Finally, FlexRay bus with time-trigger mechanism was chosen to simulate the communication of such a federated architecture designed. The experimental results show that the design is reasonable and satisfy the high reliability, high performance-price ratio and high expandability requirements of UAV flight control system.


title: A novel inflatable actuator for inflatable robotic arms

abstract: Inflatable manipulators open new trends in robotic applications. In this paper, a new inflatable actuator to articulate a long-range inflatable robotic arm, is presented. Completely made of high strength fabric, its low mass leads to large force-weight ratios and a stroke almost equal to twice its overall length. The operation principle, as well as the relations among the displacement, the pressure and force, are described in this paper. The analysis is under validation through a finite elements simulation and a test on a prototype.


title: The hippocampus: hub of brain network communication for memory

abstract: A complex brain network, centered on the hippocampus, supports episodic memories throughout their lifetimes. Classically, upon memory encoding during active behavior, hippocampal activity is dominated by theta oscillations (6–10Hz). During inactivity, hippocampal neurons burst synchronously, constituting sharp waves, which can propagate to other structures, theoretically supporting memory consolidation. This ‘two-stage' model has been updated by new data from high-density electrophysiological recordings in animals that shed light on how information is encoded and exchanged between hippocampus, neocortex and subcortical structures such as the striatum. Cell assemblies (tightly related groups of cells) discharge together and synchronize across brain structures orchestrated by theta, sharp waves and slow oscillations, to encode information. This evolving dynamical schema is key to extending our understanding of memory processes.


title: Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer

abstract: We explore scaling of the standard distributed Tensorflow with GRPC primitives on up to 512 Intel Xeon Phi (KNL) nodes of Cori supercomputer with synchronous stochastic gradient descent (SGD), and identify causes of scaling inefficiency at higher node counts. To our knowledge, this is the first exploration of distributed GRPC Tensorflow scalability on a HPC supercomputer at such large scale with synchronous SGD. We studied scaling of two convolution neural networks - ResNet-50, a state-of-the-art deep network for classification with roughly 25.5 million parameters, and HEP-CNN, a shallow topology with less than 1 million parameters for common scientific usages. For ResNet-50, we achieve>80% scaling efficiency on up to 128 workers, using 32 parameter servers (PS tasks) with a steep decline down to 23% for 512 workers using 64 PS tasks. Our analysis of the efficiency drop points to low network bandwidth utilization due to combined effect of three factors. (a) Heterogeneous distributed parallelization algorithm which uses PS tasks as centralized servers for gradient averaging is suboptimal for utilizing interconnect bandwidth. (b) Load imbalance among PS tasks hinders their efficient scaling. (c) Underlying communication primitive GRPC is currently inefficient on Cori high-speed interconnect. The HEP-CNN demands less interconnect bandwidth, and shows>80% weak scaling efficiency for up to 256 nodes with only 1 PS task. Our findings are applicable to other deep learning networks. Big networks with millions of parameters stumble upon the issues discussed here. Shallower networks like HEP-CNN with relatively lower number of parameters can efficiently enjoy weak scaling even with a single parameter server.


title: Coercion-resistant electronic elections

abstract: We introduce a model for electronic election schemes that involves a more powerful adversary than previous work. In particular, we allow the adversary to demand of coerced voters that they vote in a particular manner, abstain from voting, or even disclose their secret keys. We define a scheme to be coercion-resistant if it is infeasible for the adversary to determine if a coerced voter complies with the demands.A first contribution of this paper is to describe and characterize a new and strengthened adversary for coercion in elections. (In doing so, we additionally present what we believe to be the first formal security definitions for electronic elections of any type.) A second contribution is to demonstrate a protocol that is secure against this adversary. While it is clear that a strengthening of attack models is of theoretical relevance, it is important to note that our results lie close to practicality. This is true both in that we model real-life threats (such as vote-buying and vote-canceling), and in that our proposed protocol combines a fair degree of efficiency with an unusual lack of structural complexity. Furthermore, previous schemes have required use of an untappable channel throughout. Ours only carries the much more practical requirement of an anonymous channel during the casting of ballots, and an untappable channel during registration (potentially using postal mail).This extended abstract is a heavily truncated version of the full paper available at http://eprint.iacr.org/2002/165.


title: A New Method for Measuring Winding AC Resistance of High-Efficiency Power Inductors

abstract: Precise estimation of high-frequency winding resistance is essential for designing high-efficiency power inductors. The ac resistance for high-efficiency inductors can be estimated using analytical methods or simulation-based approaches but it must be verified with measurement. Impedance analyzers as a common method for measuring ac resistance of high-frequency inductor windings perform noticeably poor for high quality factor $Q$ inductors (high-efficiency inductor with a small resistive element). This paper proposes a new simple and highly effective method to measure the ac resistance of high-frequency power inductors (up to at least 110 MHz). In this method, a thin auxiliary winding is added to the inductor to create access to a measurement node, which effectively reduces the series inductance seen by the impedance analyzer to the parasitic leakage inductance of the inductor. Thereby it effectively eliminates the impact of inductor main magnetizing inductance in the impedance measurement. The proposed measurement method is compatible with existing four terminal pair fixture of impedance analyzers. Using a proposed modified test fixture for Agilent 4294A, the ac resistance of two high- $Q$ sample inductors is measured. Experimental results show a very substantial improvement with respect to the traditional four-terminal test fixture. A convincing agreement with the two-dimensional finite element method simulation results in COMSOL is also demonstrated.


title: Implementation of IoT: Smart Maintenance for Distribution Transformer using MQTT

abstract: Incessant distribution of power is the main impediment to modern power system era. To overcome such hurdle the necessity of distribution transformer maintenance is irrefutable. However, these maintenance works are done in the most primitive way, on field measurements by operators. This paper presents the implementation of Internet of Things (IoT) based real-time monitoring and maintenance of distribution transformers, which effectively remits the labor-intensive maintenance for the massive number of geographically dispersed transformers. Proposed system offers an efficient way to monitor various critical parameters such as load current, temperature, oil level and methods to act accordingly in abnormal conditions. This embedded IoT based smart maintenance system’s production and running cost is also economical compared to the GSM-based monitoring system which offers periodical monitoring and no means of control.


title: Functional programming in C++

abstract: This paper describes FC++: a rich library supporting functional programming in C++. Prior approaches to encoding higher order functions in C++ have suffered with respect to polymorphic functions from either lack of expressiveness or high complexity. In contrast, FC++ offers full and concise support for higher-order polymorphic functions through a novel use of C++ type inference.Another new element in FC++ is that it implements a subtype polymorphism policy for functions, in addition to the more common parametric polymorphism facilities. Subtype polymorphism is common in object oriented languages and ensures that functions in FC++ fit well within the C++ object model.Apart from these conceptual differences, FC++ is also an improvement in technical terms over previous efforts in the literature. Our function objects are reference-counted and can be aliased without needing to be copied, resulting in an efficient implementation. The reference-counting mechanism is also exported to the user as a general-purpose replacement of native C++ pointers. Finally, we supply a number of useful functional operators (a large part of the Haskell Standard Prelude) to facilitate programming with FC++. The end result is a library that is usable and efficient, while requiring no extensions to the base C++ language.


title: Convex Color Image Segmentation with Optimal Transport Distances

abstract: This work is about the use of regularized optimal-transport distances for convex, histogram-based image segmentation. In the considered framework, fixed exemplar histograms define a prior on the statistical features of the two regions in competition. In this paper, we investigate the use of various transport-based cost functions as discrepancy measures and rely on a primal-dual algorithm to solve the obtained convex optimization problem.


title: Dynamics of contraceptive use in India: Apprehension versus future intention among non-users and traditional method users

abstract: Abstract Objective This study examines the reasons for not using any method of contraception as well as reasons for not using modern methods of contraception, and factors associated with the future intention to use different types of contraceptives in India and its selected states, namely Uttar Pradesh, Assam and West Bengal. Methods Data from the third wave of District Level Household and Facility Survey, 2007–08 were used. Bivariate as well as logistic regression analyses were performed to fulfill the study objective. Results Postpartum amenorrhea and breastfeeding practices were reported as the foremost causes for not using any method of contraception. Opposition to use, health concerns and fear of side effects were reported to be major hurdles in the way of using modern methods of contraception. Results from logistic regression suggest considerable variation in explaining the factors associated with future intention to use contraceptives. Conclusion Promotion of health education addressing the advantages of contraceptive methods and eliminating apprehension about the use of these methods through effective communication by community level workers is the need of the hour.


title: Breast Cancer Diagnosis and prognosis via linear programming

abstract: Two medical applications of linear programming are described in this paper. Specifically, linear programming-based machine learning techniques are used to increase the accuracy and objectivity of breast cancer diagnosis and prognosis. The first application to breast cancer diagnosis utilizes characteristics of individual cells, obtained from a minimally invasive fine needle aspirate, to discriminate benign from malignant breast lumps. This allows an accurate diagnosis without the need for a surgical biopsy. The diagnostic system in current operation at University of Wisconsin Hospitals was trained on samples from 569 patients and has had 100% chronological correctness in diagnosing 131 subsequent patients. The second application, recently put into clinical practice, is a method that constructs a surface that predicts when breast cancer is likely to recur in patients that have had their cancers excised. This gives the physician and the patient better information with which to plan treatment, and may eliminate the need for a prognostic surgical procedure. The novel feature of the predictive approach is the ability to handle cases for which cancer has not recurred (censored data) as well as cases for which cancer has recurred at a specific time. The prognostic system has an expected error of 13.9 to 18.3 months, which is better than prognosis correctness by other available techniques.


title: Wearable ECG Based on Impulse-Radio-Type Human Body Communication

abstract: Human body communication (HBC) provides a promising physical layer for wireless body area networks (BANs) in healthcare and medical applications, because of its low propagation loss and high security characteristics. In this study, we have developed a wearable electrocardiogram (ECG) which employs impulse radio (IR)-type HBC technology for transmitting vital signals on the human body in a wearable BAN scenario. The HBC-based wearable ECG has two excellent features. First, the wideband performance of the IR scheme contributed to very low radiation power so that the transceiver is easy to satisfy the extremely weak radio laws, which does not need a license. This feature can provide big convenience in the use and spread of the wearable ECG. Second, the realization of common use of sensing and transmitting electrodes based on time sharing and capacitive coupling largely simplified the HBC-based ECG structure and contributed to its miniaturization. To verify the validity of the HBC-based ECG, we evaluated its communication performance and ECG acquisition performance. The measured bit error rate, smaller than 10 $^{-3}$ at 1.25 Mb/s, showed a good physical layer communication performance, and the acquired ECG waveform and various heart-rate variability parameters in time and frequency domains exhibited good agreement with a commercially available radio-frequency ECG and a Holter ECG. These results sufficiently showed the validity and feasibility of the HBC-based ECG for healthcare applications. This should be the first time to have realized a real-time ECG transmission by using the HBC technology.


title: A simple design of wearable device for fall detection with accelerometer and gyroscope

abstract: Falling is one of the most common injurious accidents in the elderly which could result in pain, disability, and even death. About 33% of the elderly have falling accident each year. According to the Statistics Indonesia data in 2014, the total population of over-60-year-olds increase to 8.03% of the total population in that year. In this research, we use the accelerometer and a gyroscope sensor for fall detection. The device is placed in the position of the waist. This study uses 6 type activity comparators such as walking, running, upstairs, down stairs, sit down quickly and stand up quickly. These tests are also used for 3 falling accidents such as falling forward, to the right and to the left. By comparing the results of these tests, the falling threshold values can be acquired. The threshold values of A, B and C are 0.6, 1.7 and 175 respectively. Results show that the system can detect sensitivity and specificity up to 90% and 86.7% respectively.


title: HazeRD: An outdoor scene dataset and benchmark for single image dehazing

abstract: In this paper, a new dataset, HazeRD, is proposed for benchmarking dehazing algorithms under more realistic haze conditions. HazeRD contains fifteen real outdoor scenes, for each of which five different weather conditions are simulated. As opposed to prior datasets that made use of synthetically generated images or indoor images with unrealistic parameters for haze simulation, our outdoor dataset allows for more realistic simulation of haze with parameters that are physically realistic and justified by scattering theory. All images are of high resolution, typically six to eight megapixels. We test the performance of several state-of-the-art dehazing techniques on HazeRD. The results exhibit a significant difference among algorithms across the different datasets, reiterating the need for more realistic datasets such as ours and for more careful benchmarking of the methods.


title: Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets

abstract: Previous models for the assessment of commitment towards a predicate in a sentence (also known as factuality prediction) were trained and tested against a specific annotated dataset, subsequently limiting the generality of their results. In this work we propose an intuitive method for mapping three previously annotated corpora onto a single factuality scale, thereby enabling models to be tested across these corpora. In addition, we design a novel model for factuality prediction by first extending a previous rule-based factuality prediction system and applying it over an abstraction of dependency trees, and then using the output of this system in a supervised classifier. Our model which we will make publicly available outperforms previous methods on all three datasets.


title: Counterfactual domain adversarial training of neural networks

abstract: A number of domain adaptation techniques have been developed in classical supervised machine learning to solve the problem of learning under domain shift. Unfortunately, almost no attempt has been made to exploit their potential use for causal inference. In this paper, we present a conceptual framework which leverages on one of these successes by adopting Domain Adversarial of Neural Network (DANN) [1] and make it work within the context of causal inference. More specifically, we attempt to modify an existing counterfactual prediction framework based on domain adaptation (DA) [2] with a deep neural network and discrepancy distance measure to enable us instead to perform causal inference in an adversarial way. We achieved this by jointly minimizing the risk in the feature reconstruction and the counterfactual prediction while maximizing the error in discriminating between the distribution of subjects that received intervention and those that received no intervention. This way, a domain invariant yet discriminative model that will generalize well on the counterfactual target distribution is produced.


title: Taking a HIT: Designing around Rejection, Mistrust, Risk, and Workers' Experiences in Amazon Mechanical Turk

abstract: Online crowd labor markets often address issues of risk and mistrust between employers and employees from the employers' perspective, but less often from that of employees. Based on 437 comments posted by crowd workers (Turkers) on the Amazon Mechanical Turk (AMT) participation agreement, we identified work rejection as a major risk that Turkers experience. Unfair rejections can result from poorly-designed tasks, unclear instructions, technical errors, and malicious Requesters. Because the AMT policy and platform provide little recourse to Turkers, they adopt strategies to minimize risk: avoiding new and known bad Requesters, sharing information with other Turkers, and choosing low-risk tasks. Through a series of ideas inspired by these findings-including notifying Turkers and Requesters of a broken task, returning rejected work to Turkers for repair, and providing collective dispute resolution mechanisms-we argue that making reducing risk and building trust a first-class design goal can lead to solutions that improve outcomes around rejected work for all parties in online labor markets.


MongoDB : An open source alternative for HL 7-CDA clinical documents management


title: Charliecloud: unprivileged containers for user-defined software stacks in HPC

abstract: Supercomputing centers are seeing increasing demand for user-defined software stacks (UDSS), instead of or in addition to the stack provided by the center. These UDSS support user needs such as complex dependencies or build requirements, externally required configurations, portability, and consistency. The challenge for centers is to provide these services in a usable manner while minimizing the risks: security, support burden, missing functionality, and performance. We present Charliecloud, which uses the Linux user and mount namespaces to run industry-standard Docker containers with no privileged operations or daemons on center resources. Our simple approach avoids most security risks while maintaining access to the performance and functionality already on offer, doing so in just 800 lines of code. Charliecloud promises to bring an industry-standard UDSS user workflow to existing, minimally altered HPC resources.


title: Dynamic Instrumentation of Production Systems

abstract: This paper presents DTrace, a new facility for dynamic instrumentation of production systems. DTrace features the ability to dynamically instrument both user-level and kernel-level software in a unified and absolutely safe fashion. When not explicitly enabled, DTrace has zero. probe effect--the system operates exactly as if DTrace were not present at all. DTrace allows for many tens of thousands of instrumentation points, with even the smallest of systems offering on the order of 30,000 such points in the kernel alone. We have developed a C-like high-level control language to describe the predicates and actions at a given point of instrumentation. The language features user-defined variables, including thread-local variables and associative arrays. To eliminate the need for most postprocessing, the facility features a scalable mechanism for aggregating data and a mechanism for speculative tracing. DTrace has been integrated into the Solaris operating system and has been used to find serious systemic performance problems on production systems-problems that could not be found using pre-existing facilities.


title: A Multicast Tree Router for Multichip Neuromorphic Systems

abstract: We present a tree router for multichip systems that guarantees deadlock-free multicast packet routing without dropping packets or restricting their length. Multicast routing is required to efficiently connect massively parallel systems' computational units when each unit is connected to thousands of others residing on multiple chips, which is the case in neuromorphic systems. Our tree router implements this one-to-many routing by branching recursively-broadcasting the packet within a specified subtree. Within this subtree, the packet is only accepted by chips that have been programmed to do so. This approach boosts throughput because memory look-ups are avoided enroute, and keeps the header compact because it only specifies the route to the subtree's root. Deadlock is avoided by routing in two phases-an upward phase and a downward phase-and by restricting branching to the downward phase. This design is the first fully implemented wormhole router with packet-branching that can never deadlock. The design's effectiveness is demonstrated in Neurogrid, a million-neuron neuromorphic system consisting of sixteen chips. Each chip has a 256 × 256 silicon-neuron array integrated with a full-custom asynchronous VLSI implementation of the router that delivers up to 1.17 G words/s across the sixteen-chip network with less than 1 μs jitter.


title: Force-detecting gripper and force feedback system for neurosurgery applications

abstract: Purpose For the application of less invasive robotic neurosurgery to the resection of deep-seated tumors, a prototype system of a force-detecting gripper with a flexible micromanipulator and force feedback to the operating unit will be developed.


title: Beneficial Effects of a Probiotic VSL#3 on Parameters of Liver Dysfunction in Chronic Liver Diseases

abstract: OBJECTIVES ::: To evaluate whether chronic therapy with probiotics affects plasma levels of cytokines and oxidative/nitrosative stress parameters, as well as liver damage, in patients with various types of chronic liver disease. ::: ::: ::: PATIENTS AND METHODS ::: A total of 22 nonalcoholic fatty liver disease (NAFLD) and 20 alcoholic liver cirrhosis (AC) patients were enrolled in the study and compared with 36 HCV-positive patients with chronic hepatitis without (20, CH) or with (16, CC) liver cirrhosis. All patients were treated with the probiotic VSL#3. Routine liver tests, plasma levels of tumor necrosis factor alpha (TNF-alpha), interleukin (IL)-6 and -10, malondialdehyde (MDA), and 4-hydroxynonenal (4-HNE), S-nitrosothiols (S-NO), were evaluated on days -30, 0, 90, and 120. ::: ::: ::: RESULTS ::: Treatment with VSL#3 exerted different effects in the various groups of patients: in NAFLD and AC groups, it significantly improved plasma levels of MDA and 4-HNE, whereas cytokines (TNF-alpha, IL-6, and IL-10) improved only in AC patients. No such effects were observed in HCV patients. Routine liver damage tests and plasma S-NO levels were improved at the end of treatment in all groups. ::: ::: ::: CONCLUSIONS ::: Results of the study suggest that manipulation of intestinal flora should be taken into consideration as possible adjunctive therapy in some types of chronic liver disease.


title: Biometric quality: a review of fingerprint, iris, and face

abstract: Biometric systems encounter variability in data that influence capture, treatment, and u-sage of a biometric sample. It is imperative to first analyze the data and incorporate this understanding within the recognition system, making assessment of biometric quality an important aspect of biometrics. Though several interpretations and definitions of quality exist, sometimes of a conflicting nature, a holistic definition of quality is indistinct. This paper presents a survey of different concepts and interpretations of biometric quality so that a clear picture of the current state and future directions can be presented. Several factors that cause different types of degradations of biometric samples, including image features that attribute to the effects of these degradations, are discussed. Evaluation schemes are presented to test the performance of quality metrics for various applications. A survey of the features, strengths, and limitations of existing quality assessment techniques in fingerprint, iris, and face biometric are also presented. Finally, a representative set of quality metrics from these three modalities are evaluated on a multimodal database consisting of 2D images, to understand their behavior with respect to match scores obtained from the state-of-the-art recognition systems. The analysis of the characteristic function of quality and match scores shows that a careful selection of complimentary set of quality metrics can provide more benefit to various applications of biometric quality.


title: Suppression of crosstalk by using backside deep trench isolation for 1.12μm backside illuminated CMOS image sensor

abstract: 1.12μm backside illuminated CMOS image sensor with backside deep trench isolation (DTI) has been demonstrated for the first time. DTI is fabricated on backside pixel surface after wafer bonding and grinding process. Backside DTI makes its layout simple because no transistor isolation exists on backside. We have confirmed about 50% reduction of crosstalk by using backside DTI. The crosstalk of 1.12μm backside DTI pixel is lower than that of 1.4μm BSI pixel. This technology will be promising for 1.12μm and beyond.


title: Bidirectional Gate Driver Circuit Using Recharging and Time-Division Driving Scheme for In-Cell Touch LCDs

abstract: This paper proposes a new hydrogenated amorphous silicon thin-film-transistor-based (a-Si:H TFT-based) gate driver circuit that uses the time-division driving method to ameliorate the display-to-touch crosstalk. The proposed work utilizes a recharging circuit to discharge the voltage on the gate node of the driving TFT when the touch sensing starts with the purpose of suppressing the long-term stress of the driving TFT, and charges the gate node of the driving TFT again before the end of the touch-sensing period to enhance the driving capability of the gate driver circuit. Furthermore, the proposed gate driver circuit achieves bidirectional transmission and an adjustable reporting rate of touch sensing. Measurements verify that the threshold voltage shift of the driving TFT is suppressed from 4.8 to 2.9 V after 120 h of stress at 85 °C and the output waveforms of the proposed gate driver circuits are generated correctly with forward transmission, backward transmission, and pausing in different stages. Moreover, the measured output waveforms are stable without distortion after 360 h reliability test at 85 °C. Consequently, the proposed gate driver circuit is suitable for use in 5.46-in full high-definition panels with in-cell touch technology.


title: Argument Mining on Twitter: Arguments, Facts and Sources

abstract: Social media collect and spread on the Web personal opinions, facts, fake news and all kind of information users may be interested in. Applying argument mining methods to such heterogeneous data sources is a challenging open research issue, in particular considering the peculiarities of the language used to write textual messages on social media. In addition, new issues emerge when dealing with arguments posted on such platforms, such as the need to make a distinction between personal opinions and actual facts, and to detect the source disseminating information about such facts to allow for provenance verification. In this paper, we apply supervised classification to identify arguments on Twitter, and we present two new tasks for argument mining, namely facts recognition and source identification. We study the feasibility of the approaches proposed to address these tasks on a set of tweets related to the Grexit and Brexit news topics.


title: MC-GAN: Multi-conditional Generative Adversarial Network for Image Synthesis

abstract: In this paper, we introduce a new method for generating an object image from text attributes on a desired location, when the base image is given. One step further to the existing studies on text-to-image generation mainly focusing on the object's appearance, the proposed method aims to generate an object image preserving the given background information, which is the first attempt in this field. To tackle the problem, we propose a multi-conditional GAN (MC-GAN) which controls both the object and background information jointly. As a core component of MC-GAN, we propose a synthesis block which disentangles the object and background information in the training stage. This block enables MC-GAN to generate a realistic object image with the desired background by controlling the amount of the background information from the given base image using the foreground information from the text attributes. From the experiments with Caltech-200 bird and Oxford-102 flower datasets, we show that our model is able to generate photo-realistic images with a resolution of 128 x 128. The source code of MC-GAN is released.


[Subungual squamous cell carcinoma revealed by longitudinal erythronychia].


title: A minimal model for predicting visual search in human-computer interaction

abstract: Visual search is an important part of human-computer interaction. It is critical that we build theory about how people visually search displays in order to better support the users' visual capabilities and limitations in everyday tasks. One way of building such theory is through computational cognitive modeling. The ultimate promise for cognitive modeling in HCI it to provide the science base needed for predictive interface analysis tools. This paper discusses computational cognitive modeling of the perceptual, strategic, and oculomotor processes people used in a visual search task. This work refines and rounds out previously reported cognitive modeling and eye tracking analysis. A revised "minimal model" of visual search is presented that explains a variety of eye movement data better than the original model. The revised model uses a parsimonious strategy that is not tied to a particular visual structure or feature beyond the location of objects. Three characteristics of the minimal strategy are discussed in detail.


title: Work, language and perspective: an empirical investigation of the interpretation of a computer-based information system

abstract: On the basis of empirical material, this paper will demonstrate how different perspectives on work can be pointed out and described, and how concept spheres can change when different work languages are brought together. In a process of systems development, various professions take part. Some professions have the purpose of describing and organizing the work, other professions have the purpose of performing the work. There is an important linguistic difference in perspective between those two purposes. The language of instructors and coordinators is general and normative, the language of workers is specific and descriptive. When a computer system is introduced, terms and concepts from different work languages are introduced and it is difficult to predict what the linguistic changes will be. It inevitably leads to the alteration of old concept spheres and the emergence of new ones, but the result is not always what was expected by the designers. A well intended linguistic design is sometimes accepted and sometimes rejected by the users. This fact might be explained as a result of merging or contradicting perspectives.


title: Synchronous frame PI current regulators in a virtually translated system

abstract: This paper presents a virtual translation technique for current regulation for AC machines. A virtual physical parameter is employed to effectively translate the system pole to achieve fast and robust dynamics. Applying this technique to conventional synchronous frame current regulators reduces parameter sensitivity and improves robustness. The current controllers are redesigned in the virtually translated system. Current regulators adopting the technique have been analyzed in complex vector form for symmetric AC machines and in scalar form for salient-pole AC machines. Several current regulators using this technique have been experimentally evaluated for different operating conditions in an IPMSM drive.


The evolution of female choice when the preference and the preferred trait are linked to the same inversion system


title: Deep feature learning and selection for activity recognition

abstract: Human physical activity recognition from sensor data is a growing area of research due to the widespread adoption of sensor-rich wearable and smart devices. The growing interest resulted in several formulations with multiple proposals for each of them. This paper is interested in activity recognition from short sequences of sensor readings. Traditionally, solutions to this problem have relied on handcrafted features and feature selection from large predefined feature sets. More recently, deep methods have been employed to provide an end-to-end classification system for activity recognition with higher accuracy at the expense of much slower performance. This paper proposes a middle ground in which a deep neural architecture is employed for feature learning followed by traditional feature selection and classification. This approach is shown to outperform state-of-the-art systems on six out of seven experiments using publicly available datasets.


title: CovSel: A new approach for ensemble selection applied to symbolic regression problems

abstract: Ensemble methods combine the predictions of a set of models to reach a better prediction quality compared to a single model's prediction. The ensemble process consists of three steps: 1) the generation phase where the models are created, 2) the selection phase where a set of possible ensembles is composed and one is selected by a selection method, 3) the fusion phase where the individual models' predictions of the selected ensemble are combined to an ensemble's estimate. This paper proposes CovSel, a selection approach for regression problems that ranks ensembles based on the coverage of adequately estimated training points and selects the ensemble with the highest coverage to be used in the fusion phase. An ensemble covers a training point if at least one of its models produces an adequate prediction for this training point. The more training points are covered this way, the higher is the ensemble's coverage. The selection of the "right" ensemble has a large impact on the final prediction. Results for two symbolic regression problems show that CovSel improves the predictions for various state-of-the-art fusion methods for ensembles composed of independently evolved GP models and also beats approaches based on single GP models.


title: Recurrent-OctoMap: Learning State-Based Map Refinement for Long-Term Semantic Mapping With 3-D-Lidar Data

abstract: This paper presents a novel semantic mapping approach, Recurrent-OctoMap, learned from long-term 3D Lidar data. Most existing semantic mapping approaches focus on improving semantic understanding of single frames, rather than 3D refinement of semantic maps (i.e. fusing semantic observations). The most widely-used approach for 3D semantic map refinement is a Bayesian update, which fuses the consecutive predictive probabilities following a Markov-Chain model. Instead, we propose a learning approach to fuse the semantic features, rather than simply fusing predictions from a classifier. In our approach, we represent and maintain our 3D map as an OctoMap, and model each cell as a recurrent neural network (RNN), to obtain a Recurrent-OctoMap. In this case, the semantic mapping process can be formulated as a sequence-to-sequence encoding-decoding problem. Moreover, in order to extend the duration of observations in our Recurrent-OctoMap, we developed a robust 3D localization and mapping system for successively mapping a dynamic environment using more than two weeks of data, and the system can be trained and deployed with arbitrary memory length. We validate our approach on the ETH long-term 3D Lidar dataset [1]. The experimental results show that our proposed approach outperforms the conventional"Bayesian update"approach.


title: The ferric reducing ability of plasma (FRAP) as a measure of "antioxidant power": the FRAP assay.

abstract: A simple, automated test measuring the ferric reducing ability of plasma, the FRAP assay, is presented as a novel method for assessing "antioxidant power." Ferric to ferrous ion reduction at low pH causes a colored ferrous-tripyridyltriazine complex to form. FRAP values are obtained by comparing the absorbance change at 593 nm in test reaction mixtures with those containing ferrous ions in known concentration. Absorbance changes are linear over a wide concentration range with antioxidant mixtures, including plasma, and with solutions containing one antioxidant in purified form. There is no apparent interaction between antioxidants. Measured stoichiometric factors of Trolox, alpha-tocopherol, ascorbic acid, and uric acid are all 2.0; that of bilirubin is 4.0. Activity of albumin is very low. Within- and between-run CVs are <1.0 and <3.0%, respectively, at 100-1000 micromol/liter. FRAP values of fresh plasma of healthy Chinese adults: 612-1634 micromol/liter (mean, 1017; SD, 206; n = 141). The FRAP assay is inexpensive, reagents are simple to prepare, results are highly reproducible, and the procedure is straightforward and speedy. The FRAP assay offers a putative index of antioxidant, or reducing, potential of biological fluids within the technological reach of every laboratory and researcher interested in oxidative stress and its effects.


title: Target size study for one-handed thumb use on small touchscreen devices

abstract: This paper describes a two-phase study conducted to determine optimal target sizes for one-handed thumb use of mobile handheld devices equipped with a touch-sensitive screen. Similar studies have provided recommendations for target sizes when using a mobile device with two hands plus a stylus, and interacting with a desktop-sized display with an index finger, but never for thumbs when holding a small device in a single hand. The first phase explored the required target size for single-target (discrete) pointing tasks, such as activating buttons, radio buttons or checkboxes. The second phase investigated optimal sizes for widgets used for tasks that involve a sequence of taps (serial), such as text entry. Since holding a device in one hand constrains thumb movement, we varied target positions to determine if performance depended on screen location. The results showed that while speed generally improved as targets grew, there were no significant differences in error rate between target sizes =9.6 mm in discrete tasks and targets =7.7 mm in serial tasks. Along with subjective ratings and the findings on hit response variability, we found that target size of 9.2 mm for discrete tasks and targets of 9.6 mm for serial tasks should be sufficiently large for one-handed thumb use on touchscreen-based handhelds without degrading performance and preference.


title: An identity-authentication system using fingerprints

abstract: Fingerprint verification is an important biometric technique for personal identification. We describe the design and implementation of a prototype automatic identity-authentication system that uses fingerprints to authenticate the identity of an individual. We have developed an improved minutiae-extraction algorithm that is faster and more accurate than our earlier algorithm (1995). An alignment-based minutiae-matching algorithm has been proposed. This algorithm is capable of finding the correspondences between input minutiae and the stored template without resorting to exhaustive search and has the ability to compensate adaptively for the nonlinear deformations and inexact transformations between an input and a template. To establish an objective assessment of our system, both the Michigan State University and the National Institute of Standards and Technology NIST 9 fingerprint data bases have been used to estimate the performance numbers. The experimental results reveal that our system can achieve a good performance on these data bases. We also have demonstrated that our system satisfies the response-time requirement. A complete authentication procedure, on average, takes about 1.4 seconds on a Sun ULTRA I workstation (it is expected to run as fast or faster on a 200 HMz Pentium).


title: Customizable Surprising Recommendation Based on the Tradeoff between Genre Difference and Genre Similarity

abstract: Recommendations generated by Content Based method are highly related to the user's previous choices, which may not only unattractive to the user[1], but also restrict the user's horizon [1], [2]. At the same time, a new paradigm of making recommendations hat i¨surprisei¨ the user poses new challenges in relation to the definition, formulation and performance metrics for surprising recommendation systems. Moreover, users may demand recommendations with varying degrees of surprising ness that satisfy their personal interests on the one hand, and encourage the explorations of new or unexpected areas of potential interests on another. To meet these challenges, in this paper, we proposed a framework, called Customizable GenPref, and the associated techniques for generating customizable surprising recommendations. Specifically, we contribute to the following aspects: firstly, through a review of the related works, we distinguish the difference between surprising ness and other concepts such as diversity, unexpectedness in non-traditional recommendations, secondly, we argue that the elements of surprise in a recommendation involve two conflicting goals, namely unusuality and relevance in the recommendation and proposed a framework of making recommendations such that by tuning a user-defined parameter a a user will receive recommendations which are either similar to his/her previous choices, or different and novel that surprises him/her, or combinations of both. We have evaluated our proposed framework using several relevant performance metrics, such as accuracy and diversity. Our experimental results show that Customizable GenPref is not only able to predict and recommend similar or surprising items that the user may like, but, at the same time, also serves the business objectives of e-commerce sites by recommending more distinct items to the users compared with baseline methods.


title: The Neurobiology of Addiction: Implications for Voluntary Control of Behavior

abstract: There continues to be a debate on whether addiction is best understood as a brain disease or a moral condition. This debate, which may influence both the stigma attached to addiction and access to treatment, is often motivated by the question of whether and to what extent we can justly hold addicted individuals responsible for their actions. In fact, there is substantial evidence for a disease model, but the disease model per se does not resolve the question of voluntary control. Recent research at the intersection of neuroscience and psychology suggests that addicted individuals have substantial impairments in cognitive control of behavior, but this “loss of control” is not complete or simple. Possible mechanisms and implications are briefly reviewed.


title: A survey on security and privacy issues in Wireless Mesh Networks

abstract: Wireless Mesh Networks are considered as a promising solution for offering low-cost access to broadband services. However, one of the main challenges in the design of these networks is their vulnerability to security attacks. In this paper, we analyze the fundamental security challenges and constraints of these networks, classify several possible attacks, and survey several intrusion prevention, detection, and response mechanisms found in the literature. Copyright © 2013 John Wiley & Sons, Ltd.


Effective search space reduction for spell correction using character neural embeddings.


title: Interoceptive robustness through environment-mediated morphological development

abstract: Typically, AI researchers and roboticists try to realize intelligent behavior in machines by tuning parameters of a predefined structure (body plan and/or neural network architecture) using evolutionary or learning algorithms. Another but not unrelated longstanding property of these systems is their brittleness to slight aberrations, as highlighted by the growing deep learning literature on adversarial examples. Here we show robustness can be achieved by evolving the geometry of soft robots, their control systems, and how their material properties develop in response to one particular interoceptive stimulus (engineering stress) during their lifetimes. By doing so we realized robots that were equally fit but more robust to extreme material defects (such as might occur during fabrication or by damage thereafter) than robots that did not develop during their lifetimes, or developed in response to a different interoceptive stimulus (pressure). This suggests that the interplay between changes in the containing systems of agents (body plan and/or neural architecture) at different temporal scales (evolutionary and developmental) along different modalities (geometry, material properties, synaptic weights) and in response to different signals (interoceptive and external perception) all dictate those agents' abilities to evolve or learn capable and robust strategies.


title: On Fairness, Diversity and Randomness in Algorithmic Decision Making

abstract: Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context of fairness-aware learning and demonstrate various attractive properties: (i) an ensemble of fair classifiers is guaranteed to be fair, for several different measures of fairness, (ii) an ensemble of unfair classifiers can still achieve fair outcomes, and (iii) an ensemble of classifiers can achieve better accuracy-fairness trade-offs than a single classifier. Finally, we introduce notions of distributional fairness to characterize further potential benefits of random classifier ensembles.


CULTIVATING IT-ENABLED COLLABORATIVE CONSUMPTION ECOSYSTEM: A SERVICE-DOMINANT PERSPECTIVE OF GOGET


title: An Analysis of Four Missing Data Treatment Methods for Supervised Learning

abstract: One relevant problem in data quality is missing data. Despite the frequent occurrence and the relevance of the missing data problem, many machine learning algorithms handle missing data in a rather naive way. However, missing data treatment should be carefully treated, otherwise bias might be introduced into the knowledge induced. In this work, we analyze the use of the k-nearest neighbor as an imputation method. Imputation is a term that denotes a procedure that replaces the missing values in a data set with some plausible values. One advantage of this approach is that the missing data treatment is independent of the learning algorithm used. This allows the user to select the most suitable imputation method for each situation. Our analysis indicates that missing data imputation based on the k-nearest neighbor algorithm can outperform the internal methods used by C4.5 and CN2 to treat missing data, and can also outperform the mean or mode imputation method, which is a method broadly used to treat missing ...


title: A case study: privacy preserving release of spatio-temporal density in paris

abstract: With billions of handsets in use worldwide, the quantity of mobility data is gigantic. When aggregated they can help understand complex processes, such as the spread viruses, and built better transportation systems, prevent traffic congestion. While the benefits provided by these datasets are indisputable, they unfortunately pose a considerable threat to location privacy. In this paper, we present a new anonymization scheme to release the spatio-temporal density of Paris, in France, i.e., the number of individuals in 989 different areas of the city released every hour over a whole week. The density is computed from a call-data-record (CDR) dataset, provided by the French Telecom operator Orange, containing the CDR of roughly 2 million users over one week. Our scheme is differential private, and hence, provides provable privacy guarantee to each individual in the dataset. Our main goal with this case study is to show that, even with large dimensional sensitive data, differential privacy can provide practical utility with meaningful privacy guarantee, if the anonymization scheme is carefully designed. This work is part of the national project XData (http://xdata.fr) that aims at combining large (anonymized) datasets provided by different service providers (telecom, electricity, water management, postal service, etc.).


title: Short-term traffic flow prediction with Conv-LSTM

abstract: The accurate short-term traffic flow prediction can provide timely and accurate traffic condition information which can help one to make travel decision and mitigate the traffic jam. Deep learning (DL) provides a new paradigm for the analysis of big data generated by the urban daily traffic. In this paper, we propose a novel end-to-end deep learning architecture which consists of two modules. We combine convolution and LSTM to form a Conv-LSTM module which can extract the spatial-temporal information of the traffic flow information. Furthermore, a Bi-directional LSTM module is also adopted to analyze historical traffic flow data of the prediction point to get the traffic flow periodicity feature. The experimental results on the real dataset show that the proposed approach can achieve a better prediction accuracy compared with the existing approaches.


Insects affecting regenerating conifers in Canada: natural history and management


title: The challenges of six sigma in improving service quality

abstract: Purpose – The purpose of this paper is to critically evaluate the contributions of six sigma methodology to the improvement of service quality. Since its development in the late 1980s, six sigma has been extensively applied in manufacturing and quasi‐manufacturing settings. This study aims to explore the challenges of six sigma in reaching a much wider field of application.Design/methodology/approach – Utilizing the service quality framework, the authors assess the contributions of six sigma and explore its limitations when applied to services.Findings – The relentless drive toward adopting six sigma to services has led both to a limited field of applications and to unrealistic expectations as to what six sigma is truly capable of achieving, particularly in knowledge‐based environments.Research limitations/implications – This research focuses on highlighting gaps in the six sigma as applied to services; further work is necessary to identify and develop new methods and to study their effectiveness.Practica...


title: Face spoofing detection with image quality regression

abstract: Face spoofing detection nowadays has attracted attentions regarding the biometrics authentication issue. Inspired by the observation that face spoofing detection is highly relevant with the inherent image quality which also strongly depends on the properties of the capturing devices and conditions, in this paper, we tackle the spoofing detection problem based on a two-stage learning approach. Firstly, we manually cluster the training samples based on the prior knowledge of face sample quality (e.g. camera model), and multiple quality-guided classifiers are trained based on each cluster with extracted image quality assessment (IQA) feature. Subsequently, a regression function is learned by mapping from the IQA scores to the corresponding classifier's parameters, which can be further used for classification. As such, given a new face input for verification, we can predict its classifier's coefficients based on the pre-learned regression model, with which spoofing detection can be effectively achieved. Experimental results show that we achieve significantly better classification performance compared with the strategy that directly applies the IQA features with single classifier.


Re-defining the colour rendering index.


title: A proposed system for recapitulating tweets using graph-based clustering

abstract: Twitter, a well-liked online social networking site, facilitates millions of users on a daily basis to dispatch and orate quick 140-character notes named tweets. Nowadays, twitter is cogitated as the fastest and popular intermediate of communication and is used to follow latest events. Tweets pertaining to a specific event can be effortlessly found using keyword matching, but there are numerous tweets that are likely to contain information that is semantically identical. Moreover, there exist many systems for recapitulating tweets related to a particular event, but they have numerous limitations and are unable to provide accurate results. This study aims to overcome the limitations of existing systems by proposing a system for recapitulating tweets using graph-based clustering.


title: High-Frequency Resonant Transistor DC-DC Converters

abstract: Transistor dc-dc converters which employ a resonant circuit are described. A resonant circuit is driven with square waves of current or voltage, and by adjusting the frequency around the resonant point, the voltage on the resonant components can be adjusted to any practical voltage level. By rectifying the voltage across the resonant elements, a dc voltage is obtained which can be either higher or lower than the input dc voltage to the converter. Thus, the converter can operate in either the step-up or step-down mode. In addition, the switching losses in the inverter devices and rectifiers are extremely low due to the sine waves that occur from the use of a resonant circuit (as opposed to square waves in a conventional converter); also, easier EMI filtering should result. In the voltage input version, the converter is able to use the parasitic diode associated with an FET or monolithic Darlington, while in the current input version, the converter needs the inverse blocking capability which can be obtained with an IGT or GTO device. A low-power breadboard operating at 200-300 kHz has been built. Two typical application areas are switching power supplies and battery chargers. The converter circuits offer improvements over conventional circuits due to their high efficiency (low switching losses), small reactive components (high-frequency operation), and their step-up/stepdown ability.


title: Dynamical coherent-potential approximation approach to excitation spectra in 3d transition metals

abstract: First-principles dynamical CPA (Coherent-Potential Approximation) for electron correlations has been developed further by taking into account higher-order dynamical corrections with use of the asymptotic approximation. The theory is applied to the investigations of a systematic change of excitation spectra in $3d$ transition metals from Sc to Cu at finite temperatures. It is shown that the dynamical effects damp main peaks in the densities of states (DOS) obtained by the local density approximation to the density functional theory, reduce the band broadening due to thermal spin fluctuations, create the Mott-Hubbard type bands in the case of fcc Mn and fcc Fe, and create a small hump corresponding to the `6 eV' satellite in the case of Co, Ni, and Cu. Calculated DOS explain the X-ray photoelectron spectroscopy data as well as the bremsstrahlung isochromat spectroscopy data. Moreover, it is found that screening effects on the exchange energy parameters are significant for understanding the spectra in magnetic transition metals.


Best practices in social and behavioral research: report from the Enhancing Clinical Research Professional’s Training and Qualifications project


title: Evaluating Sensor Placement for System Health Condition Monitoring

abstract: In this paper, an evaluation method of sensor placement is proposed for an optimal design of health condition monitoring systems. An evaluation procedure is presented which includes failure mode and effect analysis, testability analysis, choice of evaluation indexes, and implementing evaluation. A hidden semi-Markov model is employed to evaluation the effectiveness of sensor measurement. The proposed method is applied to a case of gear box vibration monitoring, where the model is validated using the experiment data of gear box monitoring.


CS 224 N Final Project : Sentiment analysis of news articles for financial signal prediction


title: Software-Defined Radio GNSS Instrumentation for Spoofing Mitigation: A Review and a Case Study

abstract: Recently, several global navigation satellite systems (GNSS) emerged following the transformative technology impact of the first GNSS—U.S. global positioning system (GPS). The power level of GNSS signals as measured at earth’s surface is below the noise floor and is consequently vulnerable against interference. Spoofers are smart GNSS-like interferers, which mislead the receivers into generating false position and time information. While many spoofing mitigation techniques exist, spoofers are continually evolving, producing a cycle of new spoofing attacks and counter-measures against them. Thus, upgradability of receivers becomes an important advantage for maintaining their immunity against spoofing. Software-defined radio implementations of a GPS receiver address such flexibility but are challenged by demanding computational requirements of both GNSS signal processing and spoofing mitigation. Therefore, this paper reviews reported SDRs in the context of instrumentation capabilities for both conventional and spoofing mitigation modes. This separation is necessitated by significantly increased computational loads when in spoofing domain. This is demonstrated by a case study budget analysis.


title: Progression of penile cutaneous horn to squamous cell carcinoma: A case report

abstract: The current report presents the case of a 43-year-old male suffering from a penile cutaneous horn. A surgical excision of the lesion was performed and histopathology demonstrated hyperkeratosis, dyskeratosis and epithelial hyperplasia. The cutaneous horn progressed to squamous cell carcinoma <1.5 months following surgery and a partial penectomy was conducted. The International Index of Erectile Function 5 questionnaire was used to assess the patient and the score had decreased in the one-month postoperative follow-up compared with that of the preoperative period. These findings indicate that undergoing a partial penectomy on initial diagnosis of a penile cutaneous horn should be considered in order to conserve a greater quantity of the penile tissue and improve the postoperative quality of life.


Introduction: Autophagy in kidneys


title: Sentiment analysis: From binary to multi-class classification: A pattern-based approach for multi-class sentiment analysis in Twitter

abstract: Most of the state of the art works and researches on the automatic sentiment analysis and opinion mining of texts collected from social networks and microblogging websites are oriented towards the classification of texts into positive and negative. In this paper, we propose a pattern-based approach that goes deeper in the classification of texts collected from Twitter (i.e., tweets). We classify the tweets into 7 different classes; however the approach can be run to classify into more classes. Experiments show that our approach reaches an accuracy of classification equal to 56.9% and a precision level of sentimental tweets (other than neutral and sarcastic) equal to 72.58%. Nevertheless, the approach proves to be very accurate in binary classification (i.e., classification into “positive” and “negative”) and ternary classification (i.e., classification into “positive”, “negative” and “neutral”): in the former case, we reach an accuracy of 87.5% for the same dataset used after removing neutral tweets, and in the latter case, we reached an accuracy of classification of 83.0%.


title: Relational inductive biases, deep learning, and graph networks

abstract: Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences--a hallmark of human intelligence from infancy--remains a formidable challenge for modern AI. The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between"hand-engineering"and"end-to-end"learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.


title: Multi-sensor data fusion based on a generalised belief divergence measure

abstract: Multi-sensor data fusion technology plays an important role in real applications. Because of the flexibility and effectiveness in modelling and processing the uncertain information regardless of prior probabilities, Dempster-Shafer evidence theory is widely applied in a variety of fields of information fusion. However, counter-intuitive results may come out when fusing the highly conflicting evidences. In order to deal with this problem, a novel method for multi-sensor data fusion based on a new generalised belief divergence measure of evidences is proposed. Firstly, the reliability weights of evidences are determined by considering the sufficiency and importance of the evidences. After that, on account of the reliability weights of evidences, a new Generalised Belief Jensen-Shannon divergence (GBJS) is designed to measure the discrepancy and conflict degree among multiple evidences, which can be utilised to measure the support degrees of evidences. Afterwards, the support degrees of evidences are used to adjust the bodies of the evidences before using the Dempster's combination rule. Finally, an application in fault diagnosis demonstrates the validity of the proposed method.


title: A Survey on Trust Modeling

abstract: The concept of trust and/or trust management has received considerable attention in engineering research communities as trust is perceived as the basis for decision making in many contexts and the motivation for maintaining long-term relationships based on cooperation and collaboration. Even if substantial research effort has been dedicated to addressing trust-based mechanisms or trust metrics (or computation) in diverse contexts, prior work has not clearly solved the issue of how to model and quantify trust with sufficient detail and context-based adequateness. The issue of trust quantification has become more complicated as we have the need to derive trust from complex, composite networks that may involve four distinct layers of communication protocols, information exchange, social interactions, and cognitive motivations. In addition, the diverse application domains require different aspects of trust for decision making such as emotional, logical, and relational trust. This survey aims to outline the foundations of trust models for applications in these contexts in terms of the concept of trust, trust assessment, trust constructs, trust scales, trust properties, trust formulation, and applications of trust. We discuss how different components of trust can be mapped to different layers of a complex, composite network; applicability of trust metrics and models; research challenges; and future work directions.


title: Privacy Policy Negotiation in Social Media

abstract: Social media involve many shared items, such as photos, which may concern more than one user. The challenge is that users’ individual privacy preferences for the same item may conflict, so an approach that simply merges in some way the users’ privacy preferences may provide unsatisfactory results. Previous proposals to deal with the problem were either time-consuming or did not consider compromises to solve these conflicts (e.g., by considering unilaterally imposed approaches only). We propose a negotiation mechanism for users to agree on a compromise for these conflicts. The second challenge we address in this article relates to the exponential complexity of such a negotiation mechanism. To address this, we propose heuristics that reduce the complexity of the negotiation mechanism and show how substantial benefits can be derived from the use of these heuristics through extensive experimental evaluation that compares the performance of the negotiation mechanism with and without these heuristics. Moreover, we show that one such heuristic makes the negotiation mechanism produce results fast enough to be used in actual social media infrastructures with near-optimal results.


title: Analysis of face gaze in autism using “Bubbles”

abstract: One of the components of abnormal social functioning in autism is an impaired ability to direct eye gaze onto other people's faces in social situations. Here, we investigated the relationship between gaze onto the eye and mouth regions of faces, and the visual information that was present within those regions. We used the “Bubbles” method to vary the facial information available on any given trial by revealing only small parts of the face, and measured the eye movements made as participants viewed these stimuli. Compared to ten IQ- and age-matched healthy controls, eight participants with autism showed less fixation specificity to the eyes and mouth, a greater tendency to saccade away from the eyes when information was present in those regions, and abnormal directionality of saccades. The findings provide novel detail to the abnormal way in which people with autism look at faces, an impairment that likely influences all subsequent face processing.


title: Empowering Deep Thinking to Support Critical Thinking in Teaching and Learning

abstract: This research describes learning activities and techniques that improve deep and critical thinking among students. Critical thinking is considered an ultimate goal in MIS education; and learners who reach critical thinking level can achieve the highest learning goals and outcomes. Critical thinking and English communication are considered the two most essential competencies in the 21st century. Therefore, universities have invested significantly in understanding, promoting, and delivering critical thinking in education. Moreover, the learning and education research has invested extensively in critical thinking. In this research, we present and discuss seven learning activities and techniques that initiate deep thinking and promote critical thinking in teaching and learning in order to achieve high level of quality learning. The main focus of this work is in the higher education setting at the level of colleges and universities. We discuss and explain seven learning activities, with examples and tools that will help increase the level of thinking and improve higher order thinking and critical analysis. The presented techniques and examples can be easily applied and adapted into any discipline to help increase and improve higher order thinking among the learners. The preliminary evaluation results are very encouraging.


Health Care Quality : A Question of Supply and Demand