title: How Robust is 3D Human Pose Estimation to Occlusion?

abstract: Occlusion is commonplace in realistic human-robot shared environments, yet its effects are not considered in standard 3D human pose estimation benchmarks. This leaves the question open: how robust are state-of-the-art 3D pose estimation methods against partial occlusions? We study several types of synthetic occlusions over the Human3.6M dataset and find a method with state-of-the-art benchmark performance to be sensitive even to low amounts of occlusion. Addressing this issue is key to progress in applications such as collaborative and service robotics. We take a first step in this direction by improving occlusion-robustness through training data augmentation with synthetic occlusions. This also turns out to be an effective regularizer that is beneficial even for non-occluded test cases.


title: Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks

abstract: Atrial fibrillation (AF) is the most common cardiac arrhythmias causing morbidity and mortality. AF may appear as episodes of very short (i.e., proximal AF) or sustained duration (i.e., persistent AF), either form of which causes irregular ventricular excitations that affect the global function of the heart. It is an unmet challenge for early and automatic detection of AF, limiting efficient treatment strategies for AF. In this study, we developed a new method based on continuous wavelet transform (CWT) and 2D convolutional neural networks (CNNs) to detect AF episodes. The proposed method analyzed the time-frequency features of the electrocardiogram (ECG), thus being different to conventional AF detecting methods that implement isolating atrial or ventricular activities. Then a 2D convolutional neural network was trained to improve AF detection performance. The MIT-BIH Atrial Fibrillation Database (AFDB) was used for evaluating the algorithm. The efficacy of the proposed method was compared with those of some existing methods, most of which implemented the same dataset. The newly developed algorithm using convolutional neural networks achieved 99.41%, 98.91%, 99.39% and 99.23% for the sensitivity (Se), specificity (Sp), positive predictive value (PPV) and overall accuracy (ACC) respectively. As the proposed algorithm targets the time-frequency feature of ECG signals rather than isolated atrial or ventricular activity, it has the ability to detect AF episodes for using just five beats, suggesting practical applications in the future.


title: Using bioimpedance plethysmography for measuring the pulse wave velocity of peripheral vascular

abstract: Real-time cardiovascular monitors are crucial for preventing cardiovascular diseases. In this study, a simple bioimpedance plethysmography method was employed to measure the pulse wave velocity (PWV) from the radial artery in the wrist to the middle finger. In addition, electrocardiography was combined with the bioimpedance method to calculate the PWV from R and T waves to the middle finger. The results indicated that pulse waves can be measured from the middle finger, and PWV can be calculated from the middle finger. In this experiment, cuffs were employed to temporarily block blood flow to observe changes in PWV. The statistical results indicated that temporary blockage of blood flow did not influence the PWV of typical healthy people. Moreover, multiple regression analysis was adopted to establish a regression equation for estimating two types of PWV, and the relevance of the two types of PWV with other physiological parameters were also analysed.


title: Big Data and Science: Myths and Reality

abstract: As Big Data inexorably draws attention from every segment of society, it has also suffered from many characterizations that are incorrect. This article explores a few of the more common myths about Big Data, and exposes the underlying truths.


SEPIO: A Semantic Model for the Integration and Analysis of Scientific Evidence


Pricing in Ride-Share Platforms: A Queueing-Theoretic Approach


title: Deep Cauchy Hashing for Hamming Space Retrieval

abstract: Due to its computation efficiency and retrieval quality, hashing has been widely applied to approximate nearest neighbor search for large-scale image retrieval, while deep hashing further improves the retrieval quality by end-to-end representation learning and hash coding. With compact hash codes, Hamming space retrieval enables the most efficient constant-time search that returns data points within a given Hamming radius to each query, by hash table lookups instead of linear scan. However, subject to the weak capability of concentrating relevant images to be within a small Hamming ball due to mis-specified loss functions, existing deep hashing methods may underperform for Hamming space retrieval. This work presents Deep Cauchy Hashing (DCH), a novel deep hashing model that generates compact and concentrated binary hash codes to enable efficient and effective Hamming space retrieval. The main idea is to design a pairwise cross-entropy loss based on Cauchy distribution, which penalizes significantly on similar image pairs with Hamming distance larger than the given Hamming radius threshold. Comprehensive experiments demonstrate that DCH can generate highly concentrated hash codes and yield state-of-the-art Hamming space retrieval performance on three datasets, NUS-WIDE, CIFAR-10, and MS-COCO.


title: An event-driven simulation methodology for integrated switching power supplies in SystemVerilog

abstract: Emerging power-supply-on-chip applications such as on-chip DC-DC conversion, energy harvesting, and LED drivers use switching regulator ICs integrated with digital controllers. Although the resulting mixed-signal systems call for efficient system-level behavioral simulation, this remains difficult due to the fast switching and slow transients of the regulator and the high complexity of the controller. This paper presents a truly event-driven approach for modeling and simulating such integrated power systems entirely in SystemVerilog. By modeling various switching regulator topologies as switched linear networks whose responses can be expressed as a sum of complex exponentials, ctm-1e--atu(t), the accurate voltage/current waveforms can be captured by updating the coefficients, c, at each input or switching event. The model is applied to two examples, a power factor corrector and switched-capacitor DC-DC converter, and the results demonstrate that the proposed simulator can achieve 20~100× improvements in speed while maintaining SPICE-level accuracy in evaluating power efficiency, steady-state ripples, and power factor.


title: ProvManager: a provenance management system for scientific workflows

abstract: Running scientific workflows in distributed and heterogeneous environments has been a motivating approach for provenance management, which is loosely coupled to the workflow execution engine. This kind of approach is interesting because it allows both storage and access to provenance data in a homogeneous way, even in an environment where different workflow management systems work together. However, current approaches overload scientists with many ad hoc tasks, such as script adaptations and implementations of extra functionalities to provide provenance independence. This paper proposes ProvManager, a provenance management approach that eases the gathering, storage, and analysis of provenance information in a distributed and heterogeneous environment scenario, without putting the burden of adaptations on the scientist. ProvManager leverages the provenance management at the experiment level by integrating different workflow executions from multiple workflow management systems. Copyright © 2011 John Wiley & Sons, Ltd.


title: Virgaviridae: a new family of rod-shaped plant viruses

abstract: The new plant virus family Virgaviridae is described. The family is named because its members have rod-shaped virions (from the Latin virga = rod), and it includes the genera Furovirus, Hordeivirus, Pecluvirus, Pomovirus, Tobamovirus and Tobravirus. The chief characteristics of members of the family are presented with phylogenetic analyses of selected genes to support the creation of the family. Species demarcation criteria within the genera are examined and discussed.


title: Review of automatic sarcasm detection

abstract: Sentiment Analysis has become a significant research matter for its probable in tapping into the vast amount of opinions generated by the people. Sentiment analysis deals with the computational conduct of opinion, sentiment within the text. People sometimes uses sarcastic text to express their opinion within the text. Sarcasm is a type of communication act in which the people write the contradictory of what they mean in reality. The intrinsically vague nature of sarcasm sometimes makes it hard to understand. Recognizing sarcasm can promote many sentiment analysis applications. Automatic detecting sarcasm is an approach for predicting sarcasm in text. In this paper we have tried to talk of the past work that has been done for detecting sarcasm in the text. This paper talk of approaches, features, datasets, and issues associated with sarcasm detection. Performance values associated with the past work also has been discussed. Various tables that present different dimension of past work like dataset used, features, approaches, performance values has also been discussed.


title: A method for classification of network traffic based on C5.0 Machine Learning Algorithm

abstract: Monitoring of the network performance in highspeed Internet infrastructure is a challenging task, as the requirements for the given quality level are service-dependent. Backbone QoS monitoring and analysis in Multi-hop Networks requires therefore knowledge about types of applications forming current network traffic. To overcome the drawbacks of existing methods for traffic classification, usage of C5.0 Machine Learning Algorithm (MLA) was proposed. On the basis of statistical traffic information received from volunteers and C5.0 algorithm we constructed a boosted classifier, which was shown to have ability to distinguish between 7 different applications in test set of 76,632–1,622,710 unknown cases with average accuracy of 99.3–99.9%. This high accuracy was achieved by using high quality training data collected by our system, a unique set of parameters used for both training and classification, an algorithm for recognizing flow direction and the C5.0 itself. Classified applications include Skype, FTP, torrent, web browser traffic, web radio, interactive gaming and SSH. We performed subsequent tries using different sets of parameters and both training and classification options. This paper shows how we collected accurate traffic data, presents arguments used in classification process, introduces the C5.0 classifier and its options, and finally evaluates and compares the obtained results.


title: Methods for Classifying Errors on the Raven's Standard Progressive Matrices Test

abstract: Although many psychometric tests, like Raven’s Progressive Matrices, are commonly evaluated according to total score, additional variables can lend insight into the underlying cognitive processes. We examine conceptual errors on the Raven’s Standard Progressive Matrices (SPM) test. We present a complete classification of error types on the SPM using a two-kind coding scheme, yielding � 95% inter-rater reliability. We also examine how to extract error data from a computational model, and we present a method for measuring errors through systematic ablation to create a “population” of models whose performance can be examined as a group. We present a preliminary analysis of error patterns on the SPM from typically developing individuals, individuals diagnosed with autism, and a computational model called ASTI. We discuss what the error patterns suggest regarding cognition on the SPM and routes towards improving the ASTI model.


title: Alignment of Monophonic and Polyphonic Music to a Score

abstract: Music alignment is the association of events in a score with points in the time axis of an audio signal. The signal is thus segmented according to the events in the score. We propose a new methodology for automatic alignment based on dynamic time warping, where the spectr al peak structure is used to compute the local distance, enhanced by a model of attacks and of silence. The methodology can cope with performances consider ed difficult to align, like polyphonic music, trills, fast sequences, or multi-instrument music. An optimisation of the representation of the alignment path makes the method applicable to long sound files, so that unit databases can be fully automatically segmented and labeled. On 708 sequences of synthesised music, we achieved an averag e offset of 25 ms and an error rate of 2.5%.


title: Effects of yoga versus walking on mood, anxiety, and brain GABA levels: a randomized controlled MRS study.

abstract: Abstract Objectives: Yoga and exercise have beneficial effects on mood and anxiety. γ-Aminobutyric acid (GABA)-ergic activity is reduced in mood and anxiety disorders. The practice of yoga postures is associated with increased brain GABA levels. This study addresses the question of whether changes in mood, anxiety, and GABA levels are specific to yoga or related to physical activity. Methods: Healthy subjects with no significant medical/psychiatric disorders were randomized to yoga or a metabolically matched walking intervention for 60 minutes 3 times a week for 12 weeks. Mood and anxiety scales were taken at weeks 0, 4, 8, 12, and before each magnetic resonance spectroscopy scan. Scan 1 was at baseline. Scan 2, obtained after the 12-week intervention, was followed by a 60-minute yoga or walking intervention, which was immediately followed by Scan 3. Results: The yoga subjects (n = 19) reported greater improvement in mood and greater decreases in anxiety than the walking group (n = 15). There were positiv...


title: Laser driver switching 20 A with 2 ns pulse width using GaN

abstract: A GaN-HEMT-based circuit is presented capable of switching 20 A of current with less than about 0.5 ns rise and fall time. This demonstrates the potential of GaN transistors for high-current switching applications, even if breakdown voltage requirements are low. The current driver is used to realize an optical pulse picker generating 10 ps optical pulses of more than 30 W with a variable repetition rate between 1 kHz and 100 MHz.


title: Detecting and Defending Against Third-Party Tracking on the Web

abstract: While third-party tracking on the web has garnered much attention, its workings remain poorly understood. Our goal is to dissect how mainstream web tracking occurs in the wild. We develop a client-side method for detecting and classifying five kinds of third-party trackers based on how they manipulate browser state. We run our detection system while browsing the web and observe a rich ecosystem, with over 500 unique trackers in our measurements alone. We find that most commercial pages are tracked by multiple parties, trackers vary widely in their coverage with a small number being widely deployed, and many trackers exhibit a combination of tracking behaviors. Based on web search traces taken from AOL data, we estimate that several trackers can each capture more than 20% of a user's browsing behavior. We further assess the impact of defenses on tracking and find that no existing browser mechanisms prevent tracking by social media sites via widgets while still allowing those widgets to achieve their utility goals, which leads us to develop a new defense. To the best of our knowledge, our work is the most complete study of web tracking to date.


Bloom's taxonomy question categorization using rules and N-gram approach


title: Overconfidence as a cause of diagnostic error in medicine.

abstract: The great majority of medical diagnoses are made using automatic, efficient cognitive processes, and these diagnoses are correct most of the time. This analytic review concerns the exceptions: the times when these cognitive processes fail and the final diagnosis is missed or wrong. We argue that physicians in general underappreciate the likelihood that their diagnoses are wrong and that this tendency to overconfidence is related to both intrinsic and systemically reinforced factors. We present a comprehensive review of the available literature and current thinking related to these issues. The review covers the incidence and impact of diagnostic error, data on physician overconfidence as a contributing cause of errors, strategies to improve the accuracy of diagnostic decision making, and recommendations for future research.


title: Transductive support vector machines and applications in bioinformatics for promoter recognition

abstract: This paper introduces a novel transductive support vector machine (TSVM) model and compares it with the traditional inductive SVM on a key problem in bioinformatics - promoter recognition. While inductive reasoning is concerned with the development of a model (a function) to approximate data from the whole problem space (induction), and consecutively using this model to predict output values for a new input vector (deduction), in the transductive inference systems a model is developed for every new input vector based on some closest to the new vector data from an existing database and this model is used to predict only the output for this vector. The TSVM outperforms by far the inductive SVM models applied on the same problems. Analysis is given on the advantages and disadvantages of the TSVM. Hybrid TSVM-evolving connections systems are discussed as directions for future research.


title: Fault Injection in Modern Microprocessors Using On-Chip Debugging Infrastructures

abstract: In this paper, a new fault injection approach to measure SEU sensitivity in COTS microprocessors is presented. It consists in a hardware-implemented module that performs fault injection through the available JTAG-based On-Chip Debugger (OCD). This approach can be applied to most microprocessors, since JTAG standard is a widely supported interface and OCDs are usually available in current microprocessors. Hardware implementation avoids the communication between the target system and the software debugging tool, increasing significantly the fault injection efficiency. The method has been applied to a complex microprocessor (ARM). Experimental results demonstrate the approach is a fast, efficient, and cost-effective solution.


title: Optimal combination forecasts for hierarchical

abstract: In many applications, there are multiple time series that are hierarchically organized and can be aggregated at several different levels in groups based on products, geography or some other features. We call these ''hierarchical time series''. They are commonly forecast using either a ''bottom-up'' or a ''top-down'' method. In this paper we propose a new approach to hierarchical forecasting which provides optimal forecasts that are better than forecasts produced by either a top-down or a bottom-up approach. Our method is based on independently forecasting all series at all levels of the hierarchy and then using a regression model to optimally combine and reconcile these forecasts. The resulting revised forecasts add up appropriately across the hierarchy, are unbiased and have minimum variance amongst all combination forecasts under some simple assumptions. We show in a simulation study that our method performs well compared to the top-down approach and the bottom-up method. We demonstrate our proposed method by forecasting Australian tourism demand where the data are disaggregated by purpose of travel and geographical region.


title: Privacy-Preserving Public Auditing For Secure Cloud Storage

abstract: By using Cloud storage, users can access applications, services, software whenever they requires over the internet. Users can put their data remotely to cloud storage and get benefit of on-demand services and application from the resources. The cloud must have to ensure data integrity and security of data of user. The issue about cloud storage is integrity and privacy of data of user can arise. To maintain to overkill this issue here, we are giving public auditing process for cloud storage that users can make use of a third-party auditor (TPA) to check the integrity of data. Not only verification of data integrity, the proposed system also supports data dynamics. The work that has been done in this line lacks data dynamics and true public auditability. The auditing task monitors data modifications, insertions and deletions. The proposed system is capable of supporting public auditability, data dynamics and Multiple TPA are used for the auditing process. We also extend our concept to ring signatures in which HARS scheme is used. Merkle Hash Tree is used to improve block level authentication. Further we extend our result to enable the TPA to perform audits for multiple users simultaneously through Batch auditing. Index Terms: Cloud Storage, Data Dynamics, Public Auditing, Privacy Preserving, Ring Signatures.


The Factor Structure and Reliability of the Student Athletes' Motivation toward Sports and Academics Questionnaire (SAMSAQ)


PReMVOS : Proposal-generation , Refinement and Merging for the DAVIS Challenge on Video Object Segmentation 2018


title: Compact ROSA for 100-Gb/s (4 × 25 Gb/s) Ethernet with a PLC-based AWG demultiplexer

abstract: We report a compact receiver optical sub-assembly (ROSA) for 100GbE CFP4 transceivers. All the four channels of fabricated module exhibits wide wavelength passband and high receiver sensitivity enough for 10-km-reach transmission.


title: Factors affecting food choice in relation to fruit and vegetable intake: a review

abstract: The present review provides an investigation into the food choice decisions made by individuals in relation to fruit and vegetable consumption. A comprehensive body of evidence now exists concerning the protective effect of fruit and vegetables against a number of diseases, particularly cardiovascular disease and certain forms of cancer. Current UK recommendations are to increase intakes of fruit and vegetables to 400 g/person per d. In the main body of the review the factors that affect food choice decisions of adults in relation to fruit and vegetable consumption are studied, following a suggested framework of food choice. Factors covered include sensory appeal, familiarity and habit, social interactions, cost, availability, time constraints, personal ideology, media and advertising and health. The content of the review shows just how complex the food choice process can be. Health promotion techniques can be better targeted towards certain groups of individuals, all holding similar sets of values, when making food choice decisions. Food choice, in relation to fruit and vegetable intake, needs to be studied in more depth, in order to provide effective nutrition education programmes, in particular the sets of priorities that different sub-groups of the population consider when making food choice decisions.


title: Pad++: a zooming graphical interface for exploring alternate interface physics

abstract: We describe the current status of Pad++, a zooming graphical interface that we are exploring as an alternative to traditional window and icon-based approaches to interface design. We discuss the motivation for Pad++, describe the implementation, and present prototype applications. In addition, we introduce an informational physics strategy for interface design and briefly compare it with metaphor-based design strategies.


title: Characterizing Users' Visual Analytic Activity for Insight Provenance

abstract: Insight provenance - a historical record of the process and rationale by which an insight is derived - is an essential requirement in many visual analytics applications. Although work in this area has relied on either manually recorded provenance (for example, user notes) or automaticalily recorded event-based insight provenance (for example, clicks, drags and key-presses), both approaches have fundamental limitations. Our aim is to develop a new approach that combines the benefits of both approaches while avoiding their deficiencies. Toward this goal, we characterize users' visual analytic activity at multiple levels of granularity. Moreover, we identify a critical level of abstraction, Actions, that can be used to represent visual analytic activity, with a set of general but semantically meaningful behavior types. In turn, the action types can be used as the semantic building blocks for insight provenance. We present a catalog of common actions identified through observations of several different visual analytic systems. In addition, we define a taxonomy to categorize actions into three major classes based on their semantic intent. The concept of actions has been integrated into our lab's prototype visual analytic system, HARVEST, as the basis for its insight provenance capabilities.


title: Representation Learning of Entities and Documents from Knowledge Base Descriptions

abstract: In this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB). Given a document in a KB consisting of words and entity annotations, we train our model to predict the entity that the document describes and map the document and its target entity close to each other in a continuous vector space. Our model is trained using a large number of documents extracted from Wikipedia. The performance of the proposed model is evaluated using two tasks, namely fine-grained entity typing and multiclass text classification. The results demonstrate that our model achieves state-of-the-art performance on both tasks. The code and the trained representations are made available online for further academic research.


title: Complexity thinking in ALL practice

abstract: Complexity Theory is a movement that has its beginnings in the physical sciences and mathematics. However, the understandings of this movement have led to recent developments in theories of learning and cognition. Learning is no longer seen as an act of capturing information or a process of meaning construction; learning is understood as a process of adaptation and evolution that emerges through the learner’s interactions with a dynamic and responsive environment (Davis, Sumara, & Luce-Kapler, 2000; Doll, 1993). It is important to assert here that this theory is not one that lends itself to prescriptive practices, but what it offers is insights into the nature of learning, and as such guides preparation in facilitating learning (Davis & Sumara, 2005). This paper will explore complexity theory and how it can be used to inform ALL practice.


title: Indoor robot gardening: design and implementation

abstract: This paper describes the architecture and implementation of a distributed autonomous gardening system with applications in urban/indoor precision agriculture. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry tomatoes enhanced with sensors and computation to monitor their well-being (e.g. soil humidity, state of fruits) and with networking to communicate servicing requests to the robots. By embedding sensing, computation, and communication into the pots, task allocation in the system is de-centrally coordinated, which makes the system scalable and robust against the failure of a centralized agent. We describe the architecture of this system and present experimental results for navigation, object recognition, and manipulation as well as challenges that lie ahead toward autonomous precision agriculture with multi-robot teams.


title: MiR-26b suppresses tumor cell proliferation, migration and invasion by directly targeting COX-2 in lung cancer.

abstract: OBJECTIVE ::: Lung cancer, including non-small cell lung cancer (NSCLC), is the leading cause of cancer-related mortality worldwide. Despite recent advances in clinical and experimental oncology, the prognosis of patients with NSCLC still remains poor and the average survival time of patients suffer from lung cancer is low. Therefore, the potential mechanism accounting for the tumorigenesis of NSCLC is still needed to be explored. ::: ::: ::: MATERIALS AND METHODS ::: A lentiviral vector over-expressing miR-26b in A549 lung cancer cells was constructed. Cell proliferation, migration and invasion analysis were measured by cell counting kit (MTT), would healing assay and Transwell assay. Direct target of miR-26b in A549 cells was examined using bioinformatics and Luciferase assay. ::: ::: ::: RESULTS ::: Herein, we found that over-expression of miR-26b significantly inhibited the proliferation, migration and invasion of A549 lung cancer cell in vitro and suppressed the growth of established tumors in vivo. By using bioinformatics, we found that COX-2 (Cyclooxygenase-2) is one of the potential targets of miR-26b. Moreover, miR-26b was found to negatively regulate COX-2 protein level by directly targeting its 3'UTR. In addition, depletion of endogenous COX-2 by the specific siRNA could mimic the function of miR-26b overexpression. ::: ::: ::: CONCLUSIONS ::: Taken together, our results demonstrate that miR-26b could suppress lung cancer cells proliferation, migration and invasion by directly negative regulation of COX-2. MiR-26b could serve as a novel potential marker for NSCLC therapy.


Study on ZIGBEE Technology


title: Microdrivers: A New Architecture for Device Drivers

abstract: Commodity operating systems achieve good performance by running device drivers in-kernel. Unfortunately, this architecture offers poor fault isolation. This paper introduces microdrivers, which reduce the amount of driver code running in the kernel by splitting driver functionality between a small kernel-mode component and a larger user-mode component. This paper presents the microdriver architecture and techniques to refactor existing device drivers into microdrivers, achieving most of the benefits of user-mode driverswith the performance of kernel-mode drivers. Experiments on a network driver show that 75% of its code can be removed from the kernel without affecting common-case performance.


DETECTION AND CLASSIFICATION OF PLANT DISEASES BY IMAGE PROCESSING


title: MATLAB/SIMULINK BASED ANALYSIS OF VOLTAGE SOURCE INVERTER WITH SPACE VECTOR MODULATION

abstract: Space vector modulation (SVM) is the best modulation technique to drive 3-phase load such as 3-phase induction motor. In this paper, the pulse width modulation strategy with SVM is analyzed in detail. The modulation strategy uses switching time calculator to calculate the timing of voltage vector applied to the three-phase balanced-load. The principle of the space vector modulation strategy is performed using Matlab/Simulink. The simulation result indicates that this algorithm is flexible and suitable to use for advance vector control. The strategy of the switching minimizes the distortion of load current as well as loss due to minimize number of commutations in the inverter.


title: Low cost non-invasive medical device for measuring hemoglobin

abstract: Analysis of blood substances such as glucose, hemoglobin, protein, etc. are important parameters for health condition monitoring. The method proposed here uses a couple of photo-plethysmo-graphic (PPG) signal obtained from finger by using two monochromatic light at 660nm (Red) and 940nm (IR) to measure hemoglobin non-invasively. An empirical equation for the calculation of hemoglobin content in blood is used here with well-known extinction coefficient of hemoglobin. Non-invasive measurement is much more interesting by its evident advantages such as real time monitoring. The hemoglobin is measured non-invasively and compared with the values from Massimo device. This investigation helps to rule out the diseases like anemia (low Hb level) and polycythemia (high Hb level).


title: Multi-Class and Single-Class Classification Approaches to Vehicle Model Recognition from Images

abstract: This paper investigates the use of machine learning classification techniques applied to the task of recognising the make and model of vehicles. Although a number of vehicle classification systems already exist, most of them seek only to distinguish between vehicle categories, e.g. identifying whether a vehicle is a bus, truck or car. The system presented here demonstrates that a set of features extracted from the frontal view of a vehicle may be used to determine the vehicle type (make and model) with high accuracy. The performance of some standard multi-class classification algorithms is compared for this problem. A one-class k-Nearest Neighbour classification algorithm is also implemented and tested.


title: Transformational leadership in context: Face-to-face and virtual teams.

abstract: Abstract This experimental study examined transformational leadership in the context of traditional teams using face-to-face communication and virtual teams using computer-mediated communication. Thirty-nine leaders led both face-to-face and virtual teams. Repeated-measures analyses revealed similar mean levels of transformational leadership in both team types; however, leader rank order varied across team type. Post hoc analyses revealed that the most effective leaders where those who increased their transformational leadership in virtual teams. Furthermore, analyses at the team level revealed that the effect of transformational leadership on team performance was stronger in virtual than in face-to-face teams. Team-member ratings of transformational leadership were equally linked to project satisfaction in face-to-face and virtual teams. Considered as a whole, our results suggest that transformational leadership has a stronger effect in teams that use only computer-mediated communication, and that leaders who increase their transformational leadership behaviors in such teams achieve higher levels of team performance.


title: Comparison of three control methods for an autonomous vehicle

abstract: The desirability and challenge of developing a completely autonomous vehicle and the rising need for more efficient use ::: of energy by automobiles motivate this research- a study for an optimum solution to computer control of energy efficient ::: vehicles. The purpose of this paper is to compare three control methods - mechanical, hydraulic and electric that have ::: been used to convert an experimental all terrain vehicle to drive by wire which would eventually act as a test bed for ::: conducting research on various technologies for autonomous operation. Computer control of basic operations in a vehicle ::: namely steering, braking and speed control have been implemented and will be described in this paper. The output from ::: a 3 axis motion controller is used for this purpose. The motion controller is interfaced with a software program using ::: WSDK (Windows Servo Design Kit) as an intermediate tuning layer for tuning and parameter settings in autonomous ::: operation. The software program is developed in C++. The voltage signal sent to the motion controller can be varied ::: through the control program for desired results in controlling the steering motor, activating the hydraulic brakes and ::: varying the vehicle's speed. ::: The vehicle has been tested for its basic functionality which includes testing of street legal operations and also a 1000 ::: mile test while running in a hybrid mode. The vehicle has also been tested for control when it is interfaced with devices ::: such as a keyboard, joystick and sensors under full autonomous operation. The vehicle is currently being tested in ::: various safety studies and is being used as a test bed for experiments in control courses and research studies. The ::: significance of this research is in providing a greater understanding of conventional driving controls and the possibility ::: of improving automobile safety by removing human error in control of a motor vehicle.


Using Depth Mapping to realize Bokeh effect with a single camera Android device


title: Joint Depth Map and Color Consistency Estimation for Stereo Images with Different Illuminations and Cameras

abstract: In this paper, we propose a method that infers both accurate depth maps and color-consistent stereo images for radiometrically varying stereo images. In general, stereo matching and performing color consistency between stereo images are a chicken-and-egg problem since it is not a trivial task to simultaneously achieve both goals. Hence, we have developed an iterative framework in which these two processes can boost each other. First, we transform the input color images to log-chromaticity color space, from which a linear relationship can be established during constructing a joint pdf of transformed left and right color images. From this joint pdf, we can estimate a linear function that relates the corresponding pixels in stereo images. Based on this linear property, we present a new stereo matching cost by combining Mutual Information (MI), SIFT descriptor, and segment-based plane-fitting to robustly find correspondence for stereo image pairs which undergo radiometric variations. Meanwhile, we devise a Stereo Color Histogram Equalization (SCHE) method to produce color-consistent stereo image pairs, which conversely boost the disparity map estimation. Experimental results show that our method produces both accurate depth maps and color-consistent stereo images, even for stereo images with severe radiometric differences.


title: Autonomous Modular Optical Underwater Robot (AMOUR) Design, Prototype and Feasibility Study

abstract: We propose a novel modular underwater robot which can self-reconfigure by stacking and unstacking its component modules. Applications for this robot include underwater monitoring, exploration, and surveillance. Our current prototype is a single module which contains several subsystems that later will be segregated into different modules. This robot functions as a testbed for the subsystems which are needed in the modular implementation. We describe the module design and discuss the propulsion, docking, and optical ranging subsystems in detail. Experimental results demonstrate depth control, linear motion, target module detection, and docking capabilities.


title: Deep Semantic Architecture with discriminative feature visualization for neuroimage analysis

abstract: Neuroimaging data analysis often involves \emph{a-priori} selection of data features to study the underlying neural activity. Since this could lead to sub-optimal feature selection and thereby prevent the detection of subtle patterns in neural activity, data-driven methods have recently gained popularity for optimizing neuroimaging data analysis pipelines and thereby, improving our understanding of neural mechanisms. In this context, we developed a deep convolutional architecture that can identify discriminating patterns in neuroimaging data and applied it to electroencephalography (EEG) recordings collected from 25 subjects performing a hand motor task before and after a rest period or a bout of exercise. The deep network was trained to classify subjects into exercise and control groups based on differences in their EEG signals. Subsequently, we developed a novel method termed the cue-combination for Class Activation Map (ccCAM), which enabled us to identify discriminating spatio-temporal features within definite frequency bands (23--33 Hz) and assess the effects of exercise on the brain. Additionally, the proposed architecture allowed the visualization of the differences in the propagation of underlying neural activity across the cortex between the two groups, for the first time in our knowledge. Our results demonstrate the feasibility of using deep network architectures for neuroimaging analysis in different contexts such as, for the identification of robust brain biomarkers to better characterize and potentially treat neurological disorders.


title: A comprehensive approach to data warehouse testing

abstract: Testing is an essential part of the design life-cycle of any software product. Nevertheless, while most phases of data warehouse design have received considerable attention in the literature, not much has been said about data warehouse testing. In this paper we introduce a number of data mart-specific testing activities, we classify them in terms of what is tested and how it is tested, and we discuss how they can be framed within a reference design methodology.


title: Multiphase interleaved DC-DC converter with directly and inversely coupled inductors

abstract: The main advantages of the interleaved multiphase DC-DC converters over single-phase converters are reduced current stress and reduced output current ripple. However, inductor current ripple can not be reduced only by interleaving method. The integrated magnetic structure can be used to solve this problem and this also allows to achieve higher power density. In this paper application of combination of the 2-phase directly coupled inductor and 2-phase inversely coupled inductor is analyzed to developed 4-phase interleaved DC-DC converter and compared with using of separate auxiliary inductor. The prototype of the converter has been built. The experimental results of the current ripple are presented in the paper.


title: Dietary α-cyclodextrin reduces atherosclerosis and modifies gut flora in apolipoprotein E-deficient mice

abstract: SCOPE ::: α-Cyclodextrin (α-CD), a cyclic polymer of glucose, has been shown to lower plasma cholesterol in animals and humans; however, its effect on atherosclerosis has not been previously described. ::: ::: ::: METHODS AND RESULTS ::: apoE-knockout mice were fed either low-fat diet (LFD; 5.2% fat, w/w), or Western high fat diet (21.2% fat) containing either no additions (WD), 1.5% α-CD (WDA); 1.5% β-CD (WDB); or 1.5% oligofructose-enriched inulin (WDI). Although plasma lipids were similar after 11 weeks on the WD vs. WDA diets, aortic atherosclerotic lesions were 65% less in mice on WDA compared to WD (P < 0.05), and similar to mice fed the LFD. No effect on atherosclerosis was observed for the other WD supplemented diets. By RNA-seq analysis of 16S rRNA, addition of α-CD to the WD resulted in significantly decreased cecal bacterial counts in genera Clostridium and Turicibacterium, and significantly increased Dehalobacteriaceae. At family level, Comamonadaceae significantly increased and Peptostreptococcaceae showed a negative trend. Several of these bacterial count changes correlated negatively with % atherosclerotic lesion and were associated with increased cecum weight and decreased plasma cholesterol levels. ::: ::: ::: CONCLUSION ::: Addition of α-CD to the diet of apoE-knockout mice decreases atherosclerosis and is associated with changes in the gut flora.


title: Exploratory study of a robot approaching a person in the context of handing over an object

abstract: This paper presents the results from a Human-Robot Interaction study that investigates the issues of participants’ preferences in terms of the robot approach direction (directionRAD), robot base approach interaction distance (distanceRBAID), robot handing over hand distance (distanceRHOHD), robot handing over arm gesture (gestureRHOAG), and the coordination of both the robot approaching and gestureRHOAG in the context of a robot handing over an object to a seated person. The results from this study aim at informing the development of a Human Aware Manipulation Planner. Twelve participants with some previous human-robot interaction experience were recruited for the trial. The results show that a majority of the participants prefer the robot to approach from the front and hand them a can of soft drink in the front sector of their personal zone. The robot handing over hand position had the most influence on determining from where the robot should approach (i.e distanceRAD). Legibility and perception of risk seem to be the deciding factor on how participants choose their preferred robot arm-base approach coordination for handing over a can. Detailed discussions of the results conclude the paper.


title: DDoS attack isolation using moving target defense

abstract: Among the several threats to cyber services Distributed denial-of-service (DDoS) attack is most prevailing nowadays. DDoS involves making an online service unavailable by flooding the bandwidth or resources of a targeted system. It is easier for an insider having legitimate access to the system to circumvent any security controls thus resulting in insider attack. To mitigate insider assisted DDoS attacks, this paper proposes a moving target defense mechanism that involves isolation of insiders from innocent clients by using attack proxies. Further using the concept of load balancing an effective algorithm to detect and handle insider attack is developed with the aim of maximizing attack isolation while minimizing the total number of proxies used.


title: From the wisdom of crowds to my own judgment in microfinance through online peer-to-peer lending platforms

abstract: Information asymmetry is one of the fundamental problems that online peer-to-peer (P2P) lending platforms face. This problem becomes more acute when platforms are used for microfinance, where the targeted customers are mostly economically under-privileged people. Most of the prior empirical studies have been based on data from Prosper.com or similar sites that compete in traditional consumer loan markets. Our study examines P2P lending in microfinance for which borrowers are unbankable so that signals on creditworthiness of new borrowers are very limited. In addition, microfinance customers have more incentive to repeatedly seek loans from the market. Under this microfinance setting, we examine how lenders change their decisions as creditworthiness inference becomes increasingly possible through the accumulation of transaction history. Our findings confirm that lenders seek the wisdom of crowds when information on creditworthiness is extremely limited but switch to their own judgment when more signals are transmitted through the market. Different information sets are utilized according to the structures of decisions. Due to the possibility of a repeated game, it is also shown that borrowers try to maintain a good reputation, and direct communication with lenders may adjust incorrect inference from hard data when their creditworthiness is questioned.


title: Abstract Intracranial EEG and human brain mapping

abstract: Abstract This review is an attempt to highlight the value of human intracranial recordings (intracranial electro-encephalography, iEEG) for human brain mapping, based on their technical characteristics and based on the corpus of results they have already yielded. The advantages and limitations of iEEG recordings are introduced in detail, with an estimation of their spatial and temporal resolution for both monopolar and bipolar recordings. The contribution of iEEG studies to the general field of human brain mapping is discussed through a review of the effects observed in the iEEG while patients perform cognitive tasks. Those effects range from the generation of well-localized evoked potentials to the formation of large-scale interactions between distributed brain structures, via long-range synchrony in particular. A framework is introduced to organize those iEEG studies according to the level of complexity of the spatio-temporal patterns of neural activity found to correlate with cognition. This review emphasizes the value of iEEG for the study of large-scale interactions, and describes in detail the few studies that have already addressed this point.


title: Comparison of fingerprint quality measures using an optical and a capacitive sensor

abstract: Although several image quality measures have been proposed for fingerprints, no work has taken into account the differences among capture devices, and how these differences impact on the image quality. In this paper, several representative measures for assessing the quality fingerprint images are compared using an optical and a capacitive sensor. The capability to discriminate between images of different quality and its relationship with the verification performance is studied. We report differences depending on the sensor, and interesting relationships between sensor technology and features used for quality assessment are also pointed out.


title: Epilepsy in children with cerebral palsy

abstract: Cerebral palsy (CP) is often associated with epilepsy. We sought to determine the frequency with which epilepsy is associated with CP and to define the relationship in terms of gender, etiology, pathophysiology, and topographical parameters. This was a retrospective analysis of children with CP treated at the University Clinical Centre of Kosova (UCCK) in Prishtina, over a 5-year period, 2000-2005. A total of 154 children (aged 7-15 years) with CP, treated at the UCCK were enrolled. CP was associated with epilepsy in 63 cases (40.9%), 29 (46.0%) of which had spastic quadriparetic CP; 17 (27.0%), diplegic CP; and 15 (23.8%), hemiparetic CP. For the quadriparetic children, there was no significant difference in the number associated with epilepsy between the genders (female, 45.2%; male, 46.9%). Epilepsy was frequently associated with CP, with the spastic quadriparetic group being most affected. There was no large difference in the number associated with epilepsy between the right and left hemiparetic CP cases. Tonic-clonic epilepsy was more frequently associated with hemiparetic CP, whereas partial-type epilepsy was more often associated with quadriparetic CP.


title: Low voltage sub-nanosecond pulsed current driver IC for high-resolution LIDAR applications

abstract: This paper introduces a new low voltage sub-nanosecond monolithic pulsed current driver for light detection and ranging (LIDAR) applications. Unique architecture based on a controlled current source and Vernier activation sequence, combined with a monolithic implementation that allows operation with low input voltage levels, high-resolution pulse width and sub-nanosecond rise and fall times. An on-chip low voltage pulsed driver sub-nanosecond prototype has been implemented in a TS 0.18-μm 5V-gated power management process. It incorporates an integrated wide range sesnseFET based current sensor and a rail-to-rail comparator for current regulation. To characterize the avalanche capabilities of the integrated lateral MOSFET power devices required for the driver IC, a separate line of investigation has been carried out. Several lateral diffused MOS (LDMOS) power devices have been custom designed and experimentally evaluated for a life-cycle performance characterization. Post-layout analysis of the power driver IC is in a good agreement with the theoretical predictions. For a 5V input voltage, rise and fall times of the laser pulse light output are on the order of hundreds of picoseconds, with currents up to 5A. To validate the concept of high-resolution pulse width generation and short fall time, a discrete prototype has been constructed and experimentally tested.


title: Multidisciplinary and Historical Perspectives for Developing Intelligent and Resource-Efficient Systems

abstract: As communication and computation systems become more complex and target at higher performance, the fundamental limits of nature can be expected to constrain their development and optimization. This calls for intelligent use of basic resources, that is, materials, energy, information, time, frequency, and space. We present a multidisciplinary and historical review on the body of knowledge that can be applied in researching, such as intelligent and resource-efficient systems. We review general system theory, decision theory, control theory, computer science, and communication theory. While multidisciplinarity has been recognized as important, there are no earlier reviews covering all these five disciplines. Based on the review, we build a chronology of intelligent systems and identify connections between the disciplines. Optimization, decision-making, open- and closed-loop control, hierarchy, and degree of centralization turn out to be recurring themes in these disciplines, which have converged to similar solutions that are based on remote control, automation, autonomy, and self-organization. We use future wireless networks as an example to illustrate the open questions and how they can be addressed by applying multidisciplinary knowledge. This paper can help researchers to use knowledge outside their own field and avoid repeating the work done already. The resulting consolidated view can speed up research and is especially important when the fundamental limits of nature are approached and new insights are required to overcome the challenges. The general, long-standing problem to be tackled is multiobjective optimization with autonomous and distributed decision-making in an uncertain, dynamic, and nonlinear environment where the objectives are mutually conflicting.


Creation of a dynamic model of the electrification and traction power system of a 25 kV AC feed railway line together with analysis of different operation scenarios using MATLAB/Simulink


title: Robust control for line-of-sight stabilization of a two-axis gimbal system

abstract: Line-of-sight stabilization against various disturbances is an essential property of gimbaled imaging systems mounted on mobile platforms. In recent years, the importance of target detection from higher distances has increased. This has raised the need for better stabilization performance. For that reason, stabilization loops are designed such that they have higher gains and larger bandwidths. As these are required for good disturbance attenuation, sufficient loop stability is also needed. However, model uncertainties around structural resonances impose strict restrictions on sufficient loop stability. Therefore, to satisfy high stabilization performance in the presence of model uncertainties, robust control methods are required. In this paper, a robust controller design in LQG/LTR, H$_{\infty }$, and $\mu $-synthesis framework is described for a two-axis gimbal. First, the performance criteria and weights are determined to minimize the stabilization error with moderate control effort under known platform disturbance profile. Second, model uncertainties are determined by considering locally linearized models at different operating points. Next, robust LQG/LTR, H$_{\infty }$, and $\mu $ controllers are designed. Robust stability and performance of the three designs are investigated and compared. The paper finishes with the experimental performances to validate the designed robust controllers.


1 Age progression in Human Faces: A Survey


title: Deep learning for texture classification via multi-wavelet fusion of scattering transforms

abstract: Deep learning methods have recently made a significant breakthrough in many classification problems. Deep convolutional networks realize the possibility of building large-scale data representations with multiple processing layers. A scattering transform is an effective and relatively recent deep convolutional network that builds large-scale informative representations by cascading linear and nonlinear operators in multiple layers. However, experimental results show that scattering coefficients have a significantly low energy at higher layers and subsequently have little impact on classification results. In this paper, we propose a multi-wavelet fusion-based architecture to improve the error of classification by using scattering transforms. The method effectively increases the energy of scattering coefficients at higher levels via a further step of applying a linear operator followed by a nonlinear operator. Our experimental results showed that the proposed method achieves better classification results compared to the original scattering transform, and thus can be used as an effective method for texture classification problems.


New Monasticism and the Transformation of American Evangelicalism


title: The Effect of Phonological Repetition on Cortical Magnetic Responses Evoked by Visually Presented Words

abstract: Neuroimaging studies have reported that the left superior temporal cortical area is activated by visually presented words. In the present study, we recorded cortical magnetic responses evoked by visual words and examined the effect of phonological repetition (e.g., hair–hare) on left superior temporal cortical activity, using pairs of homophonic Japanese words as stimuli. Unlike English, Japanese has a large number of homophone pairs with a totally different orthography. By taking advantage of this feature of the Japanese writing system, the effect of phonological repetition can be solely examined without being confounded by the effect of orthographic similarity. Magnetic responses were recorded over the bilateral temporal sites of the brain while subjects silently read words. The words were presented one by one; a quarter of them was immediately followed by a homophonic word. Clear magnetic responses in the latency range of 300–600 msec were observed in the left hemisphere, and the responses to the homophones were smaller than those to the first presented words. In the right hemisphere, clear responses were not consistently recorded in the same latency range, and no effect of phonological repetition was observed. The sources of the responses recorded over the left hemisphere were estimated to be in the left superior temporal cortical area adjacent to the auditory cortex and the source strength as well as the magnetic responses showed a reduction by phonological repetition. This result suggests that the activity in the left superior temporal cortical area is associated with access to the phonological representation of words.


title: Measuring Working Memory in the Spanish Population: Validation of a Multiple Shortened Complex Span Task

abstract: Working memory plays a key role in cognition as it is a major predictor of a wide range of higher order abilities and behaviors typical to daily life. Shorter versions of the complex span tasks (CSTs) have been recently developed, allowing for the reduction of test administration time without affecting validity and reliability in the measurement of working memory capacity (WMC). However, these short versions have not been validated for the Spanish-speaking population. The present work aimed to validate an English version of the shortened CSTs into Spanish in a sample of 325 university students (40% female; mean age = 21.04; SD = 2.80). Cronbach's coefficient alpha was computed for each complex span task as an index of internal consistency. Validity evidence was evaluated by comparing participants' scores on the three shortened complex span tasks (operation span, symmetry span, and rotation span) with two measures of reasoning ability (Raven's Advanced Progressive Matrices and Number Series) and using confirmatory factor analysis. Results indicated that the short version of the Spanish complex span has satisfying qualities for assessing WMC in a sample of university students, which is an initial step toward providing a valid and standardized method for assessing WMC in the Peninsular Spanish-speaking population. (PsycINFO Database Record


title: Changes in Self-Definition Impede Recovery From Rejection

abstract: Previous research highlights how adept people are at emotional recovery after rejection, but less research has examined factors that can prevent full recovery. In five studies, we investigate how changing one’s self-definition in response to rejection causes more lasting damage. We demonstrate that people who endorse an entity theory of personality (i.e., personality cannot be changed) report alterations in their self-definitions when reflecting on past rejections (Studies 1, 2, and 3) or imagining novel rejection experiences (Studies 4 and 5). Further, these changes in self-definition hinder post-rejection recovery, causing individuals to feel haunted by their past, that is, to fear the recurrence of rejection and to experience lingering negative affect from the rejection. Thus, beliefs that prompt people to tie experiences of rejection to self-definition cause rejection’s impact to linger.


title: LPaMI: A Graph-Based Lifestyle Pattern Mining Application Using Personal Image Collections in Smartphones

abstract: Normally, individuals use smartphones for a variety of purposes like photography, schedule planning, playing games, and so on, apart from benefiting from the core tasks of call-making and short messaging. These services are sources of personal data generation. Therefore, any application that utilises personal data of a user from his/her smartphone is truly a great witness of his/her interests and this information can be used for various personalised services. In this paper, we present Lifestyle Pattern MIning (LPaMI), which is a personalised application for mining the lifestyle patterns of a smartphone user. LPaMI uses the personal photograph collections of a user, which reflect the day-to-day photos taken by a smartphone, to recognise scenes (called objects of interest in our work). These are then mined to discover lifestyle patterns. The uniqueness of LPaMI lies in our graph-based approach to mining the patterns of interest. Modelling of data in the form of graphs is effective in preserving the lifestyle behaviour maintained over the passage of time. Graph-modelled lifestyle data enables us to apply variety of graph mining techniques for pattern discovery. To demonstrate the effectiveness of our proposal, we have developed a prototype system for LPaMI to implement its end-to-end pipeline. We have also conducted an extensive evaluation for various phases of LPaMI using different real-world datasets. We understand that the output of LPaMI can be utilised for variety of pattern discovery application areas like trip and food recommendations, shopping, and so on.


title: Article Asymmetry and Symmetry in the Beauty of Human Faces

abstract: Author to whom correspondence should be addressed; E-Mail: dahliaz@ucla.edu Received: 22 January 2010; in revised form: 16 February 2010 / Accepted: 16 February 2010 / Published: 23 February 2010 Abstract: The emphasis in the published literature has mostly been on symmetry as the critical source for beauty judgment. In fact, both symmetry and asymmetry serve as highly aesthetic sources of beauty, whether the context is perceptual or conceptual. The human brain is characterized by symbolic cognition and this type of cognition facilitates a range of aesthetic reactions. For example, both art and natural scenery contain asymmetrical elements, which nevertheless render the whole effect beautiful. A further good case in point is, in fact, human faces. Normally, faces are structurally left-right symmetrical content-wise but not size-wise or function-wise. Attractiveness has often been discussed in terms of content-wise full-face symmetry. To test whether or not attractiveness can be gleaned only from the presence of left-right full-faces we tested half faces. Three separate groups of participants viewed and rated the attractiveness of 56 full-faces (women’s and men’s), their 56 vertical left hemi-faces and 56 vertical right hemi-faces. We found no statistically significant differences in the attractiveness ratings of full- and hemi-faces (whether left or right). Instead, we found a strong and significant positive correlation between the ratings of the hemi- and full-faces. These results are consistent with the view that the underpinning of human facial beauty is complex and that bilateral symmetry does not constitute a principle factor in beauty assessment. We discuss that the highly evolved human brain, compared to other animals, as well as symbolic and abstract cognition in humans enable a wide variety of aesthetic reactions. Keywords: attractiveness; aesthetics; art; brain; mate selection; courtship displays; biology


title: Folliculotropic mycosis fungoides: clinicopathological analysis of 17 patients.

abstract: BACKGROUND ::: Folliculotropic mycosis fungoides (FMF) represents a variant of MF characterized by hair follicle invasion of mature, CD4-positive small lymphoid cells with cerebriform nuclei. The disease displays resistance to standard treatment modalities and has an unfavourable course. ::: ::: ::: OBJECTIVE ::: Clinical analysis of 17 patients with FMF collected between 2005 and 2012, investigation of tumour cells and involved hair follicle. ::: ::: ::: METHODS ::: Re-evaluation of clinical data, wide panel immunohistochemistry investigation on paraffin-embedded biopsy material, T-cell receptor gene rearrangement analysis of the samples. ::: ::: ::: RESULTS ::: Male and older age group predominance, frequent head-neck involvement, acneiform lesions, keratotic plugs, cysts, nodules, follicular papules, alopecia and classic mycosis fungoides-like plaques represented the main clinical characteristics. Treatment response showed a wide range from transient complete response to therapy resistance and death due to the disease. The pathological alterations: folliculotropism, mild epidermotropism, follicular plugging, mucinous degeneration of hair follicle, basaloid hyperplasia, syringotropism were similar to those observed previously. The first case of a CD8-positive folliculotropic mycosis fungoides - with unusual clinical presentation - is reported here. Nestin overexpression of mesenchymal cells of the isthmic and suprabulbar regions of hair follicle and the reappearance of dermal nestin-expressing cells were observed in association with immature dendritic cell hyperplasia. Altered CK19 expression was detected suggesting a potential role of follicular keratinocytes in the disease process. It was found that a proportion of neoplastic T cells constantly express programmed death-1 receptor in our patients contrary to classic mycosis fungoides. ::: ::: ::: CONCLUSION ::: The spectrum of the clinical manifestation and the course of folliculotropic mycosis fungoides are broad and differ from classic mycosis fungoides. Folliculotropic neoplastic T-cell proliferation is associated with activation of inflammatory reactive T- and B-lymphoid cells, mesenchymal cells and changes in the hair follicle.


title: Robust moving object tracking and trajectory prediction for visual navigation in dynamic environments

abstract: We propose a robust and efficient moving object detection system with trajectory prediction mechanism for mobile visual applications in dynamic environments. The navigation system not only supports intelligent moving object detection and collision avoidance, but also provides automatic trajectory prediction mechanism to create a safe environment for the user. To take into account recognition challenges in practice, the system addresses problems of camera shake and image blurs caused by mobile camera and fast moving objects, respectively. Experimental results show our system supports 97.1% detection rate for fast moving objects and achieves high accuracy of trajectory prediction with error estimation less than 16.5 cm.


title: Hybrid thoracoscopic surgical and transvenous catheter ablation of atrial fibrillation.

abstract: OBJECTIVES ::: The purpose of this study was to evaluate the feasibility, safety, and clinical outcomes up to 1 year in patients undergoing combined simultaneous thoracoscopic surgical and transvenous catheter atrial fibrillation (AF) ablation. ::: ::: ::: BACKGROUND ::: The combination of the transvenous endocardial approach with the thoracoscopic epicardial approach in a single AF ablation procedure overcomes the limitations of both techniques and should result in better outcomes. ::: ::: ::: METHODS ::: A cohort of 26 consecutive patients with AF who underwent hybrid thoracoscopic surgical and transvenous catheter ablation were followed, with follow-up of up to 1 year. ::: ::: ::: RESULTS ::: Twenty-six patients (42% with persistent AF) underwent successful hybrid procedures. There were no complications. The mean follow-up period was 470 ± 154 days. In 23% of the patients, the epicardial lesions were not transmural, and endocardial touch-up was necessary. One-year success, defined according to the Heart Rhythm Society, European Heart Rhythm Association, and European Cardiac Arrhythmia Society consensus statement for the catheter and surgical ablation of AF, was 93% for patients with paroxysmal AF and 90% for patients with persistent AF. Two patients underwent catheter ablation for recurrent AF or left atrial flutter after the hybrid procedure. ::: ::: ::: CONCLUSIONS ::: A combined transvenous endocardial and thoracoscopic epicardial ablation procedure for AF is feasible and safe, with a single-procedure success rate of 83% at 1 year.


title: On Matching and Thickness in Heterogeneous Dynamic Markets

abstract: We study dynamic matching in an infinite-horizon stochastic market. While all agents are potentially compatible with each other, some are hard-to-match and others are easy-to-match. Agents prefer to be matched as soon as possible and matches are formed either bilaterally or indirectly through chains. We adopt an asymptotic approach and compute tight bounds on the limit of waiting time of agents under myopic policies that differ in matching technology and prioritization. ::: We find that the market composition is a key factor in the desired matching technology and prioritization level. When hard-to-match agents arrive less frequently than easy-to-match ones (i) bilateral matching is almost as efficient as chains (waiting times scale similarly under both, though chains always outperform bilateral matching by a constant factor), and (ii) assigning priorities to hard-to-match agents improves their waiting times. When hard-to-match agents arrive more frequently, chains are much more efficient than bilateral matching and prioritization has no impact. ::: We further conduct comparative statics on arrival rates. Somewhat surprisingly, we find that in a heterogeneous market and under bilateral matching, increasing arrival rate has a non-monotone effect on waiting times, due to the fact that, under some market compositions, there is an adverse effect of competition. Our comparative statics shed light on the impact of merging markets and attracting altruistic agents (that initiate chains) or easy-to-match agents. ::: This work uncovers fundamental differences between heterogeneous and homogeneous dynamic markets, and potentially helps policy makers to generate insights on the operations of matching markets such as kidney exchange programs.


title: Learning Language Games through Interaction

abstract: We introduce a new language learning setting relevant to building adaptive natural language interfaces. It is inspired by Wittgenstein's language games: a human wishes to accomplish some task (e.g., achieving a certain configuration of blocks), but can only communicate with a computer, who performs the actual actions (e.g., removing all red blocks). The computer initially knows nothing about language and therefore must learn it from scratch through interaction, while the human adapts to the computer's capabilities. We created a game in a blocks world and collected interactions from 100 people playing it. First, we analyze the humans' strategies, showing that using compositionality and avoiding synonyms correlates positively with task performance. Second, we compare computer strategies, showing how to quickly learn a semantic parsing model from scratch, and that modeling pragmatics further accelerates learning for successful players.


title: Initial Access in 5G mm-Wave Cellular Networks

abstract: The massive amounts of bandwidth available at millimeter-wave frequencies (above 10 GHz) have the potential to greatly increase the capacity of fifth generation cellular wireless systems. However, to overcome the high isotropic propagation loss experienced at these frequencies, highly directional antennas will be required at both the base station and the mobile terminal to achieve sufficient link budget in wide area networks. This reliance on directionality has important implications for control layer procedures. In particular, initial access can be significantly delayed due to the need for the base station and the user to find the proper alignment for directional transmission and reception. This article provides a survey of several recently proposed techniques for this purpose. A coverage and delay analysis is performed to compare various techniques including exhaustive and iterative search, and context-information-based algorithms. We show that the best strategy depends on the target SNR regime, and provide guidelines to characterize the optimal choice as a function of the system parameters.


title: Deep Convolutional Neural Networks for Multi-instance Multi-task Learning

abstract: Multi-instance learning studies problems in which labels areassigned to bags that contain multiple instances. In these settings, the relations between instances and labels are usually ambiguous. Incontrast, multi-task learning focuses on the output space in whichan input sample is associated with multiple labels. In real world, asample may be associated with multiple labels that are derived fromobserving multiple aspects of the problem. Thus many real worldapplications are naturally formulated as multi-instance multi-task(MIMT) problems. A common approach to MIMT is to solve it task-by-task independently under the multi-instance learningframework. On the other hand, convolutional neural networks (CNN) have demonstrated promising performance in single-instancesingle-label image classification tasks. However, how CNN deals withmulti-instance multi-label tasks still remains an open problem. Thisis mainly due to the complex multiple-to-multiple relations betweenthe input and output space. In this work, we propose a deep leaningmodel, known as multi-instance multi-task convolutional neuralnetworks (MIMT-CNN), where a number of images representing amulti-task problem is taken as the inputs. Then a shared sub-CNN isconnected with each input image to form instance representations. Those sub-CNN outputs are subsequently aggregated as inputs toadditional convolutional layers and full connection layers toproduce the ultimate multi-label predictions. This CNN model, through transfer learning from other domains, enables transfer ofprior knowledge at image level learned from large single-labelsingle-task data sets. The bag level representations in this modelare hierarchically abstracted by multiple layers from instance levelrepresentations. Experimental results on mouse brain gene expressionpattern annotation data show that the proposed MIMT-CNN modelachieves superior performance.


title: BIM-Tool: Modeling and Reasoning Support for Strategic Business Models

abstract: The BIM-Tool provides graphical modeling and analysis support for the Business Intelligence Model (BIM). BIM-Tool is a standalone application built on top of Eclipse. The tool supports two kinds of automated reasoning: (1) bottom-up "what-if" analysis: given input labels about some elements of a BIM model (for example, success/failure for leaf goals), do these propagate to other elements in the model?; and (2) top-down "is it possible?" analysis: is there a plan that leads to the satisfaction of goals, occurence/non-occurence of situa- tions? BIM-Tool answers these questions through an encoding of BIM models in disjuntive datalog programs.


title: Genetic algorithms and unsupervised machine learning for predicting robotic manipulation failures for force-sensitive tasks

abstract: Recent advances in the state-of-the-art force-torque sensors have improved the close-loop control of robotic manipulators. However, it is still challenging to perform a force-sensitive pick-and-place task, unless a considerable number of sensors monitor the process. The predictive capability of failures in conventional robotic object-sorting systems are limited. Using fifteen force-torque samples from the University of California-Irvine (UCI) database, we demonstrate the viability of failure prediction using an unsupervised Machine Learning (ML)-based method, whose learning parameters were optimised via Genetic Algorithms (GAs). This hybrid algorithm was deployed for discriminating between manipulation failure and successful object placement. GA was used to avoid overfitting or overtraining. The proposed model could detect robotic manipulation failures with 91.95% classification accuracy, thus improving on the performance of previous classification methods. This study validates the use of GAs and unsupervised ML to predict the extent of success for force-sensitive object placement using information on forces and torques alone.


title: 25G Based PON Technology

abstract: Commercial PONs have traditionally leveraged mature components from transport systems. Starting with 25G PON, the data center ecosystem will be leveraged. A strategy to accommodate higher loss budgets at lower cost is presented.


title: Clustering-based separation of media transfers in DPI-classified cellular video and VoIP traffic

abstract: Identifying VoIP and video traffic is often useful in the context of managing a cellular network, and to perform such traffic classification deep packet inspection (DPI) approaches are often used. Commercial DPI classifiers do not necessarily differentiate between, for example, YouTube traffic that arises from browsing inside the YouTube app, and traffic arising from the actual viewing of a YouTube video. Here we apply unsupervised clustering methods on such cellular DPI-labeled VoIP and video traffic to identify the characteristic behavior of the two sub-groups of media-transfer and non media-transfer flows. The analysis is based on a measurement campaign performed inside the core network of a commercial cellular operator, collecting data for more than two billion packets in 40+ million flows. A specially instrumented commercial DPI appliance allows the simultaneous collection of per packet information in addition to the DPI classification output. We show that the majority of flows falls into clusters that are easily identifiable as belonging to one of the traffic sub-groups, and that a surprising majority of DPIlabeled VoIP and video traffic is non-media related.


title: Denying Humanness to Others A Newly Discovered Mechanism by Which Violent Video Games Increase Aggressive Behavior

abstract: Past research has provided abundant evidence that playing violent video games increases aggressive behavior. So far, these effects have been explained mainly as the result of priming existing knowledge structures. The research reported here examined the role of denying humanness to other people in accounting for the effect that playing a violent video game has on aggressive behavior. In two experiments, we found that playing violent video games increased dehumanization, which in turn evoked aggressive behavior. Thus, it appears that video-game-induced aggressive behavior is triggered when victimizers perceive the victim to be less human.


title: "I Wanted to Predict Elections with Twitter and all I got was this Lousy Paper"-- A Balanced Survey on Election Prediction using Twitter Data

abstract: Predicting X from Twitter is a popular fad within the Twitter research subculture. It seems both appealing and relatively easy. Among such kind of studies, electoral prediction is maybe the most attractive, and at this moment there is a growing body of literature on such a topic. This is not only an interesting research problem but, above all, it is extremely difficult. However, most of the authors seem to be more interested in claiming positive results than in providing sound and reproducible methods. It is also especially worrisome that many recent papers seem to only acknowledge those studies supporting the idea of Twitter predicting elections, instead of conducting a balanced literature review showing both sides of the matter. After reading many of such papers I have decided to write such a survey myself. Hence, in this paper, every study relevant to the matter of electoral prediction using social media is commented. From this review it can be concluded that the predictive power of Twitter regarding elections has been greatly exaggerated, and that hard research problems still lie ahead.


title: Optimizing NoSQL DB on Flash: A Case Study of RocksDB

abstract: A solid-state drive (SSD) gains fast I/O speed and is becoming an ideal replacement for traditional rotating storage. However, its speed and responsiveness heavily depend on internal fragmentation. With a high degree of fragmentation, an SSD may experience sharp performance degradation. Hence, minimizing fragmentation in the SSD is an effective way to sustain its high performance. In this paper, we propose an innovative file data placement strategy for Rocks DB, a widely used embedded NoSQL database. The proposed strategy steers data to a write unit exposed by an SSD according to predicted data lifetime. By placing data with similar lifetime in the same write unit, fragmentation in the SSD is controlled at the time of data write. We evaluate our proposed strategy using the Yahoo! Cloud Serving Benchmark. Our experimental results demonstrate that the proposed strategy improves the Rocks DB performance significantly: the throughput can be increased by up to 41%, 99.99%ile latency reduced by 59%, and SSD lifetime extended by up to 18%.


title: Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks

abstract: Recurrent neural networks (RNNs) have drawn interest from machine learning researchers because of their effectiveness at preserving past inputs for time-varying data processing tasks. To understand the success and limitations of RNNs, it is critical that we advance our analysis of their fundamental memory properties. We focus on echo state networks (ESNs), which are RNNs with simple memoryless nodes and random connectivity. In most existing analyses, the short-term memory (STM) capacity results conclude that the ESN network size must scale linearly with the input size for unstructured inputs. The main contribution of this paper is to provide general results characterizing the STM capacity for linear ESNs with multidimensional input streams when the inputs have common low-dimensional structure: sparsity in a basis or significant statistical dependence between inputs. In both cases, we show that the number of nodes in the network must scale linearly with the information rate and poly-logarithmically with the input dimension. The analysis relies on advanced applications of random matrix theory and results in explicit non-asymptotic bounds on the recovery error. Taken together, this analysis provides a significant step forward in our understanding of the STM properties in RNNs.


title: Graph Analytics Through Fine-Grained Parallelism

abstract: Large graphs are getting increasingly popular and even indispensable in many applications, for example, in social media data, large networks, and knowledge bases. Efficient graph analytics thus becomes an important subject of study. To increase efficiency and scalability, in-memory computation and parallelism have been explored extensively to speed up various graph analytical workloads. In many graph analytical engines (e.g., Pregel, Neo4j, GraphLab), parallelism is achieved via one of the three concurrency control models, namely, bulk synchronization processing (BSP), asynchronous processing, and synchronous processing. Among them, synchronous processing has the potential to achieve the best performance due to fine-grained parallelism, while ensuring the correctness and the convergence of the computation, if an effective concurrency control scheme is used. This paper explores the topological properties of the underlying graph to design and implement a highly effective concurrency control scheme for efficient synchronous processing in an in-memory graph analytical engine. Our design uses a novel hybrid approach that combines 2PL (two-phase locking) with OCC (optimistic concurrency control), for high degree and low degree vertices in a graph respectively. Our results show that the proposed hybrid synchronous scheduler has significantly outperformed other synchronous schedulers in existing graph analytical engines, as well as BSP and asynchronous schedulers.


title: Modern Japanese Law as an Instrument of Comparison

abstract: Comparative law courses with Japanese law as their focus are still very rare in American law schools. At present the only regular and comprehensive courses are taught at Columbia Law School, under the academic and financial sponsorship of the Parker School of Foreign and Comparative Law, and at the Law School of the University of Washington, which received important initial funding from the Ford Foundation. No law school in the United States, as far as I know, is willing at present out of its own budget to support a comparative law scholar whose primary interest is Japanese law. The purpose of this paper is to explore the use of Japanese law as an instrument of comparison in legal education in order hopefully to make a case for its more frequent inclusion in law school curricula, and to note some of the problems which arise in the teaching of Japanese law.


title: Fables of the prefrontal cortex

abstract: On the basis of neuroiinaging studies, Posner & Raichle summarily report that the prefrontal cortex is involved in executive functioning and attention. In contrast to that superficial view, we briefly describe a testable model of the kinds of representations that are stored in prefrontal cortex, which, when activated, are expressed via plans, actions, thematic knowledge, and schemas.


title: Predicting Ego-Vehicle Paths from Environmental Observations with a Deep Neural Network

abstract: Ahstract- Advanced driver assistance systems allow for increasing user comfort and safety by sensing the environment and anticipating upcoming hazards. Often, this requires to accurately predict how situations will change. Recent approaches make simplifying assumptions on the predictive model of the Ego-Vehicle motion or assume prior knowledge, such as road topologies, to be available. However, in many urban areas this assumption is not satisfied. Furthermore, temporary changes (e.g. construction areas, vehicles parked on the street) are not considered by such models. Since many cars observe the environment with several different sensors, predictive models can benefit from them by considering environmental properties. In this work, we present an approach for an Ego-Vehicle path prediction from such sensor measurements of the static vehicle environment. Besides proposing a learned model for predicting the driver's multi-modal future path as a grid-based prediction, we derive an approach for extracting paths from it. In driver assistance systems both can be used to solve varying assistance tasks. The proposed approach is evaluated on real driving data and outperforms several baseline approaches.


title: Preliminary Results on Instantaneous UAV-Based 3D Mapping for Rescue Applications

abstract: This report presents a novel approach to generate a 3D map with an UAV while flying over a disaster scene with the aim to present the map instantaneously to the operator and the rescue workers. Our approach extends the well-known ICP algorithm.


title: Automated pollen identification using microscopic imaging and texture analysis

abstract: Pollen identication is required in dierent scenarios such as prevention of allergic reactions, cilmate analysis or apiculture. However, it is a timeconsuming task since experts are required to recognize each pollen grain through the microscope. In this study, we performed an exhaustive assesment on the utility of texture analysis for automated characterization of pollen samples. A database composed of 1800 brighteld microscopy images


title: Bioinspired Visual Motion Estimation

abstract: Visual motion estimation is a computationally intensive, but important task for sighted animals. Replicating the robustness and efficiency of biological visual motion estimation in artificial systems would significantly enhance the capabilities of future robotic agents. Twenty five years ago, in this very journal, Carver Mead outlined his argument for replicating biological processing in silicon circuits. His vision served as the foundation for the field of neuromorphic engineering, which has experienced a rapid growth in interest over recent years as the ideas and technologies mature. Replicating biological visual sensing was one of the first tasks attempted in the neuromorphic field. In this paper, we focus specifically on the task of visual motion estimation. We describe the task itself, present the progression of works from the early first attempts through to the modern day state-of-the-art, and provide an outlook for future directions in the field.


title: Bitextor, a free/open-source software to harvest translation memories from multilingual websites

abstract: Bitextor is a free/open-source application for harvesting translation memories from multilingual websites. It downloads all the HTML files in a website, preprocesses them into a coherent format and, finally, applies a set of heuristics to select pairs of files which are candidates to contain the same text in two different languages (bitexts). From these parallel texts, translation memories are generated in TMX format using the library LibTagAligner, which uses the HTML tags and the length of text chunks to perform the alignment.


title: The immune system and the gut microbiota: friends or foes?

abstract: This Perspective article describes the ways in which the intestinal microbiota can interact with the host immune system to promote both pro- and anti-inflammatory immune responses. The authors discuss the important implications of this for not only intestinal, but also for systemic inflammatory diseases.


FACTORS CAUSING PATIENT’S TRUST IN NURSE FROM PATIENTS’ PERSPECTIVE


title: Relationship Banking: What Do We Know?

abstract: This paper briefly reviews the contemporary literature on relationship banking. We start out with a discussion of the raison d'etre of banks in the context of the financial intermediation literature. From there we discuss how relationship banking fits into the core economic services provided by banks, and its costs and benefits. This leads to an examination of the interrelationship between the competitive environment and relationship banking as well as a discussion of the empirical evidence.


title: Erasure Coding in Windows Azure Storage

abstract: Windows Azure Storage (WAS) is a cloud storage system that provides customers the ability to store seemingly limitless amounts of data for any duration of time. WAS customers have access to their data from anywhere, at any time, and only pay for what they use and store. To provide durability for that data and to keep the cost of storage low, WAS uses erasure coding. ::: ::: In this paper we introduce a new set of codes for erasure coding called Local Reconstruction Codes (LRC). LRC reduces the number of erasure coding fragments that need to be read when reconstructing data fragments that are offline, while still keeping the storage overhead low. The important benefits of LRC are that it reduces the bandwidth and I/Os required for repair reads over prior codes, while still allowing a significant reduction in storage overhead. We describe how LRC is used in WAS to provide low overhead durable storage with consistently low read latencies.


title: On the implementation of perfectly matched layers in a three-dimensional fourth-order velocity-stress finite difference scheme

abstract: [1] Robust absorbing boundary conditions are central to the utility and advancement of 3-D numerical wave propagation methods. It is in general preferred that an absorbing boundary method be capable of broadband absorption, be efficient in terms of memory and computation time, and be widely stable in connection with sophisticated numerical schemes. Here we discuss these issues for a promising absorbing boundary method, perfectly matched layers (PML), as implemented in the widely used fourth-order accurate three-dimensional (3-D) staggered-grid velocity-stress finite difference (FD) scheme. Numerical results for point (explosive and double couple) and extended sources, velocity structures (homogeneous, 1-D and 3-D), and different thickness PML zones are excellent, in general, leaving no observable reflections in PML seismograms compared to the amplitudes of the primary phases. For both homogeneous half-space and 1-D models, typical amplitude reduction factors (with respect to the maximum trace amplitude) range between 1/100 and 1/625 for PML thicknesses of 5–20 nodes. A PML region of thickness 5 outperforms a simple exponential damping region of thickness 20 in a homogeneous half-space model by a factor of 3. We find that PML is effective across the simulation bandwidth. For example, permanent offset artifacts due to particularly poor absorption of long-period energy by the simple exponential damping are effectively absent when PML is used. The computational efficiency and storage requirements of PML, compared to the simple exponential damping, are reduced due to the need for only narrow absorbing regions. We also discuss stability and present the complete PML model for the 3-D velocity-stress system.


The MAIN Model : A Heuristic Approach to Understanding Technology Effects on Credibility


title: Stochastic Optimization in a Cumulative Prospect Theory Framework

abstract: Cumulative prospect theory (CPT) is a popular approach for modeling human preferences. It is based on probabilistic distortions and generalizes the expected utility theory. We bring the CPT to a stochastic optimization framework and propose algorithms for both estimation and optimization of CPT-value objectives. We propose an empirical distribution function-based scheme to estimate the CPT value, and then, use this scheme in the inner loop of a CPT-value optimization procedure. We propose both gradient based as well as gradient-free CPT-value optimization algorithms that are based on two well-known simulation optimization ideas: simultaneous perturbation stochastic approximation and model-based parameter search, respectively. We provide theoretical convergence guarantees for all the proposed algorithms and also illustrate the potential of CPT-based criteria in a traffic signal control application.


title: A survey: algorithms simulating bee swarm intelligence

abstract: Swarm intelligence is an emerging area in the field of optimization and researchers have developed various algorithms by modeling the behaviors of different swarm of animals and insects such as ants, termites, bees, birds, fishes. In 1990s, Ant Colony Optimization based on ant swarm and Particle Swarm Optimization based on bird flocks and fish schools have been introduced and they have been applied to solve optimization problems in various areas within a time of two decade. However, the intelligent behaviors of bee swarm have inspired the researchers especially during the last decade to develop new algorithms. This work presents a survey of the algorithms described based on the intelligence in bee swarms and their applications.


title: Learning From Explanations Using Sentiment and Advice in RL

abstract: In order for robots to learn from people with no machine learning expertise, robots should learn from natural human instruction. Most machine learning techniques that incorporate explanations require people to use a limited vocabulary and provide state information, even if it is not intuitive. This paper discusses a software agent that learned to play the Mario Bros. game using explanations. Our goals to improve learning from explanations were twofold: 1) to filter explanations into advice and warnings and 2) to learn policies from sentences without state information. We used sentiment analysis to filter explanations into advice of what to do and warnings of what to avoid. We developed object-focused advice to represent what actions the agent should take when dealing with objects. A reinforcement learning agent used object-focused advice to learn policies that maximized its reward. After mitigating false negatives, using sentiment as a filter was approximately 85% accurate. object-focused advice performed better than when no advice was given, the agent learned where to apply the advice, and the agent could recover from adversarial advice. We also found the method of interaction should be designed to ease the cognitive load of the human teacher or the advice may be of poor quality.


title: Impact of Mental Health First aid Training on Pharmacy Students' Knowledge, Attitudes and Self-Reported Behaviour: A Controlled Trial

abstract: Objective: The aim of this study was to assess the impact of delivering Mental Health First Aid (MHFA) training for pharmacy students on their mental health literacy and stigma towards mental illness.Methods: A non-randomized controlled design was used, with all third year pharmacy students at the University of Sydney (n = 272) in 2009 invited to participate in one of two MHFA training courses, each of 12 hours duration. Of these, 174 students applied for MHFA training, of whom 60 were randomly selected and offered MHFA training. Outcome measures that were completed by all participants in the MHFA and non-MHFA groups before and after the MHFA training included an evaluation of mental health literacy, the 7-item social distance scale, and 16 items related to self-reported behaviour.Results: The survey instrument was completed by 258 participants at baseline (59 MHFA and 199 non-MHFA) and 223 participants at follow up (53 MHFA and 170 non-MHFA). The MHFA training improved the participants’ ability to correc...


title: Simulation optimization via promising region search and surrogate model approximation

abstract: We propose a random search algorithm for solving simulation optimization problems with continuous decision variables. The algorithm combines ideas from promising area search, shrinking ball methods, and surrogate model optimization. We discuss the local convergence property of the algorithm and provide numerical examples to illustrate its performance.