title: A Modified Vortex Search Algorithm for Numerical Function Optimization

abstract: The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was inspired from the vortical flow of the stirred fluids. Although the VS algorithm is shown to be a good candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS algorithm, candidate solutions are generated around the current best solution by using a Gaussian distribution at each iteration pass. This provides simplicity to the algorithm but it also leads to some problems along. Especially, for the functions those have a number of local minimum points, to select a single point to generate candidate solutions leads the algorithm to being trapped into a local minimum point. Due to the adaptive step-size adjustment scheme used in the VS algorithm, the locality of the created candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a local point as quickly as possible, it becomes much more difficult for the algorithm to escape from that point in the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to overcome above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate solutions are generated around a number of points at each iteration pass. Computational results showed that with the help of this modification the global search ability of the existing VS algorithm is improved and the MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the benchmark numerical function set.


title: Cloud detection of remote sensing images by deep learning

abstract: Cloud detection plays a major role for remote sensing image processing. Most of the existed cloud detection methods use the low-level feature of the cloud, which often cause error result especially for thin cloud and complex scene. In this paper, a novel cloud detection method based on deep learning framework is proposed. The designed deep Convolutional Neural Networks (CNNs) consists of four convolutional layers and two fully-connected layers, which can mine the deep features of cloud. The image is firstly clustered into superpixels as sub-region through simple linear iterative cluster (SLIC) method. Through the designed network model, the probability of each superpixel that belongs to cloud region is predicted, so that the cloud probability map of the image is generated. Lastly, the cloud region is obtained according to the gradient of the cloud map. Through the proposed method, both thin cloud and thick cloud can be detected well, and the result is insensitive to complex scene. Experimental results indicate that the proposed method is more robust and effective than compared methods.


title: High fidelity mathematical model building with experimental data: A Bayesian approach

abstract: Abstract Mathematical models of physicochemical systems are usually built in an iterative fashion during the course of an experimental investigation. In this paper, a novel Bayesian approach to model building is presented. This approach is now feasible because of breakthroughs in Monte Carlo sampling procedures and high performance computing, that make it possible to deal directly with the nonlinear mathematical models themselves instead of their linear approximations. By including an error model for experimental data, it is further possible to use nonlinear statistical concepts to test a given model for adequacy against experimental data and prior knowledge, and to place realistic confidence limits on the resulting model parameters. In this paper a model building work flow that takes advantage of these recent advances to enable high fidelity mathematical modeling is proposed. A set of models and their parameters are needed to initiate the process. Probability distributions for the models and their parameters based on available quantitative and subjective information must also be supplied. Finally, an error model describing the heteroscedasticity in the data along with probability distributions for the error model parameters must be generated from exploratory data. Then experiments are designed and data collected. Using Bayes’ theorem, Monte Carlo (MC) or Markov Chain Monte Carlo (MCMC) methods are used to generate a sequence of samples of parameter values for each postulated model. These sets of samples are then used to discriminate among the models using the criteria introduced in this paper. Once discrimination is achieved, a global lack of fit test is introduced to determine model adequacy. After a single adequate model is selected, highest probability density (HPD) intervals are determined for the individual parameters and HPD density regions are constructed for all model parameter pairs. Experiments are then designed to reduce the uncertainty in the joint posterior probability HPD regions. Finally, a sampling procedure is described to properly represent uncertainties in predictions made from the model. The proposed approach is demonstrated by an illustrative problem where three simple models are discriminated and the parameters in the most suitable ones are estimated rigorously.


Characteristics of Generatable Games


title: Mutual-friend based attacks in social network systems

abstract: Recently, we have seen a rapid growth of social networking systems (SNSs). In most SNSs, a user can configure his privacy settings to indicate who can or cannot see his friend list. Usually, SNSs, such as LinkedIn and Google Plus, also include a feature that allows a user to query mutual friends between him and any other user he can reach using the available public search feature in SNSs. While such a mutual friend feature is very helpful in letting users find new friends and connect to them, in this paper, we show that it also raises significant privacy concerns as an adversary can use it to find out some or all of the victim's friends, although, as per the privacy settings of the victim, the adversary is not authorized to see his friend list directly. We show that by using mutual friend queries, an attacker can launch privacy attacks that we refer to as mutual-friend based attacks to identify friends and distant neighbors of targeted users. We analyze these attacks and identify various attack structures that an attacker can use to build attack strategies, using which an attacker can identify a user's friends and his distant neighbors. Through simulations, we demonstrate that mutual-friend based attacks are effective. For instance, one of the simulation results show that an attacker using just one attacker node can identify more than 60% of a user's friends.


title: A Sales Forecast Model for the German Automobile Market Based on Time Series Analysis and Data Mining Methods

abstract: In this contribution, various sales forecast models for the German automobile market are developed and tested. Our most important criteria for the assessment of these models are the quality of the prediction as well as an easy explicability. Yearly, quarterly and monthly data for newly registered automobiles from 1992 to 2007 serve as the basis for the tests of these models. The time series model used consists of additive components: trend, seasonal, calendar and error component. The three latter components are estimated univariately while the trend component is estimated multivariately by Multiple Linear Regression as well as by a Support Vector Machine. Possible influences which are considered include macro-economic and market-specific factors. These influences are analysed by a feature selection. We found the non-linear model to be superior. Furthermore, the quarterly data provided the most accurate results.


title: Deep Generative Modeling for Scene Synthesis via Hybrid Representations

abstract: We present a deep generative scene modeling technique for indoor environments. Our goal is to train a generative model using a feed-forward neural network that maps a prior distribution (e.g., a normal distribution) to the distribution of primary objects in indoor scenes. We introduce a 3D object arrangement representation that models the locations and orientations of objects, based on their size and shape attributes. Moreover, our scene representation is applicable for 3D objects with different multiplicities (repetition counts), selected from a database. We show a principled way to train this model by combining discriminator losses for both a 3D object arrangement representation and a 2D image-based representation. We demonstrate the effectiveness of our scene representation and the deep learning method on benchmark datasets. We also show the applications of this generative model in scene interpolation and scene completion.


title: Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data

abstract: There are threefold challenges in emotion recognition. First, it is difficult to recognize human's emotional states only considering a single modality. Second, it is expensive to manually annotate the emotional data. Third, emotional data often suffers from missing modalities due to unforeseeable sensor malfunction or configuration issues. In this paper, we address all these problems under a novel multi-view deep generative framework. Specifically, we propose to model the statistical relationships of multi-modality emotional data using multiple modality-specific generative networks with a shared latent space. By imposing a Gaussian mixture assumption on the posterior approximation of the shared latent variables, our framework can learn the joint deep representation from multiple modalities and evaluate the importance of each modality simultaneously. To solve the labeled-data-scarcity problem, we extend our multi-view model to semi-supervised learning scenario by casting the semi-supervised classification problem as a specialized missing data imputation task. To address the missing-modality problem, we further extend our semi-supervised multi-view model to deal with incomplete data, where a missing view is treated as a latent variable and integrated out during inference. This way, the proposed overall framework can utilize all available (both labeled and unlabeled, as well as both complete and incomplete) data to improve its generalization ability. The experiments conducted on two real multi-modal emotion datasets demonstrated the superiority of our framework.


title: Integrating structured biological data by kernel maximum mean discrepancy

abstract: MOTIVATION ::: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based statistical test for this problem, based on the fact that two distributions are different if and only if there exists at least one function having different expectation on the two distributions. Consequently we use the maximum discrepancy between function means as the basis of a test statistic. The Maximum Mean Discrepancy (MMD) can take advantage of the kernel trick, which allows us to apply it not only to vectors, but strings, sequences, graphs, and other common structured data types arising in molecular biology. ::: ::: ::: RESULTS ::: We study the practical feasibility of an MMD-based test on three central data integration tasks: Testing cross-platform comparability of microarray data, cancer diagnosis, and data-content based schema matching for two different protein function classification schemas. In all of these experiments, including high-dimensional ones, MMD is very accurate in finding samples that were generated from the same distribution, and outperforms its best competitors. ::: ::: ::: CONCLUSIONS ::: We have defined a novel statistical test of whether two samples are from the same distribution, compatible with both multivariate and structured data, that is fast, easy to implement, and works well, as confirmed by our experiments. ::: ::: ::: AVAILABILITY ::: http://www.dbs.ifi.lmu.de/~borgward/MMD.


title: Caucasian Black Grouse (Tetrao mlokosiewiczi) breeding display sites selection in Arasbaran Region, East Azerbaijan, Iran

abstract: Micro-habitat characteristics of breeding display Sites (Leks) of Caucasian Black Grouse (Tetrao mlokosiewiczi) were studied in the mountains of Arasbaran Region, East Azerbaijan, from October 2006 to June 2008. We quantified micro-habitat characteristics around active and inactive Caucasian black grouse lek sites and random points in the 3 areas (Biosphere reserve, Kalan protected area (one of core zones of Arasbaran biosphere reserve) and unprotected areas)). Our objective was to compare micro-habitat characteristics among these sites. We also developed a logistic regression model to identify micro-habitat factors have important effect in the active lek sites. In the micro-habitat scale, we measured both biotic factors (canopy cover (%), height (cm) and frequency of vegetations) and abiotic (physical) factors (slope (%), altitude (m), aspect and etc.). Slope and altitude were different significantly between sampling sites (p = 0.05). Mean of slope in the active leks (= 57.9%) and inactive leks (= 52.0%) were higher than random sites (= 40.1%). The mean aspect differed between active leks (\( \bar X \) = 284°, S.D = 54.1°) and random sites (\( \bar X \) = 33°, S.D = 76.18°) (Watson’s U2-test, U 0.05, 22, 27 2 = 0.717). Also, variance analysis (ANOVA) revealed that grasses and forbs cover, frequency of deciduous shrubs and forbs, canopy cover and frequency of deciduous woody plants > 12 cm diameter at breast height were different significantly between sampling sites. The best model for discriminating between active lek and random sites included rock-pavement cover (parameter estimate [B] = 0.286, SE = 0.219), canopy cover of grasses (parameter estimate [B] = 0.880, SE = 0.453), distance to the nearest tree (parameter estimate [B] = −0.047, SE = 0.027).


title: A Method to Generate a Modular ifcOWL Ontology

abstract: Building Information Modeling (BIM) and Semantic Web technologies are becoming more and more popular in the Architecture Engineering Construction (AEC) and Facilities Management (FM) industry to support information management, information exchange and data interoperability. One of the key integration gateways between BIM and Semantic Web is represented by the ifcOWL ontology, i.e. the Web Ontology Language (OWL) version of the IFC standard, being one of reference technical standard for AEC/FM. Previous studies have shown how a recommended ifcOWL ontology can be automatically generated by converting the IFC standard from the official EXPRESS schema. However, the resulting ifcOWL is a large monolithic ontology that presents serious limitations for real industrial applications in terms of usability and performance (i.e. querying and reasoning). Possible enhancements to reduce the complexity and the data size consist in (1) modularization of ifcOWL making it easier to use subsets of the entire ontology, and (2) rethinking the contents and structure of an ontology for AEC/FM to better fit in the semantic web scope and make its usage more efficient. The second approach can be enabled by the first one, since it would make it easier to replace some of the ifcOWL modules with new optimized ontologies for the AEC-FM industry. This paper focuses on the first approach presenting a method to automatically generate a modular ifcOWL ontology. The method aims at minimizing the dependencies between modules to better exploit the modularization. The results are compared with simpler and more straight-forward solutions.


title: AENEID: a generic lithography-friendly detailed router based on post-RET data learning and hotspot detection

abstract: In the era of deep sub-wavelength lithography for nanometer VLSI designs, manufacturability and yield issues are critical and need to be addressed during the key physical design implementation stage, in particular detailed routing. However, most existing studies for lithography-friendly routing suffer from either huge run-time due to the intensive lithographic computations involved, or severe loss of quality of results because of the inaccurate predictive models. In this paper, we propose AENEID - a fast, generic and high performance lithography-friendly detailed router for enhanced manufacturability. AENEID combines novel hotspot detection and routing path prediction techniques through modern data learning methods and applies them at the detailed routing stage to drive high fidelity lithography-friendly routing. Compared with existing litho-friendly routing works, AENEID demonstrates 26% to 66% (avg. 50%) of lithography hotspot reduction at the cost of only 18%-38% (avg. 30%) of run-time overhead.


title: Novel Antiwindup of a Current Regulator of a Surface-Mounted Permanent-Magnet Motor for Flux-Weakening Control

abstract: This paper describes a novel anti-windup method of current regulator of surface-mounted permanent-magnet synchronous motors for flux-weakening operation. It is implemented in the synchronous-frame PI current regulator with space vector PWM. The difference of q-axis voltage between the regulator output voltage and the saturated voltage is used for flux-weakening control by modifying d-axis current reference. With this method, the anti-windup control and the flux-weakening can be achieved simultaneously. It can be easily implemented in software, and no additional hardware or sensor is needed. Moreover it guarantees more output torque than the conventional anti-windup and flux-weakening method does by better utilizing dc link voltage. The effectiveness of this method is proved by computer simulations and experimental results.


Benchmarking Web Server Architectures: A Simulation Study on Micro Performance


title: Task Parallel Implementation of Belief Propagation in Factor Graphs

abstract: Factor graphs have been increasingly used as probabilistic graphical models. Belief propagation is a prominent algorithm for inference in factor graphs. Due to the high complexity of inference, parallel techniques for belief propagation are needed. In this paper, we explore task parallelism for belief propagation in an acyclic factor graph. Our approach consists of building a task dependency graph based on the input factor graph and then using a dynamic task scheduler to exploit task parallelism. We conducted experiments on a state-of-the-art multi-core system using a variety of acyclic factor graphs. The experimental results show the efficiency and scalability of the proposed approach with a 37x speedup on a 40-core system.


title: A GPU-based convolutional neural network approach for image classification

abstract: Deep learning obtains successful results in solving many machine learning problems. In this study, image classification process is performed by using Convolutional Neural Network (CNN) which is the most used architecture of deep learning. Image classification is used in a lot of basic field like medicine, education and security. Conditions that correct classification has vital importance may be especially in medicine field. Therefore, improved methods are needed in this issue. Although several algorithms for image classification have been developed over the years, they have not been used with the discovery of Convolutional Neural Networks. Convolutional Neural Networks provide better results than existing methods in the literature due to advantages such as processing by extracting hidden features, allowing parallel processing thanks to parallel structure, and real time operation. Furthermore, we use Convolutional Neural Networks in the proposed method. In this study, the image classification process is performed by using like a LeNet network model. The caffe library, which is often used for deep learning, is utilized. Our method is trained and tested with images of cats and dogs taken from the kaggle dataset. 10.000 tagged data is used for training and 5.000 unlabeled data is used for testing. Owing to Convolutional Neural Networks allow parallel processing, GPU technology has been used. In our method is used GPU technology and classification is evaluated with acceptable accuracy rate and speed performance.


title: The relationship of service failure severity, service recovery justice and perceived switching costs with customer loyalty in the context of e-tailing

abstract: Given that e-tailing service failure is inevitable, a better understanding of how service failure and recovery affect customer loyalty represents an important topic for academics and practitioners. This study explores the relationship of service failure severity, service recovery justice (i.e., interactional justice, procedural justice, and distributive justice), and perceived switching costs with customer loyalty; as well, the moderating relationship of service recovery justice and perceived switching costs on the link between service failure severity and customer loyalty in the context of e-tailing are investigated. Data collected from 221 useful respondents are tested against the research model using the partial least squares (PLS) approach. The results indicate that service failure severity, interactional justice, procedural justice and perceived switching costs have a significant relationship with customer loyalty, and that interactional justice can mitigate the negative relationship between service failure severity and customer loyalty. These findings provide several important theoretical and practical implications in terms of e-tailing service failure and recovery.


title: Classification of fruits using Probabilistic Neural Networks - Improvement using color features

abstract: This paper presents a novel approach for the development of an intelligent fruit sorting system using techniques from Digital Image Processing and Artificial Neural Networks. The aim is to develop a fast and effective classification method along with a target of 100% efficiency. Five fruits; i.e., apples, bananas, carrots, mangoes and oranges were analysed and seventeen features were extracted based on the fruits' morphological and colour characteristics. A regular digital camera was used to acquire the images, and all manipulations were performed in a MATLAB/SIMULINK environment. The results obtained were a significant improvement over a previous study.


title: Probabilistic models of expectation violation predict psychophysiological emotional responses to live concert music

abstract: We present the results of a study testing the often-theorized role of musical expectations in inducing listeners’ emotions in a live flute concert experiment with 50 participants. Using an audience response system developed for this purpose, we measured subjective experience and peripheral psychophysiological changes continuously. To confirm the existence of the link between expectation and emotion, we used a threefold approach. (1) On the basis of an information-theoretic cognitive model, melodic pitch expectations were predicted by analyzing the musical stimuli used (six pieces of solo flute music). (2) A continuous rating scale was used by half of the audience to measure their experience of unexpectedness toward the music heard. (3) Emotional reactions were measured using a multicomponent approach: subjective feeling (valence and arousal rated continuously by the other half of the audience members), expressive behavior (facial EMG), and peripheral arousal (the latter two being measured in all 50 participants). Results confirmed the predicted relationship between high-information-content musical events, the violation of musical expectations (in corresponding ratings), and emotional reactions (psychologically and physiologically). Musical structures leading to expectation reactions were manifested in emotional reactions at different emotion component levels (increases in subjective arousal and autonomic nervous system activations). These results emphasize the role of musical structure in emotion induction, leading to a further understanding of the frequently experienced emotional effects of music.


title: Homomorphic Secret Sharing: Optimizations and Applications

abstract: We continue the study of Homomorphic Secret Sharing (HSS), recently introduced by Boyle et al. (Crypto 2016, Eurocrypt 2017). A (2-party) HSS scheme splits an input x into shares (x0,x1) such that (1) each share computationally hides x, and (2) there exists an efficient homomorphic evaluation algorithm $\Eval$ such that for any function (or "program") from a given class it holds that Eval(x0,P)+Eval(x1,P)=P(x). Boyle et al. show how to construct an HSS scheme for branching programs, with an inverse polynomial error, using discrete-log type assumptions such as DDH. We make two types of contributions. Optimizations. We introduce new optimizations that speed up the previous optimized implementation of Boyle et al. by more than a factor of 30, significantly reduce the share size, and reduce the rate of leakage induced by selective failure. Applications. Our optimizations are motivated by the observation that there are natural application scenarios in which HSS is useful even when applied to simple computations on short inputs. We demonstrate the practical feasibility of our HSS implementation in the context of such applications.


title: Mining gene expression databases for association rules

abstract: Motivation: Global gene expression profiling, both at the transcript level and at the protein level, can be a valuable tool in the understanding of genes, biological networks, and cellular states. As larger and larger gene expression data sets become available, data mining techniques can be applied to identify patterns of interest in the data. Association rules, used widely in the area of market basket analysis, can be applied to the analysis of expression data as well. Association rules can reveal biologically relevant associations between different genes or between environmental effects and gene expression. An association rule has the form LHS⇒RHS, where LHS and RHS are disjoint sets of items, the RHS set being likely to occur whenever the LHS set occurs. Items in gene expression data can include genes that are highly expressed or repressed, as well as relevant facts describing the cellular environment of the genes (e.g. the diagnosis of a tumor sample from which a profile was obtained). Results: We demonstrate an algorithm for efficiently mining association rules from gene expression data, using the data set from Hughes et al. (Cell, 102, 109‐ 126, 2000) of 300 expression profiles for yeast. Using the algorithm, we find numerous rules in the data. A cursory analysis of some of these rules reveals numerous associations between certain genes, many of which make sense biologically, others suggesting new hypotheses that may warrant further investigation. In a data set derived from the yeast data set, but with the expression values for each transcript randomly shifted with respect to the experiments, no rules were found, indicating that most all of the rules mined from the actual data set are not likely to have occurred by chance. Availability: An implementation of the algorithm using Microsoft SQL Server with Access 2000 is available at http://dot.ped.med.umich.edu:2000/pub/assoc rules/ assoc rules.zip. Our results from mining the yeast data set are available at http://dot.ped.med.umich.edu: 2000/pub/assoc rules/yeast results.zip. ∗ To whom correspondence should be addressed.


title: Employee job attitudes and organizational characteristics as predictors of cyberloafing

abstract: Cyberloafing is the personal use of the Internet by employees while at work. The purpose of this study is to examine whether employee job attitudes, organizational characteristics, attitudes towards cyberloafing, and other non-Internet loafing behaviors serve as antecedents to cyberloafing behaviors. We hypothesize that the employee job attitudes of job involvement and intrinsic involvement are related to cyberloafing. In addition, we hypothesize that organizational characteristics including the perceived cyberloafing of one's coworkers and managerial support for internet usage are related to cyberloafing. We also hypothesize that attitudes towards cyberloafing and the extent to which employees participate in non-Internet loafing behaviors (e.g., talking with coworkers, running personal errands) will both be related to cyberloafing. One hundred and forty-three working professional from a variety of industries were surveyed regarding their Internet usage at work. As hypothesized, the employee job attitudes of job involvement and intrinsic involvement were negatively related to cyberloafing. Also as predicted, the organizational characteristics of the perceived cyberloafing of one's coworkers and managerial support for internet usage were positively related to cyberloafing. Finally, results showed that attitudes towards cyberloafing and participation in non-Internet loafing behaviors were positively related to cyberloafing. Implications for both organizations and employees are discussed.


title: Optical and wireless network convergence in 5G systems – an experimental approach

abstract: This paper provides a description of key results stemming from experiments conducted at the wireless/optical boundary. The main aim is to show the advantages of a converged optical/wireless network deployment, which uses very recent technologies like SDN, Virtualization, fog and cloud computing, and Radio-over fiber. The results are mainly taken out of an international collaborative research project called FUTEBOL (Federated Union of Telecommunications Research Facilities for an EU BrazilOpenLaboratory).


title: Quasi-Newton Methods for Real-Time Simulation of Hyperelastic Materials

abstract: We present a new method for real-time physics-based simulation supporting many different types of hyperelastic materials. Previous methods such as Position-Based or Projective Dynamics are fast but support only a limited selection of materials; even classical materials such as the Neo-Hookean elasticity are not supported. Recently, Xu et al. [2015] introduced new “spline-based materials” that can be easily controlled by artists to achieve desired animation effects. Simulation of these types of materials currently relies on Newton’s method, which is slow, even with only one iteration per timestep. In this article, we show that Projective Dynamics can be interpreted as a quasi-Newton method. This insight enables very efficient simulation of a large class of hyperelastic materials, including the Neo-Hookean, spline-based materials, and others. The quasi-Newton interpretation also allows us to leverage ideas from numerical optimization. In particular, we show that our solver can be further accelerated using L-BFGS updates (Limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm). Our final method is typically more than 10 times faster than one iteration of Newton’s method without compromising quality. In fact, our result is often more accurate than the result obtained with one iteration of Newton’s method. Our method is also easier to implement, implying reduced software development costs.


title: Procedurally generated virtual reality from 3D reconstructed physical space

abstract: We present a novel system for automatically generating immersive and interactive virtual reality (VR) environments using the real world as a template. The system captures indoor scenes in 3D, detects obstacles like furniture and walls, and maps walkable areas (WA) to enable real-walking in the generated virtual environment (VE). Depth data is additionally used for recognizing and tracking objects during the VR experience. The detected objects are paired with virtual counterparts to leverage the physicality of the real world for a tactile experience. Our approach is new, in that it allows a casual user to easily create virtual reality worlds in any indoor space of arbitrary size and shape without requiring specialized equipment or training. We demonstrate our approach through a fully working system implemented on the Google Project Tango tablet device.


title: Parallelizing Sequential Graph Computations

abstract: This paper presents GRAPE, a parallel system for graph computations. GRAPE differs from prior systems in its ability to parallelize existing sequential graph algorithms as a whole. Underlying GRAPE are a simple programming model and a principled approach, based on partial evaluation and incremental computation. We show that sequential graph algorithms can be "plugged into" GRAPE with minor changes, and get parallelized. As long as the sequential algorithms are correct, their GRAPE parallelization guarantees to terminate with correct answers under a monotonic condition. Moreover, we show that algorithms in MapReduce, BSP and PRAM can be optimally simulated on GRAPE. In addition to the ease of programming, we experimentally verify that GRAPE achieves comparable performance to the state-of-the-art graph systems, using real-life and synthetic graphs.


title: Clinical application of a new hyaluronic acid filler based on its rheological properties and the anatomical site of injection

abstract: Hyaluronic acid (HA) filler is the most commonly used filler for soft tissue augmentation. There are numerous commercially available HA fillers in the cosmetic market, and there are guidelines for each filler as determined by the manufacturing company. The successful use of injectable fillers requires an understanding of each option available so that the most appropriate form of hyaluronic acid may be selected for patients. The purpose of this study was to determine whether newly developed HA fillers are appropriate for forehead augmentation considering their rheological properties and the anatomical site of injection. The rheological properties of new HA fillers were assessed e.t.p.q. S100, S300, S500 (Zetema®). Comparing the rheological properties, the authors chose e.t.p.q. S300® for forehead augmentation. The filler was injected into the foreheads of 40 consecutive patients for esthetic purposes. e.t.p.q. S300® was determined to be an appropriate filler for the forehead. The injection procedure employed was considered to be easy and safe when applied to the preperiosteal layer using a cannula. None of the patients had complications such as vascular compromise, infections, granulomas, or migration. Understanding the physical properties of new fillers is necessary. As the preperiosteal layer of the forehead is an appropriate layer for the filler injection, physicians should consider injecting fillers with enough strength to withstand the shearing forces.


title: Computing Clusters of Correlation Connected objects

abstract: The detection of correlations between different features in a set of feature vectors is a very important data mining task because correlation indicates a dependency between the features or some association of cause and effect between them. This association can be arbitrarily complex, i.e. one or more features might be dependent from a combination of several other features. Well-known methods like the principal components analysis (PCA) can perfectly find correlations which are global, linear, not hidden in a set of noise vectors, and uniform, i.e. the same type of correlation is exhibited in all feature vectors. In many applications such as medical diagnosis, molecular biology, time sequences, or electronic commerce, however, correlations are not global since the dependency between features can be different in different subgroups of the set. In this paper, we propose a method called 4C (Computing Correlation Connected Clusters) to identify local subgroups of the data objects sharing a uniform but arbitrarily complex correlation. Our algorithm is based on a combination of PCA and density-based clustering (DBSCAN). Our method has a determinate result and is robust against noise. A broad comparative evaluation demonstrates the superior performance of 4C over competing methods such as DBSCAN, CLIQUE and ORCLUS.


title: A Joyful Ode to Automatic Orchestration

abstract: Most works in automatic music generation have addressed so far specific tasks. Such a reductionist approach has been extremely successful and some of these tasks have been solved once and for all. However, few works have addressed the issue of generating automatically fully fledged music material, of human-level quality. In this article, we report on a specific experiment in holistic music generation: the reorchestration of Beethoven’s Ode to Joy, the European anthem, in seven styles. These reorchestrations were produced with algorithms developed in the Flow Machines project and within a short time frame. We stress the benefits of having had such a challenging and unifying goal, and the interesting problems and challenges it raised along the way.


Anxiety as Fear: A Behavioral Approach to One Emotion


title: Modelling Plastic Distortion in Fingerprint Images

abstract: This paper introduces a plastic distortion model to cope with the nonlinear deformations characterizing fingerprint images taken with online acquisition sensors. The problem has a great impact on several practical applications, ranging from the design of robust fingerprint matching algorithms to the generation of synthetic fingerprint images. The experimentation on real data validates the model and demonstrates its efficacy in registering minutiae data from highly distorted fingerprint samples.


title: Annual Research Review: Parenting and children’s brain development: the end of the beginning

abstract: After questioning the practical significance of evidence that parenting influences brain development – while highlighting the scientific importance of such work for understanding how family experience shapes human development – this paper reviews evidence suggesting that brain structure and function are ‘chiselled’ by parenting. Although the generalisability of most findings is limited due to a disproportionate, but understandable focus on clinical samples (e.g., maltreated children with post-traumatic stress disorder (PTSD)) and causal inferences are difficult to draw because of the observational nature of most of the evidence, it is noteworthy that some work with community samples and very new experimental work (e.g., parent training) suggests that tentative conclusions regarding effects of parenting on the developing brain may well be substantiated in future research. Such efforts should focus on parenting in the normal range, experimental manipulations of parenting, differential susceptibility to parenting effects and pathway models linking parenting to brain development and, thereby, to behavioural development. Research on parenting and children’s brain development may be regarded as at ‘the end of the beginning’.


title: Downloading textual hidden web content through keyword queries

abstract: An ever-increasing amount of information on the Web today is available only through search interfaces: the users have to type in a set of keywords in a search form in order to access the pages from certain Web sites. These pages are often referred to as the Hidden Web or the Deep Web. Since there are no static links to the Hidden Web pages, search engines cannot discover and index such pages and thus do not return them in the results. However, according to recent studies, the content provided by many Hidden Web sites is often of very high quality and can be extremely valuable to many users.In this paper, we study how we can build an effective Hidden Web crawler that can autonomously discover and download pages from the Hidden Web. Since the only "entry point" to a Hidden Web site is a query interface, the main challenge that a Hidden Web crawler has to face is how to automatically generate meaningful queries to issue to the site. Here, we provide a theoretical framework to investigate the query generation problem for the Hidden Web and we propose effective policies for generating queries automatically. Our policies proceed iteratively, issuing a different query in every iteration. We experimentally evaluate the effectiveness of these policies on 4 real Hidden Web sites and our results are very promising. For instance, in one experiment, one of our policies downloaded more than 90% of a Hidden Web site (that contains 14 million documents) after issuing fewer than 100 queries.


title: Scanpath clustering and aggregation

abstract: Eye tracking specialists often need to understand and represent aggregate scanning strategies, but methods to identify similar scanpaths and aggregate multiple scanpaths have been elusive. A new method is proposed here to identify scanning strategies by aggregating groups of matching scanpaths automatically. A dataset of scanpaths is first converted to sequences of viewed area names, which are then represented in a dotplot. Matching sequences in the dotplot are found with linear regressions, and then used to cluster the scanpaths hierarchically. Aggregate scanning strategies are generated for each cluster and presented in an interactive dendrogram. While the clustering and aggregation method works in a bottom-up fashion, based on pair-wise matches, a top-down extension is also described, in which a scanning strategy is first input by cursor gesture, then matched against the dataset. The ability to discover both bottom-up and top-down strategy matches provides a powerful tool for scanpath analysis, and for understanding group scanning strategies.


title: Employing traditional machine learning algorithms for big data streams analysis: the case of object trajectory prediction

abstract: A real-time trajectory prediction application is proposed.Performance vs. accuracy between big-data tailored solutions and traditional methods.Ignoring time element in time-series data can help in generalization of model. In this paper, we model the trajectory of sea vessels and provide a service that predicts in near-real time the position of any given vessel in 4, 10, 20 and 40 time intervals. We explore the necessary tradeoffs between accuracy, performance and resource utilization is explored given the large volume and update rates of input data. We start with building models based on well-established machine learning algorithms using static datasets and multi-scan training approaches and identify the best candidate to be used in implementing a single-pass predictive approach, under real-time constraints. The results are measured in terms of accuracy and performance and are compared against the baseline kinematic equations. Results show that it is possible to efficiently model the trajectory of multiple vessels using a single model, which is trained and evaluated using an adequately large, static dataset, thus achieving a significant gain in terms of resource usage while not compromising accuracy.


Passive and Inverse Constructions


title: Using Deep Convolutional Networks for Gesture Recognition in American Sign Language

abstract: In the realm of multimodal communication, sign language is, and continues to be, one of the most understudied areas. In line with recent advances in the field of deep learning, there are far reaching implications and applications that neural networks can have for sign language interpretation. In this paper, we present a method for using deep convolutional networks to classify images of both the the letters and digits in American Sign Language.


The Diffusion of Innovations in Social Networks ∗


title: Bottom-Gate Zinc Oxide Thin-Film Transistors (ZnO TFTs) for AM-LCDs

abstract: In this paper, high-performance bottom-gate thin-film transistors (TFTs) with transparent zinc oxide (ZnO) channels have been developed. The ZnO film for active channels was deposited by RF magnetron sputtering. The crystallinity of the ZnO film drastically improved when it was deposited on a doublelayer SiOx/SiNx gate insulator. In order to achieve a ZnO TFT back-plane for liquid-crystal display (LCD) with the required pattern accuracy, dry etching of the ZnO film in an Ar and CH4 chemistry has been developed. The etching rate and tapered profile of the ZnO film could be controlled by the Ar content in the etching gases of Ar and CH4. The saturation mobility (musat) of the ZnO TFT strongly depended on a gate voltage. A musat of 5.2 & cm2 .(V .s)-1 at VGS = 40 V and VDS = 10 V, and an on/off-current ratio of 2.7 x 107 were obtained. A drain-current uniformity of plusmn7% was achieved within a radius of 20 mm from the substrate center. A 1.46 -in diagonal LCD with 61 600 pixels has been driven by the ZnO-TFT back-plane. A moving picture image was available on fabricated LCD driven by the ZnO TFTs.


title: Hardware-In-the-Loop Simulation of Electric Vehicle Powertrain System

abstract: This paper presents a design procedure of Hardware- In-the-Loop (HIL) for Electric Vehicle powertrain system modeling and simulation. First, simulation is performed in MATLAB/Simulink platform. Then, an Electric Vehicle Powertrain Hardware-In-the-Loop system including BMS (Battery Manage System), MCU (Motor Control Unit) and VMS (vehicle management system) is built based on the dSPACE with MATLAB/Simulink, along with the RTW toolbox (real time workshop). Through this system, the Electric Vehicle Control strategy is simulated and validated, and the results of offline simulation & real time simulation with laboratory prototype are presented.


Intelligent Building Using Hybrid Inference with Building Automation System to Improve Energy Efficiency


title: The Arcade Learning Environment: An Evaluation Platform for General Agents

abstract: In this extended abstract we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by presenting a benchmark set of domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. We conclude with a brief update on the latest ALE developments. All of the software, including the benchmark agents, is publicly available.


title: A propositional theory of recognition memory

abstract: This paper modifies the Anderson and Bower (1972) theory of recognition memory for words. A propositional representation is outlined for the contextual information underlying word recognition. Logical arguments are offered for preferring this representation over the undifferentiated associative representation used earlier. The propositional representation is used to interpret effects of verbal context upon recognition memory. The implications of these context effects are considered for two-process models of recall and recognition.


title: Experimental study on velocity and flow patterns in the capillary fringe

abstract: A small-scale glass tank was used to investigate the flow patterns and velocity distribution in the capillary fringe. The experiments were done by injecting a fluorescent dye tracer (Uranin) at different depths of the capillary fringe. The results indicate that the capillary fringe can vertically be divided in two transport zones, an upper stagnant zone and a lower moving zone. The flow pattern in the middle part of the capillary fringe is almost parallel to the groundwater table slope. While there is an upward movement near the inflow, a downward movement was observed at the outflow boundary. Regarding the experimental results, the velocity increased almost linearly towards the groundwater table. Considering the same thickness for saturated and capillary zone, 35% of the total flow is in the capillary zone. The results of the study confirmed that the capillary fringe can play an important role in transport of infiltrated solutes from the surface.


title: Minimum snap trajectory generation and control for quadrotors

abstract: We address the controller design and the trajectory generation for a quadrotor maneuvering in three dimensions in a tightly constrained setting typical of indoor environments. In such settings, it is necessary to allow for significant excursions of the attitude from the hover state and small angle approximations cannot be justified for the roll and pitch. We develop an algorithm that enables the real-time generation of optimal trajectories through a sequence of 3-D positions and yaw angles, while ensuring safe passage through specified corridors and satisfying constraints on velocities, accelerations and inputs. A nonlinear controller ensures the faithful tracking of these trajectories. Experimental results illustrate the application of the method to fast motion (5–10 body lengths/second) in three-dimensional slalom courses.


title: DevOps: Making It Easy to Do the Right Thing

abstract: Wotif Group used DevOps principles to recover from the downward spiral of manual release activity that many IT departments face. Its approach involved the idea of "making it easy to do the right thing." By defining the right thing (deployment standards) for development and operations teams and making it easy to adopt, Wotif drastically improved the average release cycle time. This article is part of a theme issue on DevOps.


title: Public Intimacy: Disclosure Interpretation and Social Judgments on Facebook

abstract: This research examines how sociotechnical affordances shape interpretation of disclosure and social judgments on social networking sites. Drawing on the disclosure personalism framework, Study 1 revealed that information unavailability and relational basis underlay personalistic judgments about Facebook disclosures: Perceivers inferred greater message and relational intimacy from disclosures made privately than from those made publicly. Study 2 revealed that perceivers judged intimate disclosures shared publicly as less appropriate than intimate disclosures shared privately, and that perceived disclosure appropriateness accounted for the effects of public versus private contexts on reduced liking for a discloser. Taken together, the results show how sociotechnical affordances shape perceptions of disclosure and relationships, which has implications for understanding relational development and maintenance on SNS.


title: Automatic Retinal Minimum Distance Band (MDB)Computation from SD-OCT Images

abstract: In this paper, we have proposed a novel automatic method to determine the Minimum Distance Band (MDB) from the retinal SD-OCT image. MDB is the minimum distance between the retinal pigment epithelium (RPE) layer and the Optic Nerve Head (ONH) surface. It is an effective biomarker for early detection of glaucoma. Our proposed method is the first automatic method for computing the Minimum Distance Band (MDB). This method uses the approximate location of three benchmark reference (TBMR) layers of the retina that help to reduce the search space. This approach is highly accurate in detecting the boundary layers even in the presence of pathology. The terminal points of the RPE serve as the ONH region. The accuracies of ONH and MDB are tested against 13 manually graded optic disc centred SD-OCT volumes (11 B-scan per volume) of Glaucoma patients as a gold standard. Our method is very effective with mean and standard deviation of the error of the ONH width and MDB of 5.36±5.55 and 14.96±17.75 respectively. The precision, recall and F1 score for the ONH region are 94.90, 98.14 and 96.49 respectively.


title: Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications

abstract: This article presents a review of the methods used in recognition and analysis of the human gait from three different approaches: image processing, floor sensors and sensors placed on the body. Progress in new technologies has led the development of a series of devices and techniques which allow for objective evaluation, making measurements more efficient and effective and providing specialists with reliable information. Firstly, an introduction of the key gait parameters and semi-subjective methods is presented. Secondly, technologies and studies on the different objective methods are reviewed. Finally, based on the latest research, the characteristics of each method are discussed. 40% of the reviewed articles published in late 2012 and 2013 were related to non-wearable systems, 37.5% presented inertial sensor-based systems, and the remaining 22.5% corresponded to other wearable systems. An increasing number of research works demonstrate that various parameters such as precision, conformability, usability or transportability have indicated that the portable systems based on body sensors are promising methods for gait analysis.


title: Indexing and Query Processing Techniques in Spatio-Temporal Data

abstract: Indexing and query processing is an emerging research field in spatio - temporal data. Most of the real-time applications such as location based services, fleet management, traffic prediction and radio frequency identification and sensor networks are based on spatio-temporal indexing and query processing. All the indexing and query processing applications is any one of the forms, such as spatio index access and supporting queries or spatio-temporal indexing method and support query or temporal dimension, while in spatial data it is considered as the second priority. In this paper, give the survey of the various uncertain indexing and query processing techniques. Most of the existing survey works on spatio-temporal are based on indexing methods and query processing, but presented separately. Both the indexing and querying are related, hence state - of - art of both the indexing and query processing techniques are considered together. This paper gives the details of spatio-temporal data classification, various types of indexing methods, query processing, application areas and research direction of spatio-temporal indexing and query processing.


title: Emigration of Adult Children of Older Academics in Southwestern Nigeria and the Future of Child-Parent Support Culture

abstract: The evolving reality of many adult children emigration from Nigeria may create an ‘empty-nests’ for many elders in the future; especially among the older academics. With a qualitative approach, this study conducted 60 in-depth interviews to: establish the proportion of older academics in the Southwestern Nigeria in relationship to their children living abroad; assess the general effects of such journeys on the emigrants and their parents, and finally, to seek ways of monitoring emigration abroad to deter an ‘empty-nest’ situation. The interviews revealed that less than one-fifth of the older academics had children abroad, a result of stronger “pull factors” from countries abroad than the “retention factors” in Nigeria, and jealousy among the children.


title: In-line etching process control using dynamic scatterometry

abstract: In-line process control in microelectronics manufacturing requires real-time and non-invasive monitoring techniques. Among the different metrology techniques, scatterometry, based on the analysis of ellipsometric signatures of the light scattered by a patterned structures, is well adapted. Traditionally, the problem of defining the shape and computing the signature is dealt with modal methods and is called direct problem. On the opposite, the inverse problem allows to find the grating shape thanks to an experimental signature acquisition, and can not be solved as easily. Different classes of algorithms have been introduced (evolutionary, simplex, etc.) to address this problem, but the method of library searching seems to be the most attractive technique for industry. This technique has many advantages that will be presented in this article. However the main limitation in real-time context comes from the short data acquisition time for different wavelengths. Indeed, the lack of data leads to the method failure and several database patterns can match the experimental data. In this article, a technique for real time reconstruction of grating shape variation using dynamic scatterometry is presented. The different tools to realize this reconstruction, such as Modal Method by Fourier Expansion, regularization technique and specific software and hardware architectures are then introduced. Results issued from dynamic experiments will finally illustrate this paper.


title: SCUT: Multi-class imbalanced data classification using SMOTE and cluster-based undersampling

abstract: Class imbalance is a crucial problem in machine learning and occurs in many domains. Specifically, the two-class problem has received interest from researchers in recent years, leading to solutions for oil spill detection, tumour discovery and fraudulent credit card detection, amongst others. However, handling class imbalance in datasets that contains multiple classes, with varying degree of imbalance, has received limited attention. In such a multi-class imbalanced dataset, the classification model tends to favour the majority classes and incorrectly classify instances from the minority classes as belonging to the majority classes, leading to poor predictive accuracies. Further, there is a need to handle both the imbalances between classes as well as address the selection of examples within a class (i.e. the so-called within class imbalance). In this paper, we propose the SCUT hybrid sampling method, which is used to balance the number of training examples in such a multi-class setting. Our SCUT approach oversamples minority class examples through the generation of synthetic examples and employs cluster analysis in order to undersample majority classes. In addition, it handles both within-class and between-class imbalance. Our experimental results against a number of multi-class problems show that, when the SCUT method is used for pre-processing the data before classification, we obtain highly accurate models that compare favourably to the state-of-the-art.


title: A study on efficient compression of multi-focus images for dense Light-Field reconstruction

abstract: Light-Field enables us to observe scenes from free viewpoints. However, it generally consists of 4-D enormous data, that are not suitable for storing or transmitting without effective compression. 4-D Light-Field is very redundant because essentially it includes just 3-D scene information. Actually, although robust 3-D scene estimation such as depth recovery from Light-Field is not so easy, a method of reconstructing Light-Field directly from 3-D information composed of multi-focus images without any scene estimation is successfully derived. Previously, based on the method, Light-Field compression via synthesized multi-focus images as effective representation of 3-D scenes was proposed. In this paper, we study efficient compression of multi-focus images synthesized from dense Light-Field by using DWT instead of DCT-based compression in order to suppress degradation such as block noise. Quality of reconstructed Light-Field is evaluated by PSNR and SSIM for analyzing characteristics of residuals. Experimental results reveal that our method is much superior to Light-Field compression using disparity-compensation at low bit-rate.


title: Transparent object detection and reconstruction on a mobile platform

abstract: In this paper we propose a novel approach to detect and reconstruct transparent objects. This approach makes use of the fact that many transparent objects, especially the ones consisting of usual glass, absorb light in certain wavelengths [1]. Given a controlled illumination, this absorption is measurable in the intensity response by comparison to the background. We show the usage of a standard infrared emitter and the intensity sensor of a time of flight (ToF) camera to reconstruct the structure given we have a second view point. The structure can not be measured by the usual 3D measurements of the ToF camera. We take advantage of this fact by deriving this internal sensory contradiction from two ToF images and reconstruct an approximated surface of the original transparent object. Therefor we are using a perspectively invariant matching in the intensity channels from the first to the second view of initially acquired candidates. For each matched pixel in the first view a 3D movement can be predicted given their original 3D measurement and the known distance to the second camera position. If their line of sight did not pass a transparent object or suffered any other major defect, this prediction will highly correspond to the actual measured 3D points of the second view. Otherwise, if a detectable error occurs, we approximate a more exact point to point matching and reconstruct the original shape by triangulating the points in the stereo setup. We tested our approach using a mobile platform with one Swissranger SR4k. As this platform is mobile, we were able to create a stereo setup by moving it. Our results show a detection of transparent objects on tables while simultaneously identifying opaque objects that also existed in the test setup. The viability of our results is demonstrated by a successful automated manipulation of the respective transparent object.


Urdu Summary Corpus.


title: Some challenges and grand challenges for computational intelligence

abstract: When the terms “intelligence” or “intelligent” are used by scientists, they are referring to a large collection of human cognitive behaviors— people thinking. When life scientists speak of the intelligence of animals, they are asking us to call to mind a set of human behaviors that they are asserting the animals are (or are not) capable of. When computer scientists speak of artificial intelligence, machine intelligence, intelligent agents, or (as I chose to do in the title of this essay) computational intelligence, we are also referring to that set of human behaviors. Although intelligence means people thinking, we might be able to replicate the same set of behaviors using computation. Indeed, one branch of modern cognitive psychology is based on the model that the human mind and brain are complex computational “engines,” that is, we ourselves are examples of computational intelligence. 2. Turing’s Vision and the Turing Test for Humanoid Behavior The idea, of course, is not new. It was discussed by Turing in the 1940s. In the play about Turing’s life, Breaking the Code [Whitemore 1987], Turing is shown visiting his old grammar school and delivering a talk to the boys, in which he offers a vision of the thinking computer. The memories of those of Turing’s colleagues of the 1940s who are still alive confirm that he spoke often of this vision. In 1950, he wrote of it, in a famous article [Turing 1950], in which he proposed a test (now called the Turing Test (TT)) for computational intelligence. In the test, a human judgment must be made concerning whether a set of observed behaviors is sufficiently similar to human behaviors that the same word—intelligent—can justifiably be used. The judgment is about behavior not mechanism. Computers are not like human brains, but if they perform the same acts and one performer (the human) is labeled intelligent, then the other must be labeled intelligent also. I have always liked the Turing Test because it gave a clear and tangible vision, was reasonably objective, and made concrete the tie to human behavior by using the unarticulated criteria of a human judge. Turing Award winner Jim Gray, who works in fields of Computer Science other than AI, appears to agree. His list of challenges for the future includes: “The Turing test: Win the imitation game 30% of the time.” Significantly, he adds: “Read and understand as well as a human. Think and write as well as a human,” [Gray 2003]. I will have more to say about necessary conditions for these human activities later. But there are problems with the Turing Test (TT). Human intelligence is very multidimensional. However, the judge must fuse all of these dimensions into a


title: Towards Robust Detection of Adversarial Infection Vectors: Lessons Learned in PDF Malware

abstract: Malware still constitutes a major threat in the cybersecurity landscape, also due to the widespread use of infection vectors such as documents and other media formats. These infection vectors hide embedded malicious code to the victim users, thus facilitating the use of social engineering techniques to infect their machines. In the last decade, machine-learning algorithms provided an effective defense against such threats, being able to detect malware embedded in various infection vectors. However, the existence of an arms race in an adversarial setting like that of malware detection has recently questioned their appropriateness for this task. In this work, we focus on malware embedded in PDF files, as a representative case of how such an arms race can evolve. We first provide a comprehensive taxonomy of PDF malware attacks, and of the various learning-based detection systems that have been proposed to detect them. Then, we discuss more sophisticated attack algorithms that craft evasive PDF malware oriented to bypass such systems. We describe state-of-the-art mitigation techniques, highlighting that designing robust machine-learning algorithms remains a challenging open problem. We conclude the paper by providing a set of guidelines for designing more secure systems against the threat of adversarial malicious PDF files.


title: Extension of the Hansen–Woodyard Condition for Endfire Leaky-Wave Antennas

abstract: The Hansen-Woodyard condition is modified to include attenuation, making it applicable to leaky-wave antennas. The optimum phase constants are found for two cases, one based on the array factor only and the other including the element pattern. The optimum radiation efficiency for maximizing the endfire gain is also found in both cases. The effects of material loss on the optimization of endfire gain is also discussed.


title: Human luminance pattern-vision mechanisms: masking experiments require a new model.

abstract: A widely used model of simultaneous luminance pattern masking is based on mechanisms that sum inputs linearly and produce a response that is an S-shaped function of that sum. This model makes two predictions about masking: (1) Changing the masker spatial waveform will shift the threshold-versus-masker contrast function horizontally by a multiplicative constant. (2) Adding a second fixed-contrast masker will shift this function horizontally by an additive constant. Experimental tests do not support these predictions. The results can be explained by a new model that incorporates broadband divisive inhibition, consistent with physiology.


title: Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks

abstract: Purpose To develop a deep learning reconstruction approach to improve the reconstruction speed and quality of highly undersampled variable-density single-shot fast spin-echo imaging by using a variational network (VN), and to clinically evaluate the feasibility of this approach. Materials and Methods Imaging was performed with a 3.0-T imager with a coronal variable-density single-shot fast spin-echo sequence at 3.25 times acceleration in 157 patients referred for abdominal imaging (mean age, 11 years; range, 1-34 years; 72 males [mean age, 10 years; range, 1-26 years] and 85 females [mean age, 12 years; range, 1-34 years]) between March 2016 and April 2017. A VN was trained based on the parallel imaging and compressed sensing (PICS) reconstruction of 130 patients. The remaining 27 patients were used for evaluation. Image quality was evaluated in an independent blinded fashion by three radiologists in terms of overall image quality, perceived signal-to-noise ratio, image contrast, sharpness, and residual artifacts with scores ranging from 1 (nondiagnostic) to 5 (excellent). Wilcoxon tests were performed to test the hypothesis that there was no significant difference between VN and PICS. Results VN achieved improved perceived signal-to-noise ratio (P = .01) and improved sharpness (P < .001), with no difference in image contrast (P = .24) and residual artifacts (P = .07). In terms of overall image quality, VN performed better than did PICS (P = .02). Average reconstruction time ± standard deviation was 5.60 seconds ± 1.30 per section for PICS and 0.19 second ± 0.04 per section for VN. Conclusion Compared with the conventional parallel imaging and compressed sensing reconstruction (PICS), the variational network (VN) approach accelerates the reconstruction of variable-density single-shot fast spin-echo sequences and achieves improved overall image quality with higher perceived signal-to-noise ratio and sharpness. © RSNA, 2018 Online supplemental material is available for this article.


Virtual Team Training Model Transactive Memory Systems Virtual Team Training Model


title: The impossibility of"fairness": a generalized impossibility result for decisions

abstract: Various measures can be used to estimate bias or unfairness in a predictor. Previous work has already established that some of these measures are incompatible with each other. Here we show that, when groups differ in prevalence of the predicted event, several intuitive, reasonable measures of fairness (probability of positive prediction given occurrence or non-occurrence; probability of occurrence given prediction or non-prediction; and ratio of predictions over occurrences for each group) are all mutually exclusive: if one of them is equal among groups, the other two must differ. The only exceptions are for perfect, or trivial (always-positive or always-negative) predictors. As a consequence, any non-perfect, non-trivial predictor must necessarily be "unfair" under two out of three reasonable sets of criteria. This result readily generalizes to a wide range of well-known statistical quantities (sensitivity, specificity, false positive rate, precision, etc.), all of which can be divided into three mutually exclusive groups. Importantly, The results applies to all predictors, whether algorithmic or human. We conclude with possible ways to handle this effect when assessing and designing prediction methods.


title: The Role of Business Model Design in the Service Engineering Process: A Comparative Case Study in the Field of Cloud Computing to Join Service Engineering with Business Model Design

abstract: This paper outlines the increasing challenges and perspectives of service innovation in new business models and the relationship between service innovation and business development procedures such as service engineering and business model design concepts. It describes the main drivers of service innovation and identifies what makes service innovation so important today, consequences for (service) business modeling as well as the implications for an integrated development approach of service and business model innovation. A comparative analysis shows that in general Osterwalder's Business Model Design approach compliments the strategy-based service engineering process well in most areas. Using the toolbox of the Business Model Canvas complemented by specific methods and tools from the service engineering approach, new complex service systems can be developed in a systematic and holistic manner. Our findings from applying the theoretical results in a case study to developing a new business model for a cloud-based SaaS solution show that business modeling needs to be continuously cross-linked to the entire service engineering process to adapt to ever changing customer needs and vice versa.


title: A decision tree using CUDA GPUs

abstract: Classification is an important issue both in Machine Learning and Data Mining. Decision tree is one of the famous classification models. In the reality case, the dimension of data is high and the data size is huge. Building a decision in large data base cost much time in computation. It is a computationally expensive problem. GPU is a special design processor of graphic. The highly parallel features of graphic processing made today's GPU architecture. GPGPU means use GPU to solve non-graphic problems which need amounts of computation power. Since the high performance and capacity/price ratio, many researches use GPU to process lots computation. Compute Unified Device Architecture (CUDA) is a GPGPU solution provided by NVIDIA. This paper provides a new parallel decision tree algorithm base on CUDA. The algorithm parallel computes building phase of decision tree.


title: Pachinko allocation: DAG-structured mixture models of topic correlations

abstract: Latent Dirichlet allocation (LDA) and other related topic models are increasingly popular tools for summarization and manifold discovery in discrete data. However, LDA does not capture correlations between topics. In this paper, we introduce the pachinko allocation model (PAM), which captures arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). The leaves of the DAG represent individual words in the vocabulary, while each interior node represents a correlation among its children, which may be words or other interior nodes (topics). PAM provides a flexible alternative to recent work by Blei and Lafferty (2006), which captures correlations only between pairs of topics. Using text data from newsgroups, historic NIPS proceedings and other research paper corpora, we show improved performance of PAM in document classification, likelihood of held-out data, the ability to support finer-grained topics, and topical keyword coherence.


The Kernel-Adatron Algorithm: a Fast and Simple Learning Procedure for Support Vector Machines


title: Simultaneous Switching Noise in On-Chip CMOS Power Distribution Networks

abstract: Simultaneous switching noise (SSN) has become an important issue in the design of the internal on-chip power distribution networks in current very large scale integration/ultra large scale integration (VLSI/ULSI) circuits. An inductive model is used to characterize the power supply rails when a transient current is generated by simultaneously switching the on-chip registers and logic gates in a synchronous CMOS VLSI/ULSI circuit. An analytical expression characterizing the SSN voltage is presented here based on a lumped inductive-resistive-capacitive RLC model. The peak value of the SSN voltage based on this analytical expression is within 10% as compared to SPICE simulations. Design constraints at both the circuit and layout levels are also discussed based on minimizing the effects of the peak value of the SSN voltage.


title: Semantic priming and retrieval from lexical memory: Roles of inhibitionless spreading activation and limited-capacity attention.

abstract: Prior to each visually presented target letter string in a speeded word-nonword classification task, either BIRD, BODY, BUILDING, or xxx appeared as a priming event. When the target was a word, it was (a) a name of a type of bird on most BiRD-prime trials; (b) a name of part of a building on most BODY-prime trials; (c) a name of a part of the body on most BUiLDiNG-prime trials; (d) a name of a type of bird, part of a building, or part of the body equally often on xxx-prime trials. Thus, on BiRD-prime trials the subject expected the word target to be chosen from the same category as the category represented by the word prime itself (Nonshift), whereas on BODY-prime and BuiLDiNG-prime trials the subject's attention was to be shifted because he or she expected the word target to be chosen from a category other than the category represented by the word prime itself (Shift). The word target was an exemplar of either the category the subject expected (Expected) or a category the subject did not expect (Unexpected) and was either semantically related (Related) or semantically unrelated (Unrelated) to the word prime. Thus, there were five different types of word-prime-word-target trials: (a) BiRD-robin (Condition Nonshift-Expected-Related) ; (b) BiRD-arm (Condition Nonshift-Unexpected-Unrelated) ; (c) BODY-door (Condition Shift-Expected-Unrelated) ; (d) BODY-sparrow (Condition Shift-Unexpected-Unrelated); (e) BODYheart (Condition Shift-Unexpected-Related). The stimulus onset asynchrony (SOA) between the prime and the target letter string varied between 250 and 2,000 msec. At the 2,000-msec SOA, reaction times (RTs) on BiRD-robin type trials were faster than RTs on xxx-prime trials (a facilitation effect), whereas RTs on BIRDarm type trials were slower than RTs on xxx-prime trials (an inhibition effect). As SOA decreased, the facilitation effect on BiRD-robin trials remained constant, but the inhibition effect on BiRu-arm trials decreased until, at the 250-msec SOA, there was no inhibition. For the Shift conditions at the 2,000-msec SOA, facilitation was obtained on BODY-door type trials and inhibition was obtained on BODY-sparrow type trials. These two effects decreased in magnitude as the SOA decreased until, at the 250-msec SOA, there was no facilitation or inhibition. On BODY-heart type trials, there was an inhibition effect at the 2,000 msec SOA, which decreased as the SOA decreased until, at the 250-msec SOA, it became a facilitation effect. For the nonword targets, the facilitatory effects of the word primes decreased as SOA decreased. These results were regarded as supporting the theory of Posner and Snyder that postulates two distinct components of attention: a fast automatic inhibitionless spreading-activation process and a slow limited-capacity consciousattention mechanism.


title: A tail dependence-based dissimilarity measure for financial time series clustering

abstract: In this paper we propose a clustering procedure aimed at grouping time series with an association between extremely low values, measured by the lower tail dependence coefficient. Firstly, we estimate the coefficient using an Archimedean copula function. Then, we propose a dissimilarity measure based on tail dependence coefficients and a two-step procedure to be used with clustering algorithms which require that the objects we want to cluster have a geometric interpretation. We show how the results of the clustering applied to financial returns could be used to construct defensive portfolios reducing the effect of a simultaneous financial crisis.


title: Communicating hydrocephalus after endovascular coiling of unruptured aneurysms: Report of 2 cases

abstract: ✓ To the authors' knowledge, only 1 case of communicating hydrocephalus after endovascular coiling of unruptured brain aneurysms has been reported previously. Here, they report on 2 such cases of delayed communicating hydrocephalus after treatment with hydrogel-coated coils and offer the first histopathological evidence of foreign material, presumably related to the coils, as the cause of hydrocephalus.


Evolutionary computation and evolutionary deep learning for image analysis, signal processing and pattern recognition


Deep View-Aware Metric Learning for Person Re-Identification


title: Object Detection With Deep Learning: A Review

abstract: Due to object detection’s close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy, and optimization function. In this paper, we provide a review of deep learning-based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely, the convolutional neural network. Then, we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection, and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network-based learning systems.


title: Multi-task learning of dish detection and calorie estimation

abstract: In recent years, a rise in healthy eating has led to various food management applications, which have image recognition to automatically record meals. However, most image recognition functions in existing applications are not directly useful for multiple-dish food photos and cannot automatically estimate food calories. Meanwhile, methodologies on image recognition have advanced greatly because of the advent of Convolutional Neural Network, which has improved accuracies of various kinds of image recognition tasks, such as classification and object detection. Therefore, we propose CNN-based food calorie estimation for multiple-dish food photos. Our method estimates food calories while simultaneously detecting dishes by multi-task learning of food calorie estimation and food dish detection with a single CNN. It is expected to achieve high speed and save memory by simultaneous estimation in a single network. Currently, there is no dataset of multiple-dish food photos annotated with both bounding boxes and food calories, so in this work, we use two types of datasets alternately for training a single CNN. For the two types of datasets, we use multiple-dish food photos with bounding-boxes attached and single-dish food photos with food calories. Our results show that our multi-task method achieved higher speed and a smaller network size than a sequential model of food detection and food calorie estimation.


title: A Human-in-the-Loop System for Sound Event Detection and Annotation

abstract: Labeling of audio events is essential for many tasks. However, finding sound events and labeling them within a long audio file is tedious and time-consuming. In cases where there is very little labeled data (e.g., a single labeled example), it is often not feasible to train an automatic labeler because many techniques (e.g., deep learning) require a large number of human-labeled training examples. Also, fully automated labeling may not show sufficient agreement with human labeling for many uses. To solve this issue, we present a human-in-the-loop sound labeling system that helps a user quickly label target sound events in a long audio. It lets a user reduce the time required to label a long audio file (e.g., 20 hours) containing target sounds that are sparsely distributed throughout the recording (10% or less of the audio contains the target) when there are too few labeled examples (e.g., one) to train a state-of-the-art machine audio labeling system. To evaluate the effectiveness of our tool, we performed a human-subject study. The results show that it helped participants label target sound events twice as fast as labeling them manually. In addition to measuring the overall performance of the proposed system, we also measure interaction overhead and machine accuracy, which are two key factors that determine the overall performance. The analysis shows that an ideal interface that does not have interaction overhead at all could speed labeling by as much as a factor of four.


title: Stop Purpose Classification from GPS Data of Commercial Vehicle Fleets

abstract: Extracting stop purpose information from raw GPS data is a crucial task in most location-aware applications. With the continuous growth of GPS data collected from mobile devices, this task is becoming more and more interesting; a lot of recent research has focused on pedestrians (mobile phones) data, while the commercial vehicles sector is almost unexplored. In this paper we target the problem of stop identification and classification from vehicle GPS data, using a large and heterogeneous dataset of commercial fleets from diverse industries. Our aim is to classify stops by purpose in two categories: work related and non-work related. Our dataset consists of more than 700k stops, 160k of which are work stops. For each stop, we compute a set of 100 different features, which can be grouped in 4 main categories: stop-wise features, points of interest features, stop cluster features, and sequential features. By choosing Random Forests as classification model, we are able to assess the relative importance of each of the features in the four sets. Experimental results show that our method significantly outperforms the state of the art models for stop purpose classification in the context of commercial vehicles. The feature ranking highlights the importance, for classifying a stop, of both its duration and the duration of other stops in the same location.


title: Microstrip Patch Antenna Array at 5.8 GHz for Point to Point Communication

abstract: This paper described the design of microstrip patch array antenna with operating frequency at 5.8GHz for point to point communication. The array of four microstrip rectangular patch antennas with inset feed based on quarter-wave impedance matching technique were designed, simulated, fabricated and measured with the aid of microwave office software. The simulation and measurement result met the IEEE 802.11a standard and able to operate in upper UNII band for point to point communication. The 4times4 array has a return loss of -30.42 dB with 15% bandwidth. The gain obtained from simulation is 16 dB with 9deg half power beamwidth (HPBW)


title: Mechanism of decreased acetaminophen glucuronidation in the fasted rat.

abstract: Abstract The mechanism by which an acute fast decreases the glucuronidation of hepatotoxic doses of acetaminophen in the rat was examined. Fasting did not depress the level of the enzyme, glucuronyl transferase, or the basal level of the co-substrate, UDP-glucuronic acid (UDPGA). Administration of a hepatotoxic dose of acetaminophen rapidly depleted UDPGA levels in both fed and fasted rats to the same nadir. Fed and fasted rats differed in that the rate of repletion of UDPGA levels was markedly slower in fasted rats. The total hepatic levels of UDP-glucose dehydrogenase and its cofactor, NAD + , were not decreased by fasting. In fasted rats, hepatic levels of the UDPGA precursor, UDP-glucose, were approximately 60% those of fed rats both before and after a hepatotoxic dose of acetaminophen. In fed rats, acetaminophen induced a marked depletion of hepatic glycogen levels and a dramatic increase in blood glucose levels. Acetaminophen induced a similar marked increase in blood glucose levels in fasted rats in spite of the fact that they lacked hepatic glycogen. It is concluded that the fastinginduced decrease in the glucuronidation of hepatotoxic doses of acetaminophen results from decreased production of UDPGA. The decreased synthetic capacity for UDPGA does not appear to be due to the inability of the liver to produce glucose units perse , but rather to the fasting-induced altered activities of the enzymes of carbohydrate metabolism which, in turn, alter the fate of glucose-6-phosphate derived from gluconeogenesis.


Automatic vandalism detection in Wikipedia: Towards a machine learning approach


title: Specification and design of an industrial manufacturing middleware

abstract: The European innovation project PERFoRM (Production harmonizEd Reconfiguration of Flexible Robots and Machinery) is aiming for a harmonized integration of research results in the area of flexible and reconfigurable manufacturing systems. Based on the cyber-physical system (CPS) paradigm, existing technologies and concepts are researched and integrated in an architecture which is enabling the application of these new technologies in real industrial environments. To implement such a flexible cyber-physical system, one of the core requirements for each involved component is a harmonized communication, which enables the capability to collaborate with each other in an intelligent way. But especially when integrating multiple already existing production components into such a cyber-physical system, one of the major issues is to deal with the various communication protocols and data representations coming with each individual cyber-physical component. To tackle this issue, the solution foreseen within PERFoRM's architecture is to use an integration platform, the PERFoRM Industrial Manufacturing Middleware, to enable all connected components to interact with each other through the Middleware and without having to implement new interfaces for each. This paper describes the basic requirements of such a Middleware and how it fits into the PERFoRM architecture and gives an overview about the internal design and functionality.


title: Antenna array system for UWB-monopulse-radar

abstract: This paper describes an antenna array configuration for the implementation in a UWB monopulse radar. The measurement results of the gain in the sum and difference mode are presented. Next the transformation of the monopulse technique into the time domain by the evaluation of the impulse response is shown. A look-up table with very high dynamic of over 25 dB and flat characteristic is obtained. The unambiguous range of sensing is approx. 40° in the angular direction. This novel combination of UWB technology and the monopulse radar principle allows for very precise sensing, where UWB assures high precision in the range direction and monopulse principle in the angular direction.


title: Epileptogenic Side Effects of Psychotropic Drugs: Practical Recommendations

abstract: Patient- and drug-related factors influence the inherent epileptogenic properties of psychotropic drugs. This article suggests measures to deal with these properties prophylactically and therapeutically in epileptic and nonepileptic psychiatric patients. The appropriate use of psychotropic drugs is emphasized in light of their epileptogenic potential. (JAMA244:1460-1463, 1980)


title: Game-theory-based batch identification of invalid signatures in wireless mobile networks

abstract: Digital signature has been widely employed in wireless mobile networks to ensure the authenticity of messages and identity of nodes. A paramount concern in signature verification is reducing the verification delay to ensure the network QoS. To address this issue, researchers have proposed the batch cryptography technology. However, most of the existing works focus on designing batch verification algorithms without sufficiently considering the impact of invalid signatures. The performance of batch verification could dramatically drop, if there are verification failures caused by invalid signatures. In this paper, we propose a Game-theory-based Batch Identification Model (GBIM) for wireless mobile networks, enabling nodes to find invalid signatures with the optimal delay under heterogeneous and dynamic attack scenarios. Specifically, we design an incomplete information game model between a verifier and its attackers, and prove the existence of Nash Equilibrium, to select the dominant algorithm for identifying invalid signatures. Moreover, we propose an auto-match protocol to optimize the identification algorithm selection, when the attack strategies can be estimated based on history information. Comprehensive simulation results demonstrate that GBIM can identify invalid signatures more efficiently than existing algorithms.


title: Ontology-based extraction and structuring of information from data-rich unstructured documents

abstract: We present a new approach to extracting information from unstructured documents based on an application ontology that describes a domain of interest. Starting with such an ontology, we formulate rules to extract constants and context keywords from unstructured documents. For each unstructured document of interest, we extract its constants and keywords and apply a recognizer to organize extracted constants as attribute values of tuples in a generated database schema. To make our approach general, we fix all the processes and change only the ontological description for a different application domain. In experiments we conducted on two different types of unstructured documents taken from the Web, our approach attained recall ratios in the 80% and 90% range and precision ratios near 98%.


ELECTRIC SUPPLY OF ELECTRIFIED RAILROADS


Risks and Outcomes of Social Exclusion Insights from Longitudinal Data


title: Development of power robot hand with shape adaptability using hydraulic McKibben muscles

abstract: We have developed a hydraulic McKibben artificial muscle which realizes great force density approximately ten times larger than the other conventional actuators. In this paper, we have applied this muscle to a power robot hand. The hand finger consists of metal links and the muscles. The contraction of the muscles generates the bending motion of the fingers. This hand has large holding capacity and shape adaptability to grasp objects. The experiments show that maximum holding force of the hand is 5000N. It can hold three types of different shaped objects; cylindrical objects of o 267mm and o165mm in diameter and a square cross section of width 200mm in side. This hand can be applied to various applications, for example rescue robots in disaster area and forestry industry.


title: DOLLÁR, et al.: INTEGRAL CHANNEL FEATURES – ADDENDUM 1 Integral Channel Features – Addendum

abstract: This document is meant to serve as an addendum to [1], published at BMVC 2009. Thepurpose of this addendum is twofold: (1) to respond to feedback we’ve received since publi-cation and (2) to describe a number of changes, especially to the non-maximal suppression,that further improve performance. The performance of our updated detection increases 5%to over 91% detection rate at 1 false positive per image on the INRIA dataset, and similarlyon the Caltech Pedestrian Dataset, while overall system runtime for multiscale detectiondecreases by 1=3 to just under 1:5s per 640 480 image.We begin by rectifying an important omission to the related work. Levi and Weiss had aninnovative application of integral images to multiple image channels quite early on, demon-strating good results on face detection from few training examples [4]. This work appears tobe the earliest such use of integral images, indeed the authors even describe a precursor tointegral histograms. Many thanks to Mark Everingham for sending us this reference.


title: Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games

abstract: Poker is a family of card games that includes many variations. We hypothesize that most poker games can be solved as a pattern matching problem, and propose creating a strong poker playing system based on a unified poker representation. Our poker player learns through iterative self-play, and improves its understanding of the game by training on the results of its previous actions without sophisticated domain knowledge. We evaluate our system on three poker games: single player video poker, two-player Limit Texas Hold'em, and finally two-player 2-7 triple draw poker. We show that our model can quickly learn patterns in these very different poker games while it improves from zero knowledge to a competitive player against human experts. ::: ::: The contributions of this paper include: (1) a novel representation for poker games, extendable to different poker variations, (2) a Convolutional Neural Network (CNN) based learning model that can effectively learn the patterns in three different games, and (3) a self-trained system that significantly beats the heuristic-based program on which it is trained, and our system is competitive against human expert players.


title: Frequency of recovery of pathogens causing acute maxillary sinusitis in adults before and after introduction of vaccination of children with the 7-valent pneumococcal vaccine

abstract: The objective of the study was to compare the proportions of the recovery of pathogens of acute maxillary sinusitis in adults in the 4-year period prior to the 5-year period that followed the introduction of vaccination of children with the 7-valent pneumococcal vaccine (PCV7). Cultures were obtained through endoscopy from 385 adults with acute maxillary sinusitis, 156 between 1997 and 2000, and 229 between 2001 and 2005. One hundred and seventeen potentially pathogenic organisms were isolated from the cultures obtained between 1997 and 2000. The predominant organisms were Streptococcus pneumoniae (54 or 46 % of all isolates), Haemophilus influenzae non-type b (42 or 36 %), Moraxella catarrhalis (7 or 6 %), Streptococcus pyogenes (8 or 7 %) and Staphylococcus aureus (6 or 5 %). One hundred and sixty-seven potentially pathogenic organisms were isolated from the cultures obtained between 2001 and 2005. The most predominant organisms were H. influenzae non-type b (71 or 43 % of all isolates), Strep. pneumoniae (58 or 35 %), M. catarrhalis (13 or 8 %), Strep. pyogenes (12 or 7 %) and Staph. aureus (13 or 8 %). Significant statistical differences were noted in the rates of recovery of H. influenzae non-type b (P < 0.05) and Strep. pneumoniae (P < 0.05). A decrease occurred in the recovery of Strep. pneumoniae resistant to penicillin from 41 to 29 %, and an increase was noted in the isolation of beta-lactamase-producing H. influenzae from 33 to 39 %; however, neither change was statistically significant. These data illustrate that a significant shift occurred in the causative pathogens of acute maxillary sinusitis in adults in the 5 years after the introduction of vaccination of children with the PCV7 compared to the previous 4 years.