title: Privacy-preserving statistical estimation with optimal convergence rates

abstract: Consider an analyst who wants to release aggregate statistics about a data set containing sensitive information. Using differentially private algorithms guarantees that the released statistics reveal very little about any particular record in the data set. In this paper we study the asymptotic properties of differentially private algorithms for statistical inference. We show that for a large class of statistical estimators T and input distributions P, there is a differentially private estimator AT with the same asymptotic distribution as T. That is, the random variables AT(X) and T(X) converge in distribution when X consists of an i.i.d. sample from P of increasing size. This implies that AT(X) is essentially as good as the original statistic T(X) for statistical inference, for sufficiently large samples. Our technique applies to (almost) any pair T,P such that T is asymptotically normal on i.i.d. samples from P---in particular, to parametric maximum likelihood estimators and estimators for logistic and linear regression under standard regularity conditions. A consequence of our techniques is the existence of low-space streaming algorithms whose output converges to the same asymptotic distribution as a given estimator T (for the same class of estimators and input distributions as above).


Ontologies, Disorders and Prototypes


title: Quantitative Evaluation of BFT Protocols

abstract: Byzantine Fault Tolerant (BFT) protocols aim to improve the reliability of distributed systems. They enable systems to tolerate arbitrary failures in a bounded number of nodes. BFT protocols are usually proven correct for certain safety and liveness properties. However, recent studies have shown that the performance of state-of-the-art BFT protocols decreases drastically in the presence of even a single malicious node. This motivates a formal quantitative analysis of BFT protocols to investigate their performance characteristics under different scenarios. We present HyPerf, a new hybrid methodology based on model checking and simulation techniques for evaluating the performance of BFT protocols. We build a transition system corresponding to a BFT protocol and systematically explore the set of behaviors allowed by the protocol. We associate certain timing information with different operations in the protocol, like cryptographic operations and message transmission. After an elaborate state exploration, we use the time information to evaluate the performance characteristics of the protocol using simulation techniques. We integrate our framework in Mace, a tool for building and verifying distributed systems. We evaluate the performance of PBFT using our framework. We describe two different use-cases of our methodology. For the benign operation of the protocol, we use the time information as random variables to compute the probability distribution of the execution times. In the presence of faults, we estimate the worst-case performance of the protocol for various attacks that can be employed by malicious nodes. Our results show the importance of hybrid techniques in systematically analyzing the performance of large-scale systems.


title: Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

abstract: The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined.


title: Young Muslim Pakistani Women’s Lived Experiences of Izzat, Mental Health, and Well-Being

abstract: This article explores how six Pakistani Muslim women interpret cultural concepts of izzat (honor and self-respect); what role, if any, it has in their lives; and whether there is interplay between ...


title: Development and Rationale for a Blended Approach to Health Care Simulation Debriefing

abstract: Summary StatementWe describe an integrated conceptual framework for a blended approach to debriefing called PEARLS [Promoting Excellence And Reflective Learning in Simulation]. We provide a rationale for scripted debriefing and introduce a PEARLS debriefing tool designed to facilitate implementation


title: Bayesian Cramér-Rao Bound for nonlinear filtering with dependent noise processes

abstract: The Bayesian Cramer Rao Bound (BCRB) is derived for nonlinear state space models with dependent process and measurement noise processes. It generalizes the previously BCRB for the case of dependent noise. Two different dependence structures appearing in literature are considered, leading to two different recursions for BCRB. The special cases of Gaussian noise, and linear models are presented separately. Simulations demonstrate that correct treatment of dependencies is important for both filtering algorithms and the BCRB.


title: PID controller auto-tuning based on process step response and damping optimum criterion.

abstract: This paper presents a novel method of PID controller tuning suitable for higher-order aperiodic processes and aimed at step response-based auto-tuning applications. The PID controller tuning is based on the identification of so-called n-th order lag (PTn) process model and application of damping optimum criterion, thus facilitating straightforward algebraic rules for the adjustment of both the closed-loop response speed and damping. The PTn model identification is based on the process step response, wherein the PTn model parameters are evaluated in a novel manner from the process step response equivalent dead-time and lag time constant. The effectiveness of the proposed PTn model parameter estimation procedure and the related damping optimum-based PID controller auto-tuning have been verified by means of extensive computer simulations.


title: An analysis of open information extraction based on semantic role labeling

abstract: Open Information Extraction extracts relations from text without requiring a pre-specified domain or vocabulary. While existing techniques have used only shallow syntactic features, we investigate the use of semantic role labeling techniques for the task of Open IE. Semantic role labeling (SRL) and Open IE, although developed mostly in isolation, are quite related. We compare SRL-based open extractors, which perform computationally expensive, deep syntactic analysis, with TextRunner, an open extractor, which uses shallow syntactic analysis but is able to analyze many more sentences in a fixed amount of time and thus exploit corpus-level statistics. Our evaluation answers questions regarding these systems, including, can SRL extractors, which are trained on PropBank, cope with heterogeneous text found on the Web? Which extractor attains better precision, recall, f-measure, or running time? How does extractor performance vary for binary, n-ary and nested relations? How much do we gain by running multiple extractors? How do we select the optimal extractor given amount of data, available time, types of extractions desired?


Several Approaches for Tweet Topic Classification in COSET - IberEval 2017


title: BikeNet: A mobile sensing system for cyclist experience mapping

abstract: We present BikeNet, a mobile sensing system for mapping the cyclist experience. Built leveraging the MetroSense architecture to provide insight into the real-world challenges of people-centric sensing, BikeNet uses a number of sensors embedded into a cyclist's bicycle to gather quantitative data about the cyclist's rides. BikeNet uses a dual-mode operation for data collection, using opportunistically encountered wireless access points in a delay-tolerant fashion by default, and leveraging the cellular data channel of the cyclist's mobile phone for real-time communication as required. BikeNet also provides a Web-based portal for each cyclist to access various representations of her data, and to allow for the sharing of cycling-related data (for example, favorite cycling routes) within cycling interest groups, and data of more general interest (for example, pollution data) with the broader community. We present: a description and prototype implementation of the system architecture based on customized Moteiv Tmote Invent motes and sensor-enabled Nokia N80 mobile phones; an evaluation of sensing and inference that quantifies cyclist performance and the cyclist environment; a report on networking performance in an environment characterized by bicycle mobility and human unpredictability; and a description of BikeNet system user interfaces.


title: Maximizing Benefits from Crowdsourced Data

abstract: Crowds of people can solve some problems faster than individuals or small groups. A crowd can also rapidly generate data about circumstances affecting the crowd itself. This crowdsourced data can be leveraged to benefit the crowd by providing information or solutions faster than traditional means. However, the crowdsourced data can hardly be used directly to yield usable information. Intelligently analyzing and processing crowdsourced information can help prepare data to maximize the usable information, thus returning the benefit to the crowd. This article highlights challenges and investigates opportunities associated with mining crowdsourced data to yield useful information, as well as details how crowdsource information and technologies can be used for response-coordination when needed, and finally suggests related areas for future research.


title: Bias in OLAP Queries: Detection, Explanation, and Removal

abstract: On line analytical processing (OLAP) is an essential element of decision-support systems. OLAP tools provide insights and understanding needed for improved decision making. However, the answers to OLAP queries can be biased and lead to perplexing and incorrect insights. In this paper, we propose HypDB, a system to detect, explain, and to resolve bias in decision-support queries. We give a simple definition of a \emph{biased query}, which performs a set of independence tests on the data to detect bias. We propose a novel technique that gives explanations for bias, thus assisting an analyst in understanding what goes on. Additionally, we develop an automated method for rewriting a biased query into an unbiased query, which shows what the analyst intended to examine. In a thorough evaluation on several real datasets we show both the quality and the performance of our techniques, including the completely automatic discovery of the revolutionary insights from a famous 1973 discrimination case.


title: in cognitive and social science research*

abstract: The development of robots that closely resemble human beings can contribute to cognitive research. An android provides an experimental apparatus that has the potential to be controlled more precisely than any human actor. However, preliminary results indicate that only very humanlike devices can elicit the broad range of responses that people typically direct toward each other. Conversely, to build androids capable of emulating human behavior, it is necessary to investigate social activity in detail and to develop models of the cognitive mechanisms that support this activity. Because of the reciprocal relationship between android development and the exploration of social mechanisms, it is necessary to establish the field of android science. Androids could be a key testing ground for social, cognitive, and neuroscientific theories as well as platform for their eventual unification. Nevertheless, subtle flaws in appearance and movement can be more apparent and eerie in very humanlike robots. This uncanny phenomenon may be symptomatic of entities that elicit our model of human other but do not measure up to it. If so, very humanlike robots may provide the best means of pinpointing what kinds of behavior are perceived as human, since deviations from human norms are more obvious in them than in more mechanical-looking robots. In pursuing this line of inquiry, it is essential to identify the mechanisms involved in evaluations of human likeness. One hypothesis is that, by playing on an innate fear of death, an uncanny robot elicits culturally-supported defense responses for coping with death’s inevitability. An experiment, which borrows from methods used in terror management research, was performed to test this hypothesis. [Thomson Reuters Essential Science Indicators: Fast Breaking Paper in Social Sciences, May 2008]


title: Recommender Systems and Linked Open Data

abstract: The World Wide Web is moving from a Web of hyper-linked documents to a Web of linked data. Thanks to the Semantic Web technological stack and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets connected with each other to form the so called LOD cloud. As of today, we have tons of RDF data available in the Web of Data, but only a few applications really exploit their potential power. The availability of such data is for sure an opportunity to feed personalized information access tools such as recommender systems. We present an overview on recommender systems and we sketch how to use Linked Open Data to build a new generation of semantics-aware recommendation engines.


title: The Effects of Low– and High–Glycemic Index Meals on Time Trial Performance

abstract: PURPOSE ::: The aim of this work was to determine whether the consumption of pre-exercise high- or low-glycemic index (GI) meals has a beneficial effect on time trial performance. ::: ::: ::: METHODS ::: Eight male cyclists were provided with either a high-GI or low-GI meal, providing 1 g x kg(-1) body mass of carbohydrate, 45 min before performing a 40-km time trial on a Velotron cyclePro. ::: ::: ::: RESULTS ::: Time trial performance was significantly improved in the low-GI trial (92.5 +/- 5.2 min) compared with the high-GI trial (95.6 +/- 6.0 min) (P = .009). Blood glucose concentrations at the point of exhaustion were significantly higher in the low-GI trial (5.2 +/- 0.6 mmol x L(-1)) compared with the high-GI trial (4.7 +/- 0.7 mmol x L(-1)) (P = .001). There was no significant difference in estimated carbohydrate oxidation data between the low-GI (2.51 +/- 1.74 g x min(-1)) and high-GI (2.18 +/- 1.53 g x min(-1)) meals (P = .195). No significant difference in estimated fat oxidation was observed between the low-GI (0.15 +/- 0.15 g x min(-1)) and high-GI (0.29 +/- 0.18 g x min(-1)) diets (P = .83). ::: ::: ::: CONCLUSIONS ::: The improvement in time trial performance for the low-GI trial may be associated with an increased availability of glucose to the working muscles, contributing additional carbohydrate for oxidation and possibly sparing limited muscle and liver glycogen stores.


title: The design and implementation of fire smoke detection system based on FPGA

abstract: This paper uses modular method to design smoke detection system. On the basis of the requirement analysis, the paper designs and implements all the modules, including image capture module, SDRAM data buffer module, image display module and smoke detection module. Among them the smoke detection module is the core of the system, which decides the performance of the system. Using a synergistic manner through software and hardware, the smoke detection module is realized. First of all, using the algorithm of frame difference, erosion and dilation, it realizes suspected smoke area extraction with hardware description language. This method ensures to process real-time sequential image. And then we detect the suspected smoke areas with smoke dynamic features including smoke area rising feature and smoke regional stability feature. Finally, the paper builds smoke detect module using SOPC Builder, which reads image data captured by CMOS image sensor monitors into SRAM. Based on smoke detect hardware module this paper realizes smoke background fuzzy characteristics based on the 2d discrete wavelet transform in Nios II IDE environment. After a period of debugging and running, the system realizes real-time smoke detection.


title: Design Concept and Realization of the Humanoid Service Robot HERMES

abstract: To carry out automated service tasks at different places within large working environments, a robot system able to navigate autonomously and to execute commanded manipulation tasks is needed. In this paper the design concept and the realization of a novel service robot named HERMES are described. Its human-like appearance, highly integrated sensors and actuators together with its practical task-related intelligence enable the robot to perform various service tasks. First experiments are reported.


title: ERP institutionalization: exploring the influential factors

abstract: Purpose – The purpose of this paper is to investigate the external and internal factors that contribute to assimilation of enterprise resource planning (ERP) system in the organization through the processes of adapting, routinizing and institutionalization of technology. Design/methodology/approach – This research follows qualitative interpretive approach. The results produced in this paper are based on thematic analysis of responses from open-ended interviews with ERP stakeholders in large size Australian organizations. The research findings have been further triangulated with surveys and content analysis. Findings – This research is still in progress; therefore, this paper presents the results of the field study conducted so far along with the instrument used to collect data. This instrument consists of a list of selected questions based on the thorough review of current information systems literature. However, the major finding of this study, thus far, is that the ERP implementation is a process of ali...


title: Weighted Hausdorff Distance: A Loss Function For Object Localization.

abstract: Recent advances in Convolutional Neural Networks (CNN) have achieved remarkable results in localizing objects in images. In these networks, the training procedure usually requires providing bounding boxes or the maximum number of expected objects. In this paper, we address the task of estimating object locations without annotated bounding boxes, which are typically hand-drawn and time consuming to label. We propose a loss function that can be used in any Fully Convolutional Network (FCN) to estimate object locations. This loss function is a modification of the Average Hausdorff Distance between two unordered sets of points. The proposed method does not require one to "guess" the maximum number of objects in the image, and has no notion of bounding boxes, region proposals, or sliding windows. We evaluate our method with three datasets designed to locate people's heads, pupil centers and plant centers. We report an average precision and recall of 94% for the three datasets, and an average location error of 6 pixels in 256x256 images.


title: Psychopathic personality traits and environmental contexts: Differential correlates, gender differences, and genetic mediation.

abstract: Theory and empirical research has identified meaningful variants of psychopathy as reflected in subtypes (e.g., primary and secondary psychopathy; Karpman, 1941) and structural models that have identified distinguishable factors underlying psychopathic personality features (e.g., Factor 1 [F1] affective-interpersonal features and Factor 2 [F2] social deviance; Benning et al., 2003; Harpur et al., 1989). A prominent hypothesis based on this research is that psychopathy variants differ in their etiological underpinnings, specifically, that F1/primary psychopathy1 is more heritable whereas environmental factors contribute more to F2/secondary psychopathy as evidenced by the latter’s stronger associations with putative environmental risk factors (for a review see Skeem et al., 2003). However, few studies have directly tested this hypothesis, and substantial behavioral genetic research indicates that (1) measures presumed to reflect environmental risk exhibit robust heritability (Kendler & Baker, 2007; Plomin & Bergeman, 1991), and (2) genetic effects account for much of the covariance between measures of environmental risk and indices of personality and psychopathology (Plomin, 1995; Saudino et al., 1997). Using trait-based proxies of primary and secondary psychopathy, we utilized a large, mixed-gender sample of adolescent twins to test whether common genetic factors could account for the differential associations between psychopathic traits and environmental correlates.


title: Chitosan-Alginate Sponge: Preparation and Application in Curcumin Delivery for Dermal Wound Healing in Rat

abstract: A biodegradable sponge, composed of chitosan (CS) and sodium alginate (SA), was successfully obtained in this work. The sponge was ethereal and pliable. The chemical structure and morphology of the sponges was characterized by FTIR and SEM. The swelling ability, in vitro drug release and degradation behaviors, and an in vivo animal test were employed to confirm the applicability of this sponge as a wound dressing material. As the chitosan content in the sponge decreased, the swelling ability decreased. All types of the sponges exhibited biodegradable properties. The release of curcumin from the sponges could be controlled by the crosslinking degree. Curcumin could be released from the sponges in an extended period for up to 20 days. An in vivo animal test using SD rat showed that sponge had better effect than cotton gauze, and adding curcumin into the sponge enhanced the therapeutic healing effect.


title: Power Modeling for GPU Architectures Using McPAT

abstract: Graphics Processing Units (GPUs) are very popular for both graphics and general-purpose applications. Since GPUs operate many processing units and manage multiple levels of memory hierarchy, they consume a significant amount of power. Although several power models for CPUs are available, the power consumption of GPUs has not been studied much yet. In this article we develop a new power model for GPUs by utilizing McPAT, a CPU power tool. We generate initial power model data from McPAT with a detailed GPU configuration, and then adjust the models by comparing them with empirical data. We use the NVIDIA's Fermi architecture for building the power model, and our model estimates the GPU power consumption with an average error of 7.7p and 12.8p for the microbenchmarks and Merge benchmarks, respectively.


title: Trainable Sentence Planning For Complex Information Presentations In Spoken Dialog Systems

abstract: A challenging problem for spoken dialog systems is the design of utterance generation modules that are fast, flexible and general, yet produce high quality output in particular domains. A promising approach is trainable generation, which uses general-purpose linguistic knowledge automatically adapted to the application domain. This paper presents a trainable sentence planner for the MATCH dialog system. We show that trainable sentence planning can produce output comparable to that of MATCH's template-based generator even for quite complex information presentations.


title: Transverse flux permanent magnet motor with double-C stator hoops and flux-concentrated rotor for in-wheel drive electric vehicle

abstract: This paper proposes a novel transverse flux permanent magnet motor (TFPMM) with double-C stator hoops. The proposed TFPMM carries significantly increased power density due to higher coefficient of utilization of the volume compared with the traditional double-side counterparts. The topology, principle and parametric calculation of the TFPMM are described elaborately. The magnetic field and characteristics analysis are conducted by three-dimensional finite element method (3D FEM) including flux density, EMF and winding inductance, and cogging torque are obtained to verify the feasibility of proposed TFPMM design. The mechanism of cogging torque of the proposed double-C stator hoops TFPMM is also analyzed. The results of transient simulation of power density well align with the theoretical analysis, which confirm that the power density is significantly increased due to its higher volumetric utilization ratio.


title: Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods

abstract: Author(s): Janzamin, M; Sedghi, H; Anandkumar, A | Abstract: Training neural networks is a challenging non-convex optimization problem, and backpropagation or gradient descent can get stuck in spurious local optima. We propose a novel algorithm based on tensor decomposition for guaranteed training of two-layer neural networks. We provide risk bounds for our proposed method, with a polynomial sample complexity in the relevant parameters, such as input dimension and number of neurons. While learning arbitrary target functions is NP-hard, we provide transparent conditions on the function and the input for learnability. Our training method is based on tensor decomposition, which provably converges to the global optimum, under a set of mild non-degeneracy conditions. It consists of simple embarrassingly parallel linear and multi-linear operations, and is competitive with standard stochastic gradient descent (SGD), in terms of computational complexity. Thus, we propose a computationally efficient method with guaranteed risk bounds for training neural networks with one hidden layer.


title: Design and empirical identification of a lumped parameter thermal network for permanent magnet synchronous motors with physically motivated constraints

abstract: Monitoring critical temperatures in permanent magnet synchronous motors (PMSM) is essential to prevent device failures or excessive motor life time reduction due to thermal stress. A lumped parameter thermal network (LPTN) consisting of four nodes is designed to model the most important motor parts, i.e. the stator yoke, stator winding, stator teeth and the permanent magnets. An empirical approach based on comprehensive experimental training data and an global particle swarm optimisation are used to identify the LPTN parameters of a 60 kW automotive traction PMSM. Here, varying parameters and physically motivated constraints are taken into account to extend the model scope beyond the training data domain. The model accuracy is cross-validated with independent load profiles and a maximum estimation error of 5 ° C regarding all considered motor temperatures is achieved.


title: Learning to Forecast and Refine Residual Motion for Image-to-Video Generation

abstract: We consider the problem of image-to-video translation, where an input image is translated into an output video containing motions of a single object. Recent methods for such problems typically train transformation networks to generate future frames conditioned on the structure sequence. Parallel work has shown that short high-quality motions can be generated by spatiotemporal generative networks that leverage temporal knowledge from the training data. We combine the benefits of both approaches and propose a two-stage generation framework where videos are generated from structures and then refined by temporal signals. To model motions more efficiently, we train networks to learn residual motion between the current and future frames, which avoids learning motion-irrelevant details. We conduct extensive experiments on two image-to-video translation tasks: facial expression retargeting and human pose forecasting. Superior results over the state-of-the-art methods on both tasks demonstrate the effectiveness of our approach.


title: Improved Training with Curriculum GANs

abstract: In this paper we introduce Curriculum GANs, a curriculum learning strategy for training Generative Adversarial Networks that increases the strength of the discriminator over the course of training, thereby making the learning task progressively more difficult for the generator. We demonstrate that this strategy is key to obtaining state-of-the-art results in image generation. We also show evidence that this strategy may be broadly applicable to improving GAN training in other data modalities.


title: Tranalyzer: Versatile high performance network traffic analyser

abstract: IP-based networks are prone to hardware failures, software errors and misconfigurations. This leads to service outages, such as those experienced by American Airlines in 2015. Moreover, cyber threats are becoming ever more sophisticated. As demonstrated by recent success stories of malware, such as the crimeware BlackEnergy, current defence solutions are insufficient to detect those anomalies and threats. Indeed, the widespread use of cryptography and obfuscation techniques limits the effectiveness of standard solutions relying on content inspection. Although statistical based approaches are able to deal with some of these limitations, threats such as data exfiltration and covert channels remain challenging to detect. This paper presents Tranalyzer, a flow-based traffic analyser built upon a flexible plugin-based architecture, allowing efficient processing and analysis of network traffic. The program is presented through a series of real-life scenarios dealing with traffic mining and troubleshooting and provides the analyst with a methodology on how to tackle such challenges, even when encryption or obfuscation techniques are being used.


title: A hybrid estimation approach for autonomous dirt road following using multiple clothoid segments

abstract: In this paper we describe a novel approach to autonomous dirt road following. The algorithm is able to recognize highly curved roads in cluttered color images quite often appearing in offroad scenarios. To cope with large curvatures we apply gaze control and model the road using two different clothoid segments. A Particle Filter incorporating edge and color intensity information is used to simultaneously detect and track the road farther away from the ego vehicle. In addition the particles are used to generate static road segment estimations in a given look ahead distance. These estimations are predicted with respect to ego motion and fused utilizing Kalman filter techniques to generate a smooth local clothoid segment for lateral control of the vehicle.


title: “I want that”: Human-in-the-loop control of a wheelchair-mounted robotic arm

abstract: Wheelchair-mounted robotic arms have been commercially available for a decade. In order to operate these robotic arms, a user must have a high level of cognitive function. Our research focuses on replacing a manufacturer-provided, menu-based interface with a vision-based system while adding autonomy to reduce the cognitive load. Instead of manual task decomposition and execution, the user explicitly designates the end goal, and the system autonomously retrieves the object. In this paper, we present the complete system which can autonomously retrieve a desired object from a shelf. We also present the results of a 15-week study in which 12 participants from our target population used our system, totaling 198 trials.


title: Exploring Customer Reviews for Music Genre Classification and Evolutionary Studies

abstract: The 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York City, United States of America, 7-11 August 2016


title: Data-driven Human Behavior Models: Opportunities and Challenges

abstract: We live in a world of data, of big data, a big part of which has been generated by humans through their interactions with both the physical and digital world. A key element in the exponential growth of human behavioral data is the mobile phone. There are more mobile phones in the world as humans which has turned the mobile phone into the piece of technology with the highest levels of adoption in human history. We carry them with us all through the day (and night, in many cases), leaving digital traces of our physical interactions. Mobile phones have become sensors of human activity both in the large scale and also as the most personal devices. In my talk, I present some of the work that we are doing at Telefonica Research in the area of modeling humans from human behavioral data collected from mobile phones. The data may be data that is automatically collected by the mobile network infrastructure --in particular Call Detail Records or CDRs---or data that is collected by an experimental mobile application through a user study. All projects entail data analytics and machine learning in order to build accurate models of individual or aggregate behavior. In particular, I describe four projects: (1) a project to automatically infer personality from Call Detail Records [1]; (2) MobiScore, a project to automatically assess a credit score from Call Detail Records [2]; Borapp, a mobile app that is able to detect boredom from patterns of phone usage [3] and a project to automatically predict crime hotspots in a city from human dynamics and demographic data [4]. I conclude by highlighting opportunities and challenges associated with building data-driven models of human behavior.


title: An empirical test of conclusions from Effective schools research

abstract: Abstract Effective schools variables identified in several literature reviews were collapsed into 6 constructs, and the independent effect of each construct was empirically tested on student achievement level. The data for this analysis were taken from the National Educational Longitudinal Study for the years 1988, 1990, and 1992. The regression analysis of the data indicates that the most important effective schools characteristics are an achievement-oriented school culture, principal autonomy in hiring and firing teachers, and high teacher morale. No evidence was found that teacher empowerment, teacher education level, most principal influences, and quality of relations between the administration and the school are related to student performance.


title: RF Engineering: Live Construction of A Coil "Let's build an RF human coil."

abstract: Introduction Last year, we gave our live demonstration entitled “Live construction of coils in 60 minutes: an 8-channel cardiac array coil [1].” This is a continued program in which we will be constructing another RF human coil. Recent advances of MR scanner design involve an ever-increasing number of receiver channels (32-128) for faster scans. While 1.5T and 3T fast MRI clinical applications are now realized and accepted as everyday routine clinical practices, 7T MRI is also emerging because of its capability to study neurodegenerative diseases, e.g., Alzheimer, and biochemical functional imaging to analyze physico-chemical parameters used for examining tissue morphology, blood flow, metabolism and chemistry in vivo. Regardless of the field strengths, the key requirements are maximum achievable signal-to-noise ratio and multi-detector array coil optimization within the framework of the parallel imaging scheme to enable more advanced and faster clinical MR scans. Preamplifiers are also one of the key components in the detector array coils. In this talk, while appreciating the RF coil theory, we will construct an RF human coil. To prepare ourselves for the demonstration, the important and fundamental principles of MRI RF array coils are reviewed. The reader is also encouraged to review our last year’s presentation which is available online.


GestureRNN: A neural gesture system for the Roli Lightpad Block


title: Quantitative analysis of Euclidean distance to complement qualitative analysis of facial expression during deception

abstract: Background: Accurate evaluation of an individuals' veracity is a fundamental aspect of social functioning that allows individuals to act in adaptive ways. The domain of deception detection ability is still young, and many components in this field are yet to be touched which demands more research in this field. Aims: The present study aims at deciphering the structural composition of face during felt, posed, and deceived emotions in facial expression unique to Indian culture, using Facial Action Coding System (FACS). Quantitative analysis of Euclidean distance has been done to complement qualitative FACS analysis. Methods: In this study, thirty female, young adults with age range of 23–27 years were chosen randomly for portraying their (felt, posed, and deceived) facial expression. All facial expressions were captured through instruction, and videos were converted into static images. The static images were coded on the basis of FACS to decipher the felt, posed, and deceived expressions. Quantitative analysis of the data has been done using MATLAB to meet the objectives of the study and to complement the qualitative analysis. Results: Felt and posed emotions differ in terms of intensity of the expression and subjective experience. Posed emotional and deceived expressions differ in intent. Facial asymmetry is an important indicator for detecting deception.


title: Wavelet transform in image recognition

abstract: Texture segmentation and classification form a very important topic of the interdisciplinarg area of signal processing with many applications in diflerent areas including satellite image processing, biomedical image analysis and microscopic image processing. The paper presents selected mathematical methods used for image segmentation and the following segments classification using multiresolution decomposition of segments boundary szgnals. The wavelet transform has been applied here for feature extraction and image de-noising. Results of feature extraction obtained by the discrete wavelet transform are compared with that evaluated by the discrete Fourier transform. For the following feature classification the self-organizing neural networks are applied. Proposed methods have been verified for simulated structures and then used for analysis of microscopic images of crystals of diflerent shapes and sizes.


title: Optimization and modeling of FPGA implementation of the Katan Cipher

abstract: Lightweight ciphers (e.g., Katan) are crucial for secure communication for resource-constrained devices. The Katan cipher algorithm was proposed for low-resource devices. This paper examines implementing Katan Cipher on field programmable gate array (FPGA) platform. The paper discusses several implementations, with 80-bits key size and 64-bits block size. The energy and power dissipations are examined to select the optimum design. Models for resources and power are derived with average error of 12% and 17%.


D Mapping with an RGB-D Camera


title: The Thought Worlds of Marketing and Sales: Which Differences Make a Difference?

abstract: Abstract The literature is divided on whether differences between the thought worlds of marketing and sales are deleterious or beneficial. This article empirically investigates various facets of thought-world differences and their effects on various outcomes. It confirms that, in general, differences hamper the cooperation between marketing and sales, which leads to a lower market performance of the business unit. However, some facets of thought-world differences enhance the market performance of the business unit through a direct effect that outweighs the negative effect mediated by the quality of cooperation between marketing and sales. Market performance is enhanced if one side plays the customers' advocate while the other plays the products' advocate. Market performance is also enhanced if one side plays the advocate of short-term considerations while the other plays the advocate of long-term considerations. In contrast, differences between marketing and sales in regard to product knowledge and interp...


title: Humphrey Ridley (1653-1708): 17th century evolution in neuroanatomy and selective cerebrovascular injections for cadaver dissection.

abstract: Humphrey Ridley, M.D. (1653–1708), is a relatively unknown historical figure, belonging to the postmedieval era of neuroanatomical discovery. He was born in the market town of Mansfield, 14 miles from the county of Nottinghamshire, England. After studying at Merton College, Oxford, he pursued medicine at Leiden University in the Netherlands. In 1688, he was incorporated as an M.D. at Cambridge. Ridley authored the first original treatise in English language on neuroanatomy, The Anatomy of the Brain Containing its Mechanisms and Physiology: Together with Some New Discoveries and Corrections of Ancient and Modern Authors upon that Subject. Ridley described the venous anatomy of the eponymous circular sinus in connection with the parasellar compartment. His methods were novel, unique, and effective. To appreciate the venous anatomy, he preferred to perform his anatomical dissections on recently executed criminals who had been hanged. These cadavers had considerable venous engorgement, which made the skull ba...


title: RSAW, A Highly Reconfigurable Wave Robot: Analysis, Design, and Experiments

abstract: This letter presents a highly reconfigurable wave robot named RSAW. RSAW is a reconfigurable version of our recently developed single actuator wave robot (SAW) that produces a continuous wave motion. The robot is composed of two waves attached to each other through an actuated universal (U) joint which allows the robot to rotate its yaw to steer its direction in the plane and to raise its pitch to climb over obstacles. Its symmetrical design enables the RSAW to operate even when flipped over. This letter presents the design of the robot, an analysis of its kinematics and dynamics and a prototype of RSAW that was designed based on this analysis. We describe multiple experiments using the robot to verify its crawling and climbing capabilities, and its turning radius. The RSAW can travel at a speed of up to 19 cm/s and climb over obstacles that are 10 cm high. The RSAW can crawl on multiple challenging surfaces including grass, gravel, stones and dirt (see video).


title: TexT - Text Extractor Tool for Handwritten Document Transcription and Annotation

abstract: This paper presents a framework for semi-automatic transcription of large-scale historical handwritten documents and proposes a simple user-friendly text extractor tool, TexT for transcription. The proposed approach provides a quick and easy transcription of text using computer assisted interactive technique. The algorithm finds multiple occurrences of the marked text on-the-fly using a word spotting system. TexT is also capable of performing on-the-fly annotation of handwritten text with automatic generation of ground truth labels, and dynamic adjustment and correction of user generated bounding box annotations with the word being perfectly encapsulated. The user can view the document and the found words in the original form or with background noise removed for easier visualization of transcription results. The effectiveness of TexT is demonstrated on an archival manuscript collection from well-known publicly available dataset.


title: Deep Learning for Tumor Classification in Imaging Mass Spectrometry

abstract: Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS. jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de Supplementary data are available at Bioinformatics online.


title: RELATIONSHIP BETWEEN JOB SATISFACTION AND TURNOVER INTENTION: An Empirical Study on the IT Firms in Palestine

abstract: Studies investigating the relationship between job satisfaction and turnover intention are abundant. Yet, this relationship has not been fully addressed in the IT field particularly in the developing countries. Moving from this point, this study aims at further probe this area by evaluating the levels of job satisfaction and turnover intention among a sample of IT employees in the Palestinian IT firms. Then, it attempts to examine the sources of job satisfaction and the causes of turnover intention among those employees. The findings show job security, work conditions, pay and benefits, work nature, coworkers, career advancement, supervision and management were all significantly correlated with overall job satisfaction. Only job security, pay, and coworkers were able to significantly influence turnover intention. Implications of the findings and future research directions are discussed


title: Blind Channel Estimation for MIMO-OFDM Systems

abstract: By combining multiple-input multiple-output (MIMO) communication with the orthogonal frequency division multiplexing (OFDM) modulation scheme, MIMO-OFDM systems can achieve high data rates over broadband wireless channels. In this paper, to provide a bandwidth-efficient solution for MIMO-OFDM channel estimation, we establish conditions for channel identifiability and present a blind channel estimation technique based on a subspace approach. The proposed method unifies and generalizes the existing subspace-based methods for blind channel estimation in single-input single-output OFDM systems to blind channel estimation for two different MIMO-OFDM systems distinguished according to the number of transmit and receive antennas. In particular, the proposed method obtains accurate channel estimation and fast convergence with insensitivity to overestimates of the true channel order. If virtual carriers (VCs) are available, the proposed method can work with no or insufficient cyclic prefix (CP), thereby potentially increasing channel utilization. Furthermore, it is shown under specific system conditions that the proposed method can be applied to MIMO-OFDM systems without CPs, regardless of the presence of VCs, and obtains an accurate channel estimate with a small number of OFDM symbols. Thus, this method improves the transmission bandwidth efficiency. Simulation results illustrate the mean-square error performance of the proposed method via numerical experiments


title: Structuring the Transition From Example Study to Problem Solving in Cognitive Skill Acquisition: A Cognitive Load Perspective

abstract: Cognitive load research has shown that learning from worked-out examples, in comparison to problem solving, is very effective during the initial stages of cognitive skill acquisition. In later stages, however, solving problems is superior. In this contribution, theoretical analyses of different types of cognitive load and their changes over the stages of skill acquisition are presented. Two basic arguments are put forth: (a) Intrinsic cognitive load gradually decreases so that a gradual increase of problem-solving demands is possible without inducing cognitive overload. (b) In contrast to the earlier stages, different learner activities during the later stages constitute either germane or extraneous load, because different instructional goals are to be achieved. Based on these analyses, we propose a fading procedure in which problem-solving elements are successively integrated into example study until the learners are expected to solve problems on their own. Empirical evidence supporting this fading proce...


title: Will this car change the lane? - Turn signal recognition in the frequency domain

abstract: Understanding the intention of other road users is a key requirement for autonomous driving. In this regard, one particularly relevant cue is a flashing turn signal, since it gives an important hint regarding the intended driving direction of another vehicle in the next few seconds. As such, turn signals can be considered as one of the first methods invented for car-to-car communication. In contrast to modern radio-based approaches, turn signals are installed in almost every vehicle. However, only image-based methods are able to detect, recognize and understand those signals. In this paper, we present a new method to recognize turn signals of other vehicles in images. Our approach builds upon a robust vehicle detector and involves three major steps applied to each detected vehicle: light spot detection, feature extraction through FFT-based analysis of the temporal signal behavior at each detected light spot, and AdaBoost classification of the extracted feature set. In our experiments, we use solely virtually-generated data for training and evaluate the proposed approach on a large 30 minute real-world image sequence. Our results indicate competitive performance at real-time speeds.


title: Optimal Design of a 3.5-kV/11-kW DC–DC Converter for Charging Capacitor Banks of Power Modulators

abstract: For the generation of short high-power pulses in many applications, power modulators based on capacitor discharge are used, where the peak power is drawn from the input capacitor bank. In order to continuously recharge the energy buffer during operation at a lower average power, usually, power supplies connected to the mains are used. Due to the worldwide variation in mains voltages and the desired ability to adapt the capacitor voltage of the modulator, the power supply has to support a wide input and output voltage range, whereby the supply should draw a sinusoidal current from the mains due to EMI regulations. Additionally, depending on the modulator concept, a galvanic isolation also has to be provided. In order to achieve the mentioned specifications for the considered power supply, a combination of an ac-dc and a dc-dc converter is proposed, whereas the mains voltage is rectified by a three-phase buck-boost converter to 400 Vdc, and thereafter, an isolated dc-dc converter charges the input capacitor bank of the power modulator up to 3.5 kV. This paper focuses on the basic operation and the design of the 3.5-kV/11-kW isolated dc-dc converter, which includes transformer design, efficiency-volume optimization, and component selection. In this paper, compared with the well-known flyback converter, the proposed full-bridge-based topology results in a much higher efficiency and power density.


title: Pitfall in Pigmentation: Pseudopods in the Nail Plate

abstract: background. We present here a patient with an unusual type of nail plate pigmentation featuring pseudopod formation. ::: ::: objective. To identify additional dermoscopic criteria. ::: ::: methods. Dermoscopy of the nail with control biopsy. ::: ::: results. Pseudopods of the nail resulted from erythrocytes entrapped between the layers of the nail plate. ::: ::: conclusion. Pseudopod formation of the nail may represent a diagnostic criterion to distinguish hemorrhage and melanoma of the nail apparatus.


title: Optical camouflage using retro-reflective projection technology

abstract: This paper describes a kind of active camouflage system named optical camouflage. Optical camouflage uses the retro-reflective projection technology, a projection-based augmented-reality system composed of a projector with a small iris and a retro-reflective screen. The object that needs to be made transparent is painted or covered with retro-reflective material. Then a projector projects the background image on it making the masking object virtually transparent.


title: Ultra-wideband sampling down converter with sampling phase detector

abstract: Ultra-wideband radar is an excellent tool for nondestructive examination of walls and highway structures. Therefore often steep edged narrow pulses with rise-, fall-times in the range of 100 ps are used. For digitizing of the reflected pulses a down conversion has to be accomplished. A new low cost sampling down converter with a sampling phase detector for use in ultra-wideband radar applications is presented.


title: Big data analytics for logistics and transportation

abstract: Nowadays, there are many challenges for the logistics industry mainly with the integration of E-commerce and new sources of data such as smartphones, sensors, GPS and other devices. Those new data sources generate daily a huge quantity of unstructured data, to deal with such complex data, the use of big data analytic tools becomes an obligation. In this context, many works have been done recently in the integration of big data analytics in the logistics industry. In this paper, we propose to give a review of the latest applications of big data analytics in the field of logistics and transportation industry and to propose a novel approach to detect and recognize containers code based on a Hadoop big data analytics system.


Natural-Language Interface


title: INTERSPEECH 2010 Binary Coding of Speech Spectrograms Using a Deep Auto-encoder

abstract: This paper reports our recent exploration of the layer-by-layer learning strategy for training a multi-layer generative model of patches of speech spectrograms. The top layer of the generative model learns binary codes that can be used for efficient compression of speech and could also be used for scalable speech recognition or rapid speech content retrieval. Each layer of the generative model is fully connected to the layer below and the weights on these connections are pretrained efficiently by using the contrastive divergence approximation to the log likelihood gradient. After layer-bylayer pre-training we “unroll” the generative model to form a deep auto-encoder, whose parameters are then fine-tuned using back-propagation. To reconstruct the full-length speech spectrogram, individual spectrogram segments predicted by their respective binary codes are combined using an overlapand-add method. Experimental results on speech spectrogram coding demonstrate that the binary codes produce a logspectral distortion that is approximately 2 dB lower than a subband vector quantization technique over the entire frequency range of wide-band speech. Index Terms: deep learning, speech feature extraction, neural networks, auto-encoder, binary codes, Boltzmann machine


title: Detecting online auction shilling frauds using supervised learning

abstract: Online auction sites are a target for fraud due to their anonymity, number of potential targets and low likelihood of identification. Researchers have developed methods for identifying fraud. However, these methods must be individually tailored for each type of fraud, since each differs in the characteristics important for their identification. Using supervised learning methods, it is possible to produce classifiers for specific types of fraud by providing a dataset where instances with behaviours of interest are assigned to a separate class. However this requires multiple labelled datasets: one for each fraud type of interest. It is difficult to use real-world datasets for this purpose since they are difficult to label, often limited in size, and contain zero or multiple suspicious behaviours that may or may not be under investigation. The aims of this work are to: (1) demonstrate the approach of using supervised learning together with a validated synthetic data generator to create fraud detection models that are experimentally more accurate than existing methods and that is effective over real data, and (2) to evaluate a set of features for use in general fraud detection is shown to further improve the performance of the created detection models. The approach is as follows: the data generator is an agent-based simulation modelled on users in commercial online auction data. The simulation is extended using fraud agents which model a known type of online auction fraud called competitive shilling. These agents are added to the simulation to produce the synthetic datasets. Features extracted from this data are used as training data for supervised learning. Using this approach, we optimise an existing fraud detection algorithm, and produce classifiers capable of detecting shilling fraud. Experimental results with synthetic data show the new models have significant improvements in detection accuracy. Results with commercial data show the models identify users with suspicious behaviour.


title: Strategy, Choice of Performance Measures, and Performance

abstract: We examine the relationship between quality‐based manufacturing strategy and the use of different types of performance measures, as well as their separate and joint effects on performance. A key part of our investigation is the distinction between financial and both objective and subjective nonfinancial measures. Our results support the view that performance measurement diversity benefits performance as we find that, regardless of strategy, firms with more extensive performance measurement systems—especially those that include objective and subjective nonfinancial measures—have higher performance. But our findings also partly support the view that the strategy‐measurement “fit” affects performance. We find that firms that emphasize quality in manufacturing use more of both objective and subjective nonfinancial measures. However, there is only a positive effect on performance from pairing a qualitybased manufacturing strategy with extensive use of subjective measures, but not with objective nonfinancial me...


title: The influence of muscles activation on the dynamical behaviour of the tympano-ossicular system of the middle ear

abstract: The human ear is a complex biomechanical system and is divided into three parts: outer, middle and inner ear. The middle ear is formed by ossicles (malleus, incus and stapes), ligaments, muscles and tendons, which transfers sound vibrations from the eardrum to the inner ear, linking with mastoid and Eustachian tube. In this work, a finite element modelling of the tympano-ossicular system of the middle ear was developed. A dynamic study based on a structural response to harmonic vibrations, for a sound pressure level (SPL) of 110, 120 and 130 dB SPL applied in the eardrum, is presented. The connection between the ossicles is made using a contact formulation. The model includes the different ligaments considering its hyperelastic behaviour. The activation of the muscles is based on the constitutive model proposed by previous work. The harmonic responses of displacement and pressure obtained on the stapes footplate, for a frequency range between 100 Hz and 10 kHz, are obtained simulating the muscle activatio...


title: Hard-less Common Mode Active EMI filter

abstract: Active EMI filters appear as an alternative to classical EMI filters based on passive components. Active EMI filters can provide more compact, cheaper and lighter solutions than passive approach. In this paper we present a theoretical analysis of the 4 possible configurations. Expressions of the transfer function and insertion losses considering impedances of mains and source of noise are presented. The voltage-current topology is identified as the more convenient solution. Finally, preliminary experimental results of a prototype capable to provide attenuation up to 100MHz are shown.


title: Reaction-Diffusion Systems in Epidemiology

abstract: A key problem in modelling the evolution dynamics of infectious diseases is the mathematical representation of the mechanism of transmission of the contagion. Models with a finite number of subpopulations can be described via systems of ordinary differential equations. When dealing with populations with space structure the relevant quantities are spatial densities, whose evolution in time requires nonlinear partial differential equations, which are known as reaction-diffusion systems. Here we present an (historical) outline of mathematical epidemiology, with a particular attention to the role of spatial heterogeneity and dispersal in the population dynamics of infectious diseases. Two specific examples are discussed, which have been the subject of intensive research by the authors, i.e. man-environment-man epidemics, and malaria. In addition to the epidemiological relevance of these epidemics all over the world, their treatment requires a large amount of different sophisticate mathematical methods, and has even posed new non trivial mathematical problems, as one can realize from the list of references. One of the most relevant problems posed by the authors, i.e. regional control, has been emphasized here: the public health concern consists of eradicating the disease in the relevant population, as fast as possible. On the other hand, very often the entire domain of interest for the epidemic, is either unknown, or difficult to manage for an affordable implementation of suitable environmental programmes. For regional control instead it might be sufficient to implement such programmes only in a given subregion conveniently chosen so to lead to an effective (exponentially fast) eradication of the epidemic in the whole habitat; it is evident that this practice may have an enormous importance in real cases with respect to both financial and practical affordability.


1 Introduction to Real-Time Digital Signal Processing


title: Agent Architectures for Flexible, Practical Teamwork

abstract: Teamwork in complex, dynamic, multi-agent domains mandates highly flexible coordination and communication. Simply fitting individual agents with precomputed coordination plans will not do, for their inflexibility can cause severe failures in teamwork, and their domain-specificity hinders reusability. Our central hypothesis is that the key to such flexibility and reusability is agent architectures with integrated teamwork capabilities. This fundamental shift in agent architectures is illustrated via an implemented candidate: STEAM. While STEAM is founded on the joint intentions theory, practical operationalization has required it to integrate several key novel concepts: (i) team synchronization to establish joint intentions; (ii) constructs for monitoring joint intentions and repair; and (iii) decision-theoretic communication selectivity (to pragmatically extend the joint intentions theory). Applications in three different complex domains, with empirical results, are presented.


title: Frappé: Querying the Linux Kernel Dependency Graph

abstract: Frappe is a developer tool for querying and visualizing the dependencies of large C/C++ software systems to the order of 10s of millions of lines of code in size. It supports developers with a range of code comprehension queries such as Does function X or something it calls write to global variable Y? and How much code could be affected if I change this macro? Results are overlaid on a visualization of the dependency graph data based on a cartographic map metaphor. In this paper, we give a brief overview of Frappe and describe our experiences implementing it on top of the Neo4j graph database. We detail the graph model used by Frappe and outline its key use cases using representative queries and their runtimes with the dependency graph data of the Unbreakable Enterprise Kernel. Finally, we discuss some of the open challenges in supporting source code queries across single and multiple versions of an evolving codebase with current property graph database technologies: performance, efficient storage, and the expressivity of the graph querying language given a graph model.


title: Human recombinant follistatin-288 suppresses plasma concentrations of follicle-stimulating hormone but is not a significant regulator of luteinizing hormone in castrated rams.

abstract: We have tested the hypothesis that treatment of castrated rams (wethers) with human recombinant follistatin-288 (FS-288) suppresses plasma concentrations of FSH but has no effect on plasma concentrations of LH. Wethers were given an i.v. injection of vehicle or 50 micrograms FS-288 followed by a 12-h i.v. infusion of vehicle or 800 micrograms FS-288, respectively. This dose and treatment regimen was identical to that used in a previous study in which wethers were treated with vehicle or human recombinant inhibin A (hr-inhibin). Human recombinant follistatin significantly (p < 0.05) suppressed plasma concentrations of FSH, but vehicle had no effect. The maximal suppression of FSH occurred 12-15 h after the start of treatment, at which time the plasma concentrations were reduced by 20.6%. The reduction in plasma concentrations of FSH caused by FS-288 was about 2.6-fold less than that observed after the same treatment with hr-inhibin. FS-288 had no effect on plasma concentrations of LH; this result was similar to the findings we obtained with hr-inhibin. These data suggest that follistatin is a less potent negative regulator of FSH secretion than inhibin and that follistatin is not a significant regulator of the secretion of LH in rams.


title: Effectiveness of a hospital-wide programme to improve compliance with hand hygiene

abstract: BACKGROUND ::: Hand hygiene prevents cross infection in hospitals, but compliance with recommended instructions is commonly poor. We attempted to promote hand hygiene by implementing a hospital-wide programme, with special emphasis on bedside, alcohol-based hand disinfection. We measured nosocomial infections in parallel. ::: ::: ::: METHODS ::: We monitored the overall compliance with hand hygiene during routine patient care in a teaching hospital in Geneva, Switzerland, before and during implementation of a hand-hygiene campaign. Seven hospital-wide observational surveys were done twice yearly from December, 1994, to December, 1997. Secondary outcome measures were nosocomial infection rates, attack rates of methicillin-resistant Staphylococcus aureus (MRSA), and consumption of handrub disinfectant. ::: ::: ::: FINDINGS ::: We observed more than 20,000 opportunities for hand hygiene. Compliance improved progressively from 48% in 1994, to 66% in 1997 (p<0.001). Although recourse to handwashing with soap and water remained stable, frequency of hand disinfection substantially increased during the study period (p<0.001). This result was unchanged after adjustment for known risk factors of poor adherence. Hand hygiene improved significantly among nurses and nursing assistants, but remained poor among doctors. During the same period, overall nosocomial infection decreased (prevalence of 16.9% in 1994 to 9.9% in 1998; p=0.04), MRSA transmission rates decreased (2.16 to 0.93 episodes per 10,000 patient-days; p<0.001), and the consumption of alcohol-based handrub solution increased from 3.5 to 15.4 L per 1000 patient-days between 1993 and 1998 (p<0.001). ::: ::: ::: INTERPRETATION ::: The campaign produced a sustained improvement in compliance with hand hygiene, coinciding with a reduction of nosocomial infections and MRSA transmission. The promotion of bedside, antiseptic handrubs largely contributed to the increase in compliance.


The Role of Hope and Optimism in Breast Cancer Patients: A Systematic Review


title: The Hypothenar Ulnar Artery: A Case Series of Aneurysm, Tortuosity and Thrombosis

abstract: Presented is a case series of duplex ultrasound examination for evaluation of the hypothenar ulnar artery. Because of its superficial location, the distal ulnar artery is well characterized by duplex ultrasound. Aneurysm, thrombosis, and ectasia are the most common abnormal findings. The study is a single-center database query of all arterial duplex examinations performed from January 1, 2000 through December 31, 2006, and containing the words ulnar artery. A total of 2766 upper-extremity arterial duplex scans were queried yielding 155 scans meeting the search criteria. A total of 9 ulnar arteries were interrogated into the hypothenar area of the hand. Seven of them were abnormal, and 2 were follow-up studies of a patient who had undergone repair of bilateral ulnar artery aneurysms. Reported symptoms include pain, digital lesions, pallor or a pulsatile mass. Of the 7 abnormal ulnar arteries, 3 arteries were aneurysmal (diameter greater than 1.5 times the proximal segment), and 3 were ectatic (increase in diameter less than 1.5 times the proximal segment). Two arteries were tortuous, and one was occluded. All but one patient was a past or present smoker. All abnormalities were attributed to occupational repetitive trauma and one patient experienced a single traumatic event. The mean patient age was 42 years (median 37) and all were male. Of the 3 aneurysms, the largest was 17 mm and was associated with mural thrombus and a biphasic Doppler signal. The other 2 aneurysms were 5.0 and 6.9 mm with mural thrombus and triphasic Doppler signals. One patient underwent successful thrombolysis, 3 patients were followed conservatively, and 2 of the aneurysms and one of the thrombosed arteries were surgically repaired. Although abnormal findings of the distal ulnar artery are rare, they may be well visualized and characterized with duplex ultrasound. Duplex ultrasound is an ideal diagnostic modality for evaluation and follow-up of distal ulnar artery abnormalities in experienced vascular laboratories.


title: CODOMs: protecting software with code-centric memory domains

abstract: Today's complex software systems are neither secure nor reliable. The rudimentary software protection primitives provided by current hardware forces systems to run many distrusting software components (e.g., procedures, libraries, plugins, modules) in the same protection domain, or otherwise suffer degraded performance from address space switches. We present CODOMs (COde-centric memory DOMains), a novel architecture that can provide finer-grained isolation between software components with effectively zero run-time overhead, all at a fraction of the complexity of other approaches. An implementation of CODOMs in a cycle-accurate full-system x86 simulator demonstrates that with the right hardware support, finer-grained protection and run-time performance can peacefully coexist.


title: A Taxonomy of Software-Defined Networking (SDN)-Enabled Cloud Computing

abstract: Software-Defined Networking (SDN) opened up new opportunities in networking with its concept of the segregated control plane from the data-forwarding hardware, which enables the network to be programmable, adjustable, and reconfigurable dynamically. These characteristics can bring numerous benefits to cloud computing, where dynamic changes and reconfiguration are necessary with its on-demand usage pattern. Although researchers have studied utilizing SDN in cloud computing, gaps still exist and need to be explored further. In this article, we propose a taxonomy to depict different aspects of SDN-enabled cloud computing and explain each element in details. The detailed survey of studies utilizing SDN for cloud computing is presented with focus on data center power optimization, traffic engineering, network virtualization, and security. We also present various simulation and empirical evaluation methods that have been developed for SDN-enabled clouds. Finally, we analyze the gap in current research and propose future directions.


title: Nonoperative management of stable thoracolumbar burst fractures with early ambulation and bracing.

abstract: Eighteen neurologically intact patients with burst fractures at the thoracolumbar junction were treated with early ambulation in a total contact orthosis. No attempt was made to reduce the associated deformity. Selection criteria excluded patients with posterior column disruption. Hospital stay averaged 10 days. Follow-up averaged 19 months. Mean kyphosis was 19 at time of injury and 20 at follow-up. At follow-up, 15 patients rated their pain as little or none. Seventeen patients had little ot no restriction of activity


title: Further Meta-Evaluation of Machine Translation

abstract: This paper analyzes the translation quality of machine translation systems for 10 language pairs translating between Czech, English, French, German, Hungarian, and Spanish. We report the translation quality of over 30 diverse translation systems based on a large-scale manual evaluation involving hundreds of hours of effort. We use the human judgments of the systems to analyze automatic evaluation metrics for translation quality, and we report the strength of the correlation with human judgments at both the system-level and at the sentence-level. We validate our manual evaluation methodology by measuring intra- and inter-annotator agreement, and collecting timing information.


title: Inferring clinical depression from speech and spoken utterances

abstract: In this paper, we investigate the problem of detecting depression from recordings of subjects' speech using speech processing and machine learning. There has been considerable interest in this problem in recent years due to the potential for developing objective assessments from real-world behaviors, which may provide valuable supplementary clinical information or may be useful in screening. The cues for depression may be present in “what is said” (content) and “how it is said” (prosody). Given the limited amounts of text data, even in this relatively large study, it is difficult to employ standard method of learning models from n-gram features. Instead, we learn models using word representations in an alternative feature space of valence and arousal. This is akin to embedding words into a real vector space albeit with manual ratings instead of those learned with deep neural networks [1]. For extracting prosody, we employ standard feature extractors such as those implemented in openSMILE and compare them with features extracted from harmonic models that we have been developing in recent years. Our experiments show that our features from harmonic model improve the performance of detecting depression from spoken utterances than other alternatives. The context features provide additional improvements to achieve an accuracy of about 74%, sufficient to be useful in screening applications.


title: Combinatorial test design in practice

abstract: Combinatorial testing is a specification based sampling technique that provides a systematic way to select combinations of program inputs or features for testing. It has been applied over the years to test input data, configurations, web forms, protocols, graphical user interfaces and for testing software product lines. This tutorial introduces the fundamentals of combinatorial testing, including both practical and theoretical foundations, to provide a comprehensive introduction that is relevant to both test practitioners and software engineering researchers. The tutorial will present an overview of Combinatorial Test Design (CTD) and describe some state of the art research advances and domains where CTD has been applied. It will present the theoretical underpinnings of CTD and explain a few algorithmic techniques used to generate CTD samples, as well as describe recent work on practical extensions to these algorithms that allow for a broader use of CTD. A session devoted to modeling test problems using CTD will follow, with attendees obtaining hands-on experience using several realistic problems.


title: Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads

abstract: This paper addresses the problem of predicting popularity of comments in an online discussion forum using reinforcement learning, particularly addressing two challenges that arise from having natural language state and action spaces. First, the state representation, which characterizes the history of comments tracked in a discussion at a particular point, is augmented to incorporate the global context represented by discussions on world events available in an external knowledge source. Second, a two-stage Q-learning framework is introduced, making it feasible to search the combinatorial action space while also accounting for redundancy among sub-actions. We experiment with five Reddit communities, showing that the two methods improve over previous reported results on this task.


title: Enhancing privacy through negative representations of data

abstract: Abstract : The paper introduces the concept of a negative database, in which a set of records DB is represented by its complement set. That is, all the records not in DB are represented, and DB itself is not explicitly stored. After introducing the concept, several results are given regarding the feasibility of such a scheme and its potential for enhancing privacy. It is shown that a database consisting of n, l-bit records can be represented negatively using only O"ln" records. It is also shown that membership queries for DB can be processed against the negative representation in time no worse than linear in its size and that reconstructing the database DB represented by a negative database NDB given as input is an NP-hard problem when time complexity is measured as a function of the size of NDB.


title: A Joint Power Control Scheme with Game Theory, Waveform Adaptation and Multiuser Detection

abstract: In the paper, we combine waveform adaptation with multi-user detection to propose a new game-theoretic power control algorithm, which both the transmitter signature waveforms and receiver filter coefficients for all users are sequentially updated in a distributed way. We show that the proposed algorithm converges to a fixed-point power vector where the information rate for each of the users is maximized, the transmitter signature waveforms converge to be orthogonal waveforms that the interferences between users are minimized and the receiver filter converges to the minimum mean squared error (MMSE) multiuser detector. Moreover, the simulation results show that the new power control scheme outperforms the game model in [4] in terms of the maximum of SINR or the net information rate obtained on condition that total transmitted power is given.


title: State of the Art in Procedural Noise Functions

abstract: Procedural noise functions are widely used in Computer Graphics, from off-line rendering in movie production to interactive video games. The ability to add complex and intricate details at low memory and authoring cost is one of its main attractions. This state-of-the-art report is motivated by the inherent importance of noise in graphics, the widespread use of noise in industry, and the fact that many recent research developments justify the need for an up-to date survey. Our goal is to provide both a valuable entry point into the field of procedural noise functions, as well as a comprehensive view of the field to the informed reader. In this report, we cover procedural noise functions in all their aspects. We outline recent advances in research on this topic, discussing and comparing recent and well established methods. We first formally define procedural noise functions based on stochastic processes and then classify and review existing procedural noise functions. We discuss how procedural noise functions are used for modeling and how they are applied on surfaces. We then introduce analysis tools and apply them to evaluate and compare the major approaches to noise generation. We finally identify several directions for future work.


title: Image segmentation by histogram thresholding using fuzzy sets

abstract: Methods for histogram thresholding based on the minimization of a threshold-dependent criterion function might not work well for images having multimodal histograms. We propose an approach to threshold the histogram according to the similarity between gray levels. Such a similarity is assessed through a fuzzy measure. In this way, we overcome the local minima that affect most of the conventional methods. The experimental results demonstrate the effectiveness of the proposed approach for both bimodal and multimodal histograms.


title: Selectivities in the 1,3-dipolar cycloaddition of nitrile oxides to dicyclopentadiene and its derivatives

abstract: Abstract The 1,3-dipolar cycloaddition of nitrile oxides, generated from aldoximes and nitroalkanes, to dicyclopentadiene proceeds with complete chemo- and stereoselectivity. The approach of the dipole takes place exclusively from the exo-face of the bicycloheptane moiety providing a mixture of regioisomers in approximately 55:45 ratio. On the other hand, nitrile oxide cycloaddition to dimethyldicyclopentadiene dicarboxylate (Thiele's ester), besides exhibiting chemo- and stereoselectivity as in the case of dicyclopentadiene, exhibits complete regioselectivity as well providing a single isomer in good yield. The Influence of remote substituents, including sterically ‘sterile’ ones, on the regioselectivity has also been investigated using 8-hydroxy and 1-keto derivatives of dicyclopentadiene. These experimental observations have been investigated through gas phase and solvent model MO calculations on the transition state geometries at semiempirical (PM3) and hybrid ab initio-DFT levels of theory. The Computational methods employed in this study were rigorously tested by performing model calculations on well-established experimental observations.


title: Automatic Article Commenting: the Task and Dataset

abstract: Comments of online articles provide extended views and improve user engagement. Automatically making comments thus become a valuable functionality for online forums, intelligent chatbots, etc. This paper proposes the new task of automatic article commenting, and introduces a large-scale Chinese dataset with millions of real comments and a human-annotated subset characterizing the comments' varying quality. Incorporating the human bias of comment quality, we further develop automatic metrics that generalize a broad set of popular reference-based metrics and exhibit greatly improved correlations with human evaluations.


title: Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson's method

abstract: Within-subject ANOVAs are a powerful tool to analyze data because the variance associated to differences between the participants is removed from the analysis. Hence, small differences, when present for most of the participants, can be significant even when the participants are very different from one another. Yet, graphs showing standard error or confidence interval bars are misleading since these bars include the between-subject variability. Loftus and Masson (1994) noticed this fact and proposed an alternate method to compute the error bars. However, i) their approach requires that the ANOVA be performed first, which is paradoxical since a graph is an aid to decide whether to perform analyses or not; ii) their method provides a single error bar for all the conditions, masking information such as the heterogeneity of variances across conditions; iii) the method proposed is difficult to implement in commonly-used graphing software. Here we propose a simple alternative and sow how it can be implemented in SPSS.


title: Optimal Joint Power and Subcarrier Allocation for Full-Duplex Multicarrier Non-Orthogonal Multiple Access Systems

abstract: In this paper, we investigate resource allocation algorithm design for multicarrier non-orthogonal multiple access (MC-NOMA) systems employing a full-duplex (FD) base station for serving multiple half-duplex (HD) downlink and uplink users simultaneously. The proposed algorithm is obtained from the solution of a non-convex optimization problem for the maximization of the weighted sum system throughput. We apply monotonic optimization to develop an optimal joint power and subcarrier allocation policy. The optimal resource allocation policy serves as a system performance benchmark due to its high computational complexity. Furthermore, a suboptimal iterative scheme based on successive convex approximation is proposed to strike a balance between computational complexity and optimality. Our simulation results reveal that the proposed suboptimal algorithm achieves a close-to-optimal performance. In addition, FD MC-NOMA systems employing the proposed resource allocation algorithms provide a substantial system throughput improvement compared with conventional HD multicarrier orthogonal multiple access (MC-OMA) systems and other baseline schemes. In addition, our results unveil that FD MC-NOMA systems enable a fairer resource allocation compared with traditional HD MC-OMA systems.


title: A robust algorithm for detection and classification of traffic signs in video data

abstract: The accurate identification and recognition of the traffic signs is a challenging problem as the developed systems have to address a large number of imaging problems such as motion artifacts, various weather conditions, shadows and partial occlusion, issues that are often encountered in video traffic sequences that are captured from a moving vehicle. These factors substantially degrade the performance of the existing traffic sign recognition (TSR) systems and in this paper we detail the implementation of a new strategy that entails three distinct computational stages. The first component addresses the robust identification of the candidate traffic signs in each frame of the video sequence. The second component discards the traffic sign candidates that do not comply with stringent shape constraints, and the last component implements the classification of the traffic signs using Support Vector Machines (SVMs). The main novel elements of our TSR algorithm are given by the approach that has been developed for traffic sign classification and by the experimental evaluation that was employed to identify the optimal image attributes that are able to maximize the traffic sign classification performance. The TSR algorithm has been validated using video sequences that include the most important categories of signs that are used to regulate the traffic on the Irish and UK roads, and it achieved 87.6% sign detection, 99.2% traffic sign classification accuracy and 86.7% overall traffic sign recognition.


title: Robust greedy deep dictionary learning for ECG arrhythmia classification

abstract: This work proposes a new deep learning method which we call robust deep dictionary learning RDDL. RDDL is suitable for learning representations from signals corrupted with sparse but large outliers such as artifacts and noise that are more heavy tailed than Gaussian distributions. Such outliers are common in biomedical signals e.g. EEG and ECG. RDDL learns multiple levels of non-linear dictionaries for representing the data. Instead of the standard Euclidean cost function that is usually employed in dictionary learning, we propose a robust l 1 -norm cost function. In order to achieve sparse representation, an l 1 -norm is imposed on the learned representation. The ‘depth’ arises from the fact that multiple levels of dictionaries are learnt. The full formulation is solved in a greedy fashion, one layer at a time. To study the extent of usefulness of RDDL, we first benchmark it with two wellknown deep learning tools — the stacked denoising autoencoder and the deep belief network methods; experiments are carried out on benchmark deep learning datasets — MNIST, CIFAR-10 and SVHN. In all cases, our method yields the best results. Then the proposed method is used for learning representations of ECG data (containing arficacts) and for their classification using the MIT-BIH arrhythmia classification database. We compare it with traditional techniques as well as on deep learning tools. Our method yields the best results.


title: Psychological Stress and the Human Immune System: A Meta-Analytic Study of 30 Years of Inquiry.

abstract: The present report meta-analyzes more than 300 empirical articles describing a relationship between psychological stress and parameters of the immune system in human participants. Acute stressors (lasting minutes) were associated with potentially adaptive upregulation of some parameters of natural immunity and downregulation of some functions of specific immunity. Brief naturalistic stressors (such as exams) tended to suppress cellular immunity while preserving humoral immunity. Chronic stressors were associated with suppression of both cellular and humoral measures. Effects of event sequences varied according to the kind of event (trauma vs. loss). Subjective reports of stress generally did not associate with immune change. In some cases, physical vulnerability as a function of age or disease also increased vulnerability to immune change during stressors.


title: SOI-based devices and technologies for High Voltage ICs

abstract: This paper reviews the current status Silicon-On-Insulator (SOI) devices and technologies for high voltage integrated circuits (HVICs) and discusses new trends in the field. The paper focuses on novel SOI-based RESURF concepts such as superjunction, linearly graded profile or SOI membrane technology. Due to its intrinsic isolation properties, the SOI is the ideal substrate for bipolar MOS switches such as the LIGBT. In fact, the only LIGBT products on the market are fabricated using Dielectric Isolation (type of SOI) and SOI technology. The paper finishes with an overview of the fierce fight of technology survival in terms of specific Ron vs breakdown voltage. Here the SOI competes with quasi-vertical DMOS technologies and advanced bulk BCD technologies.


title: Do intermittent diets provide physiological benefits over continuous diets for weight loss? A systematic review of clinical trials

abstract: Energy restriction induces physiological effects that hinder further weight loss. Thus, deliberate periods of energy balance during weight loss interventions may attenuate these adaptive responses to energy restriction and thereby increase the efficiency of weight loss (i.e. the amount of weight or fat lost per unit of energy deficit). To address this possibility, we systematically searched MEDLINE, PreMEDLINE, PubMed and Cinahl and reviewed adaptive responses to energy restriction in 40 publications involving humans of any age or body mass index that had undergone a diet involving intermittent energy restriction, 12 with direct comparison to continuous energy restriction. Included publications needed to measure one or more of body weight, body mass index, or body composition before and at the end of energy restriction. 31 of the 40 publications involved ‘intermittent fasting’ of 1–7-day periods of severe energy restriction. While intermittent fasting appears to produce similar effects to continuous energy restriction to reduce body weight, fat mass, fat-free mass and improve glucose homeostasis, and may reduce appetite, it does not appear to attenuate other adaptive responses to energy restriction or improve weight loss efficiency, albeit most of the reviewed publications were not powered to assess these outcomes. Intermittent fasting thus represents a valid – albeit apparently not superior – option to continuous energy restriction for weight loss.


title: Targeted Employee Retention : Performance-Based and Job-Related Differences in Reported Reasons for Staying

abstract: What makes employees stay with a particular employer? To find out, we developed a content model of 12 retention factors in the context of previous theory and research and analyzed open-ended responses from 24,829 employees in the leisure and hospitality industry. Our coding of their comments supported the identified framework and revealed that job satisfaction, extrinsic rewards, constituent attachments, organizational commitment, and organizational prestige were the most frequently mentioned reasons for staying. High performers and nonhourly workers were more likely to cite advancement opportunities and organizational prestige as reasons for staying, whereas low performers and hourly employees were more likely to cite extrinsic rewards. These findings highlight the importance of differentiating human resource management practices when the goal is to retain valued employees. © 2009 Wiley Periodicals, Inc.


title: A measurement-driven analysis of information propagation in the flickr social network

abstract: Online social networking sites like MySpace, Facebook, and Flickr have become a popular way to share and disseminate content. Their massive popularity has led to viral marketing techniques that attempt to spread content, products, and ideas on these sites. However, there is little data publicly available on viral propagation in the real world and few studies have characterized how information spreads over current online social networks. In this paper, we collect and analyze large-scale traces of information dissemination in the Flickr social network. Our analysis, based on crawls of the favorite markings of 2.5 million users on 11 million photos, aims at answering three key questions: (a) how widely does information propagate in the social network? (b) how quickly does information propagate? and (c) what is the role of word-of-mouth exchanges between friends in the overall propagation of information in the network? Contrary to viral marketing ``intuition,'' we find that (a) even popular photos do not spread widely throughout the network, (b) even popular photos spread slowly through the network, and (c) information exchanged between friends is likely to account for over 50 of all favorite-markings, but with a significant delay at each hop.


title: Rotor structure in 50 kW spoke-type interior permanent magnet synchronous motor with ferrite permanent magnets for automotive applications

abstract: In light of global warming and the rapidly increasing prices of rare earths, electric vehicles with motors free of rare earths are needed. We are therefore developing an interior permanent magnet synchronous motor (IPMSM) that utilizes ferrite permanent magnets. Because the residual flux density of a ferrite magnet is lower than that of a rare earth magnet, we have adopted a spoke-type IPMSM with radially arranged ferrite magnets. This paper proposes two rotor structure designs for spoke-type IPMSMs with ferrite magnets, and presents the results of two-dimensional finite element analysis (FEA) for the two designs. The results indicate that the performance loss from using ferrite magnets can be moderated by effectively utilizing reluctance torque in a spoke-type IPMSM. Finally, a rotor structure that has sufficient performance to be usable in a traction motor free of rare earths is examined through experiments on a prototype and three-dimensional FEA.


title: A truly two-dimensional systolic array FPGA implementation of QR decomposition

abstract: We have implemented a two-dimensional systolic array QR decomposition on a Xilinx Virtex5 FPGA using the Givens rotation algorithm. QR decomposition is a key step in many DSP applications including sonar beamforming, channel equalization, and 3G wireless communication. Compared to previous work that implements Givens rotations using a one-dimensional systolic array, our implementation uses a truly two-dimensional systolic array architecture. As a result, latency scales well for larger matrices. In addition, prior work avoids divide and square root operations in the Givens rotation algorithm by using special operations such as CORDIC or special number systems such as the logarithmic number system (LNS). In contrast, our design uses straightforward floating-point divide and square root implementations, which makes it easier to be used within a larger system. In our design, the input matrix size can be configured at compile time to many different sizes, making it easily scalable to future large FPGAs or over multiple FPGAs. The QR module is fully pipelined with a throughput of over 130MHz for the IEEE single-precision floating-point format. The peak performance for a 12 × 12 input matrix is approximately 35 GFLOPs.