title: Improved mean shift segmentation approach for natural images

abstract: This paper proposes an improved natural image segmentation approach that is more effective, more controllable and more stable under various backgrounds than the traditional mean shift segmentation. The proposed approach employs following four new aspects: the changeable color bandwidth, the direct density searching, the global optimization for mode merging, and the elimination of texture patches. In bandwidth selection, the optimal color bandwidth under Plug-in rule used by the traditional approach is not suitable for actual vision tasks, and a changeable color bandwidth makes it easy to control the segmentation result. The performance of the direct density searching is better than that of mean shift under the same spatial bandwidth, A global optimization criterion for mode merging stabilizes the segmentation result of different images. The elimination of texture patches mostly removes the small patches resulting from texture. In addition, after mode detection, an image is partitioned into some local patches, each of which corresponds to a local mode. These patches are got with color information, and they can be taken as the initial segmentation for further processing that is based on a global optimization criterion constructed by texture features.


title: Directional Statistics-based Deep Metric Learning for Image Classification and Retrieval

abstract: Abstract L2-normalization is an effective method to enhance the discriminant power of deep representation learning. However, without exploiting the geometric properties of the feature space, the generally used gradient based optimization methods are failed to track the global information during training. In this paper, we propose a novel deep metric learning model based on the directional distribution. By defining the loss function based on the von Mises–Fisher distribution, we propose an effective alternative learning algorithm by periodically updating the class centers. The proposed metric learning not only captures the global information about the embedding space but also yields an approximate representation of the class distribution during training. Considering classification and retrieval tasks, our experiments on benchmark datasets demonstrate an improvement from the proposed algorithm. Particularly, with a small number of convolutional layers, a significant accuracy upsurge can be observed compared to widely used gradient-based methods.


title: Natural Language Dialogue in a Virtual Assistant Interface

abstract: Dialogue management and language processing appear as key points in virtual assistants for e-shops, as their main goal is to assist the user both in his navegation through the shop product pages and in his search for the most appropriate product. In this paper we present the details of the semantic and pragmatic approach and the dialogue management done in the ADVICE project (IST-1999-11305) for a bricolage tools shop, as well as how this modules have been evaluated.


title: Qi-ferry: Energy-constrained wireless charging in wireless sensor networks

abstract: Using the technology of wireless energy transfer, the paper proposes Qi-ferry which physically carries energy, roves a wireless sensor network, and wirelessly charges sensor nodes to extend their lifetime. To optimize the usage of the entire energy reserve on a Qi-ferry, both the movement of the Qi-ferry itself and its wireless charging of sensor nodes share the same pool of battery energy reserve, resulting in a tradeoff between how many sensors the Qi-ferry could charge and how far it could travel. The paper formulates an energy-constrained Qi-ferry wireless charging problem in wireless sensor networks, which maximizes the number of sensors wirelessly charged by a Qi-ferry subject to an energy constraint limiting the total energy consumed by the Qi-ferry for both moving and charging. Due to the NP-hardness of the problem, the paper proposes heuristic solutions based on the meta-heuristics of Particle Swarm Optimization. Evaluation results validate the effectiveness of the solutions.


title: High added value of a population-based participatory surveillance system for community acute gastrointestinal, respiratory and influenza-like illnesses in Sweden, 2013–2014 using the web

abstract: In 2013-2014, the Public Health Agency of Sweden developed a web-based participatory surveillance system, Hӓlsorapport, based on a random sample of individuals reporting symptoms weekly online, to estimate the community incidence of self-reported acute gastrointestinal (AGI), acute respiratory (ARI) and influenza-like (ILI) illnesses and their severity. We evaluated Hӓlsorapport's acceptability, completeness, representativeness and its data correlation with other surveillance data. We calculated response proportions and Spearman correlation coefficients (r) between (i) incidence of illnesses in Hӓlsorapport and (ii) proportions of specific search terms to medical-advice website and reasons for calling a medical advice hotline. Of 34 748 invitees, 3245 (9·3%) joined the cohort. Participants answered 81% (139 013) of the weekly questionnaires and 90% (16 351) of follow-up questionnaires. AGI incidence correlated with searches on winter-vomiting disease [r = 0·81, 95% confidence interval (CI) 0·69-0·89], and ARI incidence correlated with searches on cough (r = 0·77, 95% CI 0·62-0·86). ILI incidence correlated with the web query-based estimated incidence of ILI patients consulting physicians (r = 0·63, 95% CI 0·42-0·77). The high response to different questionnaires and the correlation with other syndromic surveillance systems suggest that Hӓlsorapport offers a reasonable representation of AGI, ARI and ILI patterns in the community and can complement traditional and syndromic surveillance systems to estimate their burden in the community.


title: Robust Structured Light Coding for 3D Reconstruction

abstract: In this paper we present a new monochromatic pattern for a robust structured light coding based on the spatial neighborhood scheme using the M-array approach. The proposed pattern is robust as it allows a high error rate characterized by an average Hamming distance higher than 6. We tackle the design problem with the definition of a small set of symbols associated to simple geometrical primitives. One of these primitives embeds the local orientation of the pattern. This is helpful while performing the search for the relevant neighborhood during the decoding process. The aim of this work is to use this pattern for the real-time 3-D reconstruction of dynamic scenes, particularly in endoscopic surgery, with fast and reliable detection and decoding stages. Ongoing results are presented to assess both the capabilities of the proposed pattern and the reliable decoding algorithm with projections onto simple 3-D scenes and onto internal structures of a pig abdomen.


title: Towards a performance interference-aware virtual machine placement strategy for supporting soft real-time applications in the cloud.

abstract: It is standard practice for cloud service providers (CSPs) to overbook physical system resources to maximize the resource utilization and make their business model more profitable. Resource overbooking can lead to performance interference, however, among the virtual machines (VMs) hosted on the physical resources causing performance unpredictability for soft real-time applications hosted in the VMs, which is unacceptable to these applications. Balancing these conflicting requirements needs a careful design of the placement strategies for hosting soft real-time applications such that the performance interference effects are minimized while still allowing resource overbooking. These placement decisions cannot be made offline because workloads change at run time. Moreover, satisfying the priorities of collocated VMs may require VM migrations, which require an online solution. This paper presents a machine learning-based, online placement solution to this problem where the system is trained using a publicly available trace of a large data center owned by Google. Our approach first classifies the VMs based on their historic mean CPU and memory usage, and performance features. Subsequently, it learns the best patterns of collocating the classified VMs by employing machine learning techniques. These extracted patterns are those that provide the lowest performance interference level on the specified host machines making them amenable to hosting soft real-time applications while still allowing resource overbooking. Keywords-virtual machine placement, cloud computing, performance interference, resource overbooking, application QoS.


title: Optimized graphene transfer: Influence of polymethylmethacrylate (PMMA) layer concentration and baking time on graphene final performance

abstract: Abstract The influence of transfer parameters on the final structure, morphology and electrical properties of graphene were investigated in this work. Optical microscopy and atomic force microscopy (AFM) images showed that a double layer of PMMA can enhance or degrade graphene quality depending on its concentration. When properly diluted (15% in anisole, resulting in a PMMA layer of 1.35%) the transfer technique using double layer PMMA produces high quality graphene with fewer PMMA residues, non-cracked surface and sheet resistance around 247 ohm/square. We also investigated the influence of different baking times and temperature, and observed that the increase in baking time can degrade graphene quality thus leaving higher amounts of PMMA residues. Several works regarding graphene transfer are reported in the literature, but PMMA-based transfer processes still present challenges in yielding a clean and high quality graphene.


title: Seismo: Blood Pressure Monitoring using Built-in Smartphone Accelerometer and Camera

abstract: Although cost-effective at-home blood pressure monitors are available, a complementary mobile solution can ease the burden of measuring BP at critical points throughout the day. In this work, we developed and evaluated a smartphone-based BP monitoring application called textitSeismo. The technique relies on measuring the time between the opening of the aortic valve and the pulse later reaching a periphery arterial site. It uses the smartphone's accelerometer to measure the vibration caused by the heart valve movements and the smartphone's camera to measure the pulse at the fingertip. The system was evaluated in a nine participant longitudinal BP perturbation study. Each participant participated in four sessions that involved stationary biking at multiple intensities. The Pearson correlation coefficient of the blood pressure estimation across participants is 0.20-0.77 ($mu$=0.55, $sigma$=0.19), with an RMSE of 3.3-9.2 mmHg ($mu$=5.2, $sigma$=2.0).


title: Global Optimization Of Cerebral Cortex Layout

abstract: Functional areas of mammalian cerebral cortex seem positioned to minimize costs of their interconnections, down to a best-in-a-billion optimality level. The optimization problem here, originating in microcircuit design, is: Given connections among components, what is the physical placement of the components on a surface that minimizes total length of connections? Because of unfeasibility of measuring long-range “wire length” in the cortex, a simpler adjacency cost was validated. To deal with incomplete information on brain networks, a size law was developed that predicts optimization patterns in subnetworks. Macaque and cat cortex rank better in this connection optimization than the wiring of comparably structured computer chips, but somewhat worse than the macroeconomic commodity-flow network among U.S. states. However, cortex wiring conforms to the size law better than the macroeconomic patterns, which may indicate cortex optimizing mechanisms involve more global processes.


title: It Usually Works: The Temporal Logic of Stochastic Systems

abstract: In this paper the branching time logic pCTL* is defined. pCTL* expresses quantitative bounds on the probabilities of correct behavior; it can be interpreted over discrete Markov processes. A bisimulation relation is defined on finite Markov processes, and shown to be sound and complete with respect to pCTL*. We extend the universe of models to generalized Markov processes in order to support notions of refinement, abstraction, and parametrization. Model checking pCTL* over generalized Markov processes is shown to be elementary by a reduction to RCF. We conclude by describing practical and theoretical avenues for further work.


Towards Automatically Classifying Depressive Symptoms from Twitter Data for Population Health.


title: Solving systems of polynomial equations

abstract: Geometric and solid modelling deal with the representation and manipulation of physical objects. Currently most geometric objects are formulated in terms of polynomial equations, thereby reducing many application problems to manipulating polynomial systems. Solving systems of polynomial equations is a fundamental problem in these geometric computations. The author presents an algorithm for solving polynomial equations. The combination of multipolynomial resultants and matrix computations underlies this efficient, robust and accurate algorithm. >


The Fundamental System of Spatial Schemas in Language


title: Improving binary classification on text problems using differential word features

abstract: We describe an efficient technique to weigh word-based features in binary classification tasks and show that it significantly improves classification accuracy on a range of problems. The most common text classification approach uses a document's ngrams (words and short phrases) as its features and assigns feature values equal to their frequency or TFIDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference of its inverse document frequencies in the positive and negative training sets. While this technique is remarkably easy to implement, it gives a statistically significant improvement over the standard bag-of-words approaches using support vector machines on a range of classification tasks. Our results show that our technique is robust and broadly applicable. We provide an analysis of why the approach works and how it can generalize to other domains and problems.


title: Chronotype and time-of-day effects on mood during school day

abstract: Existing evidence suggests an association between mood, time-of-day and morningness–eveningness (M-E). Since few studies have been carried out among adolescents, in this study daily mood fluctuations were analyzed in the naturalistic school context during 2 d in order to test how chronotype and time-of-day are related to mood during the school schedule period and check if sleep length is involved in the above relation. A sample of 655 adolescents (12–16 years) reported mood levels (current level of pleasantness) three times during school day (8:10–8:30 h, 10:20–11:40 h, 13:50–14:10 h). They also reported M-E preference and time in bed. Neither age nor sex were related to mood. However, the results indicated that regardless of chronotype mood increased throughout the school day from the lowest morning levels. Moreover, morning types showed better mood compared to other chronotypes, while evening types exhibited the lowest mood. Evening-oriented students slept less than other chronotypes, but time in bed wa...


title: Web-based expert systems: benefits and challenges

abstract: Convergence of technologies in the Internet and the field of expert systems have offered new ways of sharing and distributing knowledge. However, there has been a general lack of research in the area of web-based expert systems (ES). This paper addresses the issues associated with the design, development, and use of web-based ES from a standpoint of the benefits and challenges of developing and using them. The original theory and concepts in conventional ES were reviewed and a knowledge engineering framework for developing them was revisited. The study considered three web-based ES: WITS-advisor -- for e-business strategy development, Fish-Expert -- for fish disease diagnosis, and IMIS -- to promote intelligent interviews. The benefits and challenges in developing and using ES are discussed by comparing them with traditional standalone systems from development and application perspectives.


title: Thread and memory placement on NUMA systems: asymmetry matters

abstract: It is well known that the placement of threads and memory plays a crucial role for performance on NUMA (Non-Uniform Memory-Access) systems. The conventional wisdom is to place threads close to their memory, to collocate on the same node threads that share data, and to segregate on different nodes threads that compete for memory bandwidth or cache resources. While many studies addressed thread and data placement, none of them considered a crucial property of modern NUMA systems that is likely to prevail in the future: asymmetric interconnect. When the nodes are connected by links of different bandwidth, we must consider not only whether the threads and data are placed on the same or different nodes, but how these nodes are connected. ::: ::: We study the effects of asymmetry on a widely available ×86 system and find that performance can vary by more than 2× under the same distribution of thread and data across the nodes but different inter-node connectivity. The key new insight is that the best-performing connectivity is the one with the greatest total bandwidth as opposed to the smallest number of hops. Based on our findings we designed and implemented a dynamic thread and memory placement algorithm in Linux that delivers similar or better performance than the best static placement and up to 218% better performance than when the placement is chosen randomly.


title: Distributed denial of service attack detection using Naive Bayes Classifier through Info Gain Feature Selection

abstract: Distributed Denial of Service (DDoS) attack brings revenue loss, productivity loss, reputation damage, theft, etc. to huge banking and business firms. This leads to the necessity of a good DDoS prevention and detection techniques. The paper aims to provide a better solution to these problems using features analysis. The statistical characteristics or parameters of the incoming packets viz. MTI (mean time intervals), POIP (probability of occurrence of IP), TTL (time to live), ACK value, SYN value, time stamp field, differentiated service field and sequence number are analyzed. The incoming packets are classified into normal and attack packets by deploying Naive Bayes classifier algorithm using the attack and normal profile from previously available datasets. Information gain algorithm is used to decrease the computation time, memory usage and increase efficiency of detection by reducing the number of parameters. The performance increases with more consistency after the application of information gain. The efficiencies of detection before and after the application of information gain are 98% and 99.5% respectively. The computation time is also reduced by 46.6%. In the paper, we use CAIDA 2008 and CAIDA anonymous trace 2015 datasets for feature selection and classification.


title: Applications of LDPC Codes to the Wiretap Channel

abstract: With the advent of quantum key distribution (QKD) systems, perfect (i.e., information-theoretic) security can now be achieved for distribution of a cryptographic key. QKD systems and similar protocols use classical error-correcting codes for both error correction (for the honest parties to correct errors) and privacy amplification (to make an eavesdropper fully ignorant). From a coding perspective, a good model that corresponds to such a setting is the wire tap channel introduced by Wyner in 1975. In this correspondence, we study fundamental limits and coding methods for wire tap channels. We provide an alternative view of the proof for secrecy capacity of wire tap channels and show how capacity achieving codes can be used to achieve the secrecy capacity for any wiretap channel. We also consider binary erasure channel and binary symmetric channel special cases for the wiretap channel and propose specific practical codes. In some cases our designs achieve the secrecy capacity and in others the codes provide security at rates below secrecy capacity. For the special case of a noiseless main channel and binary erasure channel, we consider encoder and decoder design for codes achieving secrecy on the wiretap channel; we show that it is possible to construct linear-time decodable secrecy codes based on low-density parity-check (LDPC) codes that achieve secrecy.


title: Automatic P-Wave Arrival Detection and Picking with Multiscale Wavelet Analysis for Single-Component Recordings

abstract: We have developed an automatic P -wave arrival detection and picking algorithm based on the wavelet transform and Akaike information criteria (AIC) picker. Wavelet coefficients at high resolutions show the fine structure of the time series, and those at low resolutions characterize its coarse features. Primary features such as the P -wave arrival are retained over several resolution scales, whereas secondary features such as scattered arrivals decay quickly at lower resolutions. We apply the discrete wavelet transform to single-component seismograms through a series of sliding time windows. In each window the AIC autopicker is applied to the thresholded absolute wavelet coefficients at different scales, and we compare the consistency of those picks to determine whether a P -wave arrival has been detected in the given time window. The arrival time is then determined using the AIC picker on the time window chosen by the wavelet transform. We test our method on regional earthquake data from the Dead Sea Rift region and local earthquake data from the Parkfield, California region. We find that 81% of picks are within 0.2-sec of the corresponding analyst pick for the Dead Sea dataset, and 93% of picks are within 0.1 sec of the analyst pick for the Parkfield dataset. We attribute the lower percentage of agreement for the Dead Sea dataset to the substantially lower signal-to-noise ratio of those data, and the likelihood that some percentage of the analyst picks are in error.


Relevance theory


title: A COMPARATIVE STUDY OF FOUR EVOLUTIONARY ALGORITHMS FOR ECONOMIC AND ECONOMIC-STATISTICAL DESIGNS OF MEWMA CONTROL CHARTS

abstract: The multivariate exponentially weighted moving average (MEWMA) control chart is one of the best statistical control chart that are usually used to detect simultaneous small deviations on the mean of more than one cross-correlated quality characteristics. The economic design of MEWMA control charts involves solving a combinatorial optimization model that is composed of a nonlinear cost function and traditional linear constraints. The cost function in this model is a complex nonlinear function that formulates the cost of implementing the MEWMA chart economically. An economically designed MEWMA chart to possess desired statistical properties requires some additional statistical constraints to be an economic-statistical model. In this paper, the efficiency of some major evolutionary algorithms that are employed in economic and economic-statistical design of a MEWMA control chart are discussed comparatively and the results are presented. The investigated evolutionary algorithms are simulated annealing (SA), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO), which are the most well known algorithms to solve complex combinatorial optimization problems. The major metrics to evaluate the algorithms are (i) the quality of the best solution obtained, (ii) the trends of responses in approaching the optimum value, (iii) the average objective-function-value in all trials, and (iv) the computer processing time to achieve the optimum value. The result of the investigation for the economic design shows that while GA is the most powerful algorithm, PSO is the second to the best, and then DE and SA come to the picture. For economic-statistical design, while PSO is the best and GA is the second to the best, DE and SA have similar performances.


title: Automobile and fixed wireless sensor network for radiation detection

abstract: Monitoring of radiation sources becomes a very important job now days. In this paper a wireless sensor network (WSN) is designed from a group of radiation detector stations with different types of sensors. These stations are located in different areas and each sensor transmits its data through GSM network to the main monitoring and control station. The design include GPS module to determine the location of mobile and fixed station. The data is transmitted with GSM/GPRS modem. Instead of using traditional SMS data string or word messages a digital data frame is constructed and transmitted as SMS data. In the main monitoring station graphical user interface (GUI) software is designed to shows information and statues of the all stations in the network. It reports any radiation leaks. In addition to the data, the GUI contains a geographical map to display the location of the leakage station and can control the stations power consumption by sending a special command to it.


title: Motif Spotting in an Alapana in Carnatic Music

abstract: This work addresses the problem of melodic motif spotting, given a query, in Carnatic music. Melody in Carnatic music is based on the concept of raga. Melodic motifs are signature phrases which give a raga its identity. They are also the fundamental units that enable extempore elaborations of a raga. In this paper, an attempt is made to spot typical melodic motifs of a raga queried in a musical piece using a two pass dynamic programming approach, with pitch as the basic feature. In the first pass, the rough longest common subsequence (RLCS) matching is performed between the saddle points of the pitch contours of the reference motif and the musical piece. These saddle points corresponding to quasi-stationary points of the motifs, are relevant entities of the raga. Multiple sequences are identified in this step, not all of which correspond to the the motif that is queried. To reduce the false alarms, in the second pass a fine search using RLCS is performed between the continuous pitch contours of the reference motif and the subsequences obtained in the first pass. The proposed methodology is validated by testing on Alapanas of 20 different musicians.


title: The application of ROC analysis in threshold identification, data imbalance and metrics selection for software fault prediction

abstract: Software engineers have limited resources and need metrics analysis tools to investigate software quality such as fault-proneness of modules. There are a large number of software metrics available to investigate quality. However, not all metrics are strongly correlated with faults. In addition, software fault data are imbalanced and affect quality assessment tools such as fault prediction or threshold values that are used to identify risky modules. Software quality is investigated for three purposes. First, the receiver operating characteristics (ROC) analysis is used to identify threshold values to identify risky modules. Second, the ROC analysis is investigated for imbalanced data. Third, the ROC analysis is considered for feature selection. This work validated the use of ROC to identify thresholds for four metrics (WMC, CBO, RFC and LCOM). The ROC results after sampling the data are not significantly different from before sampling. The ROC analysis selects the same metrics (WMC, CBO and RFC) in most datasets, while other techniques have a large variation in selecting metrics.


title: Automatic Detection of Diabetic Retinopathy using Deep Convolutional Neural Network

abstract: The purpose of this project is to design an automated and efficient solution that could detect the symptoms of DR from a retinal image within seconds and simplify the process of reviewing and examination of images. Diabetic Retinopathy (DR) is a complication of diabetes that is caused by changes in the blood vessel of the retina and it is one of the leading causes of blindness in the developed world. Currently, detecting DR symptoms is manual and time-consuming process. Recently, fully-connected and convolutional neural networks have been trained to achieve state-of-the-art performance on a wide variety of tasks such as speech recognition, image classification, natural language processing, and bioinformatics. In our approach, we trained a deep Convolutional Neural Network model on large dataset consisting around 35,000 images and used dropout layer techniques to achieve higher accuracy.


title: Multitrial Evaluation of Progression-Free Survival as a Surrogate End Point for Overall Survival in First-Line Extensive-Stage Small-Cell Lung Cancer

abstract: INTRODUCTION ::: We previously reported that progression-free survival (PFS) may be a candidate surrogate end point for overall survival (OS) in first-line extensive-stage small-cell lung cancer (ES-SCLC) using data from three randomized trials (Foster, Cancer 2011). In this validation study (N0424-Alliance), we assessed the patient-level and trial-level surrogacy of PFS using data from seven new first-line phase II/III ES-SCLC trials and across all 10 trials as well (seven new, three previous). ::: ::: ::: METHODS ::: Individual patient data were utilized across the seven new trials (2259 patients) and all 10 trials (2855 patients). Patient-level surrogacy (Kendall's τ) was assessed using the Clayton copula bivariate survival model. Trial-level surrogacy was assessed through association of the log hazard ratios on OS and PFS across trials, including weighted (by trial size) least squares regression (WLS R²) of Cox model effects and correlation of the copula effects (copula R²). The minimum effect on the surrogate (MES) needed to detect a nonzero treatment effect on OS was also calculated. ::: ::: ::: RESULTS ::: The median OS and PFS across all 10 trials were 9.8 and 5.9 months, respectively. PFS showed strong surrogacy within the 7 new trials (copula R² = 0.90 [standard error = 0.27], WLS R² = 0.83 [95% confidence interval: 0.43, 0.95]; MES = 0.67, and Kendall's τ = 0.58) and across all 10 trials (copula R² = 0.81 [standard errors = 0.25], WLS R² = 0.77 [95% confidence interval: 0.47-0.91], MES = 0.70, and Kendall's τ = 0.57). ::: ::: ::: CONCLUSIONS ::: PFS demonstrated strong surrogacy for OS in first-line ES-SCLC based on this external validation study of individual patient data. PFS is a good alternative end point to OS and should be considered when resource constraints (time or patient) might make it useful or desirable in place of OS. Additional analyses are needed to assess its appropriateness for targeted agents in this disease setting.


title: Control for Aerial Transportation Using a Quadrotor with a Multi-DOF Manipulator

abstract: This paper presents a control method for a quadrotor equipped with a multi-DOF manipulator to transport a common object to the desired position. By considering a quadrotor and robot arm as a combined system, called quadrorot-manipulator system, the kinematic and dynamitic models are built together in a general version using Euler-Lagrange(EL) equations. The impact on the quadrotormanipulator system caused by the object is also considered. The transportation task can be decomposed into five steps. The planning trajectory can be obtained when the initial and the fianl position of the object is given. With the combined dynamic model, we propose a control scheme consisting of position controller, attitude controller and manipulator controller to track the planning trajectory. To validate our approach, the simulation results of a transportation task with a quadrotor with a 2-DOF manipulator was presented.


title: Ant algorithms and stigmergy

abstract: Ant colonies, and more generally social insect societies, are distributed systems that, in spite of the simplicity of their individuals, present a highly structured social organization. As a result of this organization, ant colonies can accomplish complex tasks that in some cases far exceed the individual capacities of a single ant. The study of ant colonies behavior and of their self-organizing capacities is interesting for computer scientists because it provides models of distributed organization which are useful to solve difficult optimization and distributed control problems. In this paper we overview some models derived from the observation of real ants, emphasizing the role played by stigmergy as distributed communication paradigm, and we show how these models have inspired a number of novel algorithms for the solution of distributed optimization and distributed control problems.


title: Development and evaluation of single sided flux couplers for contactless electric vehicle charging

abstract: Inductive Power Transfer (IPT) is a practical and preferable method for recharging stationary and moving Electric Vehicles (EV) because it is safe, convenient and reliable. The performance of the magnetic couplers that transfer power determines the overall feasibility of a complete system. Single sided flux couplers have a unidirectional flux pattern and can be used under an EV with minimal loss to the (steel) chassis. Circular pads are single sided and the most common topology and these are investigated with consideration of magnetic field leakage guidelines. Following this, a new polarized coupler topology (referred to as a “Double-D-Quadrature” (DDQ)) is presented. An interoperability study with conventional circular couplers shows this DDQ pad is completely compatible and offers a significantly larger charge zone.


title: Modeling of PV panels performance based on datasheet values for solar micro energy harvesting

abstract: Typically photovoltaic (PV) panel datasheets provide the electrical parameters at only Standard Test Condition (STC) i.e. at irradiance of 1000 W/m2 and cell temperature of 25°C. However, the PV panels operate at various operating conditions. Therefore the datasheet information is not sufficient to determine its behavior. There is a need of a novel model to predict the behavior of PV panel under all conditions. Single Mechanism Five Parameters (1M5P) model uses datasheet values along with series and parallel resistance of the PV panel. The basic equations of the model and parameters extraction from datasheet are shown. The advantage of this model is that it only requires the minimum parameters from datasheet. Furthermore, it can predict the behavior of PV panel under all operating conditions and can be used for solar micro energy harvesting. At STC, the model is validated with both datasheet and experimental values with difference less than 5%.


title: Modeling the speed of text entry with a word prediction interface

abstract: This study analyzes user performance of text entry tasks with word prediction by applying modeling techniques developed in the field of human-computer interaction. Fourteen subjects transcribed text with and without a word prediction feature for seven test sessions. Eight subjects were able-bodied and used mouthstick typing, while six subjects bad high-level spinal cord injuries and used their usual method of keyboard access. Use of word prediction decreased text generation rate for the spinal cord injured subjects and only modestly enhanced it for the able-bodied subjects. This suggests that the cognitive cost of using word prediction had a major impact on the performance of these subjects. Performance was analyzed in more detail by deriving subjects' times for keypress and list search actions during word prediction use. All subjects had slower keypress times during word prediction use as compared to letters-only typing, and spinal cord injured subjects had much slower list search times than able-bodied subjects. These parameter values were used in a two-parameter model to simulate subjects' word entry times during word prediction use, with an average model error of 16%. These simulation results are an encouraging first step toward demonstrating the ability of analytical models to represent user performance with word prediction. >


title: Design of Organic TFT Pixel Electrode Circuit for Active-Matrix OLED Displays

abstract: A new current-programming pixel circuit for active-matrix organic light-emitting diode (AM-OLED) displays, composed of four organic thin-film transistors (OTFTs) and one capacitor, has been designed, simulated and evaluated. The most critical issue in realizing AMOLED displays with OTFTs is the variation and aging of the driving-TFTs and degradation of OLEDs. This can cause image sticking or degradation of image quality. These problems require compensation methods for high-quality display applications, and pixel level approach is considered to be one of the most important factors for improving display image quality. Our design shows that the current OLED and OTFT technology can be implemented for AMOLED displays, compensating the degradation of OTFT device characteristics.


title: Constructing places from spatial footprints

abstract: Place is an essential concept in human discourse. It is people's interaction and experience with their surroundings that identify place from non-place in space. This paper explores the use of spatial footprints as a record of human interaction with the environment. Specifically, we use geotagged photos collected in Flickr to provide a collective view of sense of place, in terms of significance and location. Spatial footprints associated with photographs can not only describe individual place locations and spatial extents but also the relationship between places, such as hierarchy. This type of information about place may be utilized to study the way people understand their landscape, or can be incorporated into existing gazetteers for geographic information retrieval and location-based services. Other sources of user-generated geographic information, such as Foursquare and Twitter, may also be harvested and aggregated to study place in a similar way.


title: ZeroBlock: Preventing Selfish Mining in Bitcoin

abstract: Bitcoin was recently introduced as a peer-to-peer electronic currency in order to facilitate transactions outside the traditional financial system. The core of Bitcoin, the Blockchain, is the history of the transactions in the system maintained by all nodes as a distributed shared register. New blocks in the Blockchain contain the last transactions in the system and are added by nodes (miners) after a block mining process that consists in solving a resource consuming proof-of-work (cryptographic puzzle). The reward is a motivation for mining process but also could be an incentive for attacks such as selfish mining. In this paper we propose a solution for one of the major problems in Bitcoin : selfish mining or block withholding attack. This attack is conducted by adversarial or selfish nodes in order to either earn undue rewards or waste the computational power of honest nodes. Contrary to recent solutions, our solution, ZeroBlock, prevents block withholding using a technique free of forgeable timestamps. Moreover, we show that our solution is also compliant with nodes churn.


title: Short time fourier transform with coefficient optimization for detecting salient regions in stereoscopic 3D images: GSDU

abstract: In multimedia scenario, number of saliency detection designs has been portrayed for different intelligent applications regarding the accurate saliency detection like human visual system. More challenges exist regarding complexity in natural images and lesser scale prototypes in salient objects. In lots of the prevailing methods, the competency of identifying objects’ instances in the discovered salient regions is still not up to the mark. Hence it is planned to assist a new strategy by considering certain parameters of feature evaluation under optimization algorithms which diverts the new method of capturing the optimal parameters to acquire better outcomes. The given paper proposes a new saliency detection design that encompasses 2 phases like Feature extraction and depth saliency detection. In which Gaussian kernel model is processed for extracting the STFT features (Short-Time Fourier Transform), Texture features, and Depth features; and Gabor filter is used to get the depth saliency map. Here, the color space information is progressed under STFT model for extracting the STFT features. This is the major contribution, where all the STFT feature, Texture feature and Depth features are taken out to gain the depth saliency map. Additionally, this paper proffers a new optimization prototype that optimizes 2 coefficients namely feature difference amongst image patches from feature evaluation, and fine scale, by which more precise saliency detection outcomes can be attained through the proposed model. Subsequently, the proposed GSDU (Glowworm Swarm optimization with Dragonfly Update) contrasts its performance over other conventional methodologies concerning i) ROC (Receiver Operator Curve), ii) PCC (Pearson Correlation Coefficient), iii) KLD (Kullback Leibler Divergence) as well as iv) AUC (Area Under the Curve), and the efficiency of proposed model is proven grounded on higher accuracy rate.


title: Meditation practices for health: state of the research.

abstract: OBJECTIVES ::: To review and synthesize the state of research on a variety of meditation practices, including: the specific meditation practices examined; the research designs employed and the conditions and outcomes examined; the efficacy and effectiveness of different meditation practices for the three most studied conditions; the role of effect modifiers on outcomes; and the effects of meditation on physiological and neuropsychological outcomes.


title: Efficient Distributed Learning with Sparsity

abstract: We propose a novel, efficient approach for distributed sparse learning in high-dimensions, where observations are randomly partitioned across machines. Computationally, at each round our method only requires the master machine to solve a shifted ell_1 regularized M-estimation problem, and other workers to compute the gradient. In respect of communication, the proposed approach provably matches the estimation error bound of centralized methods within constant rounds of communications (ignoring logarithmic factors). We conduct extensive experiments on both simulated and real world datasets, and demonstrate encouraging performances on high-dimensional regression and classification tasks.


title: Quantitative CRISPR interference screens in yeast identify chemical-genetic interactions and new rules for guide RNA design

abstract: BackgroundGenome-scale CRISPR interference (CRISPRi) has been used in human cell lines; however, the features of effective guide RNAs (gRNAs) in different organisms have not been well characterized. Here, we define rules that determine gRNA effectiveness for transcriptional repression in Saccharomyces cerevisiae.ResultsWe create an inducible single plasmid CRISPRi system for gene repression in yeast, and use it to analyze fitness effects of gRNAs under 18 small molecule treatments. Our approach correctly identifies previously described chemical-genetic interactions, as well as a new mechanism of suppressing fluconazole toxicity by repression of the ERG25 gene. Assessment of multiple target loci across treatments using gRNA libraries allows us to determine generalizable features associated with gRNA efficacy. Guides that target regions with low nucleosome occupancy and high chromatin accessibility are clearly more effective. We also find that the best region to target gRNAs is between the transcription start site (TSS) and 200 bp upstream of the TSS. Finally, unlike nuclease-proficient Cas9 in human cells, the specificity of truncated gRNAs (18 nt of complementarity to the target) is not clearly superior to full-length gRNAs (20 nt of complementarity), as truncated gRNAs are generally less potent against both mismatched and perfectly matched targets.ConclusionsOur results establish a powerful functional and chemical genomics screening method and provide guidelines for designing effective gRNAs, which consider chromatin state and position relative to the target gene TSS. These findings will enable effective library design and genome-wide programmable gene repression in many genetic backgrounds.


title: A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks

abstract: Abstract Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuroimaging datasets requires adequate sample size for training and well-defined signal intensity based structural differentiation. There is a lack of effective automated diagnostic tools for the reliable detection of brain dysmaturation in the neonatal period, related to small sample size and complex undifferentiated brain structures, despite both translational research and clinical importance. Volumetric information alone is insufficient for diagnosis. In this study, we developed a computational framework for the automated classification of brain dysmaturation from neonatal MRI, by combining a specific deep neural network implementation with neonatal structural brain segmentation as a method for both clinical pattern recognition and data-driven inference into the underlying structural morphology. We implemented three-dimensional convolution neural networks (3D-CNNs) to specifically classify dysplastic cerebelli, a subset of surface-based subcortical brain dysmaturation, in term infants born with congenital heart disease. We obtained a 0.985 ± 0. 0241-classification accuracy of subtle cerebellar dysplasia in CHD using 10-fold cross-validation. Furthermore, the hidden layer activations and class activation maps depicted regional vulnerability of the superior surface of the cerebellum, (composed of mostly the posterior lobe and the midline vermis), in regards to differentiating the dysplastic process from normal tissue. The posterior lobe and the midline vermis provide regional differentiation that is relevant to not only to the clinical diagnosis of cerebellar dysplasia, but also genetic mechanisms and neurodevelopmental outcome correlates. These findings not only contribute to the detection and classification of a subset of neonatal brain dysmaturation, but also provide insight to the pathogenesis of cerebellar dysplasia in CHD. In addition, this is one of the first examples of the application of deep learning to a neuroimaging dataset, in which the hidden layer activation revealed diagnostically and biologically relevant features about the clinical pathogenesis. The code developed for this project is open source, published under the BSD License, and designed to be generalizable to applications both within and beyond neonatal brain imaging.


title: When complexity science meets implementation science: a theoretical and empirical analysis of systems change

abstract: Implementation science has a core aim – to get evidence into practice. Early in the evidence-based medicine movement, this task was construed in linear terms, wherein the knowledge pipeline moved from evidence created in the laboratory through to clinical trials and, finally, via new tests, drugs, equipment, or procedures, into clinical practice. We now know that this straight-line thinking was naïve at best, and little more than an idealization, with multiple fractures appearing in the pipeline. The knowledge pipeline derives from a mechanistic and linear approach to science, which, while delivering huge advances in medicine over the last two centuries, is limited in its application to complex social systems such as healthcare. Instead, complexity science, a theoretical approach to understanding interconnections among agents and how they give rise to emergent, dynamic, systems-level behaviors, represents an increasingly useful conceptual framework for change. Herein, we discuss what implementation science can learn from complexity science, and tease out some of the properties of healthcare systems that enable or constrain the goals we have for better, more effective, more evidence-based care. Two Australian examples, one largely top-down, predicated on applying new standards across the country, and the other largely bottom-up, adopting medical emergency teams in over 200 hospitals, provide empirical support for a complexity-informed approach to implementation. The key lessons are that change can be stimulated in many ways, but a triggering mechanism is needed, such as legislation or widespread stakeholder agreement; that feedback loops are crucial to continue change momentum; that extended sweeps of time are involved, typically much longer than believed at the outset; and that taking a systems-informed, complexity approach, having regard for existing networks and socio-technical characteristics, is beneficial. Construing healthcare as a complex adaptive system implies that getting evidence into routine practice through a step-by-step model is not feasible. Complexity science forces us to consider the dynamic properties of systems and the varying characteristics that are deeply enmeshed in social practices, whilst indicating that multiple forces, variables, and influences must be factored into any change process, and that unpredictability and uncertainty are normal properties of multi-part, intricate systems.


title: Automated energy saving and safety system

abstract: This paper reports on a system that can save electricity. Many times we leave the room and hall without switching off lights and fans, thus electricity is wasted. In this work we have presented a system in which energy will be saved based on number of people entering in or leaving out of the room. If there is no person in the room, then light and fan will be switched OFF. On the other hand as soon as any person enters the room, light and fan will be switched ON. On the basis of the intensity of sunlight we can increase or decrease the intensity of light. Simultaneously we can vary the speed of the fan sensing the room temperature. The more temperature the more speed of the fan. A smoke sensor module is used for detecting any smoke within the room and provides safety by alarming the audio device.


title: Deep Residual Learning for Small-Footprint Keyword Spotting

abstract: We explore the application of deep residual learning and dilated convolutions to the keyword spotting task, using the recently-released Google Speech Commands Dataset as our benchmark. Our best residual network (ResNet) implementation significantly outperforms Google's previous convolutional neural networks in terms of accuracy. By varying model depth and width, we can achieve compact models that also outperform previous small-footprint variants. To our knowledge, we are the first to examine these approaches for keyword spotting, and our results establish an open-source state-of-the-art reference to support the development of future speech-based interfaces.


title: Efficient Multi-task Feature and Relationship Learning

abstract: We consider a multitask learning problem, in which several predictors are learned jointly. Prior research has shown that learning the relations between tasks, and between the input features, together with the predictor, can lead to better generalization and interpretability, which proved to be useful for applications in many domains. In this paper, we consider a formulation of multitask learning that learns the relationships both between tasks and between features, represented through a task covariance and a feature covariance matrix, respectively. First, we demonstrate that existing methods proposed for this problem present an issue that may lead to ill-posed optimization. We then propose an alternative formulation, as well as an efficient algorithm to optimize it. Using ideas from optimization and graph theory, we propose an efficient coordinate-wise minimization algorithm that has a closed form solution for each block subproblem. Our experiments show that the proposed optimization method is orders of magnitude faster than its competitors. We also provide a nonlinear extension that is able to achieve better generalization than existing methods.


title: Reasoning on the Raven's Advanced Progressive Matrices Test with Iconic Visual Representations

abstract: Although the problems on Raven’s Progressive Matrices intelligence tests resemble geometric analogies, studies of human behavior suggest the existence of two qualitatively distinct types of strategies: verbal strategies that use propositional representations and visual strategies that use iconic representations. However, all prior computational models implemented to solve these tests have modeled only verbal strategies: they translate problems into purely propositional representations. We examine here the other half of what may be a dual-process mechanism of reasoning in humans: visual strategies that use iconic representations. In particular, we present two different algorithms that use iconic visual representations to address problems found on the Advanced Progressive Matrices test, the best of which yields performances at levels equivalent to the 75th percentile for human test takers aged from 20 to 62 years-old. We discuss implications of our work for understanding the computational nature of Raven’s and visual analogy in problem solving.


Combining Logical Abduction and Statistical Induction: Discovering Written Primitives with Human Knowledge.


title: Surgical management for abnormal head position in nystagmus: the augmented modified Kestenbaum procedure.

abstract: Patients with nystagmus and an eccentric null point in lateral gaze may assume an abnormal head position to maximise visual acuity. Surgical procedures for this condition can result in significant undercorrection of the head turn. A follow-up of 15 patients for an average of 33 months revealed a sustained improvement in head position with the use of the augmented modified Kestenbaum procedure.


title: Interactive simulation of needle insertion models

abstract: A novel interactive virtual needle insertion simulation is presented. The simulation models are based on measured planar tissue deformations and needle insertion forces. Since the force-displacement relationship is only of interest along the needle shaft, a condensation technique is shown to reduce the computational complexity of linear simulation models significantly. As the needle penetrates or is withdrawn from the tissue model, the boundary conditions that determine the tissue and needle motion change. Boundary condition and local material coordinate changes are facilitated by fast low-rank matrix updates. A large-strain elastic needle model is coupled to the tissue models to account for needle deflection and bending during simulated insertion. A haptic environment, based on these novel interactive simulation techniques, allows users to manipulate a three-degree-of-freedom virtual needle as it penetrates virtual tissue models, while experiencing steering torques and lateral needle forces through a planar haptic interface.


Response of N and P fertilization on China aster


title: Persistence of improvements in postural strategies following motor control training in people with recurrent low back pain.

abstract: This study investigated long-term effects of training on postural control using the model of deficits in activation of transversus abdominis (TrA) in people with recurrent low back pain (LBP). Nine volunteers with LBP attended four sessions for assessment and/or training (initial, two weeks, four weeks and six months). Training of repeated isolated voluntary TrA contractions were performed at the initial and two-week session with feedback from real-time ultrasound imaging. Home program involved training twice daily for four weeks. Electromyographic activity (EMG) of trunk and deltoid muscles was recorded with surface and fine-wire electrodes. Rapid arm movement and walking were performed at each session, and immediately after training on the first two sessions. Onset of trunk muscle activation relative to prime mover deltoid during arm movements, and the coefficient of variation (CV) of EMG during averaged gait cycle were calculated. Over four weeks of training, onset of TrA EMG was earlier during arm movements and CV of TrA EMG was reduced (consistent with more sustained EMG activity). Changes were retained at six months follow-up (p<0.05). These results show persistence of motor control changes following training and demonstrate that this training approach leads to motor learning of automatic postural control strategies.


title: Design and implementation of digital Costas loop and Bit synchronizer in FPGA for BPSK demodulation

abstract: This paper describes the design and implementation of digital Costas loop for carrier recovery and demodulation of the data with Bit synchronizer for clock recovery in Field Programmable Gate Array (FPGA) for Binary Phase Shift Keying (BPSK) demodulation. For coherent demodulation, synchronization system is essential to extract carrier frequency and phase information from the received signal. The classical BPSK demodulation technique is the Costas loop, which can be either analog or digital that obtains the phase and frequency information of the modulated carrier as well as demodulates the data. In analog Costas loop, an imbalance between the in-phase and quadrature-phase exists which requires fine tuning. To overcome this problem of analog design, digital design is used. The digital implementation in a Digital Signal Processor (DSP) or FPGA is beneficial in terms of size, complexity and it is also tunable. In the present invention, the whole system is implemented in a single FPGA. The early-late gate technique is used for the design of Bit Synchronizer. The digital system design is simulated in MATLAB and the VHDL code developed in ACTEL LIBERO software is simulated in ModelSim simulator. Finally, the whole system is implemented in ACTEL PROASIC3E FPGA. The major advantages of the system include reprogrammablility, repeatability, reduced cost and low power consumption with less hardware.


title: The most highly cited Library and Information Science articles: Interdisciplinarity, first authors and citation patterns

abstract: Highly cited articles are interesting because of the potential association between high citation counts and high quality research. This study investigates the 82 most highly cited Information Science and Library Science’ (IS&LS) articles (the top 0.1%) in the Web of Science from the perspectives of disciplinarity, annual citation patterns, and first author citation profiles. First, the relative frequency of these 82 articles was much lower for articles solely in IS&LS than for those in IS&LS and at least one other subject, suggesting that that the promotion of interdisciplinary research in IS&LS may be conducive to improving research quality. Second, two thirds of the first authors had an h-index in IS&LS of less than eight, show that much significant research is produced by researchers without a high overall IS&LS research productivity. Third, there is a moderate correlation (0.46) between citation ranking and the number of years between peak year and year of publication. This indicates that high quality ideas and methods in IS&LS often are deployed many years after being published.


title: Broken Rotor Bar Fault Detection of IM Based on the Counter-Current Braking Method

abstract: This paper presents an improved method for the broken rotor bar detection in a squirrel-cage induction motor (IM). The method is based on the spectral analysis of the transient stator current signal during the counter-current braking (CCB). Contrary to the classical CCB, the proposed method results in the low braking current, which is a small fraction of the rated value and serves as a broken bar detection test signal only. This kind of the broken rotor bar fault diagnosis is independent of loading conditions and can be carried out even for a free shaft motor. The existence of spectral components in the low CCB current signal indicating the faulty conditions is first proven with the generalized theory of symmetrical components. The method is then verified via simulations, using an IM model based on the finite-element analysis and the magnetically coupled multiple circuits approach. Afterward, the experiments are performed, showing a good agreement with both the theoretical prediction and the simulation results, confirming the presence of the fault-induced components in the stator current spectra.


title: A phase-adjustable Delay-Locked Loop utilizing embedded phase interpolation

abstract: This paper presents the design of a 1 GHz Delay-Locked Loop (DLL) with embedded phase interpolation. The DLL was designed in a 0.13µm SiGe BiCMOS process and provides a measured single-tap phase range of 50ps (18°) with an average and worst-case phase resolution of 1.36ps (0.49°) and 5ps (1.8°), respectively. This translates to beamsteering resolutions of < 3.5mm in free space when integrated into mm-wave imagers that utilize time-domain beamforming. The DLL has an average output rms-jitter (across all interpolation steps) of 860fs. The DLL consumes 33 mW of power and occupies an area of 0.185mm2.


title: No Need to Pay Attention: Simple Recurrent Neural Networks Work! (for Answering "Simple" Questions)

abstract: First-order factoid question answering assumes that the question can be answered by a single fact in a knowledge base (KB). While this does not seem like a challenging task, many recent attempts that apply either complex linguistic reasoning or deep neural networks achieve 65%-76% accuracy on benchmark sets. Our approach formulates the task as two machine learning problems: detecting the entities in the question, and classifying the question as one of the relation types in the KB. We train a recurrent neural network to solve each problem. On the SimpleQuestions dataset, our approach yields substantial improvements over previously published results --- even neural networks based on much more complex architectures. The simplicity of our approach also has practical advantages, such as efficiency and modularity, that are valuable especially in an industry setting. In fact, we present a preliminary analysis of the performance of our model on real queries from Comcast's X1 entertainment platform with millions of users every day.


title: Riemannian Optimization for High-Dimensional Tensor Completion

abstract: Tensor completion aims to reconstruct a high-dimensional data set where the vast majority of entries is missing. The assumption of low-rank structure in the underlying original data allows us to cast the completion problem into an optimization problem restricted to the manifold of fixed-rank tensors. Elements of this smooth embedded submanifold can be efficiently represented in the tensor train or matrix product states format with storage complexity scaling linearly with the number of dimensions. We present a nonlinear conjugate gradient scheme within the framework of Riemannian optimization which exploits this favorable scaling. Numerical experiments and comparison to existing methods show the effectiveness of our approach for the approximation of multivariate functions. Finally, we show that our algorithm can obtain competitive reconstructions from uniform random sampling of few entries compared to adaptive sampling techniques such as cross-approximation.


title: Design of Control Application Using Processor Expert Blockset

abstract: This paper describes design of a controller of the brushless DC motor using Processor Expert blockset in Mathworks Simulink. The controller algorithm is designed in Simulink and the Processor Expert blockset provides access to the microcontroller hardware through its Hardware Abstraction Layer. The behavior of the controller (including the peripherals represented by Processor Expert blocks) is verified by simulation. The Real-Time Workshop is used for the C source code generation from the controller model. The objective of this paper is to show on this non-trivial case-study, how the model based design methodology can be successfully used for effective rapid development and quality production code generation.


title: Fluctuations of spontaneous EEG topographies predict disease state in relapsing-remitting multiple sclerosis

abstract: Spontaneous fluctuations of neuronal activity in large-scale distributed networks are a hallmark of the resting brain. In relapsing-remitting multiple sclerosis (RRMS) several fMRI studies have suggested altered resting-state connectivity patterns. Topographical EEG analysis reveals much faster temporal fluctuations in the tens of milliseconds time range (termed “microstates”), which showed altered properties in a number of neuropsychiatric conditions. ::: ::: We investigated whether these microstates were altered in patients with RRMS, and if the microstates' temporal properties reflected a link to the patients' clinical features. ::: ::: We acquired 256-channel EEG in 53 patients (mean age 37.6 years, 45 females, mean disease duration 9.99 years, Expanded Disability Status Scale ≤ 4, mean 2.2) and 49 healthy controls (mean age 36.4 years, 33 females). We analyzed segments of a total of 5 min of EEG during resting wakefulness and determined for both groups the four predominant microstates using established clustering methods. ::: ::: We found significant differences in the temporal dynamics of two of the four microstates between healthy controls and patients with RRMS in terms of increased appearance and prolonged duration. Using stepwise multiple linear regression models with 8-fold cross-validation, we found evidence that these electrophysiological measures predicted a patient's total disease duration, annual relapse rate, disability score, as well as depression score, and cognitive fatigue measure. ::: ::: In RRMS patients, microstate analysis captured altered fluctuations of EEG topographies in the sub-second range. This measure of high temporal resolution provided potentially powerful markers of disease activity and neuropsychiatric co-morbidities in RRMS.


title: When trees collide: an approximation algorithm for the generalized Steiner problem on networks

abstract: We give the first approximation algorithm for the {\em generalized network Steiner tree problem}, a problem in network design. An instance consists of a network with link-costs and, for each pair ${i,j}$ of nodes, an edge-connectivity requirement. The goal is to find a minimum-cost network using the available links and satisfying the requirements. Our algorithm outputs a solution whose cost is within $ 2 \log R $ of optimal, where $R$ is the highest requirement value. In the course of proving the performance guarantee, we prove a combinatorial min-max approximate equality relating minimum-cost networks to maximum packings of certain kinds of cuts. As a consequence of the proof of this theorem, we obtain an approximation algorithm for optimally packing these cuts; we show that this algorithm has application to estimating the reliability of a probabilistic network.


title: MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation

abstract: Optical flow estimation is one of the most studied problems in computer vision, yet recent benchmark datasets continue to reveal problem areas of today's approaches. Occlusions have remained one of the key challenges. In this paper, we propose a symmetric optical flow method to address the well-known chicken-and-egg relation between optical flow and occlusions. In contrast to many state-of-the-art methods that consider occlusions as outliers, possibly filtered out during post-processing, we highlight the importance of joint occlusion reasoning in the optimization and show how to utilize occlusion as an important cue for estimating optical flow. The key feature of our model is to fully exploit the symmetry properties that characterize optical flow and occlusions in the two consecutive images. Specifically through utilizing forward-backward consistency and occlusion-disocclusion symmetry in the energy, our model jointly estimates optical flow in both forward and backward direction, as well as consistent occlusion maps in both views. We demonstrate significant performance benefits on standard benchmarks, especially from the occlusion-disocclusion symmetry. On the challenging KITTI dataset we report the most accurate two-frame results to date.


title: A compact magnetic directional proximity sensor for spherical robots

abstract: Spherical robots have recently attracted significant interest due to their ability to offer high speed motion with excellent locomotion efficiency. As a result of the presence of a sealed outer shell, its obstacle avoidance strategy has been simply “hit and run”. While this is convenient due to the specific geometry of the spherical robots, it however could pose serious issues when the robots are small and light. For portable spherical robots with on-board cameras, a high speed collision with a hard surface may damage the robot or the camera. This paper proposes a novel and compact proximity sensor that utilizes passive magnetic field to detect the ferromagnetic obstacles through perturbation of the magnetic field. Compared with the existing works that utilize the Earth's weak magnetic field as a means of detection, the approach undertaken here seeks to harness the same principle but uses an intelligently designed magnetic assembly. It efficiently amplifies the perturbation and therefore improves the detection performance. The presented method is able to simultaneously determine both the distance and direction of the nearby ferromagnetic obstacles. Both simulation and experimental results are presented to validate the sensing principle and operational performance.


title: Time – motion analysis of professional rugby union players during match-play

abstract: Abstract The aim of this study was to quantify the movement patterns of various playing positions during professional rugby union match-play, such that the relative importance of aerobic and anaerobic energy pathways to performance could be estimated. Video analysis was conducted of individual players (n=29) from the Otago Highlanders during six “Super 12” representative fixtures. Each movement was coded as one of six speeds of locomotion (standing still, walking, jogging, cruising, sprinting, and utility), three states of non-running intensive exertion (rucking/mauling, tackling, and scrummaging), and three discrete activities (kicking, jumping, passing). The results indicated significant demands on all energy systems in all playing positions, yet implied a greater reliance on anaerobic glycolytic metabolism in forwards, due primarily to their regular involvement in non-running intense activities such as rucking, mauling, scrummaging, and tackling. Positional group comparisons indicated that while the gr...


title: A multi-lane MIPI CSI receiver for mobile camera applications

abstract: In this paper, we present a multi-lane Mobile Industry Processor Interface (MIPI) Camera Serial Interface (CSI) receiver which is suitable for high-resolution camera and High-Density (HD) video applications. The presented receiver is designed to achieve parallel processing so that it reduces dynamic power consumption for multi-lane configuration. In order to facilitate parallel processing, we propose two techniques for the multi-lane receiver. The presented design receives data at the maximum rate of 4Gb/s and is verified in a standard CMOS technology.


title: Grasping analysis for a 3-Finger Adaptive Robot Gripper

abstract: A 3-Finger Adaptive Robot Gripper is an advanced robotic research that provides a robotic hand-like capabilities due to its flexibility and versatility. However, the grasping performance has to be analyzed and monitored based on the motor encoder, motor current, and force feedback so that the finger position and grasping force can be effectively controlled. This paper provides an open-loop grasping analysis for a 3-Finger Adaptive Robot Gripper. A series of grasping tests has been conducted to demonstrate the robot capabilities and functionalities. Different stiffness levels of the grasped objects have been chosen to demonstrate the grasping ability. In the experiment, a Modbus RTU protocol and Matlab/Simulink are used as communication and control platform. A specially modified interlink FSR sensor is proposed where a special plastic cover has been developed to enhance the sensor sensitivity. The Arduino IO Package is employed to interface the sensor and Matlab/Simulink. The results show that the significant relationships between finger position, motor current, and force sensor are found and the results can be used for a proper grasping performance.


title: Computational Modeling of Synthetic Microbial Biofilms

abstract: Microbial biofilms are complex, self-organized communities of bacteria, which employ physiological cooperation and spatial organization to increase both their metabolic efficiency and their resistance to changes in their local environment. These properties make biofilms an attractive target for engineering, particularly for the production of chemicals such as pharmaceutical ingredients or biofuels, with the potential to significantly improve yields and lower maintenance costs. Biofilms are also a major cause of persistent infection, and a better understanding of their organization could lead to new strategies for their disruption. Despite this potential, the design of synthetic biofilms remains a major challenge, due to the complex interplay between transcriptional regulation, intercellular signaling, and cell biophysics. Computational modeling could help to address this challenge by predicting the behavior of synthetic biofilms prior to their construction; however, multiscale modeling has so far not been achieved for realistic cell numbers. This paper presents a computational method for modeling synthetic microbial biofilms, which combines three-dimensional biophysical models of individual cells with models of genetic regulation and intercellular signaling. The method is implemented as a software tool (CellModeller), which uses parallel Graphics Processing Unit architectures to scale to more than 30,000 cells, typical of a 100 μm diameter colony, in 30 min of computation time.


title: Real-Time PID Control Strategy for Maglev Transportation System via Particle Swarm Optimization

abstract: This paper focuses on the design of a real-time particle-swarm-optimization-based proportional-integral-differential (PSO-PID) control scheme for the levitated balancing and propulsive positioning of a magnetic-levitation (maglev) transportation system. The dynamic model of a maglev transportation system, including levitated electromagnets and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics, is first constructed. The control objective is to design a real-time PID control methodology via PSO gain selections and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. The effectiveness of the proposed PSO-PID control scheme for the maglev transportation system is verified by numerical simulations and experimental results, and its superiority is indicated in comparison with PSO-PID in previous literature and conventional sliding-mode (SM) control strategies. With the proposed PSO-PID control scheme, the controlled maglev transportation system possesses the advantages of favorable control performance without chattering phenomena in SM control and robustness to uncertainties superior to fixed-gain PSO-PID control.


title: Exploring Influencing Factors in E-Commerce Transaction Behaviors

abstract: With the rapid growth of the Internet, e-commerce has been growing and expanding significantly, according to Fortune, almost all of the global top 500 companies have started online marketing; e-commerce is presently far from the mainstream of modern trade behaviors. The influencing factors in e-commerce transaction behaviors are explored in this paper. This paper takes network customers of a large e-commerce enterprise as research object; the influencing factors are integrated into three categories, respectively managerial factors, external environmental factors and internal environmental factors. And analyzes the influencing factors in the process of e-commerce transaction in terms of customer behaviors, constructs a functional process model for influencing factors in e-commerce transaction activities, integrates such factors into the model, and illustrates the functional mechanism of all the factors in e-commerce transaction. Studies on influencing factors for e-commerce transaction in the perspective of e-commerce customers will help shed light on such contradiction.


title: Ontology for colaborative development of product service systems based on basic formal ontology

abstract: The paper is one of the few attempts to develop a Product Service System (PSS) ontology aiming to facilitate Knowledge Management in collaborative PSS design, focusing upon machine industry. The PSS ontology includes concepts such as products, services, PSS, PSS lifecycle, process and stakeholders, including direct customers, consumers and their feedback. The context sensitivity approach is proposed to fully support the use of different tools for PSS development by various stakeholders. The context model includes both PSS ontology and a so-called user-centric ontology. The process to develop the ontologies is described. The approach to build the PSS ontology is based on the so-called Basic Formal Ontology (BFO). The foreseen applications of both ontologies in industrial practice of machine vendors and the expected benefits are being elaborated.


title: Mahalanobis Distance Learning for Person Re-identification

abstract: Recently, Mahalanobis metric learning has gained a considerable interest for single-shot person re-identification. The main idea is to build on an existing image representation and to learn a metric that reflects the visual camera-to-camera transitions, allowing for a more powerful classification. The goal of this chapter is twofold. We first review the main ideas of Mahalanobis metric learning in general and then give a detailed study on different approaches for the task of single-shot person re-identification, also comparing to the state of the art. In particular, for our experiments, we used Linear Discriminant Metric Learning (LDML), Information Theoretic Metric Learning (ITML), Large Margin Nearest Neighbor (LMNN), Large Margin Nearest Neighbor with Rejection (LMNN-R), Efficient Impostor-based Metric Learning (EIML), and KISSME. For our evaluations we used four different publicly available datasets (i.e., VIPeR, ETHZ, PRID 2011, and CAVIAR4REID). Additionally, we generated the new, more realistic PRID 450S dataset, where we also provide detailed segmentations. For the latter one, we also evaluated the influence of using well-segmented foreground and background regions. Finally, the corresponding results are presented and discussed.


title: Followee recommendation based on text analysis of micro-blogging activity

abstract: Nowadays, more and more users keep up with news through information streams coming from real-time micro-blogging activity offered by services such as Twitter. In these sites, information is shared via a followers/followees social network structure in which a follower receives all the micro-blogs from his/her followees. Recent research efforts on understanding micro-blogging as a novel form of communication and news spreading medium have identified three different categories of users in these systems: information sources, information seekers and friends. As social networks grow in the number of registered users, finding relevant and reliable users to receive interesting information becomes essential. In this paper we propose a followee recommender system based on both the analysis of the content of micro-blogs to detect users' interests and in the exploration of the topology of the network to find candidate users for recommendation. Experimental evaluation was conducted in order to determine the impact of different profiling strategies based on the text analysis of micro-blogs as well as several factors that allows the identification of users acting as good information sources. We found that user-generated content available in the network is a rich source of information for profiling users and finding like-minded people.


title: Locality-Driven Dynamic Flash Cache Allocation

abstract: Flash-based SSDs are widely deployed as the storage caches to boost the system performance. However, unlike the traditional cache devices such as SRAM and DRAM, SSDs have internal garbage collection activities, which can severely degrade the cache performance. Moreover, SSD can only sustain limited P/E cycles. Therefore, traditional cache hit ratio oriented optimizations might not obtain the optimal performance for the SSD cache and can even shorten the device lifetime, especially for the SSD write cache, which could introduce more internal garbage collection processes and lifetime concerns. In this paper, we propose a reuse distance aware cache management to improve both the performance and lifetime of SSD-based write cache by compromising the cache hit ratio and the internal garbage collection overhead.


COMPENSATION OF CAPACITIVE DIFFERENTIAL PRESSURE SENSOR USING MULTI LAYER PERCEPTRON NEURAL NETWORK


title: Text Categorization for Deriving the Application Quality in Enterprises Using Ticketing Systems

abstract: Today’s enterprise services and business applications are often centralized in a small number of data centers. Employees located at branches and side offices access the computing infrastructure via the internet using thin client architectures. The task to provide a good application quality to the employers using a multitude of different applications and access networks has thus become complex. Enterprises have to be able to identify resource bottlenecks and applications with a poor performance quickly to take appropriate countermeasures and enable a good application quality for their employees. Ticketing systems within an enterprise use large databases for collecting complaints and problems of the users over a long period of time and thus are an interesting starting point to identify performance problems. However, manual categorization of tickets comes with a high workload.


title: Least-Squares Fitting of Circles and Ellipses

abstract: Fitting circles and ellipses to given points in the plane is a problem that arises in many application areas, e.g., computer graphics, coordinate meteorology, petroleum engineering, statistics. In the past, algorithms have been given which fit circles and ellipses insome least-squares sense without minimizing the geometric distance to the given points.


title: Using real time computer vision algorithms in automatic attendance management systems

abstract: This paper introduces a new approach in automatic attendance management systems, extended with computer vision algorithms. We propose using real time face detection algorithms integrated on an existing Learning Management System (LMS), which automatically detects and registers students attending on a lecture. The system represents a supplemental tool for instructors, combining algorithms used in machine learning with adaptive methods used to track facial changes during a longer period of time. This new system aims to be less time consuming than traditional methods, at the same time being nonintrusive and not interfere with the regular teaching process. The tool promises to offer accurate results and a more detailed reporting system which shows student activity and attendance in a classroom.


title: ACC+Stop&go maneuvers with throttle and brake fuzzy control

abstract: Research on adaptive cruise control (ACC) with Stop&Go maneuvers is presently one of the most important topics in the field of intelligent transportation systems. The main feature of such controllers is that there is adaptation to a user-preset speed and, if necessary, speed reduction to keep a safe distance from the vehicle ahead in the same lane of the road, whatever the speed. The extreme case is the stop and go operation in which the lead car stops and the vehicle at the rear must also do so. This paper presents the development of an ACC system and related experiments. The system input information is acquired by a real-time kinematic phase differential global positioning system (GPS) (i.e., centimetric GPS) and wireless local area network links. The outputs are the variables that control the pressure on the throttle and brake pedals, which is calculated by an onboard computer. In addition, the car control is based on fuzzy logic. The system has been installed in two mass-produced Citroe/spl uml/n Berlingo electric vans, in which all the actuators have been automated to achieve humanlike driving. The results from real experiments show that the unmanned vehicles behave very similarly to human-driven cars and are very adaptive to any kind of situation at a broad range of speeds, thus raising the safety of the driving and allowing cooperation with manually driven cars.


title: A federated architecture for database systems

abstract: The contemporary approach to database system architecture requires the complete integration of data into a single, centralized database; while multiple logical databases can be supported by current database management software, techniques for relating these databases are strictly ad hoc. This problem is aggravated by the trend toward networks of small to medium size computer systems, as opposed to large, stand-alone main-frames. Moreover, while current research on distributed databases aims to provide techniques that support the physical distribution of data items in a computer network environment, current approaches require a distributed database to be logically centralized.


title: The mediating role of the employee relations climate in the relationship between strategic HRM and organizational performance in Chinese banks

abstract: Abstract In today's business environment, establishing a positive climate is becoming increasingly important for organizational growth and performance. A positive employee relations climate creates a social atmosphere, which encourages high employee involvement and an employee-centered culture. In response, employees feel comfortable and contribute positively to organizational performance. Given the intensity of competition in the service industry, banks must understand how the employee relations climate and HRM practices affect organizational performance. Banks also need to know which employee relations climate best meets the needs of the organization. Accordingly, this study examined the way the employee relations climate mediates the relationship between strategic HRM practices and organizational performance in Chinese banks. Senior employees of Chinese banks based in Shanghai provided data by responding to a survey questionnaire. CFA and SEM in AMOS 18 were used to test the hypotheses and evaluate measurement validity. The results indicate that strategic HRM has a significant positive relationship with operational performance. In addition, the employee relations climate was found to mediate the relationship between strategic HRM and organizational performance. Cross-sectional data, a restricted target sample (i.e., bank employees), and a restricted target area (i.e., Shanghai) are limitations of this study. In addition, the dimensions of organizational performance were not identified in detail. Finally, the hypothesized relationship was explored only for a specific sector (i.e., the banking sector).


title: Automated Colour Segmentation of Malaria Parasite with Fuzzy and Fractal Methods

abstract: Malaria is an endemic, global and life threatening disease. Technically skilled person or an expert is needed to analyze the microscopic blood smears for long hours. This paper presents an efficient approach to segment the parasites of malaria. Three different colour space namely LAB, HSI and gray has been used effectively to pre-process and segment the parasite in digital images with noise, debris and stain. L and B plane of LAB, S plane of HSI of input image is extracted with convolution and DCT. Fuzzy based segmentation has been proposed to segment the malaria parasite. Colour features, fractal features are extracted and feature vectors are prepared as a result of segmentation. Adaptive Resonance Theory Neural Network (ARTNN), Back Propagation Network (BPN) and SVM classifiers are used with Fuzzy segmentation and fractal feature extraction methods. Automated segmentation with ARTNN has recorded an accuracy of 95 % compared to other classifiers.


TCP/IP Illustrated, Volume 1: The Protocols


title: Impact of Depression and Antidepressant Treatment on Heart Rate Variability: A Review and Meta-Analysis

abstract: Background Depression is associated with an increase in the likelihood of cardiac events; however, studies investigating the relationship between depression and heart rate variability (HRV) have generally focused on patients with cardiovascular disease (CVD). The objective of the current report is to examine with meta-analysis the impact of depression and antidepressant treatment on HRV in depressed patients without CVD. Methods Studies comparing 1) HRV in patients with major depressive disorder and healthy control subjects and 2) the HRV of patients with major depressive disorder before and after treatment were considered for meta-analysis. Results Meta-analyses were based on 18 articles that met inclusion criteria, comprising a total of 673 depressed participants and 407 healthy comparison participants. Participants with depression had lower HRV (time frequency: Hedges' g = −.301, p g = −.293, p g = −1.955, p = .05; Valsalva ratio: Hedges' g = −.712, p r = −.354, p Conclusions Depression without CVD is associated with reduced HRV, which decreases with increasing depression severity, most apparent with nonlinear measures of HRV. Critically, a variety of antidepressant treatments do not resolve these decreases despite resolution of symptoms, highlighting that antidepressant medications might not have HRV-mediated cardioprotective effects and the need to identify individuals at risk among patients in remission.


title: Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach

abstract: As an alternative to manual pixel-wise annotation, synthetic data has been increasingly used for training semantic segmentation models. Such synthetic images and semantic labels can be easily generated from virtual 3D environments. In this work, we propose an approach to cross-domain semantic segmentation with the auxiliary geometric information, which can also be easily obtained from virtual environments. The geometric information is utilized on two levels for reducing domain shift: on the input level, we augment the standard image translation network with the geometric information to translate synthetic images into realistic style; on the output level, we build a task network which simultaneously performs semantic segmentation and depth estimation. Meanwhile, adversarial training is applied on the joint output space to preserve the correlation between semantics and depth. The proposed approach is validated on two pairs of synthetic to real dataset: from Virtual KITTI to KITTI, and from SYNTHIA to Cityscapes, where we achieve a clear performance gain compared to the baselines and various competing methods, demonstrating the effectiveness of the geometric information for cross-domain semantic segmentation.


title: Gestures: their role in teaching and learning

abstract: Gestures are central to human cognition and constitute a pervasive element of human communication across cultures; even congenitally blind individuals use gestures when they talk. Yet there exists virtually no educational research that focuses on the role of gestures in knowing and learning and the implications they have for designing and evaluating learning environments. The purpose of this article is to provide a review of the existing literature in anthropology, linguistics, psychology, and education and, in the context of several concrete analyses of gesture use, to articulate potential focus questions that are relevant to educational research of knowing, learning, and teaching.


title: Advances in direct transesterification of algal oils from wet biomass.

abstract: An interest in biodiesel as an alternative fuel for diesel engines has been increasing because of the issue of petroleum depletion and environmental concerns related to massive carbon dioxide emissions. Researchers are strongly driven to pursue the next generation of vegetable oil-based biodiesel. Oleaginous microalgae are considered to be a promising alternative oil source. To commercialize microalgal biodiesel, cost reductions in oil extraction and downstream biodiesel conversion are stressed. Herein, starting from an investigation of oil extraction from wet microalgae, a review is conducted of transesterification using enzymes, homogeneous and heterogeneous catalysts, and yield enhancement by ultrasound, microwave, and supercritical process. In particular, there is a focus on direct transesterification as a simple and energy efficient process that omits a separate oil extraction step and utilizes wet microalgal biomass; however, it is still necessary to consider issues such as the purification of microalgal oils and upgrading of biodiesel properties.


title: Forecasting methods for cloud hosted resources, a comparison

abstract: The emergence of cloud management systems, and the adoption of elastic cloud services enable dynamic adjustment of cloud hosted resources and provisioning. In order to effectively provision for dynamic workloads presented on cloud platforms, an accurate forecast of the load on the cloud resources is required. In this paper, we investigate various forecasting methods presented in recent research, identify and adapt evaluation metrics used in literature and compare forecasting methods on prediction performance. We investigate the performance gain of ensemble models when combining three of the best performing models into one model. We find that our 30th order Auto-regression model and Feed-Forward Neural Network method perform the best when evaluated on Google's Cluster dataset and using the provision specific metrics identified. We also show an improvement in forecasting accuracy when evaluating two ensemble models.


title: A novel vehicle for local protein delivery to the inner ear: injectable and biodegradable thermosensitive hydrogel loaded with PLGA nanoparticles.

abstract: Delivery of biomacromolecular drugs into the inner ear is challenging, mainly because of their inherent instability as well as physiological and anatomical barriers. Therefore, protein-friendly, hydrogel-based delivery systems following local administration are being developed for inner ear therapy. Herein, biodegradable poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) containing interferon α-2 b (IFN α-2 b) were loaded in chitosan/glycerophosphate (CS/GP)-based thermosensitive hydrogel for IFN delivery by intratympanic injection. The injectable hydrogel possessed a physiological pH and formed semi-solid gel at 37 °C, with good swelling and deswelling properties. The CS/GP hydrogel could slowly degrade as visualized by scanning electron microscopy (SEM). The presence of NPs in CS/GP gel largely influenced in vitro drug release. In the guinea pig cochlea, a 1.5- to 3-fold increase in the drug exposure time of NPs-CS/GP was found than those of the solution, NPs and IFN-loaded hydrogel. Most importantly, a prolonged residence time was attained without obvious histological changes in the inner ear. This biodegradable, injectable, and thermosensitive NPs-CS/GP system may allow longer delivery of protein drugs to the inner ear, thus may be a potential novel vehicle for inner ear therapy.