title: Author name disambiguation in MEDLINE

abstract: Background: We recently described “Author-ity,” a model for estimating the probability that two articles in MEDLINE, sharing the same author name, were written by the same individual. Features include shared title words, journal name, coauthors, medical subject headings, language, affiliations, and author name features (middle initial, suffix, and prevalence in MEDLINE). Here we test the hypothesis that the Author-ity model will suffice to disambiguate author names for the vast majority of articles in MEDLINE. Methods: Enhancements include: (a) incorporating first names and their variants, email addresses, and correlations between specific last names and affiliation words; (b) new methods of generating large unbiased training sets; (c) new methods for estimating the prior probability; (d) a weighted least squares algorithm for correcting transitivity violations; and (e) a maximum likelihood based agglomerative algorithm for computing clusters of articles that represent inferred author-individuals. Results: Pairwise comparisons were computed for all author names on all 15.3 million articles in MEDLINE (2006 baseline), that share last name and first initial, to create Author-ity 2006, a database that has each name on each article assigned to one of 6.7 million inferred author-individual clusters. Recall is estimated at ∼98.8p. Lumping (putting two different individuals into the same cluster) affects ∼0.5p of clusters, whereas splitting (assigning articles written by the same individual to >1 cluster) affects ∼2p of articles. Impact: The Author-ity model can be applied generally to other bibliographic databases. Author name disambiguation allows information retrieval and data integration to become person-centered, not just document-centered, setting the stage for new data mining and social network tools that will facilitate the analysis of scholarly publishing and collaboration behavior. Availability: The Author-ity 2006 database is available for nonprofit academic research, and can be freely queried via http://arrowsmith.psych.uic.edu.


title: Privacy-Preserving Machine Learning Algorithms for Big Data Systems

abstract: Machine learning has played an increasing important role in big data systems due to its capability of efficiently discovering valuable knowledge and hidden information. Often times big data such as healthcare systems or financial systems may involve with multiple organizations who may have different privacy policy, and may not explicitly share their data publicly while joint data processing may be a must. Thus, how to share big data among distributed data processing entities while mitigating privacy concerns becomes a challenging problem. Traditional methods rely on cryptographic tools and/or randomization to preserve privacy. Unfortunately, this alone may be inadequate for the emerging big data systems because they are mainly designed for traditional small-scale data sets. In this paper, we propose a novel framework to achieve privacy-preserving machine learning where the training data are distributed and each shared data portion is of large volume. Specifically, we utilize the data locality property of Apache Hadoop architecture and only a limited number of cryptographic operations at the Reduce() procedures to achieve privacy-preservation. We show that the proposed scheme is secure in the semi-honest model and use extensive simulations to demonstrate its scalability and correctness.


title: Modeling and Control of Legged Robots

abstract: The promise of legged robots over wheeled robots is to provide improved mobility over rough terrain. Unfortunately, this promise comes at the cost of a significant increase in complexity. We now have a good understanding of how to make legged robots walk and run dynamically, but further research is still necessary to make them walk and run efficiently in terms of energy, speed, reactivity, versatility, and robustness. In this chapter, we will discuss how legged robots are usually modeled, how their stability analysis is approached, how dynamic motions are generated and controlled, and finally summarize the current trends in trying to improve their performance. The main problem is avoiding to fall. This can prove difficult since legged robots have to rely entirely on available contact forces to do so. The temporality of leg motions appears to be a key aspect in this respect, as current control solutions include continuous anticipation of future motion (using some form of model predictive control), or focusing more specifically on limit cycles and orbital stability.


title: The role of idioms in sentiment analysis

abstract: Idiom-based features significantly improve sentiment classification results.This study provides resources that can support further research into sentiment analysis.A comprehensive collection of 580 idioms annotated with sentiment polarity.A set of local grammars that can be used to recognize occurrences of these idioms.A corpus of 2521 annotated sentences in which idioms are used in context. In this paper we investigate the role of idioms in automated approaches to sentiment analysis. To estimate the degree to which the inclusion of idioms as features may potentially improve the results of traditional sentiment analysis, we compared our results to two such methods. First, to support idioms as features we collected a set of 580 idioms that are relevant to sentiment analysis, i.e. the ones that can be mapped to an emotion. These mappings were then obtained using a web-based crowdsourcing approach. The quality of the crowdsourced information is demonstrated with high agreement among five independent annotators calculated using Krippendorff's alpha coefficient (α=0.662). Second, to evaluate the results of sentiment analysis, we assembled a corpus of sentences in which idioms are used in context. Each sentence was annotated with an emotion, which formed the basis for the gold standard used for the comparison against two baseline methods. The performance was evaluated in terms of three measures - precision, recall and F-measure. Overall, our approach achieved 64% and 61% for these three measures in two experiments improving the baseline results by 20 and 15 percent points respectively. F-measure was significantly improved over all three sentiment polarity classes: Positive, Negative and Other. Most notable improvement was recorded in classification of positive sentiments, where recall was improved by 45 percent points in both experiments without compromising the precision. The statistical significance of these improvements was confirmed by McNemar's test.


title: EvalAI: Towards Better Evaluation Systems for AI Agents

abstract: We introduce EvalAI, an open source platform for evaluating and comparing machine learning (ML) and artificial intelligence algorithms (AI) at scale. EvalAI is built to provide a scalable solution to the research community to fulfill the critical need of evaluating machine learning models and agents acting in an environment against annotations or with a human-in-the-loop. This will help researchers, students, and data scientists to create, collaborate, and participate in AI challenges organized around the globe. By simplifying and standardizing the process of benchmarking these models, EvalAI seeks to lower the barrier to entry for participating in the global scientific effort to push the frontiers of machine learning and artificial intelligence, thereby increasing the rate of measurable progress in this domain.


title: Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters

abstract: We present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms. Motivated by established model-based fitting methods such as active shapes, we use a PCA of the landmark positions to allow generative modeling of facial landmarks. Instead of computing the model parameters using iterative optimization, the PCA is included in a deep neural network using a novel layer type. The network predicts model parameters in a single forward pass, thereby allowing facial landmark detection at several hundreds of frames per second. Our architecture allows direct end-to-end training of a model-based landmark detection method and shows that deep neural networks can be used to reliably predict model parameters directly without the need for an iterative optimization. The method is evaluated on different datasets for facial landmark detection and medical image segmentation. PyTorch code is freely available at this https URL


title: An efficient antialiasing technique

abstract: An intuitive concept of antialiasing is developed into very efficient antialiased line and circle generators that require even less amount of integer arithmetic than Bresenham's line and circle algorithms. Unlike its predecessors, the new antialiasing technique is derived in spatial domain (raster plane) under a subjectively meaningful error measure to preserve the dynamics of curve and object boundaries. A formal analysis of the new antialiasing technique in frequency domain is also conducted. It is shown that our antialiasing technique computes the same antialiased images as Fujimoto-Iwata's algorithm but at a fraction of the latter's computational cost. The simplicities of the new antialiased line and circle generators also mean their easy hardware implementations.


title: Representation Learning Based Speech Assistive System for Persons With Dysarthria

abstract: An assistive system for persons with vocal impairment due to dysarthria converts dysarthric speech to normal speech or text. Because of the articulatory deficits, dysarthric speech recognition needs a robust learning technique. Representation learning is significant for complex tasks such as dysarthric speech recognition. We focus on robust representation for dysarthric speech recognition that involves recognizing sequential patterns of varying length utterances. We propose a hybrid framework that uses a generative learning based data representation with a discriminative learning based classifier. In this hybrid framework, we propose to use Example Specific Hidden Markov Models (ESHMMs) to obtain log-likelihood scores for a dysarthric speech utterance to form fixed dimensional score vector representation. This representation is used as an input to discriminative classifier such as support vector machine.The performance of the proposed approach is evaluatedusingUA-Speechdatabase.The recognitionaccuracy is much better than the conventional hidden Markov model based approach and Deep Neural Network-Hidden Markov Model (DNN-HMM). The efficiency of the discriminative nature of score vector representation is proved for “very low” intelligibility words.


title: LabVIEW controlled robot for object handling using NI myRIO

abstract: There is significant interest in industry, library and warehouse automation, which frequently involves pick-and-place tasks for products located in shelving units. This system identifies the object, picks that object from source location and places at desired location. The objective of this paper is to control robotic arm using NI myRIO. In industry we need labour for handling and movement of objects. To defeat this problem we suggest robotic arm with degrees of freedom. To implement above mentioned tasks we have used National Instruments myRIO model which has wide variety of sensors, actuators, and displays. The myRIO is control unit of this system. A robot is virtual agent, usually an electromechanical device that is guided by LabVIEW programming. This system has tested for library management application.


title: A design of rotor bar inclination in squirrel cage induction motor

abstract: The shape of rotor bar is important in squirrel cage induction motor (SCIM) performance, such as starting torque, operating torque, and operating efficiency. Some designs of increasing starting torque of motor may decrease motor efficiency. Moreover, some designed shapes of bar are so complicated to be manufactured. In this paper, the design is focused on the simple inclination of conventional shape of bar with keeping the rotor bar area constant. The effects of rotor bar inclination on starting torque, operating torque, and operating efficiency in SCIM are analyzed. The results showed that there is optimum inclination of rotor bar to improve all the performances together. An application to the 3.4 kW SCIM showed that the starting torque is increased from 17.53 to 17.64 $\text {N}\cdot \text {m}$ , the operating torque from 11.19 to $11.26~\text {N}\cdot \text {m}$ , and the operating efficiency from 94.18% to 94.81%.


title: Non-orthogonal multiple access (NOMA) for future downlink radio access of 5G

abstract: Multiple access (MA) technology is of most importance for 5G. Non-orthogonal multiple access (NOMA) utilizing power domain and advanced receiver has been considered as a promising candidate MA technology recently. In this paper, the NOMA concept is presented toward future enhancements of spectrum efficiency in lower frequency bands for downlink of 5G system. Key component technologies of NOMA are presented and discussed including multiuser transmission power allocation, scheduling algorithm, receiver design and combination of NOMA with multi-antenna technology. The performance gains of NOMA are evaluated by system-level simulations with very practical assumptions. Under multiple configurations and setups, the achievable system-level gains of NOMA are shown promising even when practical considerations were taken into account.


SOFTWARE CONSIDERATIONS IN AIRBORNE SYSTEMS AND EQUIPMENT CERTIFICATION


title: Cataloging Public Objects Using Aerial and Street-Level Images — Urban Trees

abstract: Each corner of the inhabited world is imaged from multiple viewpoints with increasing frequency. Online map services like Google Maps or Here Maps provide direct access to huge amounts of densely sampled, georeferenced images from street view and aerial perspective. There is an opportunity to design computer vision systems that will help us search, catalog and monitor public infrastructure, buildings and artifacts. We explore the architecture and feasibility of such a system. The main technical challenge is combining test time information from multiple views of each geographic location (e.g., aerial and street views). We implement two modules: det2geo, which detects the set of locations of objects belonging to a given category, and geo2cat, which computes the fine-grained category of the object at a given location. We introduce a solution that adapts state-of the-art CNN-based object detectors and classifiers. We test our method on "Pasadena Urban Trees", a new dataset of 80,000 trees with geographic and species annotations, and show that combining multiple views significantly improves both tree detection and tree species classification, rivaling human performance.


title: Availability and Load Balancing in Cloud Computing

abstract: Availability of cloud systems is one of the main concerns of cloud computing. The term, availability of clouds, is mainly evaluated by ubiquity of information comparing with resource scaling. In clouds, load balancing, as a method, is applied across different data centers to ensure the network availability by minimizing use of computer hardware, software failures and mitigating recourse limitations. This work discusses the load balancing in cloud computing and then demonstrates a case study of system availability based on a typical Hospital Database Management solution.


title: Asynchronous Byzantine Machine Learning

abstract: Asynchronous distributed machine learning solutions have proven very effective so far, but always assuming perfectly functioning workers. In practice, some of the workers can however exhibit Byzantine behavior, caused by hardware failures, software bugs, corrupt data, or even malicious attacks. We introduce \emph{Kardam}, the first distributed asynchronous stochastic gradient descent (SGD) algorithm that copes with Byzantine workers. Kardam consists of two complementary components: a filtering and a dampening component. The first is scalar-based and ensures resilience against $\frac{1}{3}$ Byzantine workers. Essentially, this filter leverages the Lipschitzness of cost functions and acts as a self-stabilizer against Byzantine workers that would attempt to corrupt the progress of SGD. The dampening component bounds the convergence rate by adjusting to stale information through a generic gradient weighting scheme. We prove that Kardam guarantees almost sure convergence in the presence of asynchrony and Byzantine behavior, and we derive its convergence rate. We evaluate Kardam on the CIFAR-100 and EMNIST datasets and measure its overhead with respect to non Byzantine-resilient solutions. We empirically show that Kardam does not introduce additional noise to the learning procedure but does induce a slowdown (the cost of Byzantine resilience) that we both theoretically and empirically show to be less than $f/n$, where $f$ is the number of Byzantine failures tolerated and $n$ the total number of workers. Interestingly, we also empirically observe that the dampening component is interesting in its own right for it enables to build an SGD algorithm that outperforms alternative staleness-aware asynchronous competitors in environments with honest workers.


title: Robust fetal ECG extraction and detection from abdominal leads

abstract: The fetal ECG derived from abdominal leads provides an alternative to standard means of fetal monitoring. Furthermore, it permits long-term and ambulant recordings, which expands the range diagnostic possibilities for evaluating the fetal health state. However, due to the temporal and spectral overlap of maternal and fetal signals, the usage of abdominal leads imposes the need for elaborated signal processing routines.In this work a modular combination of processing techniques is presented. Its core consists of two maternal ECG estimation techniques, namely the extended Kalman smoother (EKS) and template adaption (TA) in combination with an innovative detection algorithm. Our detection method employs principles of evolutionary computing to detect fetal peaks by considering the periodicity and morphological characteristics of the fetal signal. In a postprocessing phase, single channel detections are combined by means of kernel density estimation and heart rate correction.The described methodology was presented during the Computing in Cardiology Challenge 2013. The entry was the winner of the closed-source events with average scores for events 4/5 with 15.1/3.32 (TA) and 69.5/4.58 (EKS) on training set-A and 20.4/4.57 (TA) and 219/7.69 (EKS) on test set-B, respectively. Using our own clinical data (24 subjects each 20 min recordings) and statistical measures beyond the Challenge's scoring system, we further validated the proposed method. For our clinical data we obtained an average detection rate of 82.8% (TA) and 83.4% (EKS). The achieved results show that the proposed methods are able produce reliable fetal heart rate estimates from a restricted number of abdominal leads.


Visualizing Science by Citation Mapping


title: Social temporal collaborative ranking for context aware movie recommendation

abstract: Most existing collaborative filtering models only consider the use of user feedback (e.g., ratings) and meta data (e.g., content, demographics). However, in most real world recommender systems, context information, such as time and social networks, are also very important factors that could be considered in order to produce more accurate recommendations. In this work, we address several challenges for the context aware movie recommendation tasks in CAMRa 2010: (1) how to combine multiple heterogeneous forms of user feedbackq (2) how to cope with dynamic user and item characteristicsq (3) how to capture and utilize social connections among usersq For the first challenge, we propose a novel ranking based matrix factorization model to aggregate explicit and implicit user feedback. For the second challenge, we extend this model to a sequential matrix factorization model to enable time-aware parametrization. Finally, we introduce a network regularization function to constrain user parameters based on social connections. To the best of our knowledge, this is the first study that investigates the collective modeling of social and temporal dynamics. Experiments on the CAMRa 2010 dataset demonstrated clear improvements over many baselines.


title: The Resolution of Ruptures in the Therapeutic Alliance.

abstract: This article outlines some of the main features of a research program on ruptures in the therapeutic alliance and reports some of the major findings to date. A rupture in the therapeutic alliance is a deterioration in the quality of the relationship between patient and therapist; it is an interpersonal marker that indicates a critical opportunity for exploring and understanding the processes that maintain a maladaptive interpersonal schema. Following the task-analytic research paradigm, a preliminary model of the resolution process was developed and then tested and revised with 2 different data sets. A series of lag 1 sequential analyses were used to confirm the hypothesized sequences of events within resolution sessions and to demonstrate a difference between resolution and nonresolution sessions. This article describes the evolution of a model of rupture resolution and then discusses its implications for treatment development and evaluation. This article provides an outline of the main features of our programmatic research on ruptures in the therapeutic alliance, as well as our major findings to date. Although the concept of the therapeutic alliance originated in early psychoanalytic literature (e.g., Sterba, 1934; Zetzel, 1956), in the past 15 years it has become a central focus for psychotherapy researchers. There is now a considerable amount of evidence demonstrating that the quality of the therapeutic alliance is one of the better predictors of outcome, regardless of the particular type of therapy (e.g., Horvath & Symonds, 1991). Our research program moves beyond establishing the predictive validity of the construct and toward elucidating the processes involved in resolving ruptures in the therapeutic alliance. We have been attempting to develop a model of the patterns of patient-thera pist transactional sequences associated with improvements in the therapeutic alliance (Safran, Crocker, McMain, & Murray, 1990; Safran, Muran, & Samstag, 1994). Our assumption is that this type of fine-grained change process model has the potential of providing clinicians with information about the subtleties of dynamic interactional sequences, which can have a substantial impact on their clinical practice. The development of a finely detailed model of recurrent patterns can sensitize clinicians to some of the nuances of the clinical process, which are typically relegated to the domain of intuitive clinical wisdom rather than explicated and tested empirically (Rice & Greenberg, 1984).


title: Influence of Pokémon Go on physical activity levels of university players: a cross-sectional study

abstract: BACKGROUND ::: The prevalence of overweight is increasing and the effectiveness of various weight management and exercise programs varied. An augmented reality smartphone game, Pokémon Go, appears to increase activity levels of players. This study assessed the players and ex-players' frequencies and durations of staying outdoors, and walking/jogging before and during the time they played Pokémon Go, evaluated the physical activity levels of players, ex-players and non-players, and investigated the potential factors which determined their play statuses. ::: ::: ::: METHODS ::: Students in a university answered an online-questionnaire survey. The IPAQ-short form was incorporated to measure vigorous-intensity activities, moderate-intensity activities and walking. Chi square tests were used to compare frequencies and durations of staying outdoors and walking/jogging, health discomforts and physical activity levels between players, ex-players and non-players. Wilcoxon signed ranks tests were performed to assess the changes prior to and during the time when the players and ex-players played Pokémon Go. Logistic regression analyses were performed to assess factors contributing to playing, quitting or not playing Pokémon Go. ::: ::: ::: RESULTS ::: 644 university students answered the questionnaire. Compared with the ex-players, the players were significantly more frequent to stay outdoors when playing Pokémon Go (P < 0.001), walk/jog to a location to catch Pokémon, to Pokéstops or Gyms (P < 0.005), as well as walking/jogging to hatch eggs (P < 0.001). Players who never or rarely walked/jogged before spent a mean of 108.19 ± 158.21 min/week to walk/jog in order to play the game which is equivalent to burning 357 kcal/week for a 60-kg person walking a moderate pace. Compared with the non-players, players were more likely to be aged 18-25 years [OR (95% CI) 3.28 (1.28-8.40), P = 0.013], never [OR (95% CI) 10.51 (1.12-98.57), P = 0.039] or rarely [OR (95% CI) 4.00 (1.95-8.23), P < 0.001] stayed outdoors and rarely walked/jogged prior to playing the game [OR (95% CI) 3.88 (1.86-8.05), P < 0.001]. However, there was no significant difference in physical activity levels between the three groups (P = 0.573). ::: ::: ::: CONCLUSIONS ::: Players who used to be sedentary benefited the most from Pokémon Go. The game can be used as a starting point for sedentary people to begin an active lifestyle. The impact of Pokémon Go on physical activity can provide insights to public health workers in using novel strategies in health promotion.


title: Inflammation and Activated Innate Immunity in the Pathogenesis of Type 2 Diabetes

abstract: There is increasing evidence that an ongoing cytokine-induced acute-phase response (sometimes called low-grade inflammation, but part of a widespread activation of the innate immune system) is closely involved in the pathogenesis of type 2 diabetes and associated complications such as dyslipidemia and atherosclerosis. Elevated circulating inflammatory markers such as C-reactive protein and interleukin-6 predict the development of type 2 diabetes, and several drugs with anti-inflammatory properties lower both acute-phase reactants and glycemia (aspirin and thiazolidinediones) and possibly decrease the risk of developing type 2 diabetes (statins). Among the risk factors for type 2 diabetes, which are also known to be associated with activated innate immunity, are age, inactivity, certain dietary components, smoking, psychological stress, and low birth weight. Activated immunity may be the common antecedent of both type 2 diabetes and atherosclerosis, which probably develop in parallel. Other features of type 2 diabetes, such as fatigue, sleep disturbance, and depression, are likely to be at least partly due to hypercytokinemia and activated innate immunity. Further research is needed to confirm and clarify the role of innate immunity in type 2 diabetes, particularly the extent to which inflammation in type 2 diabetes is a primary abnormality or partly secondary to hyperglycemia, obesity, atherosclerosis, or other common features of the disease.


title: A reinforcement learning approach to power control and rate adaptation in cellular networks

abstract: Optimizing radio transmission power and user data rates in wireless systems requires full system observability. While the problem has been extensively studied in the literature, practical solutions approaching optimality exploiting only the partial observability available in real systems are still lacking. This paper proposes a reinforcement learning approach to downlink power control and rate adaptation in cellular networks that closes this gap. We present a comprehensive design of the learning framework that includes the characterization of the system state, a general reward function, and an efficient learning algorithm. System level simulations show that our design quickly learns a power control policy that brings significant energy savings and fairness across users in the system.


title: A Context-Aware Model for Proactive Recommender Systems in the Tourism Domain

abstract: A Proactive Recommender System (PRS) actively pushes recommendations to users when the current context seems appropriate. Despite the advantages of PRSs, especially in the mobile scenario where users could be provided with relevant items on-the-fly when needed, the area of PRSs is still unexplored with many challenges. In particular, it is crucial to identify the relevant items for the target users as well as to determine the right context for pushing these items, since otherwise the user acceptance, and therefore system success, will be negatively impacted. In this paper, we propose a new model that scores each item on two dimensions, preference fit and context fit, to proactively push relevant items to the target user in the right context. Furthermore, we present the preliminary design of a prototype of a mobile Point of Interest (POI) recommender which will be implemented in order to evaluate the practicality and effectiveness of our proposed model.


The ONE Simulator for DTN Protocol Evaluation


title: Metrics measurement model: To measure the object oriented design metrics

abstract: There are many tools and methodologies are available for the development of software. The development consists of activities such as analyze, design, code, test, implement and maintenance. This paper provides the information about measuring the metrics of Object Oriented Software (OOS) development and proposed a Metric Measurement Model (MMM) to ensure that metrics of quality attributes are very important in Object Oriented Software improvement. OOS is a system to enlarge objects that are easily reused, modified and replaced. All objects have attributes and methods, so it works at a privileged level of generalization. Objects are collected to outline a class. The quality of OOS design can be improved by measuring the object oriented design metrics. The OO design is done by UML diagrams; particularly class diagram holds the important part of designing in the development process. Early measurement of metrics will leads to good quality software.


title: Strength and stiffness assessment technologies for improving grading effectiveness of radiata pine wood.

abstract: This work was designed to provide the Australian structural radiata pine processing industry with some indications for improving stress grading methods and/or technologies to give an increase in structural grade yields, and significantly reduce processing costs without compromising product quality. To achieve this, advanced statistical techniques were used in conjunction with state-of-the-art property measurement systems applied to the same sample of sawn timber. Acoustic vibration analyses were conducted on green and dry boards. Raw data from existing in-line systems was captured on the same boards. The Metriguard HCLT stress rating system was used as the "reference" machine grading because of its current common use in the industry. A WoodEye optical scanning system and an X-ray LHG scanner were also able to provide relevant information on knots. The data set was analyzed using classical and advanced statistical tools to provide correlations between data sets, and to develop efficient strength and stiffness prediction equations. Reductions in non-structural dry volumes can be achieved..


title: Segmented Architecture for Successive Approximation Analog-to-Digital Converters

abstract: In this paper, the structure of a binary-weighted capacitive digital-to-analog converter (DAC) in a successive-approximation analog-to-digital converter (SA-ADC) is modified to a unary or segmented configuration to reduce the power consumption and improve the static linearity performance. In order to be able to choose the optimum value of the segmentation degree (i.e., the number of unary bits), the power consumption and the static linearity behavior of the segmented architecture as functions of the segmentation degree are analyzed. Circuit-level simulation results are presented to show the accuracy of the proposed equations. It is shown that for moderate and high-resolution ADCs, a segmentation degree of four or five bits is the optimum choice from the power-consumption viewpoint. Simulation results of a 1 V, 10-bit, 100-kS/s SA-ADC show that the power consumption of the entire capacitive DAC and the digital circuit of the segmented implementation with a segmentation degree of 4 is 30% less than the conventional design while the standard deviation of the differential nonlinearity is reduced by a factor of 2√(2).


title: ESSAC: Enhanced Scalable Secure Access Control Framework for Cloud Storage

abstract: Outsourcing data storage and IT workloads to a third-party cloud provider introduces some security risks and time performance degradation. Moreover, controlling access to this data becomes very difficult when the volume of the data and number of users is very high. Various access control techniques have been proposed to address this issue. However, those techniques have complex schemes which are costly to be applied in real scenarios and they have limited flexibility and scalability to large volumes of data and users. In this paper we propose ESSAC which is an enhanced version of the SSAC scheme. ESSAC introduces a fine-grained access control scheme based on a classified Attribute Based Encryption, Role Based Encryption and Single Key Encryption methodology which achieves highest security without degrading the performance. We validate our scheme using a simulation on top of Amazon S3 and compare it to current schemes.


title: Wireless infidelity I: war driving

abstract: Although WiFi technology security vulnerabilities are well known, the extent of these vulnerabilities may be surprising: War driving experiences identify many potential points of entry.


title: Probabilistic discovery of time series motifs

abstract: Several important time series data mining problems reduce to the core task of finding approximately repeated subsequences in a longer time series. In an earlier work, we formalized the idea of approximately repeated subsequences by introducing the notion of time series motifs. Two limitations of this work were the poor scalability of the motif discovery algorithm, and the inability to discover motifs in the presence of noise.Here we address these limitations by introducing a novel algorithm inspired by recent advances in the problem of pattern discovery in biosequences. Our algorithm is probabilistic in nature, but as we show empirically and theoretically, it can find time series motifs with very high probability even in the presence of noise or "don't care" symbols. Not only is the algorithm fast, but it is an anytime algorithm, producing likely candidate motifs almost immediately, and gradually improving the quality of results over time.


title: Outcomes Research: The History, Debate, and Implications for the Field of Occupational Therapy

abstract: The field of occupational therapy, as with all facets of health care, has been profoundly affected by the changing climate of health care delivery. The combination of cost-effectiveness and quality of care has become the benchmark for and consequent drive behind the rise of managed health care delivery systems. The spawning of outcomes research is in direct response to the need for comparative databases to provide results of effectiveness in health care treatment protocols, evaluations of health-related quality of life, and cost containment measures. Outcomes management is the application of outcomes research data by all levels of health care providers. The challenges facing occupational therapists include proving our value in an economic trend of downsizing, competing within the medical profession, developing and affiliating with new payer sources, and reengineering our careers to meet the needs of the new, nontraditional health care marketplace.


title: Dynamics of projective adaptive resonance theory model: the foundation of PART algorithm

abstract: Projective adaptive resonance theory (PART) neural network developed by Cao and Wu recently has been shown to be very effective in clustering data sets in high dimensional spaces. The PART algorithm is based on the assumptions that the model equations of PART (a large scale and singularly perturbed system of differential equations coupled with a reset mechanism) have quite regular computational performance. This paper provides a rigorous proof of these regular dynamics of the PART model when the signal functions are special step functions, and provides additional simulation results to illustrate the computational performance of PART.


title: Why creating web page objects manually if it can be done automatically?

abstract: Page Object is a design pattern aimed at making web test scripts more readable, robust and maintainable. The effort to manually create the page objects needed for a web application may be substantial and unfortunately existing tools do not help web developers in such task. In this paper we present A pogen , a tool for the automatic generation of page objects for web applications. Our tool automatically derives a testing model by reverse engineering the target web application and uses a combination of dynamic and static analysis to generate Java page objects for the popular Selenium WebDriver framework. Our preliminary evaluation shows that it is possible to use around 3/4 of the automatic page object methods as they are, while the remaining 1/4 need only minor modifications.


title: Phenomenological models of synaptic plasticity based on spike timing

abstract: Synaptic plasticity is considered to be the biological substrate of learning and memory. In this document we review phenomenological models of short-term and long-term synaptic plasticity, in particular spike-timing dependent plasticity (STDP). The aim of the document is to provide a framework for classifying and evaluating different models of plasticity. We focus on phenomenological synaptic models that are compatible with integrate-and-fire type neuron models where each neuron is described by a small number of variables. This implies that synaptic update rules for short-term or long-term plasticity can only depend on spike timing and, potentially, on membrane potential, as well as on the value of the synaptic weight, or on low-pass filtered (temporally averaged) versions of the above variables. We examine the ability of the models to account for experimental data and to fulfill expectations derived from theoretical considerations. We further discuss their relations to teacher-based rules (supervised learning) and reward-based rules (reinforcement learning). All models discussed in this paper are suitable for large-scale network simulations.


title: Computational sprinting on a hardware/software testbed

abstract: CMOS scaling trends have led to an inflection point where thermal constraints (especially in mobile devices that employ only passive cooling) preclude sustained operation of all transistors on a chip --- a phenomenon called "dark silicon." Recent research proposed computational sprinting --- exceeding sustainable thermal limits for short intervals --- to improve responsiveness in light of the bursty computation demands of many media-rich interactive mobile applications. Computational sprinting improves responsiveness by activating reserve cores (parallel sprinting) and/or boosting frequency/voltage (frequency sprinting) to power levels that far exceed the system's sustainable cooling capabilities, relying on thermal capacitance to buffer heat. Prior work analyzed the feasibility of sprinting through modeling and simulation. In this work, we investigate sprinting using a hardware/software testbed. First, we study unabridged sprints, wherein the computation completes before temperature becomes critical, demonstrating a 6.3x responsiveness gain, and a 6% energy efficiency improvement by racing to idle. We then analyze truncated sprints, wherein our software runtime system must intervene to prevent overheating by throttling parallelism and frequency before the computation is complete. To avoid oversubscription penalties (context switching inefficiencies after a truncated parallel sprint), we develop a sprint-aware task-based parallel runtime. We find that maximal-intensity sprinting is not always best, introduce the concept of sprint pacing, and evaluate an adaptive policy for selecting sprint intensity. We report initial results using a phase change heat sink to extend maximum sprint duration. Finally, we demonstrate that a sprint-and-rest operating regime can actually outperform thermally-limited sustained execution.


title: IDH1 mutations as molecular signature and predictive factor of secondary glioblastomas.

abstract: PURPOSE ::: To establish the frequency of IDH1 mutations in glioblastomas at a population level, and to assess whether they allow reliable discrimination between primary (de novo) glioblastomas and secondary glioblastomas that progressed from low-grade or anaplastic astrocytoma. ::: ::: ::: EXPERIMENTAL DESIGN ::: We screened glioblastomas from a population-based study for IDH1 mutations and correlated them with clinical data and other genetic alterations. ::: ::: ::: RESULTS ::: IDH1 mutations were detected in 36 of 407 glioblastomas (8.8%). Glioblastoma patients with IDH1 mutations were younger (mean, 47.9 years) than those with EGFR amplification (60.9 years) and were associated with significantly longer survival (mean, 27.1 versus 11.3 months; P < 0.0001). IDH1 mutations were frequent in glioblastomas diagnosed as secondary (22 of 30; 73%), but rare in primary glioblastomas (14 of 377; 3.7%: P < 0.0001). IDH1 mutations as genetic marker of secondary glioblastoma corresponded to the respective clinical diagnosis in 95% of cases. Glioblastomas with IDH1 mutation diagnosed as primary had clinical and genetic profiles similar to those of secondary glioblastomas, suggesting that they may have rapidly progressed from a less malignant precursor lesion that escaped clinical diagnosis and were thus misclassified as primary. Conversely, glioblastomas without IDH1 mutations clinically diagnosed as secondary typically developed from anaplastic rather than low-grade gliomas, suggesting that at least some were actually primary glioblastomas, that may have been misclassified, possibly due to histologic sampling error. ::: ::: ::: CONCLUSION ::: IDH1 mutations are a strong predictor of a more favorable prognosis and a highly selective molecular marker of secondary glioblastomas that complements clinical criteria for distinguishing them from primary glioblastomas.


title: On the End-to-End Solution to Mandarin-English Code-switching Speech Recognition

abstract: Code-switching (CS) refers to a linguistic phenomenon where a speaker uses different languages in an utterance or between alternating utterances. In this work, we study end-to-end (E2E) approaches to the Mandarin-English code-switching speech recognition (CSSR) task. We first examine the effectiveness of using data augmentation and byte-pair encoding (BPE) subword units. More importantly, we propose a multitask learning recipe, where a language identification task is explicitly learned in addition to the E2E speech recognition task. Furthermore, we introduce an efficient word vocabulary expansion method for language modeling to alleviate data sparsity issues under the code-switching scenario. Experimental results on the SEAME data, a Mandarin-English CS corpus, demonstrate the effectiveness of the proposed methods.


title: Improved Synaptic Behavior Under Identical Pulses Using AlOx/HfO2 Bilayer RRAM Array for Neuromorphic Systems

abstract: We analyze the response of identical pulses on a filamentary resistive memory (RRAM) to implement the synapse function in neuromorphic systems. Our findings show that the multilevel states of conductance are achieved by varying the measurement conditions related to the formation and rupture of a conductive filament. Furthermore, abrupt set switching behavior in the RRAM leads to an unchanged conductance state, leading to degradation in the accuracy of pattern recognition. Thus, we demonstrate a linear potentiation (or depression) behavior of conductance under identical pulses using the effect of barrier layer on the switching, which was realized by fabricating an RRAM on top of an Al electrode. As a result, when the range of the conductance is symmetrically controlled at both polarities, a significantly improved accuracy is achieved for pattern recognition using a neural network with a multilayer perceptron.


title: Simultaneous generation of oxygen vacancies on ultrathin BiOBr nanosheets during visible-light-driven CO2 photoreduction evoked superior activity and long-term stability

abstract: Abstract Under the tremendous pressure of imminent energy crisis and anthropogenic climate change, photocatalytic conversion of abandoned CO2 into energy-rich hydrocarbon fuels is highly desirable. However, this solar-to-fuel conversion is unavoidably suppressed by the fast recombination of electron-hole pairs and lack of stability of photocatalysts. To overcome this, we have developed ultrathin BiOBr nanosheets (BOB-NS) with primarily exposed {001} facets. The {001} facets of BOB-NS are comprised of high density O atoms, which are linked to the neighbouring Bi atoms via weak Bi O bonds with long bond length and low bond energy. After visible-light-driven CO2 photoreduction over BOB-NS, we found that the Bi O bonds were broken and oxygen vacancy (OV) defects formed on the sample surface. The presence of these OVs was proven to be beneficial for photoactivities as photoinduced electrons were effectively trapped at the OV sites and recombination of charge carriers were inhibited. Generally, the free O atoms from dissociation of CO2 would reoxidize the sample surface, thereby deteriorating the performance of photocatalysts. In contrast, we demonstrated in this study that the OV sites on BOB-NS could be simultaneously regenerated and refreshed as the reactions proceeded, leading to a sustainable activity and long-term stability for CO2 photoreduction.


title: Driver Behavior Model Based on Ontology for Intelligent Transportation Systems

abstract: Intelligent transportation systems are a set of technological solutions used to improve the performance and safety of road transportation. A crucial element that affects road safety is driver behavior, because driver errors are usually the principal cause of traffic accidents. Therefore, understanding and modeling human driver behavior is extremely important for the safety of the road transportation. In this paper, a driver behavior model based in ontology for intelligent transportation system is proposed. The ontology will allows predicting different undesirable situations, such as traffic accidents, and road congestion, taking into account the environment information and the driver behavior.


title: Application of Photonic Crystal Enhanced Fluorescence to a Cytokine Immunoassay

abstract: Photonic crystal surfaces are demonstrated as a means for enhancing the detection sensitivity and resolution for assays that use a fluorescent tag to quantify the concentration of an analyte protein molecule in a liquid test sample. Computer modeling of the spatial distribution of resonantly coupled electromagnetic fields on the photonic crystal surface are used to estimate the magnitude of enhancement factor compared to performing the same fluorescent assay on a plain glass surface, and the photonic crystal structure is fabricated and tested to experimentally verify the performance using a sandwich immunoassay for the protein tumor necrosis factor-alpha (TNF-α). The demonstrated photonic crystal fabrication method utilizes a nanoreplica molding technique that allows for large-area inexpensive fabrication of the structure in a format that is compatible with confocal microarray laser scanners. The signal-to-noise ratio for fluorescent spots on the photonic crystal is increased by at least 5-fold relative t...


title: DNA methylation profiles at birth and child ADHD symptoms

abstract: Abstract Attention deficit/hyperactivity disorder (ADHD) is a common and highly heritable psychiatric disorder. In addition, early life environmental factors contribute to the occurrence of ADHD. Recently, DNA methylation has emerged as a mechanism potentially mediating genetic and environmental effects. Here, we investigated whether newborn DNA methylation patterns of selected candidate genes involved in psychiatric disorders or fetal growth are associated with ADHD symptoms in childhood. Participants were 426 children from a large population based cohort of Dutch national origin. Behavioral data were obtained at age 6 years with the Child Behavior Checklist. For the current study, 11 regions at 7 different genes were selected. DNA methylation levels of cord blood DNA were measured for the 11 regions combined and for each region separately. We examined the association between DNA methylation levels at different regions and ADHD symptoms with linear mixed models. DNA methylation levels were negatively associated with ADHD symptom score in the overall analysis of all 11 regions. This association was largely explained by associations of DRD4 and 5-HTT regions. Other candidate genes showed no association between DNA methylation levels and ADHD symptom score. Associations between DNA methylation levels and ADHD symptom score were attenuated by co-occurring Oppositional defiant disorder and total symptoms. Lower DNA methylation levels of the 7 genes assessed at birth, were associated with more ADHD symptoms of the child at 6 years of age. Further studies are needed to confirm our results and to investigate the possible underlying mechanism.


title: A High-Efficiency MOSFET Transformerless Inverter for Nonisolated Microinverter Applications

abstract: State-of-the-art low-power-level metal-oxide-semiconductor field-effect transistor (MOSFET)-based transformerless photovoltaic (PV) inverters can achieve high efficiency by using latest super junction MOSFETs. However, these MOSFET-based inverter topologies suffer from one or more of these drawbacks: MOSFET failure risk from body diode reverse recovery, increased conduction losses due to more devices, or low magnetics utilization. By splitting the conventional MOSFET-based phase leg with an optimized inductor, this paper proposes a novel MOSFET-based phase leg configuration to minimize these drawbacks. Based on the proposed phase leg configuration, a high efficiency single-phase MOSFET transformerless inverter is presented for the PV microinverter applications. The pulsewidth modulation (PWM) modulation and circuit operation principle are then described. The common-mode and differential-mode voltage model is then presented and analyzed for circuit design. Experimental results of a 250 W hardware prototype are shown to demonstrate the merits of the proposed transformerless inverter on nonisolated two-stage PV microinverter application.


A UML Profile for the e3-Value e-Business Modeling Ontology


title: Designing gaze gestures for gaming: an investigation of performance

abstract: To enable people with motor impairments to use gaze control to play online games and take part in virtual communities, new interaction techniques are needed that overcome the limitations of dwell clicking on icons in the games interface. We have investigated gaze gestures as a means of achieving this. We report the results of an experiment with 24 participants that examined performance differences between different gestures. We were able to predict the effect on performance of the numbers of legs in the gesture and the primary direction of eye movement in a gesture. We also report the outcomes of user trials in which 12 experienced gamers used the gaze gesture interface to play World of Warcraft. All participants were able to move around and engage other characters in fighting episodes successfully. Gestures were good for issuing specific commands such as spell casting, and less good for continuous control of movement compared with other gaze interaction techniques we have developed.


title: Class-Boundary Alignment for Imbalanced Dataset Learning

abstract: In this paper, we propose the class-boundaryalignment algorithm to augment SVMs to deal with imbalanced training-data problems posed by many emerging applications (e.g., image retrieval, video surveillance, and gene profiling). Through a simple example, we first show that SVMs can be ineffective in determining the class boundary when the training instances of the target class are heavily outnumbered by the nontarget training instances. To remedy this problem, we propose to adjust the class boundary either by transforming the kernel function when the training data can be represented in a vector space, or by modifying the kernel matrix when the data do not have a vector-space representation (e.g., sequence data). Through theoretical analysis and empirical study, we show that the classboundary-alignment algorithm works effectively with images (data that have a vector-space representation) and video sequences (data that do not have a vector-space representation).


title: Feature analysis and selection for training an end-to-end autonomous vehicle controller using deep learning approach

abstract: Deep learning-based approaches have been widely used for training controllers for autonomous vehicles due to their powerful ability to approximate nonlinear functions or policies. However, the training process usually requires large labeled data sets and takes a lot of time. In this paper, we analyze the influences of features on the performance of controllers trained using the convolutional neural networks (CNNs), which gives a guideline of feature selection to reduce computation cost. We collect a large set of data using The Open Racing Car Simulator (TORCS) and classify the image features into three categories (sky-related, roadside-related, and road-related features).We then design two experimental frameworks to investigate the importance of each single feature for training a CNN controller.The first framework uses the training data with all three features included to train a controller, which is then tested with data that has one feature removed to evaluate the feature's effects. The second framework is trained with the data that has one feature excluded, while all three features are included in the test data. Different driving scenarios are selected to test and analyze the trained controllers using the two experimental frameworks. The experiment results show that (1) the road-related features are indispensable for training the controller, (2) the roadside-related features are useful to improve the generalizability of the controller to scenarios with complicated roadside information, and (3) the sky-related features have limited contribution to train an end-to-end autonomous vehicle controller.


title: Note‐Taking and Secondary Students with Learning Disabilities: Challenges and Solutions

abstract: As more secondary students with learning disabilities (LD) enroll in advanced content-area classes and are expected to pass state exams, they are faced with the challenge of mastering difficult concepts and abstract vocabulary while learning content. Once in these classes, students must learn from lectures that move at a quick pace, record accurate and complete notes, and then demonstrate their mastery of the content on tests. This article provides an overview of the challenges faced by students with LD in content-area classes and discusses the problems that students have learning from lectures and recording notes. Further, the article discusses theory and research related to note-taking and presents note-taking interventions that teachers can use to help students improve their note-taking skills, and ultimately, improve their achievement in these classes.


title: Design Techniques for Fully Integrated Switched-Capacitor DC-DC Converters

abstract: This paper describes design techniques to maximize the efficiency and power density of fully integrated switched-capacitor (SC) DC-DC converters. Circuit design methods are proposed to enable simplified gate drivers while supporting multiple topologies (and hence output voltages). These methods are verified by a proof-of-concept converter prototype implemented in 0.374 mm2 of a 32 nm SOI process. The 32-phase interleaved converter can be configured into three topologies to support output voltages of 0.5 V-1.2 V from a 2 V input supply, and achieves 79.76% efficiency at an output power density of 0.86 W/mm2 .


Relational Agents: Effecting Change through Human-Computer Relationships


title: Effect of information quality on decision performance at regency development priorities

abstract: Information quality (IQ) is essential for decision-making. Several research have been undertaken to analysis relationship between information quality and decision-making. Since the eGovernment has emerged in Indonesia, many government institutions have deployed Information System (IS) in their public services. Unfortunately, deployment of IS in government institutions are not followed by mature planning. Consequently, IS deployment did not give significant impact on information quality. It is time for Local Government to review their IS from customer perspective. We recommend that IS deployment should not only improve its accessibility IQ but should also improve its contextual IQ, intrinsic IQ and representational IQ.


title: Link Prediction on Evolving Data Using Matrix and Tensor Factorizations

abstract: The data in many disciplines such as social networks, web analysis, etc. is link-based, and the link structure can be exploited for many different data mining tasks. In this paper, we consider the problem of temporal link prediction: Given link data for time periods 1 through T, can we predict the links in time period T +1? Specifically, we look at bipartite graphs changing over time and consider matrix- and tensor-based methods for predicting links. We present a weight-based method for collapsing multi-year data into a single matrix. We show how the well-known Katz method for link prediction can be extended to bipartite graphs and, moreover, approximated in a scalable way using a truncated singular value decomposition. Using a CANDECOMP/PARAFAC tensor decomposition of the data, we illustrate the usefulness of exploiting the natural three-dimensional structure of temporal link data. Through several numerical experiments, we demonstrate that both matrix and tensor-based techniques are effective for temporal link prediction despite the inherent difficulty of the problem.


title: Deep Counterfactual Regret Minimization

abstract: Counterfactual Regret Minimization (CFR) is the leading algorithm for solving large imperfect-information games. It iteratively traverses the game tree in order to converge to a Nash equilibrium. In order to deal with extremely large games, CFR typically uses domain-specific heuristics to simplify the target game in a process known as abstraction. This simplified game is solved with tabular CFR, and its solution is mapped back to the full game. This paper introduces Deep Counterfactual Regret Minimization (Deep CFR), a form of CFR that obviates the need for abstraction by instead using deep neural networks to approximate the behavior of CFR in the full game. We show that Deep CFR is principled and achieves strong performance in large poker games. This is the first non-tabular variant of CFR to be successful in large games.


title: Kinematics analysis of a novel 4-dof stabilized platform

abstract: This paper presents a novel stabilized platform with three UPS-type and one RPU-type active limbs based on the shipboard helicopter's landing process and its kinematics are studied systematically. First, a prototype of this stabilized platform is constructed and its dof is analyzed, Second, analytic formulas for solving the inverse displacement kinematics are derived Third, analytic formulas for solving the forward displacement kinematics are derived and verified. Theoretical formulas and results provide a basis for the further optimization, control, and manufacturing.


title: Cluster-based sampling of multiclass imbalanced data

abstract: The aim of this paper is to improve the classification performance based on the multiclass imbalanced datasets. In this paper, we introduce a new resampling approach based on Clustering with sampling for Multiclass Imbalanced classification using Ensemble (C-MIEN). C-MIEN uses the clustering approach to create a new training set for each cluster. The new training sets consist of the new label of instances with similar characteristics. This step is applied to reduce the number of classes then the complexity problem can be easily solved by C-MIEN. After that, we apply two resampling techniques (oversampling and undersampling) to rebalance the class distribution. Finally, the class distribution of each training set is balanced and ensemble approaches are used to combine the models obtained with the proposed method through majority vote. Moreover, we carefully design the experiments and analyze the behavior of C-MIEN with different parameters (imbalance ratio and number of classifiers). The experimental results show that C-MIEN achieved higher performance than state-of-the-art methods.


title: Where are we? The anatomy of the murine cortical meninges revisited for intravital imaging, immunology, and clearance of waste from the brain

abstract: Rapid progress is being made in understanding the roles of the cerebral meninges in the maintenance of normal brain function, in immune surveillance, and as a site of disease. Most basic research on the meninges and the neural brain is now done on mice, major attractions being the availability of reporter mice with fluorescent cells, and of a huge range of antibodies useful for immunocytochemistry and the characterization of isolated cells. In addition, two-photon microscopy through the unperforated calvaria allows intravital imaging of the undisturbed meninges with sub-micron resolution. The anatomy of the dorsal meninges of the mouse (and, indeed, of all mammals) differs considerably from that shown in many published diagrams: over cortical convexities, the outer layer, the dura, is usually thicker than the inner layer, the leptomeninx, and both layers are richly vascularized and innervated, and communicate with the lymphatic system. A membrane barrier separates them and, in disease, inflammation can be localized to one layer or the other, so experimentalists must be able to identify the compartment they are studying. Here, we present current knowledge of the functional anatomy of the meninges, particularly as it appears in intravital imaging, and review their role as a gateway between the brain, blood, and lymphatics, drawing on information that is scattered among works on different pathologies.


title: Undervalued or Overvalued Customers: Capturing Total Customer Engagement Value

abstract: Customers can interact with and create value for firms in a variety of ways. This article proposes that assessing the value of customers based solely upon their transactions with a firm may not be sufficient, and valuing this engagement correctly is crucial in avoiding undervaluation and overvaluation of customers. We propose four components of a customer's engagement value (CEV) with a firm. The first component is customer lifetime value (the customer's purchase behavior), the second is customer referral value (as it relates to incentivized referral of new customers), the third is customer influencer value (which includes the customer's behavior to influence other customers, that is increasing acquisition, retention, and share of wallet through word of mouth of existing customers as well as prospects), and the fourth is customer knowledge value (the value added to the firm by feedback from the customer). CEV provides a comprehensive framework that can ultimately lead to more efficient marketing strategies that enable higher long-term contribution from the customer. Metrics to measure CEV, future research propositions regarding relationships between the four components of CEV are proposed and marketing strategies that can leverage these relationships suggested.


title: The Significance of Evaluation in AI and Law A Case Study Re-examining ICAIL Proceedings ABSTRACT

abstract: This paper examines the presence of performance evaluation in works published at ICAIL conferences since 2000. As such, it is a self-reflexive, meta-level study that investigates the proportion of works that include some form of performance assessment in their contribution. It also reports on the categories of evaluation present as well as their degree. In addition, the paper compares current trends in performance measurement with those of earlier ICAILs, as reported in the Hall and Zeleznikow work on the same topic (ICAIL 2001). The paper also develops an argument for why evaluation in formal Artificial Intelligence and Law reports such as ICAIL proceedings is imperative. It underscores the importance of answering the question: how good is the system?, how reliable is the approach?, or, more succinctly, does it work? The paper argues that the presence of a performance-based ethic within a scientific research community is a sign of maturity and essential scientific rigor. Finally the work references an evaluation checklist and presents a set of recommended best practices for the inclusion of evaluation methods going forward.


title: Satisfaction assessment with malleable prosthetic implant of Spectra (AMS) and Genesis (Coloplast) models

abstract: The malleable prosthetic implant is widely accepted among patients and physicians owing to a lower degree of surgical complexity, its rare mechanic failures and lower cost. We have compared the degree of satisfaction with malleable prosthetic implant in 60 patients, 36 with Spectra (AMS) and 24 with Genesis (Coloplast). For assessment purposes, we implemented the Erectile Dysfunction Inventory of Treatment Satisfaction (EDITS) satisfaction questionnaire adapted for penile prosthetic implants. The mean age and follow-up was 61.7 years (31-82) and 19.9 months (1-61), respectively. Mean EDITS scores did not indicate superiority of one implant over the other, overall satisfaction index being 77.1% and 75.6% for Genesis and Spectra prosthesis, respectively (P=0.4970). Our results revealed that these two models of malleable prostheses present a high level of satisfaction and confirm that the malleable prosthetic implant is an excellent option to treat patients with ED refractory to medical treatment.


title: Contribution behavior in virtual communities: cognitive, emotional, and social influences

abstract: The long-term viability of virtual communities depends critically on contribution behavior by their members. We deepen and extend prior research by conceptualizing contributions to virtual communities in terms of small friendship group-referent intentional actions. Specifically, we investigate cognitive, emotional, and social determinants of shared we-intentions and their consequences for member contribution behavior to the small friendship group to which they belong within a larger community. Using multiple measurement sources and a longitudinal quasi-experimental design, we show that group norms and social identity, as well as attitudes and anticipated emotions, contribute to the development of behavioral desires, which in turn influence we-intentions. In addition, subjective norms are less effective than either group norms or social identity in encouraging contribution behavior. Finally, members' experience levels positively moderate the relationship between we-intentions and contribution behaviors, and differences between collectivistic versus individualistic orientations moderate the effects of social identity and anticipated emotions on the desire to contribute to one's friendship group in the virtual community. Tests for methods biases were conducted, as well as rival hypotheses. These findings have significant research and managerial implications.


title: Tridimensional Yagi Antenna

abstract: This work presents the concept and development of the so called Tridimensional Yagi Antenna, which is a novel type of Yagi antenna operating in P band. It was idealized from two other types of Yagi antennas: Quasi-Yagi and Yagi-Uda. The resultant antenna is based on a microstrip balun, as in Quasi-Yagi, and its driver and directors are made from metal tubes, as in Yagi-Uda, both integrated to a reflecting plane. The key point of this new antenna model is its asymmetric and reconfigurable radiation pattern and therefore the possibility of a high rejection between bands in a plane. This feature was accomplished with the new concept of progressive rotation of directors around driver. We present a design methodology to adjust return loss and radiation pattern characteristics. The final prototype presents 9.4 dB gain at 450 MHz and 47% bandwidth centered at 425 MHz.


title: Drawn to distraction: A qualitative study of off-task use of educational technology

abstract: Today's educational system increasingly integrates digital devices such as laptops and tablets in the classroom on the assumption that the use of these technologies will increase student motivation and learning. However, research shows that students often use technologies for distractive purposes like off-task activity and multitasking. Few studies address the processes involved in this activity. This article offers a postphenomenologically informed qualitative study of students' off-task use of technology during class. Building on interviews with students in a Danish business college about their off-task technology use, findings suggest that off-task activity is not always a conscious choice. Because of deeply sedimented bodily habits, students often experience habitual distraction in the form of prereflective attraction towards certain frequently visited websites (e.g., Facebook). Laptops are experienced as endowed with an attractive allure that "pulls you in". Students sometimes go as far as closing the lids of their laptops to avoid this habitual distraction. Theoretical and practical implications are discussed. Increasing integration of technology in the educational system.Detrimental off-task activity and multitasking occur frequently.Engaging in this off-task activity is not always a conscious choice.Students experience a prereflective attraction towards frequently visited websites.Closing the lids of student laptops removes this habitual distraction.


title: Quartz: A Lightweight Performance Emulator for Persistent Memory Software

abstract: Next-generation non-volatile memory (NVM) technologies, such as phase-change memory and memristors, can enable computer systems infrastructure to continue keeping up with the voracious appetite of data-centric applications for large, cheap, and fast storage. Persistent memory has emerged as a promising approach to accessing emerging byte-addressable non-volatile memory through processor load/store instructions. Due to lack of commercially available NVM, system software researchers have mainly relied on emulation to model persistent memory performance. However, existing emulation approaches are either too simplistic, or too slow to emulate large-scale workloads, or require special hardware. To fill this gap and encourage wider adoption of persistent memory, we developed a performance emulator for persistent memory, called Quartz. Quartz enables an efficient emulation of a wide range of NVM latencies and bandwidth characteristics for performance evaluation of emerging byte-addressable NVMs and their impact on applications performance (without modifying or instrumenting their source code) by leveraging features available in commodity hardware. Our emulator is implemented on three latest Intel Xeon-based processor architectures: Sandy Bridge, Ivy Bridge, and Haswell. To assist researchers and engineers in evaluating design decisions with emerging NVMs, we extend Quartz for emulating the application execution on future systems with two types of memory: fast, regular volatile DRAM and slower persistent memory. We evaluate the effectiveness of our approach by using a set of specially designed memory-intensive benchmarks and real applications. The accuracy of the proposed approach is validated by running these programs both on our emulation platform and a multisocket (NUMA) machine that can support a range of memory latencies. We show that Quartz can emulate a range of performance characteristics with low overhead and good accuracy (with emulation errors 0.2% - 9%).


title: The Development of a Self-Report Questionnaire on Coping with Cyberbullying: The Cyberbullying Coping Questionnaire

abstract: The negative effects and the continuation of cyberbullying seem to depend on the coping strategies the victims use. To assess their coping strategies, self-report questionnaires (SRQs) are used. However, these SRQs are often subject to several shortcomings: the (single and topological) categorizations used in SRQs do not always adequately differentiate among various coping responses, in addition the strategies of general SRQs fail to accurately measure coping with cyberbullying. This study is therefore aimed to develop a SRQ that specifically measures coping with cyberbullying ( i.e. , Cyberbullying Coping Questionnaire; CCQ) and to discover whether other, not single and topological, categorizations of coping strategies can be found. Based on previous SRQs used in the (cyber)bullying ( i.e. , traditional and cyberbullying) literature ( i.e. , 49 studies were found with three different SRQs measuring coping with traditional bullying, cyberbullying or (cyber)bullying) items and categorizations were selected, compared and merged into a new questionnaire. In compliance with recommendations from the classical test-theory, a principal component analysis and a confirmatory factor analysis were done, and a final model was constructed. Seventeen items loaded onto four different coping categorizations: mental-, passive-, social-, and confrontational-coping. The CCQ appeared to have good internal consistency, acceptable test-retest reliability, good discriminant validity and the development of the CCQ fulfilled many of the recommendations from classical test-theory. The CCQ omits working in single and topological categorizations and measures cognitive, behavioral, approach and avoidance strategies.


title: Prediction of human emergency behavior and their mobility following large-scale disaster

abstract: The frequency and intensity of natural disasters has significantly increased over the past decades and this trend is predicted to continue. Facing these possible and unexpected disasters, accurately predicting human emergency behavior and their mobility will become the critical issue for planning effective humanitarian relief, disaster management, and long-term societal reconstruction. In this paper, we build up a large human mobility database (GPS records of 1.6 million users over one year) and several different datasets to capture and analyze human emergency behavior and their mobility following the Great East Japan Earthquake and Fukushima nuclear accident. Based on our empirical analysis through these data, we find that human behavior and their mobility following large-scale disaster sometimes correlate with their mobility patterns during normal times, and are also highly impacted by their social relationship, intensity of disaster, damage level, government appointed shelters, news reporting, large population flow and etc. On the basis of these findings, we develop a model of human behavior that takes into account these factors for accurately predicting human emergency behavior and their mobility following large-scale disaster. The experimental results and validations demonstrate the efficiency of our behavior model, and suggest that human behavior and their movements during disasters may be significantly more predictable than previously thought.


title: Lessons Learned: A Pilot Study on Occupational Therapy Effectiveness for Children With Sensory Modulation Disorder

abstract: OBJECTIVE. The purpose of this pilot study was to prepare for a randomized controlled study of the effectiveness of occupational therapy using a sensory integration approach (OT-SI) with children who have sensory processing disorders (SPD). ::: ::: METHOD. A one-group pretest, posttest design with 30 children was completed with a subset of children with SPD, those with sensory modulation disorder. ::: ::: RESULTS. Lessons learned relate to (a) identifying a homogeneous sample with quantifiable inclusion criteria, (b) developing an intervention manual for study replication and a fidelity to treatment measure, (c) determining which outcomes are sensitive to change and relate to parents� priorities, and (d) clarifying rigorous methodologies (e.g., blinded examiners, randomization, power). ::: ::: CONCLUSION. A comprehensive program of research is needed, including multiple pilot studies to develop enough knowledge that high-quality effectiveness research in occupational therapy can be completed. Previous effectiveness studies in OT-SI have been single projects not based on a unified long-term program of research.


title: On Obstructing Obscenity Obfuscation

abstract: Obscenity (the use of rude words or offensive expressions) has spread from informal verbal conversations to digital media, becoming increasingly common on user-generated comments found in Web forums, newspaper user boards, social networks, blogs, and media-sharing sites. The basic obscenity-blocking mechanism is based on verbatim comparisons against a blacklist of banned vocabulary; however, creative users circumvent these filters by obfuscating obscenity with symbol substitutions or bogus segmentations that still visually preserve the original semantics, such as writing shit as $h¡;t or s.h.i.t or even worse mixing them as $.h….¡.t. The number of potential obfuscated variants is combinatorial, yielding the verbatim filter impractical. Here we describe a method intended to obstruct this anomaly inspired by sequence alignment algorithms used in genomics, coupled with a tailor-made edit penalty function. The method only requires to set up the vocabulary of plain obscenities; no further training is needed. Its complexity on screening a single obscenity is linear, both in runtime and memory, on the length of the user-generated text. We validated the method on three different experiments. The first one involves a new dataset that is also introduced in this article; it consists of a set of manually annotated real-life comments in Spanish, gathered from the news user boards of an online newspaper, containing this type of obfuscation. The second one is a publicly available dataset of comments in Portuguese from a sports Web site. In these experiments, at the obscenity level, we observed recall rates greater than 90%, whereas precision rates varied between 75% and 95%, depending on their sequence length (shorter lengths yielded a higher number of false alarms). On the other hand, at the comment level, we report recall of 86%, precision of 91%, and specificity of 98%. The last experiment revealed that the method is more effective in matching this type of obfuscation compared to the classical Levenshtein edit distance. We conclude discussing the prospects of the method to help enforcing moderation rules of obscenity expressions or as a preprocessing mechanism for sequence cleaning and/or feature extraction in more sophisticated text categorization techniques.


title: Dynamic description logic model for data integration

abstract: Data integration is the issue of retrieving and combining data residing at distributed and heterogeneous sources, and of providing users with transparent access without being aware of the details of the sources. Data integration is a very important issue because it deals with data infrastructure issues of coordinated computing systems. Despite its importance, the following key challenges make data integration one of the longest standing problems around: 1) how to solve the system heterogeneity; 2) how to build a global model; 3) how to process queries automatically and correctly; and 4) how to solve semantic heterogeneity.


title: Similarity in Relationships as Niche Construction: Choice, Stability, and Influence Within Dyads in a Free Choice Environment

abstract: A series of field studies focused on the role of similarity as niche construction in friendships. Using a free-range dyad harvest method, we collected 11 independent samples with 1,523 interacting pairs, and compared dyad members’ personality traits, attitudes, values, recreational activities, and alcohol and drug use. Within-dyad similarity was statistically significant on 86% of variables measured. To determine whether similarity was primarily attributable to niche construction (i.e., selection) or social influence, we tested whether similarity increased as closeness, intimacy, discussion, length of relationship, and importance of the attitude increased. There were no effects on similarity of closeness, relationship length, or discussion of the attitude. There were quite modest effects of intimacy, and a reliable effect of the shared importance of the attitude. Because relationship length, intimacy, closeness, and discussion can all serve as markers of opportunity for, or potency of social influence, these data are consistent with the “niche construction” account of similarity. In 2 follow-up controlled longitudinal field studies, participants interacted with people they did not know from their large lecture classes, and at a later time completed a survey of attitudes, values, and personality traits. Interacting pairs were not more similar than chance, but for the 23% of dyads that interacted beyond the first meeting, there was significant similarity within dyad members. These 2 lines of inquiry converge to suggest that similarity is mainly due to niche construction, and is most important in the early stages of a relationship; its importance to further relationship development wanes. (PsycINFO Database Record (c) 2017 APA, all rights reserved)


title: An Energy-Efficient Architecture for the Internet of Things (IoT)

abstract: Internet of things (IoT) is a smart technology that connects anything anywhere at any time. Such ubiquitous nature of IoT is responsible for draining out energy from its resources. Therefore, the energy efficiency of IoT resources has emerged as a major research issue. In this paper, an energy-efficien t architecture for IoT has been proposed, which consists of three layers, namely, sensing and control, information processing, and presentation. The architectural design allows the system to predict the sleep interval of sensors based upon their remaining battery level, their previous usage history, and quality of information required for a particular application. The predicted value can be used to boost the utilization of cloud resources by reprovisioning the allocated resources when the corresponding sensory nodes are in sleep mode. This mechanism allows the energy-efficient utilization of all the IoT resources. The experimental results show a significant amount of energy saving in the case of sensor nodes and improved resource utilization of cloud resources.


title: Learning to overtake in TORCS using simple reinforcement learning

abstract: In modern racing games programming non-player characters with believable and sophisticated behaviors is getting increasingly challenging. Recently, several works in the literature suggested that computational intelligence can provide effective solutions to support the development process of NPCs. In this paper, we applied the Behavior Analysis and Training (BAT) methodology to define a behavior-based architecture for the NPCs in The Open Racing Car Simulator (TORCS), a well-known open source racing game. Then, we focused on two major overtaking behaviors: (i) the overtaking of a fast opponent either on a straight stretch or on a large bend; (ii) the overtaking on a tight bend, which typically requires a rather advanced braking policy. We applied simple reinforcement learning, namely Q-learning, to learn both these overtaking behaviors. We tested our approach in several overtaking situations and compared the learned behaviors against one of the best NPC provided with TORCS. Our results suggest that, exploiting the proposed behavior-based architecture, Q-learning can effectively learn sophisticated behaviors and outperform programmed NPCs. In addition, we also show that the same approach can be successfully applied to adapt a previously learned behavior to a dynamically changing game situation.


title: Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence

abstract: We present a simple, easy-to-replicate monolingual aligner that demonstrates state-of-the-art performance while relying on almost no supervision and a very small number of external resources. Based on the hypothesis that words with similar meanings represent potential pairs for alignment if located in similar contexts, we propose a system that operates by finding such pairs. In two intrinsic evaluations on alignment test data, our system achieves F 1 scores of 88–92%, demonstrating 1–3% absolute improvement over the previous best system. Moreover, in two extrinsic evaluations our aligner outperforms existing aligners, and even a naive application of the aligner approaches state-of-the-art performance in each extrinsic task.


title: View Definition and Generalization for Database Integration in a Multidatabase System

abstract: Access to a heterogeneous distributed collection of databases can be simplified by providing users with a logically integrated interface or global view. There are two aspects to database integration. Firstly, the local schemas may model objects and relationships differently and, secondly, the databases may contain mutually inconsistent data. This paper identifies several kinds of structural and data inconsistencies that might exist. It describes a versatile view definition facility for the functional data model and illustrates the use of this facility for resolving inconsistencies. In particular, the concept of generalization is extended to this model, and its importance to database integration is emphasized. The query modification algorithm for the relational model is extended to the semantically richer functional data model with generalization.


title: On the analysis of QoE-based performance degradation in YouTube traffic

abstract: YouTube is the most popular service in today's Internet. Google relies on its massive Content Delivery Network (CDN) to push YouTube videos as close as possible to the end-users to improve their Quality of Experience (QoE), using dynamic server selection strategies. Such traffic delivery policies can have a relevant impact on the traffic routed through the Internet Service Providers (ISPs) providing the access, but most importantly, they can have negative effects on the end-user QoE. In this paper we shed light on the problem of diagnosing QoE-based performance degradation events in YouTube's traffic. Through the analysis of one month of YouTube flow traces collected at the network of a large European ISP, we particularly identify and drill down a Google's CDN server selection policy negatively impacting the watching experience of YouTube users during several days at peak-load times. The analysis combines both the user-side perspective and the CDN perspective of the end-to-end YouTube delivery service to diagnose the problem. The main contributions of the paper are threefold: firstly, we provide a large-scale characterization of the YouTube service in terms of traffic characteristics and provisioning behavior of the Google CDN servers. Secondly, we introduce simple yet effective QoE-based KPIs to monitor YouTube videos from the end-user perspective. Finally and most important, we analyze and provide evidence of the occurrence of QoE-based YouTube anomalies induced by CDN server selection policies, which are somehow normally hidden from the common knowledge of the end-user. This is a main issue for ISPs, who see their reputation degrade when such events occur, even if Google is the culprit.


title: Face Recognition from Video by Matching Image Sets

abstract: As opposed to still-image based paradigms, video-based face recognition involves identifying a person from a video input. In video-based approaches, face detection and tracking are performed together with recognition, as usually the background is included in the video and the person could be moving or being captured unknowingly. By detecting and raster-scanning a face sub-image to be a vector, we can concatenate all extracted vectors to form an image set, thus allowing the application of face recognition algorithms based on matching image sets. It has been reported that linear subspace-based methods for face recognition using image sets achieve good recognition results. The challenge that remains is to update the linear subspace representation and perform recognition on-the-fly so that the recognition-from-video objective is not defeated. Here, we demonstrate how this can be achieved by using a well-studied incremental SVD updating procedure. We then present our online face recognition-from-video framework and the recognition results obtained.


title: An Introduction to Factor Graphs

abstract: Graphical models such as factor graphs allow a unified approach to a number of key topics in coding and signal processing such as the iterative decoding of turbo codes, LDPC codes and similar codes, joint decoding, equalization, parameter estimation, hidden-Markov models, Kalman filtering, and recursive least squares. Graphical models can represent complex real-world systems, and such representations help to derive practical detection/estimation algorithms in a wide area of applications. Most known signal processing techniques -including gradient methods, Kalman filtering, and particle methods -can be used as components of such algorithms. Other than most of the previous literature, we have used Forney-style factor graphs, which support hierarchical modeling and are compatible with standard block diagrams.


title: Ranking on Data Manifolds

abstract: The Google search engine has enjoyed huge success with its web page ranking algorithm, which exploits global, rather than local, hyperlink structure of the web using random walks. Here we propose a simple universal ranking algorithm for data lying in the Euclidean space, such as text or image data. The core idea of our method is to rank the data with respect to the intrinsic manifold structure collectively revealed by a great amount of data. Encouraging experimental results from synthetic, image, and text data illustrate the validity of our method.


title: A multi-view annotation tool for people detection evaluation

abstract: In this paper we introduce a novel multi-view annotation tool for generating 3D ground truth data of the real location of people in the scene. The proposed tool allows the user to accurately select the ground occupancy of people by aligning an oriented rectangle on the ground plane. In addition, the height of the people can also be adjusted. In order to achieve precise ground truth data the user is aided by the video frames of multiple synchronized and calibrated cameras. Finally, the 3D annotation data can be easily converted to 2D image positions using the available calibration matrices. One key advantage of the proposed technique is that different methods can be compared against each other, whether they estimate the real world ground position of people or the 2D position on the camera images. Therefore, we defined two different error metrics, which quantitatively evaluate the estimated positions. We used the proposed tool to annotate two publicly available datasets, and evaluated the metrics on two state of the art algorithms.


title: An evaluation of OPTC and EPYC as candidate genes for high myopia

abstract: Purpose: The small leucine-rich repeat proteins (SLRPs) are involved in organizing the collagen fibrils of the sclera and vitreous. The shape of the eyeball is determined by the sclera and vitreous, so defects in SLRP family members may contribute to myopia. The purpose of this study was to test whether mutations in the two members of the class III SLRPs, opticin (OPTC) and dermatan sulfate proteoglycan 3 (EPYC), are responsible for high myopia. Methods: DNA was prepared from venous leukocytes of 93 patients with high myopia (refraction of spherical equivalent ≤-6.00D) and 96 controls (refraction of spherical equivalent between -0.50D and +1.00D). The coding regions and adjacent intronic sequences of OPTC and EPYC were amplified by the polymerase chain reaction (PCR), and the products were then analyzed by cycle sequencing. The detected variations were further evaluated in normal controls and available family members by a heteroduplex-single strand conformation polymorphism (heteroduplex-SSCP) analysis or sequencing. Results: Two substitutions in OPTC, including c.491G>T and c.803T>C, were identified. The c.491G>T mutation (p.Arg164Leu), a novel heterozygous variation, was detected in one of the 93 patients but in none of the 96 controls. The c.803T>C mutation (p.Pro267Leu), a known polymorphism, was detected in 22 of the 93 patients and in 15 of 48 controls. No variation was observed in EPYC. Conclusions: Only one novel variation in OPTC was detected in a Chinese patient with high myopia. Our results imply that OPTC and EPYC are unlikely to play a major role in high myopia.


title: Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery

abstract: Fault diagnosis in rotating machinery is significant to avoid serious accidents; thus, an accurate and timely diagnosis method is necessary. With the breakthrough in deep learning algorithm, some intelligent methods, such as deep belief network (DBN) and deep convolution neural network (DCNN), have been developed with satisfactory performances to conduct machinery fault diagnosis. However, only a few of these methods consider properly dealing with noises that exist in practical situations and the denoising methods are in need of extensive professional experiences. Accordingly, rethinking the fault diagnosis method based on deep architectures is essential. Hence, this study proposes an automatic denoising and feature extraction method that inherently considers spatial and temporal correlations. In this study, an integrated deep fault recognizer model based on the stacked denoising autoencoder (SDAE) is applied to both denoise random noises in the raw signals and represent fault features in fault pattern diagnosis for both bearing rolling fault and gearbox fault, and trained in a greedy layer-wise fashion. Finally, the experimental validation demonstrates that the proposed method has better diagnosis accuracy than DBN, particularly in the existing situation of noises with superiority of approximately 7% in fault diagnosis accuracy.


title: Formation and Distribution of Marine Biogenic Halocarbons with Emphasis on Polar Regions

abstract: It is well-established that marine microand macroalgae form volatile halogenated compounds (halocarbons). Production occurs throughout the world ocean with strong regional sources in coastal areas. In the atmosphere, these compounds are converted to reactive inorganic halogen compounds, which degrade ozone. Biogenic halocarbons typically contain iodine or bromine, which have higher ozone depletion potentials than chlorine. The main pathway of halocarbon formation involves haloperoxidases, which reduce hydrogen peroxide, formed during photosynthesis, by oxidation of halide ions. Therefore, this is a universal process in the oceans, but there are large spatial and temporal variations. Estimations of the oceanic source of these compounds rely on accurate assessments of production and degradation rates, as well as on air-sea flux calculations. In this work, the distribution of biogenic halocarbons in the ice-covered central Arctic Ocean, and in the Pacific sector of the Southern Ocean, was investigated. Elevated concentrations were found under the sea ice, and even higher concentration in the ice. The production, attributed to sea ice algae, was highly variable. Degradation was also observed with half-lives of as short as 1 day. Production was also found in snow, which could be attributed to biological activity. The studies were performed in a period when the sea ice was melting, and low nutrient levels indicated that a previous bloom had now been followed by heterotrophic activity. The fast degradation, presumably biotic since known abiotic reactions are much slower, showed the importance of this parameter when studying natural populations, as compared to culture studies. The highest concentrations in surface water and sea ice brine in the Arctic Ocean coincided with high concentrations of dissolved organic matter (DOM) carried by river water from the Russian rivers. It was hypothesized that this is a key parameter for the production of halocarbons in this region, as an organic substrate is needed for halocarbon production. In the Southern Ocean it was found that large regions may act as a sink for bromoform. This was highly dependent on the wind direction. When air was transported from coastal regions, under-saturation was measured in open ocean regimes. This has implications for the estimation of local sources to the atmosphere in the Southern Ocean, as the net air-sea flux may be lower due to these nearby sinks.


title: Next generation of IGBT-modules applied to high power traction

abstract: Modern high power traction converters are equipped with IGBT-modules. For a reliable operation the modules have to fulfil the following requirements: High junction temperature limit, large safe operating area, high surge current capability and sufficient thermal cycling capability. In this paper current and next generation of IGBT modules will be characterised regarding the first three aspects.


The Relationship Between Health and Growth: When Lucas Meets Nelson-Phelps ∗


Comparative study of Antiplatelet Aggregation Activity of Bacopa monnieri extracted using Microwave and Ultrasonication Rency