title: Handling Pedestrians in Crosswalks Using Deep Neural Networks in the IARA Autonomous Car

abstract: In this work, we propose a subsystem to handle pedestrians in crosswalks using deep neural networks for the IARA autonomous car, which relies on camera and LIDAR data fusion. Crosswalks’ positions were manually annotated in IARA’s map. Pedestrians are detected in the camera image using a convolutional neural network (CNN). Then, pedestrians’ positions in the map are obtained by fusing their positions in the image with the LIDAR point cloud. Subsequently, if a pedestrian position is inside the crosswalk area, the crosswalk is set as busy. Finally, a busy crosswalk message is published to the High-Level Decision Maker subsystem. This subsystem selects the car’s behavior according to the crosswalk condition and propagates this decision down through the control pipeline, in order to make the car drive correctly through the crosswalk area. The Pedestrian Handler subsystem was evaluated on IARA, which was driven autonomously for various laps along a real and complex circuit with various crosswalks. In all passages through crosswalks, the Pedestrian Handler dealt with pedestrians as expected, i.e., without any human intervention.


title: Factors to increase the stocking density using BioFloc Technology in tilapia production : A mini review

abstract: Since the emergence of BioFloc Technology, much information has been published to test this system in different contexts and under different nutritional conditions. One of the limitations of this system in aquaculture farms is the difficulty of working with high stocking densities. However, each author manages his own density to manipulate productivity in biofloc systems. The objective of this work is to analyze the variables involved that directly affect the stocking density such as, the C: N ratio, the aeration systems, the amount of food supplied and the tilapia species. Based on the results of the experiences that have been published in previous works, the results of each variable are analyzed to propose an increase in the density of tilapia culture.


Time series forecasting with neural networks : a comparative study using the airline data


title: Precise Calibration of Channel Imbalance for Very High Resolution SAR With Stepped Frequency

abstract: Synthetic aperture radar (SAR) images require a high-resolution system for accurate interpretations. This high range resolution can be achieved by stepped frequency chirp signals. To reconstruct a wideband waveform from each subband signal, amplitude/phase/delay imbalance between the channels should be precisely compensated. In this paper, the system configuration is first presented. Further, a calibration strategy was proposed based on three calibration loops: the reference calibration, transmitting calibration, and receiving calibration loops for coarsely compensating the channel imbalance. Then, two different methods based on the cost functions were proposed to remove the residual channel imbalance. The proposed methods were validated using stepped frequency SAR data acquired by an X-band airborne SAR system with a total bandwidth of 3.6 GHz, yielding (unweighted) a 3-dB range resolution of 4 cm.


title: A 14Gb/s high-swing thin-oxide device SST TX in 45nm CMOS SOI

abstract: The limited supply voltage of today's state-of-the-art CMOS technologies makes the design of high-speed transmitters at signaling swings above the typical 1V supply a challenging task. Higher-voltage TX amplitude is not only required in older I/O standards and legacy applications, but also in emerging electro-optical extensions where high voltage swing combined with high-speed operation is desired. Higher swing also helps meet certain I/O standards in applications where losses introduced by high-density package constraints can be compensated to some extent. The source-series terminated (SST) driver is a versatile building block in a multistandard I/O TX thanks to its potential for low-power operation, its low area consumption, high CMOS-style circuit content, and flexible termination capability [1]. Also, the SST driver supports single-ended output and differential operation.


title: Present status of clinical care for postpartum patients with hypertensive disorders of pregnancy in Japan: findings from a nationwide questionnaire survey

abstract: Objective: To assess the present status of clinical care for postpartum patients with hypertensive disorders of pregnancy (HDP) in Japan. Methods: We conducted a nationwide questionnaire survey of obstetricians, internists and hypertension specialists and analyzed 686 valid responses. Results: Though HDP is widely known as a risk factor for subsequent hypertension and cardiovascular disease, over one-third of obstetricians terminated their postpartum follow-up of HDP patients without referring them to other departments. Conclusion: It is important to establish an effective referral system, whereby patients with HDP can be smoothly transferred to primary care or a specialist physician after childbirth for long-term monitoring and management of blood pressure.


Delta-Sigma Fractional-N Phase-Locked Loops


title: Correlates of school dropout and absenteeism among adolescent girls from marginalized community in north Karnataka, south India

abstract: Abstract Secondary education among lower caste adolescent girls living in rural Karnataka, South India, is characterized by high rates of school drop-out and absenteeism. A cross-sectional baseline survey (N=2275) was conducted in 2014 as part of a cluster-randomized control trial among adolescent girls (13-14 year) and their families from marginalized communities in two districts of north Karnataka. Bivariate and multivariate logistic regression models were used. Overall, 8.7% girls reported secondary school dropout and 8.1% reported frequent absenteeism (past month). In adjusted analyses, economic factors (household poverty; girls' work-related migration), social norms and practices (child marriage; value of girls' education), and school-related factors (poor learning environment and bullying/harassment at school) were associated with an increased odds of school dropout and absenteeism. Interventions aiming to increase secondary school retention among marginalized girls may require a multi-level approach, with synergistic components that address social, structural and economic determinants of school absenteeism and dropout.


title: Towards Deep Cellular Phenotyping in Placental Histology

abstract: The placenta is a complex organ, playing multiple roles during fetal development. Very little is known about the association between placental morphological abnormalities and fetal physiology. In this work, we present an open sourced, computationally tractable deep learning pipeline to analyse placenta histology at the level of the cell. By utilising two deep Convolutional Neural Network architectures and transfer learning, we can robustly localise and classify placental cells within five classes with an accuracy of 89%. Furthermore, we learn deep embeddings encoding phenotypic knowledge that is capable of both stratifying five distinct cell populations and learn intraclass phenotypic variance. We envisage that the automation of this pipeline to population scale studies of placenta histology has the potential to improve our understanding of basic cellular placental biology and its variations, particularly its role in predicting adverse birth outcomes.


title: Multi-Objective Evolutionary Federated Learning

abstract: Federated learning is an emerging technique used to prevent the leakage of private information. Unlike centralized learning that needs to collect data from users and store them collectively on a cloud server, federated learning makes it possible to learn a global model while the data are distributed on the users' devices. However, compared with the traditional centralized approach, the federated setting consumes considerable communication resources of the clients, which is indispensable for updating global models and prevents this technique from being widely used. In this paper, we aim to optimize the structure of the neural network models in federated learning using a multi-objective evolutionary algorithm to simultaneously minimize the communication costs and the global model test errors. A scalable method for encoding network connectivity is adapted to federated learning to enhance the efficiency in evolving deep neural networks. Experimental results on both multilayer perceptrons and convolutional neural networks indicate that the proposed optimization method is able to find optimized neural network models that can not only significantly reduce communication costs but also improve the learning performance of federated learning compared with the standard fully connected neural networks.


title: Smart contract-based campus demonstration of decentralized transactive energy auctions

abstract: Transactive energy paradigms will enable the exchange of energy from a distributed set of prosumers. While prosumers have access to distributed energy resources, these resources are intermittently available. There is a need for distributed markets to enable the exchange of energy in transactive environments, however, the large number of potential prosumers introduces challenges in the establishment of trust between prosumers. Markets for transactive environments create other challenges, such as establishing clearing prices for energy and exchanging money between prosumers. Blockchains provide a unique technology to address this distributed trust problem through the use of a distributed ledger, cryptocurrencies, and the execution of smart contracts. This paper introduces a smart contract that implements a transactive energy auction that operates without the need for a trusted entitys oversight. The auction mechanism implements a Vickrey second price auction, which guarantees bidders will submit honest bids. The contract is implemented on transactive agents on the WSU campus interacting with a 72kW PV array and the Ethereum blockchain. The contract is then used to execute auctions based on the energy from the the PV array and simulated building loads to demonstrate the auctions operations.


title: Neocolonialism in education: Cooperative Learning in an Asian context

abstract: This article is concerned with the influence of western educational approaches in non‐western countries and societies. This influence is frequently referred to as educational neocolonialism in the sense that western paradigms tend to shape and influence educational systems and thinking elsewhere through the process of globalisation. Given the perceived pressure to modernise and reform in order to attain high international standards, educational policy makers in non‐western countries tend to look to the west. Thus they may ‘borrow’ policies and practices that were originally developed and operated, and which appeared to be effective, in a very different cultural context to that of their own societies. In effecting such transfer, detailed consideration of particular aspects of the culture and heritage of the originating country is often neglected. To illustrate some of the problems that result from this, the article presents a case study of the application of Cooperative Learning, an educational method deve...


title: Power-Sharing Method of Multiple Distributed Generators Considering Control Modes and Configurations of a Microgrid

abstract: This paper describes the active power and frequency-control principles of multiple distributed generators (DGs) in a microgrid. Microgrids have two operating modes: 1) a grid-connected mode and 2) an islanded mode. During islanded operation, one DG unit should share output generation power with other units in exact accordance with the load. Two different options for controlling the active power of DGs are introduced and analyzed: 1) unit output-power control (UPC) and 2) feeder flow control (FFC). Taking into account the control mode and the configuration of the DGs, we investigate power-sharing principles among multiple DGs under various system conditions: 1) load variation during grid-connected operation, 2) load variation during islanded operation, and 3) loss of mains (disconnected from the main grid). Based on the analysis, the FFC mode is advantageous to the main grid and the microgrid itself under load variation conditions. However, when the microgrid is islanded, the FFC control mode is limited by the existing droop controller. Therefore, we propose an algorithm to modify the droop constant of the FFC-mode DGs to ensure proper power sharing among DGs. The principles and the proposed algorithm are verified by PSCAD simulation.


title: Smart, low power, wearable multi-sensor data acquisition system for environmental monitoring

abstract: The wireless sensor network is becoming a technology of huge interest for a wide variety of applications. Despite the technological developments of smart sensing, issues related to system integration, sensor miniaturization and especially low-power sensors interface circuitry design, still represent major technical challenges. This paper describes the prototype of a smart, low power, wearable system that can be used for environmental monitoring. The wearable system uses an array of sensors connected to a central processing unit with firmware for continuously monitoring. The data acquisition process has been performed via Bluetooth with a Graphical User Interface (GUI) designed within LabVIEW. This system will be also interfaced with an Android device as an alternative for ease of access and portability. This work is meant to prove basic concepts about smart sensing in the context of wearable systems along with Bluetooth communication, data acquisition as well as mobile computers.


title: Generalizing DTW to the multi-dimensional case requires an adaptive approach

abstract: In recent years Dynamic Time Warping (DTW) has emerged as the distance measure of choice for virtually all time series data mining applications. For example, virtually all applications that process data from wearable devices use DTW as a core sub-routine. This is the result of significant progress in improving DTW's efficiency, together with multiple empirical studies showing that DTW-based classifiers at least equal (and generally surpass) the accuracy of all their rivals across dozens of datasets. Thus far, most of the research has considered only the one-dimensional case, with practitioners generalizing to the multi-dimensional case in one of two ways, dependent or independent warping. In general, it appears the community believes either that the two ways are equivalent, or that the choice is irrelevant. In this work, we show that this is not the case. The two most commonly used multi-dimensional DTW methods can produce different classifications, and neither one dominates over the other. This seems to suggest that one should learn the best method for a particular application. However, we will show that this is not necessary; a simple, principled rule can be used on a case-by-case basis to predict which of the two methods we should trust at the time of classification. Our method allows us to ensure that classification results are at least as accurate as the better of the two rival methods, and, in many cases, our method is significantly more accurate. We demonstrate our ideas with the most extensive set of multi-dimensional time series classification experiments ever attempted.


title: Learning, innovation, and sustainability among mobile phone repairers in Dhaka, Bangladesh

abstract: Acts of technology maintenance and repair constitute important and often overlooked moments in the operation of complex interactive systems. They also provide fresh insight on a series of problems -- innovation, learning, and sustainability -- long core to HCI concern. This paper builds on original ethnographic fieldwork in the repair markets of Dhaka, Bangladesh to advance three basic arguments: first, that repair activities in such locations reveal novel and significant forms of craft-based knowledge and innovation; second, that repair work is embedded in local and transnational flows that connect local practices to global networks and institutions; and third, that taking repair work seriously can cast new light on problems of learning and sustainability in the design and operation of complex interactive systems. We conclude with observations that relate our repair-based findings back to problems in interactive systems research and design.


title: Silicon carbide pinched barrier rectifier (PBR)

abstract: In this paper, a new rectifier structure in silicon carbide (SiC) is presented for the first time. The proposed structure involves neither Schottky contact nor minor carrier injection via P-N junction. With adjacent P+ areas placed sufficiently close, pinched barrier is formed for rectifier purpose. Numerical simulations are carried out to verify its function, and optimize its performance. Based on the simulation results, a good trade-off can be achieved between forward drop and reverse leakage, provided that this new structure offers flexibility of controlling onset voltage by adjusting channel parameters continuously.


title: Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier

abstract: Purpose To differentiate the visual fields (VFs) of preperimetric open-angle glaucoma (OAG) patients from the VFs of healthy eyes using a deep learning (DL) method. Design Cohort study. Participants One hundred seventy-one preperimetric glaucoma VFs (PPGVFs) from 53 eyes in 51 OAG patients and 108 healthy eyes of 87 healthy participants. Methods Preperimetric glaucoma VFs were defined as all VFs before a first diagnosis of manifest glaucoma (Anderson-Patella's criteria). In total, 171 PPGVFs from 53 eyes in 51 OAG patients and 108 VFs from 108 healthy eyes in 87 healthy participants were analyzed (all VFs were tested using the Humphrey Field Analyzer 30-2 program; Carl Zeiss Meditec, Dublin, CA). The 52 total deviation, mean deviation, and pattern standard deviation values were used as predictors in the DL classifier: a deep feed-forward neural network (FNN), along with other machine learning (ML) methods, including random forests (RF), gradient boosting, support vector machine, and neural network (NN). The area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy of discrimination for each method. Main Outcome Measures The AUCs obtained with each classifier method. Results A significantly larger AUC of 92.6% (95% confidence interval [CI], 89.8%–95.4%) was obtained using the deep FNN classifier compared with all other ML methods: 79.0% (95% CI, 73.5%–84.5%) with RF, 77.6% (95% CI, 71.7%–83.5%) with gradient boosting, 71.2% (95% CI, 65.0%–77.5%), and 66.7% (95% CI, 60.1%–73.3%) with NN. Conclusions Preperimetric glaucoma VFs can be distinguished from healthy VFs with very high accuracy using a deep FNN classifier.


title: Androgen Receptor in Prostate Cancer

abstract: The normal development and maintenance of the prostate is dependent on androgen acting through the androgen receptor (AR). AR remains important in the development and progression of prostate cancer. AR expression is maintained throughout prostate cancer progression, and the majority of androgen-independent or hormone refractory prostate cancers express AR. Mutation of AR, especially mutations that result in a relaxation of AR ligand specificity, may contribute to the progression of prostate cancer and the failure of endocrine therapy by allowing AR transcriptional activation in response to antiandrogens or other endogenous hormones. Similarly, alterations in the relative expression of AR coregulators have been found to occur with prostate cancer progression and may contribute to differences in AR ligand specificity or transcriptional activity. Prostate cancer progression is also associated with increased growth factor production and an altered response to growth factors by prostate cancer cells. The kinas...


title: S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization

abstract: Particle Swarm Optimization (PSO) is one of the most widely used heuristic algorithms. The simplicity and inexpensive computational cost makes this algorithm very popular and powerful in solving a wide range of problems. The binary version of this algorithm has been introduced for solving binary problems. The main part of the binary version is a transfer function which is responsible to map a continuous search space to a discrete search space. Currently there appears to be insufficient focus on the transfer function in the literature despite its apparent importance. In this study six new transfer functions divided into two families, s-shaped and v-shaped, are introduced and evaluated. Twenty-five benchmark optimization functions provided by CEC 2005 special session are employed to evaluate these transfer functions and select the best one in terms of avoiding local minima and convergence speed. In order to validate the performance of the best transfer function, a comparative study with six recent modifications of BPSO is provided as well. The results prove that the new introduced v-shaped family of transfer functions significantly improves the performance of the original binary PSO.


title: Effects of Interactivity on Website Involvement and Purchase Intention

abstract: This study aims to understand how website interactivity (active control and reciprocal communication) can impact purchase intention through website involvement and how the impacts are moderated by the type of products featured on websites. In a laboratory experiment, 186 participants were asked to perform purchasing tasks of non-fictional books or greeting cards on websites of varying levels of interactivity. Results indicate that websites with a high level of active control lead to cognitive involvement and, in some instances, affective involvement. Websites with reciprocal communication lead to affective involvement for functional products but not expressive products. Responses from the participants also reveal that an increase in website involvement leads to higher purchase intention. Implications for research and practice are discussed.


title: When positive stereotypes threaten intellectual performance: the psychological hazards of “model minority” status

abstract: Asian-American women's performance on a test of quan- titative skill was studied as a function of whether their Asian, female, or individual identity was salient at the time of testing. In previous research, ethnicity salience was found to result in enhanced math performance among Asian women. However, the investigators relied on a subtle manipulation of ethnicity salience that likely did not in- voke concerns about group reputation nor make salient the common cultural stereotypes concerning Asians' mathematical prowess. We induced a focus on ethnic identity in a manner that was likely to make other people's high performance expectations more salient. Under these conditions, ethnicity salience resulted in diminished ability to concentrate, which in turn led to significantly impaired math perfor- mance. Thus, although people commonly hold positive stereotypes about Asians' mathematical skills, making these stereotypes salient prior to performance can create the potential for "choking" under the pressure of high expectations. A number of recent investigations have shown that negative ste- reotypes can undermine the academic performance of even very tal- ented members of the stereotyped group. For example, African- American students were found to perform significantly more poorly than their European-American counterparts on a test that was charac- terized as diagnostic of intelligence, but when the same test was


title: A Multi-Fusion Pattern Matching Algorithm for Signature-Based Network Intrusion Detection System

abstract: Security has become a critical issue in today's highly distributed and networked systems. Network intrusion detection systems (NIDSs), especially signature-based NIDSs, are being widely deployed in a distributed network environment with the purpose of defending against a variety of network attacks. Most of the commercially available NIDSs are software based and rely on pattern matching to extract the threat from network traffic. The increase in network speed and traffic may make existing algorithms to become a performance bottleneck. Therefore it is very necessary to develop faster and more efficient pattern matching algorithm in order to overcome the troubles on performance of NIDSs. Therefore, we propose a multi fusion pattern matching algorithm for Network Intrusion Detection Systems. The results obtained in percentages from the proposed fusion algorithm given better values in terms processing time in milliseconds than the existing algorithms when data English text are applied to evaluate the fusion performances.


Descriptions of Eleven New Species of Indigenous Melanidæ


title: Tuning Simulated Annealing Using the Sequential Parameter Optimization Toolbox spot

abstract: The sequential parameter optimization (spot) package for R (R Development Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Model-based investigations are common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimization algorithms. spot includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as CART and random forest; Gaussian process models (Kriging), and combinations of dierent metamodeling approaches. This article exemplies how an existing optimization algorithms, namely simulated annealing, can be tuned using the spot framework


title: Potency Trends of Δ9-THC and Other Cannabinoids in Confiscated Cannabis Preparations from 1993 to 2008*

abstract: The University of Mississippi has a contract with the National Institute on Drug Abuse (NIDA) to carry out a variety of research activities dealing with cannabis, including the Potency Monitoring (PM) program, which provides analytical potency data on cannabis preparations confiscated in the United States. This report provides data on 46,211 samples seized and analyzed by gas chromatography-flame ionization detection (GC-FID) during 1993-2008. The data showed an upward trend in the mean Δ(9)-tetrahydrocannabinol (Δ(9)-THC) content of all confiscated cannabis preparations, which increased from 3.4% in 1993 to 8.8% in 2008. Hashish potencies did not increase consistently during this period; however, the mean yearly potency varied from 2.5-9.2% (1993-2003) to 12.0-29.3% (2004-2008). Hash oil potencies also varied considerably during this period (16.8 ± 16.3%). The increase in cannabis preparation potency is mainly due to the increase in the potency of nondomestic versus domestic samples.


title: Multi-shot Imaging: Joint Alignment, Deblurring, and Resolution-Enhancement

abstract: The capture of multiple images is a simple way to increase the chance of capturing a good photo with a light-weight hand-held camera, for which the camera-shake blur is typically a nuisance problem. The naive approach of selecting the single best captured photo as output does not take full advantage of all the observations. Conventional multi-image blind deblurring methods can take all observations as input but usually require the multiple images are well aligned. However, the multiple blurry images captured in the presence of camera shake are rarely free from mis-alignment. Registering multiple blurry images is a challenging task due to the presence of blur while deblurring of multiple blurry images requires accurate alignment, leading to an intrinsically coupled problem. In this paper, we propose a blind multi-image restoration method which can achieve joint alignment, non-uniform deblurring, together with resolution enhancement from multiple low-quality images. Experiments on several real-world images with comparison to some previous methods validate the effectiveness of the proposed method.


title: Honey: A Potential Therapeutic Agent for Managing Diabetic Wounds

abstract: Diabetic wounds are unlike typical wounds in that they are slower to heal, making treatment with conventional topical medications an uphill process. Among several different alternative therapies, honey is an effective choice because it provides comparatively rapid wound healing. Although honey has been used as an alternative medicine for wound healing since ancient times, the application of honey to diabetic wounds has only recently been revived. Because honey has some unique natural features as a wound healer, it works even more effectively on diabetic wounds than on normal wounds. In addition, honey is known as an "all in one" remedy for diabetic wound healing because it can combat many microorganisms that are involved in the wound process and because it possesses antioxidant activity and controls inflammation. In this review, the potential role of honey's antibacterial activity on diabetic wound-related microorganisms and honey's clinical effectiveness in treating diabetic wounds based on the most recent studies is described. Additionally, ways in which honey can be used as a safer, faster, and effective healing agent for diabetic wounds in comparison with other synthetic medications in terms of microbial resistance and treatment costs are also described to support its traditional claims.


title: A scaled conjugate gradient algorithm for fast supervised learning

abstract: A supervised learning algorithm (Scaled Conjugate Gradient, SCG) is introduced. The performance of SCG is benchmarked against that of the standard back propagation algorithm (BP) (Rumelhart, Hinton, & Williams, 1986), the conjugate gradient algorithm with line search (CGL) (Johansson, Dowla, & Goodman, 1990) and the one-step Broyden-Fletcher-Goldfarb-Shanno memoriless quasi-Newton algorithm (BFGS) (Battiti, 1990). SCG is fully-automated, includes no critical user-dependent parameters, and avoids a time consuming line search, which CGL and BFGS use in each iteration in order to determine an appropriate step size. Experiments show that SCG is considerably faster than BP, CGL, and BFGS.


title: VHDL generator for a high performance convolutional neural network FPGA-based accelerator

abstract: Convolutional Neural Network (CNN) has been proven as a highly accurate and effective algorithm that has been used in a variety of applications such as handwriting digit recognition, visual recognition, and image classification. As a matter of fact, state-of-the-art CNNs are computationally intensive; however, their parallel and modular nature make platforms like FPGAs well suited for the acceleration process. A typical CNN takes a very long development round on FPGAs, hence in this paper, we propose a tool which allows developers, through a configurable user-interface, to automatically generate VHDL code for their desired CNN model. The generated code or architecture is modular, massively parallel, reconfigurable, scalable, fully pipelined, and adaptive to different CNN models. We demonstrate the automatic VHDL generator and its adaptability by implementing a small-scale CNN model “LeNet” and a large-scale one “AlexNet”. The parameters of small scale models are automatically hard-coded as constants (part of the programmable logic) to overcome the memory bottleneck issue. On a Xilinx Virtex-7 running at 200 MHz, the system is capable of processing up to 125k images/s of size 28×28 for LeNet and achieved a peak performance of 611.52 GOP/s and 414 FPS for AlexNet.


title: "Doing" reflexive modernization in pig husbandry: the hard work of changing the course of a river

abstract: The Dutch animal production sector faces significant pressure for change. We discuss a project for the design of a sustainable husbandry system for pigs. Named after the Greek hero Hercules, the project aimed for structural changes in both animal and crop production. However, instead of changing the course of the river, the project ended up merely adapting its flow. The Hercules project ran into difficulties typical for projects aiming at reflexive modernization. It relapsed from an effort for reflexive modernization to ecological modernization, by ultimately leaving the structural features of the sociotechnical regime intact. We show how this resulted from the biases and limitations implied by existing institutions, in which the project was unavoidably embedded. We introduce the idea of reflexive design, as "doing" reflexive modernization, which implies working on action and structure at the same time. A number of recommendations are given for reflexive design projects like this.


title: Robust Median Reversion Strategy for Online Portfolio Selection

abstract: Online portfolio selection has attracted increasing attention from data mining and machine learning communities in recent years. An important theory in financial markets is mean reversion, which plays a critical role in some state-of-the-art portfolio selection strategies. Although existing mean reversion strategies have been shown to achieve good empirical performance on certain datasets, they seldom carefully deal with noise and outliers in the data, leading to suboptimal portfolios, and consequently yielding poor performance in practice. In this paper, we propose to exploit the reversion phenomenon by using robust $L_1$ -median estimators, and design a novel online portfolio selection strategy named “Robust Median Reversion” (RMR), which constructs optimal portfolios based on the improved reversion estimator. We examine the performance of the proposed algorithms on various real markets with extensive experiments. Empirical results show that RMR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results. Finally, RMR runs in linear time, and thus is suitable for large-scale real-time algorithmic trading applications.


title: Not Competent but Warm... Really? Compensatory Stereotypes in the French-speaking World Group Processes & Intergroup Relations

abstract: Two studies examined the compensation hypothesis that members of both high- and low-status groups associate high-status groups with high levels of competence and low levels of warmth on the one hand, and low-status groups with low levels of competence and high levels of warmth, on the other. Building upon existing linguistic relations between the French and the Belgians, Study 1 had standard, i.e. French, and non-standard, i.e. Belgian, speakers rate the linguistic skills, competence, and warmth of both groups and report their meta-stereotypes. As predicted, both groups of participants saw the French as more skilled linguistically than Belgians and evaluated standard speakers as more competent than warm and non-standard speakers as more warm than competent. This pattern also emerged in respondents’ meta-stereotypes. Study 2 revealed that compensation was less marked among a third group of Francophone speakers, i.e. Swiss, even if the latter respondents seemed well aware of the pattern guiding Belgian and French representations of each other. We discuss the implications of the findings in terms of motivated intergroup stereotypes.


title: The beacon openflow controller

abstract: Beacon is a Java-based open source OpenFlow controller created in 2010. It has been widely used for teaching, research, and as the basis of Floodlight. This paper describes the architectural decisions and implementation that achieves three of Beacon's goals: to improve developer productivity, to provide the runtime ability to start and stop existing and new applications, and to be high performance.


title: Learning Temporal Transformations From Time-Lapse Videos

abstract: Based on life-long observations of physical, chemical, and biologic phenomena in the natural world, humans can often easily picture in their minds what an object will look like in the future. But, what about computers? In this paper, we learn computational models of object transformations from time-lapse videos. In particular, we explore the use of generative models to create depictions of objects at future times. These models explore several different prediction tasks: generating a future state given a single depiction of an object, generating a future state given two depictions of an object at different times, and generating future states recursively in a recurrent framework. We provide both qualitative and quantitative evaluations of the generated results, and also conduct a human evaluation to compare variations of our models.


title: Improving Sampling from Generative Autoencoders with Markov Chains

abstract: We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. Generative autoencoders are those which are trained to softly enforce a prior on the latent distribution learned by the inference model. We call the distribution to which the inference model maps observed samples, the learned latent distribution, which may not be consistent with the prior. We formulate a Markov chain Monte Carlo (MCMC) sampling process, equivalent to iteratively decoding and encoding, which allows us to sample from the learned latent distribution. Since, the generative model learns to map from the learned latent distribution, rather than the prior, we may use MCMC to improve the quality of samples drawn from the generative model, especially when the learned latent distribution is far from the prior. Using MCMC sampling, we are able to reveal previously unseen differences between generative autoencoders trained either with or without a denoising criterion.


title: Criminal investigation using Call Data Records (CDR) through Big Data technology

abstract: The growing crime rate of any country is always one of the biggest obstacles to its growth and development. With more manpower it certainly helps to keep the crime rate at bay, but is manpower the ideal solution? No. This may come as a surprise that despite Big Data being the boom of the century is yet to take firm roots when it comes to helping solve a Criminal Investigation. With the aim of changing this, our paper describes the need of a system which analyzes the Call Data Records (CDR) using Big Data technology. Our proposed idea through this research paper is to develop a system which takes as input mobile number/s and extracts corresponding CDRs, thereby generating multiple databases of CDRs. After this it analyzes these databases and finds various links between various suspects (mobile numbers) and generates as output, conclusions of its analysis. This conclusion may consist of phone numbers, call durations, locations etc. With proper analysis of the CDRs of the various suspects, the Anti — Crime team can move forward on multiple fronts simultaneously.


title: Visualizing impacts of database schema changes - A controlled experiment

abstract: Research in schema evolution has been driven by the need for more effective software development and maintenance. Finding impacts of schema changes on the applications and presenting them in an appropriate way are particularly challenging. We have developed a tool that finds impacts of schema changes on applications in object-oriented systems. This tool displays components (packages, classes, interfaces, methods and fields) of a database application system as a graph. Components potentially affected by a change are indicated by changing the shape of the boxes representing those components. Two versions of the tool are available. One version identifies affected parts of applications at the granularity of packages, classes, and interfaces, whereas the other version identifies affected parts at the finer granularity of fields and methods. This paper presents the design and results of a controlled student experiment testing these two granularity levels with respect to productivity and user satisfaction. There are indications that identifying impacts at the finer granularity can reduce the time needed to conduct schema changes and reduce the number of errors. Our results also show that the subjects of the experiment appreciated the idea of visualizing the impacts of schema changes.


title: A Novel ZVS PWM Interleaved Flyback Converter

abstract: In this paper, a novel ZVS PWM interleaved flyback converter topology is presented. Operation principle and steady state of the presented converter are analyzed. Comparing to conventional interleaved flyback converter, two transformers of the proposed converter can be reset with a clamp capacitor Cc and input voltage Vi, respectively. Thus, it can reduce losses which are produced by snubber circuit and increase efficiency. Moreover, all switches can achieve zero-voltage-switching (ZVS) through the resonance which exists between the transformer leakage inductance, parasitic capacitance of switches and clamp capacitor. As a result, it has some advantages such as simple topology, soft-switching, lower output ripple and higher efficiency. Finally, the analyses above are verified by simulation and experimental results.


title: Swearing, Euphemisms, and Linguistic Relativity

abstract: Participants read aloud swear words, euphemisms of the swear words, and neutral stimuli while their autonomic activity was measured by electrodermal activity. The key finding was that autonomic responses to swear words were larger than to euphemisms and neutral stimuli. It is argued that the heightened response to swear words reflects a form of verbal conditioning in which the phonological form of the word is directly associated with an affective response. Euphemisms are effective because they replace the trigger (the offending word form) by another word form that expresses a similar idea. That is, word forms exert some control on affect and cognition in turn. We relate these findings to the linguistic relativity hypothesis, and suggest a simple mechanistic account of how language may influence thinking in this context.


title: The IIT Bombay English-Hindi Parallel Corpus

abstract: We present the IIT Bombay English-Hindi Parallel Corpus. The corpus is a compilation of parallel corpora previously available in the public domain as well as new parallel corpora we collected. The corpus contains 1.49 million parallel segments, of which 694k segments were not previously available in the public domain. The corpus has been pre-processed for machine translation, and we report baseline phrase-based SMT and NMT translation results on this corpus. This corpus has been used in two editions of shared tasks at the Workshop on Asian Language Translation (2016 and 2017). The corpus is freely available for non-commercial research. To the best of our knowledge, this is the largest publicly available English-Hindi parallel corpus.


title: Coarse-to-Fine Localization for a Mobile Robot Based on Place Learning With a 2-D Range Scan

abstract: This paper presents a new coarse-to-fine global localization approach based on place learning with a 2-D range scan; we use a support vector machine (SVM) to train a set of classifiers for place recognition. Exploiting the graphical property of generalized Voronoi graph extracted from an occupancy grid map, we segment the map into local places with a spectral clustering algorithm and wavefront propagation algorithm. A bag-of-words approach is employed to build feature vectors for training the SVM classifiers, which utilizes the shape context descriptors computed from each scan point. The global localization process consists of two stages: coarse localization and fine localization. Coarse localization from the SVM-based place recognition selects candidate places where the robot may be located, and fine localization computes the relative poses to the candidate places with fast spectral scan matching and estimates the correct robot pose with a particle filter algorithm, in which the candidate poses are exploited as seeds for initial random sampling. The feasibility of the proposed methods is demonstrated by the results of extensive simulations and experiments.


title: Threshold-based probabilistic top-k dominating queries

abstract: Recently, due to intrinsic characteristics in many underlying data sets, a number of probabilistic queries on uncertain data have been investigated. Top-k dominating queries are very important in many applications including decision making in a multidimensional space. In this paper, we study the problem of efficiently computing top-k dominating queries on uncertain data. We first formally define the problem. Then, we develop an efficient, threshold-based algorithm to compute the exact solution. To overcome some inherent computational deficiency in an exact computation, we develop an efficient randomized algorithm with an accuracy guarantee. Our extensive experiments demonstrate that both algorithms are quite efficient, while the randomized algorithm is quite scalable against data set sizes, object areas, k values, etc. The randomized algorithm is also highly accurate in practice.


title: Gender Identity and Adjustment in Middle Childhood

abstract: This article has two purposes. The first is to present a brief (and speculative) account of the developmental origins of the several components of gender identity featured in the multidimensional model of gender identity proposed by Egan and Perry (2001). The second is to offer additional empirical support for the construct and discriminant validity of the various gender identity dimensions. Children (M age = 11.5 years) were assessed for 4 components of gender identity: (a) felt gender typicality, (b) contentment with gender assignment, (c) felt pressure for gender conformity, and (d) intergroup bias (the sentiment that one's own sex is superior). Gender typicality, gender contentedness, and felt pressure (but not intergroup bias) related to indexes of psychosocial adjustment in specific and theoretically meaningful ways. The case for a multidimensional approach to gender identity is strengthened.


title: Real-Time Robot Localization, Vision, and Speech Recognition on Nvidia Jetson TX1

abstract: Robotics systems are complex, often consisted of basic services including SLAM for localization and mapping, Convolution Neural Networks for scene understanding, and Speech Recognition for user interaction, etc. Meanwhile, robots are mobile and usually have tight energy constraints, integrating these services onto an embedded platform with around 10 W of power consumption is critical to the proliferation of mobile robots. In this paper, we present a case study on integrating real-time localization, vision, and speech recognition services on a mobile SoC, Nvidia Jetson TX1, within about 10 W of power envelope. In addition, we explore whether offloading some of the services to cloud platform can lead to further energy efficiency while meeting the real-time requirements


title: Enhanced Framework for Sentiment Analysis in Text using Distance based Classification Scheme

abstract: Social media platform like blogs, social networks and content communities have become valuable resource for mining the sentiments. In fact information gained from the social platform like Facebook and twitter has been expressed to be more important to industries, market, news, public opinion organization and many more. Sentiment analysis in a text is a procedure in which the polarities like positive and negative of a specified text is determined. In this project we are using twitter comments as the text input. The proposed method consists of two phases called testing and training phase. For extracting the features we are using the sentiwordnet. The distance based classifier is used to classify the features. Finally analyses of the sentiments are done by polarities classified by the distance classifier.


title: "Siri Talks at You": An Empirical Investigation of Voice-Activated Personal Assistant (VAPA) Usage by Individuals Who Are Blind

abstract: Voice-activated personal assistants (VAPAs)--like Amazon Echo or Apple Siri--offer considerable promise to individuals who are blind due to widespread adoption of these non-visual interaction platforms. However, studies have yet to focus on the ways in which these technologies are used by individuals who are blind, along with whether barriers are encountered during the process of interaction. To address this gap, we interviewed fourteen legally-blind adults with experience of home and/or mobile-based VAPAs. While participants appreciated the access VAPAs provided to inaccessible applications and services, they faced challenges relating to the input, responses from VAPAs, and control of information presented. User behavior varied depending on the situation or context of the interaction. Implications for design are suggested to support inclusivity when interacting with VAPAs. These include accounting for privacy and situational factors in design, examining ways to support concerns over trust, and synchronizing presentation of visual and non-visual cues.


title: The Forest, the Trees, and the Chameleon: Context Dependence and Mimicry.

abstract: Three studies examined the relation between context dependence in information processing and behavioral mimicry. In Experiment 1, a field-dependent cognitive style was related to a greater tendency to mimic a target's behavior. In Experiment 2, context dependence was experimentally manipulated, and results showed more mimicry in the session where a context-dependent processing style was induced compared with the session where a context-independent processing style was induced. Experiment 3 provided evidence for bidirectionality in the relation between context dependence and mimicry. Specifically, participants whose posture and behavior had been unobtrusively mimicked by an experimenter subsequently processed information in a more context-dependent manner than did nonmimicked participants. Taken together, these results illustrate the interplay between basic cognitive and behavioral processes.


title: An Experimental Study on Ubiquitous Commerce Adoption: Impact of Personalization and Privacy Concerns *

abstract: U-commerce represents “anytime, anywhere” commerce. U-commerce can provide a high level of personalization, which can bring significant benefits to customers. However, customers’ privacy is a major concern and obstacle to the adoption of u-commerce. As customers’ intention to adopt u-commerce is based on the aggregate effect of perceived benefits and risk exposure (e.g., privacy concerns), this research examines how personalization and context can impact on customers’ perceived benefits and privacy concerns, and how this aggregated effect in turn affects u-commerce adoption intention.


title: An opensource simulator for cognitive robotics research: The prototype of the icub humanoid robot simulator

abstract: This paper presents the prototype of a new computer simulator for the humanoid robot iCub. The iCub is a new open-source humanoid robot developed as a result of the "RobotCub" project, a collaborative European project aiming at developing a new open-source cognitive robotics platform. The iCub simulator has been developed as part of a joint effort with the European project "ITALK" on the integration and transfer of action and language knowledge in cognitive robots. This is available open-source to all researchers interested in cognitive robotics experiments with the iCub humanoid platform.


title: CRAM — A Cognitive Robot Abstract Machine for everyday manipulation in human environments

abstract: This paper describes CRAM (Cognitive Robot Abstract Machine) as a software toolbox for the design, the implementation, and the deployment of cognition-enabled autonomous robots performing everyday manipulation activities. CRAM equips autonomous robots with lightweight reasoning mechanisms that can infer control decisions rather than requiring the decisions to be preprogrammed. This way CRAM-programmed autonomous robots are much more flexible, reliable, and general than control programs that lack such cognitive capabilities. CRAM does not require the whole domain to be stated explicitly in an abstract knowledge base. Rather, it grounds symbolic expressions in the knowledge representation into the perception and actuation routines and into the essential data structures of the control programs. In the accompanying video, we show complex mobile manipulation tasks performed by our household robot that were realized using the CRAM infrastructure.


title: Active ESD protection circuit design against charged-device-model ESD event in CMOS integrated circuits

abstract: CDM ESD event has become the main ESD reliability concern for integrated-circuits products using nanoscale CMOS technology. A novel CDM ESD protection design, using self-biased current trigger (SBCT) and source pumping, has been proposed and successfully verified in 0.13-μm CMOS technology to achieve 1-kV CDM ESD robustness.


title: A single-pixel wireless contact lens display

abstract: We present the design, construction and in vivo rabbit testing of a wirelessly powered contact lens display. The display consists of an antenna, a 500 × 500 µm2 silicon power harvesting and radio integrated circuit, metal interconnects, insulation layers and a 750 × 750 µm2 transparent sapphire chip containing a custom-designed micro-light emitting diode with peak emission at 475 nm, all integrated onto a contact lens. The display can be powered wirelessly from ~1 m in free space and ~2 cm in vivo on a rabbit. The display was tested on live, anesthetized rabbits with no observed adverse effect. In order to extend display capabilities, design and fabrication of micro-Fresnel lenses on a contact lens are presented to move toward a multipixel display that can be worn in the form of a contact lens. Contact lenses with integrated micro-Fresnel lenses were also tested on live rabbits and showed no adverse effect.


title: Velocity based artificial bee colony algorithm for high dimensional continuous optimization problems

abstract: Artificial bee colony (ABC) is a swarm optimization algorithm which has been shown to be more effective than the other population based algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). Since it was invented, it has received significant interest from researchers studying in different fields because of having fewer control parameters, high global search ability and ease of implementation. Although ABC is good at exploration, the main drawback is its poor exploitation which results in an issue on convergence speed in some cases. Inspired by particle swarm optimization, we propose a modified ABC algorithm called VABC, to overcome this insufficiency by applying a new search equation in the onlooker phase, which uses the PSO search strategy to guide the search for candidate solutions. The experimental results tested on numerical benchmark functions show that the VABC has good performance compared with PSO and ABC. Moreover, the performance of the proposed algorithm is also compared with those of state-of-the-art hybrid methods and the results demonstrate that the proposed method has a higher convergence speed and better search ability for almost all functions. We propose a hybrid ABC algorithm so called VABC for numerical function optimization.Inspiring from the PSO, the VABC improves the ABC's exploitation strategy.The VABC considers a velocity value for each particle in the onlooker search equation.The VABC is compared with ABC, PSO and the seven state-of-the-art hybrid methods.The results show that the VABC has higher convergence speed and better search ability.


title: Joint Application of Speech and Speaker Recognition for Automation and Security in Smart Home

abstract: This paper describes the deployment of speech technologies in STARHome, a fully functional smart home prototype. We make use of speech and speaker recognition technologies to provide three voice services, namely, voice command for controlling home appliances, voice biometric for entrancedoor access control, and service customization (speaker-loaded command control). Voice applications for STARHome have been designed to deal with short utterances and low SNR. Index Terms: home security and automation, command control, smart home, speaker recognition


title: ChARMin: The First Actuated Exoskeleton Robot for Pediatric Arm Rehabilitation

abstract: ChARMin is the first actuated exoskeleton robot for pediatric arm rehabilitation. The device was specifically designed to provide intensive rehabilitative training for children with affected arm motor function, e.g., due to cerebral palsy; therewith complementing conventional therapies. This paper gives a comprehensive overview of the ChARMin robot, which provides six actuated degrees of freedom, and is designed to cover the complete target group of children and adolescents aged 5–18 years. Moreover, the new audiovisual game-like interface is presented, which motivates active participation of the child. To support the child's arm movements with the exoskeleton, a patient-cooperative control strategy was implemented. The controller enables free arm movements, assistance-as-needed, and complete guidance of the arm. Five children aged 5–17 years with impaired arm motor function due to cerebral palsy, stroke, and traumatic brain injury tested the various training scenarios with different amounts of support, and robot settings depending on the therapeutic goals and the children's movement capabilities and preferences. These preliminary tests suggest that the ChARMin setup can be used as an advanced exercise tool for arm neurorehabilitation that optimally challenges children and adolescents with severely to moderately affected arm motor functions.


title: Circularly Polarized Waveguide Antenna With Dual Pairs of Radiation Slots at Ka-Band

abstract: In this letter, a circularly polarized (CP) waveguide antenna with dual pairs of slots is presented for Ka-band applications. Circular polarization is realized by combining two orthogonal linear polarizations, which are generated by two pairs of radiation slots on a cavity surface, respectively. Two approaches of radiation slots, i.e., edge slots and corner slots, are discussed. The corner slots approach is employed to design a CP waveguide antenna at Ka-band. The measured axial ratio (AR) is below 3 dB from 29.69 to 30.06 GHz, or 1.2% relative bandwidth, with a minimal value of 0.4 dB at 29.9 GHz. The measured antenna gain is 9.8 dBi at 30 GHz, and the return loss is below –13 dB within the 3 dB AR bandwidth. The all-metal structure of the proposed antenna is very appropriate for tough environment applications.


title: Active object recognition by view integration and reinforcement learning

abstract: Abstract A mobile agent with the task to classify its sensor pattern has to cope with ambiguous information. Active recognition of three-dimensional objects involves the observer in a search for discriminative evidence, e.g., by change of its viewpoint. This paper defines the recognition process as a sequential decision problem with the objective to disambiguate initial object hypotheses. Reinforcement learning provides then an efficient method to autonomously develop near-optimal decision strategies in terms of sensorimotor mappings. The proposed system learns object models from visual appearance and uses a radial basis function (RBF) network for a probabilistic interpretation of the two-dimensional views. The information gain in fusing successive object hypotheses provides a utility measure to reinforce actions leading to discriminative viewpoints. The system is verified in experiments with 16 objects and two degrees of freedom in sensor motion. Crucial improvements in performance are gained using the learned in contrast to random camera placements.


title: Biometrie identification from raw ECG signal using deep learning techniques

abstract: The paper presents and discusses a novel method of biometrie identification based on ECG data. The main idea of the study is to apply Deep Neural Networks (DNN) for human identification based on the raw ECG signal. To improve overall system accuracy various signal pre-processing and outlier detection techniques have been applied. Also, to make ECG identification approach more user friendly, three-finger measurement scheme has been proposed. All experiments have been made using self-collected Lviv Biometric Data Set.


title: AIS-Based Evaluation of Target Detectors and SAR Sensors Characteristics for Maritime Surveillance

abstract: This paper studies the performances of different ship detectors based on adaptive threshold algorithms. The detection algorithms are based on various clutter distributions and assessed automatically with a systematic methodology. Evaluation using large datasets of medium resolution SAR images and AIS (automatic identification system) data as ground truths allows to evaluate the efficiency of each detector. Depending on the datasets used for testing, the detection algorithms offer different advantages and disadvantages. The systematic method used in discriminating real detected targets and false alarms in order to determine the detection rate, allows us to perform an appropriate and consistent comparison of the detectors. The impact of SAR sensors characteristics (incidence angle, polarization, frequency and spatial resolution) is fully assessed, the vessels’ length being also considered. Experiments are conducted on Radarsat-2 and CosmoSkymed ScanSAR datasets and AIS data acquired by coastal stations.


title: Establishing test form and individual task comparability: a case study of a semi-direct speaking test:

abstract: Examination boards are often criticized for their failure to provide evidence of comparability across forms, and few such studies are publicly available. This study aims to investigate the extent t...


title: Analysing the Relationship between Risk and Trust

abstract: Among the various human factors impinging upon making a decision in an uncertain environment, risk and trust are surely crucial ones. Several models for trust have been proposed in the literature but few explicitly take risk into account. This paper analyses the relationship between the two concepts by first looking at how a decision is made to enter into a transaction based on the risk information. We then draw a model of the invested fraction of the capital function of a decision surface. We finally define a model of trust composed of a reliability trust as the probability of transaction success and a decision trust derived from the decision surface.


title: Load balancing system for IPTV web application virtualization

abstract: Web applications are easy to develop and maintain. It is desirable to provide Web application services to IPTV. However many legacy set-top boxes lack enough computing power to run Web applications. Desktop virtualization technologies can be used to provide Web application services to legacy set-top boxes. Multiple servers and a load balancing system are required to serve many clients. This paper describes a load balancing system for IPTV Web application virtualization. The load balancing system distributes requests based on server loads and supports high availability.


title: A wideband RF link at the 5.8 GHz ISM band for multi-channel neural telemetry for small animals

abstract: The ability to record single unit neural activity from the brain of awake and naturally behaving animals will open new frontiers in neuroscience research. We present the prototype design of a battery powered, high bandwidth and miniature neural telemetry system operating in the 5.8 GHz ISM band. It was designed for spike recordings from freely moving marmosets (Callithrix jacchus) while they engaged in natural vocal behavior. The wireless system was tested on chronically implanted adult marmosets with multi-electrode arrays in the frontal cortex. With a baseband bandwidth of 3 MHz this system allows simultaneous transmission of 15 channels of neural data to a receiver 10 meters away. It has additional bandwidth for behavioral data such as multi-channel acoustic information. The transmitter measures 2.5×2×1.3 cm and weighs 10g. Our system provides researchers with design flexibility compared to expensive commercial alternatives and proved comparable to tethered systems during in vivo tests.


title: Phonetic variation in Northern Wales: preaspiration

abstract: Preaspiration (aspiration preceding the closure of a voiceless stop) is typologically rare (Laver 1994: 356) and, in the case of many languages and dialects in North West Europe, is noted as being an areal phenomenon. In some varieties preaspiration has been normalised in the phonology, whereas in others it occurs as a non-obligatory feature (Helgason 2002: 2123). The aim of this paper is to ascertain whether preaspiration is present in the repertoire of Welsh-English bilinguals in North Wales. Data was collected via word-list from four Welsh-English bilinguals and was analysed acoustically. Preaspiration was detected in 60.5% of Welsh tokens and was also found, to a lesser extent, in the English tokens (33.3%). As in previous studies, preaspiration is more common in female speech (78.3%) than amongst males (57.7%). The female informants, who were also more active users of the language, were significantly more prone to produce preaspirated English tokens.


title: Self-Referenced Deep Learning

abstract: Knowledge distillation is an effective approach to transferring knowledge from a teacher neural network to a student target network for satisfying the low-memory and fast running requirements in practice use. Whilst being able to create stronger target networks compared to the vanilla non-teacher based learning strategy, this scheme needs to train additionally a large teacher model with expensive computational cost. In this work, we present a Self-Referenced Deep Learning (SRDL) strategy. Unlike both vanilla optimisation and existing knowledge distillation, SRDL distils the knowledge discovered by the in-training target model back to itself to regularise the subsequent learning procedure therefore eliminating the need for training a large teacher model. SRDL improves the model generalisation performance compared to vanilla learning and conventional knowledge distillation approaches with negligible extra computational cost. Extensive evaluations show that a variety of deep networks benefit from SRDL resulting in enhanced deployment performance on both coarse-grained object categorisation tasks (CIFAR10, CIFAR100, Tiny ImageNet, and ImageNet) and fine-grained person instance identification tasks (Market-1501).


title: Intelligent cyber-physical system for an efficient detection of Parkinson disease using fog computing

abstract: Parkinson’s disease is one of the notable neurodegenerative disorders caused by insufficient production of dopamine which damages the motor skills and voice. Advancement of the Internet of Things (IoT) has fuelled the development of healthcare systems. In this article, we propose an intelligent system for detecting Parkinson’s disease to provide proper medication by analysing voice samples. Instead of relying on limited storage capacity and computational resources of IoT, the recent healthcare systems take advantages of the cloud server. On the other hand, the utilization of cloud computing incurs the issues of data privacy and additional communication costs to the healthcare systems. To address this issue, we propose to utilize Fog computing as a midway layer between end devices and the cloud server. The proposed system employs the combinatorial Fuzzy K-nearest Neighbor and Case-based Reasoning classifier for the better classification of the Parkinson patients from healthy individuals. On the detection of abnormality, the proposed healthcare system is designed to generate an immediate alert to the patient. The proposed system is experimentally evaluated on the UCI-Parkinson dataset, and the results reveal the improved performance of our system over baseline approaches.


title: Machine Learning Augmented Fuzzing

abstract: The proposed thesis introduces cutting edge Machine Learning (ML) tools into Search Based Software Engineering (SBST). The contribution is three-fold. The first is an ML driven property targeting search strategy. It uses a deep neural network to process execution trace information to yield a likelihood score of a presence of a crash, which is in turn used as a fitness function for search. This method clearly outperforms the baseline search technique. The second contribution is a method for defining a property agnostic search landscape. This is achieved by training an autoencoder on a corpus of execution traces to produce a "latent space" representation. The expectation is to observe a tendency for arbitrary properties of executions to group in distinct regions of the latent space. Location in this space would in turn be used to direct an SBST process. The third contribution is to augment an automated tool with a generative model. The intention is to produce approximately valid input seeds that would target desired locations of a fitness landscape. These contributions will provide novel ideas for future research in the intersection of SBST and ML.


title: The Impact of Emotional Intelligence Development on Nurse Managers

abstract: Background:The role of nurse managers has a scope and range of accountability that place them at risk for high turnover and role exhaustion. Emotional intelligence (EI) was chosen as a focus for this study because of overwhelming evidence in the literature that relates EI with leadership effectiveness, retention, and both physical and emotional wellness. Purpose:This pilot study was undertaken to explore the impact of a peer coaching intervention on EI abilities of nurse managers. Design:An exploratory, quantitative, pre- and posttest design was utilized. Setting and Population:The study took place at a private tertiary care medical center in Honolulu, Hawaii. From an initial sample of 31 nurse managers, 48% (15) completed the 6-month intervention and study posttest. Method:The intervention consisted of initial training sessions on EI and peer coaching skills, followed by weekly one-to-one peer coaching and monthly check-in group meetings. Findings:All participants who completed the study perceived that the peer coaching intervention improved their EI abilities and general management performance. Conclusion:Participants reported peer coaching's positive effect on their EI and performance skills during a period of unusually high organizational stress, providing evidence for the positive effect of the study intervention.


title: Transparent and Nontransparent Microstrip Antennas on a CubeSat: Novel low-profile antennas for CubeSats improve mission reliability.

abstract: This article reviews the development of some novel low-profile antennas for CubeSats. The integrated antennas were developed using microstrip-antenna technology, and the antennas were designed to be low profile while having minimal (or zero) blockage of the solar panels on the CubeSat. Two types of designs were investigated: 1) transparent antennas, which are placed above the solar panels (supersolar) and 2) nontransparent antennas, which are placed below the solar panels (subsolar). For past: using transparent metal and using a wire mesh design. The transparent metal indium tin oxide has a comparatively high sheet impedance, which makes an antenna design less efficient. Also, there is a tradeoff between conductivity and transparency [6]. However, a highly conductive thin mesh structure has demonstrated a reasonable efficiency and a high transparency [7]. The substrate must then also be transparent, e.g., glass or quartz.


title: Development of motion instruction system with interactive robot suit HAL

abstract: This paper proposes motion instruction system with the interactive robot suit HAL (Hybrid Assistive Limb) for supporting physically challenged persons, enhancing and expanding the human ability. HAL consists of external frames, actuators, various sensors, and a control PC with ethernet device and has not only been developed to assist the motion of wearer's body, but also can be applied to motion instruction between the doctor and the patient in rehabilitation by building the unilateral/bilateral master-slave system with two HALs. In order to realize the motion instruction system, it is required to transfer the information of motion to each other. We performed fundamental experiments for unilateral/bilateral motion instruction between HALs to verify whether the system satisfies the required basic specification, and could confirm the effectiveness of the suggested system.


title: Dynamic Opinion Model and Invasion Percolation

abstract: We propose a "nonconsensus" opinion model that allows for stable coexistence of two opinions by forming clusters of agents holding the same opinion. We study this nonconsensus model on lattices, several model complex networks, and a real-life social network. We find that the model displays a phase transition behavior characterized by a large spanning cluster of nodes holding the same opinion appearing when the concentration of nodes holding the same opinion (even minority) is above a certain threshold. Because of the clustering (community support) of agents holding the same opinion, these clusters cannot be invaded by the other opinion (similar to incompressible fluids). Our extensive simulations show that the nonconsensus opinion model appears to belong to the same universality class as invasion percolation.


title: Using Agile Practices to Solve Global Software Development Problems -- A Case Study

abstract: Although challenges in Global Software Development (GSD)are now well known, companies are still looking for workable solutions to issues relating to poor communication, lack of control, low staff morale, and ambiguous requirements, all of which are prevalent in distributed development settings. Solutions offered are often difficult to implement, are disparate, and can be theoretical rather than practical. We conducted a case study with a GSD company, currently using a plan driven approach to development, and uncovered many GSD problems. We mapped each problem to solutions known to work in Agile development settings. Although work has been undertaken in Agile for GSD, we look to the literature to identify practices that specifically address challenges for organisations already engaged in GSD, who are open to moving from a plan-driven/waterfall development process to a more agile approach. Many challenges are addressed by Agile practices, for example requirements ambiguity, need for autonomy, or unpredictable customer expectations. We hypothesise that many GSD problems can be solved by organisations taking an agile approach to their development.


title: Test Expectancy and Memory for Important Information

abstract: Prior research suggests that learners study and remember information differently depending upon the type of test they expect to later receive. The current experiments investigate how testing expectations impact the study of and memory for valuable information. Participants studied lists of words ranging in value from 1 to 10 points with the goal being to maximize their score on a later memory test. Half of the participants were told to expect a recognition test after each list, whereas the other half were told to expect a recall test. After several lists of receiving tests congruent with expectations, participants studying for a recognition test instead received an unexpected recall test. In Experiment 1, participants who had studied for a recognition test recalled less of the valuable information than participants anticipating the recall format. These participants continued to attend less to item value on future (expected) recall tests than participants who had only ever experienced recall testing. When the recognition tests were made more demanding in Experiment 2, value-based recall improved relative to Experiment 1: though memory for the valuable information remained superior when participants studied with the expectation of having to recall the information, there were no longer significant differences after accounting for recall testing experience. Thus, recall-based testing encouraged strategic, value-based encoding and enhanced retrieval of important information, whereas recognition testing in some cases limited value-based study and memory. These results extend prior work concerning the impact of testing expectations on memory, offering further insight into how people study important information. (PsycINFO Database Record


title: The Discrete Fractional Fourier Transform

abstract: We propose and consolidate a definition of the discrete fractional Fourier transform that generalizes the discrete Fourier transform (DFT) in the same sense that the continuous fractional Fourier transform generalizes the continuous ordinary Fourier transform. This definition is based on a particular set of eigenvectors of the DFT matrix, which constitutes the discrete counterpart of the set of Hermite-Gaussian functions. The definition is exactly unitary, index additive, and reduces to the DFT for unit order. The fact that this definition satisfies all the desirable properties expected of the discrete fractional Fourier transform supports our confidence that it will be accepted as the definitive definition of this transform.


title: A new method to generate and maintain a WiFi fingerprinting database automatically by using RFID

abstract: Location fingerprinting in WiFi positioning has been widely used in indoor environments. The key issue of the fingerprinting technology is the fingerprint database. The disadvantages of this technology are the database generation and maintenance requirements. The conventional method to create the database is that people carry out the survey manually (that is what the commercial products are doing). When the environment changes significantly (such as after a building renovation, or moving of furniture), the database has to be rebuilt. This paper proposes a new method to build and maintain the database in an efficient manner. This method only requires persons to carry a specific device which consists of a Radio Frequency Identification (RFID) reader and WiFi scanner to log the coordinates and the WiFi signal strengths. The coordinates are provided by some pre-deployed medium range (1–2 meters) RFID tags in the building with a location technique based on ‘cell ID’. As the persons conducting the survey are moving around the area of interest for purposes other than the fingerprint survey (such as a security guard who regularly patrols the whole building anyway), so the fingerprint database can be generated “automatically”. Also, the database can be refined as the data are being accumulated. When the environment changes, it can be detected by the self-refining database. A preliminary test was carried out for the proposed method. The results show that it works well.


title: Computational Social Scientist Beware: Simpson's Paradox in Behavioral Data

abstract: Observational data about human behavior are often heterogeneous, i.e., generated by subgroups within the population under study that vary in size and behavior. Heterogeneity predisposes analysis to Simpson’s paradox, whereby the trends observed in data that have been aggregated over the entire population may be substantially different from those of the underlying subgroups. I illustrate Simpson’s paradox with several examples coming from studies of online behavior and show that aggregate response leads to wrong conclusions about the underlying individual behavior. I then present a simple method to test whether Simpson’s paradox is affecting results of analysis. The presence of Simpson’s paradox in social data suggests that important behavioral differences exist within the population, and failure to take these differences into account can distort the studies’ findings.


title: An Introduction to Neural Networks

abstract: From the Publisher: ::: An Introduction to Nueral Networks will be warmly welcomed by a wide readership seeking an authoritative treatment of this key subject without an intimidating level of mathematics in the presentation.


title: Control of a Single-Phase Cascaded H-Bridge Multilevel Inverter for Grid-Connected Photovoltaic Systems

abstract: This paper presents a single-phase cascaded H-bridge converter for a grid-connected photovoltaic (PV) application. The multilevel topology consists of several H-bridge cells connected in series, each one connected to a string of PV modules. The adopted control scheme permits the independent control of each dc-link voltage, enabling, in this way, the tracking of the maximum power point for each string of PV panels. Additionally, low-ripple sinusoidal-current waveforms are generated with almost unity power factor. The topology offers other advantages such as the operation at lower switching frequency or lower current ripple compared to standard two-level topologies. Simulation and experimental results are presented for different operating conditions.


title: Surface feature detection and description with applications to mesh matching

abstract: In this paper we revisit local feature detectors/descriptors developed for 2D images and extend them to the more general framework of scalar fields defined on 2D manifolds. We provide methods and tools to detect and describe features on surfaces equiped with scalar functions, such as photometric information. This is motivated by the growing need for matching and tracking photometric surfaces over temporal sequences, due to recent advancements in multiple camera 3D reconstruction. We propose a 3D feature detector (MeshDOG) and a 3D feature descriptor (MeshHOG) for uniformly triangulated meshes, invariant to changes in rotation, translation, and scale. The descriptor is able to capture the local geometric and/or photometric properties in a succinct fashion. Moreover, the method is defined generically for any scalar function, e.g., local curvature. Results with matching rigid and non-rigid meshes demonstrate the interest of the proposed framework.


title: Estimating Outlier Score Probabilities

abstract: Outlier detection is a critical function across a diverse range of tasks and domains. There are numerous outlier detection methods, the majority of which produce scores to indicate an outlier versus inlier. An issue with these scores is that they can be difficult to interpret and do not allow for comparisons between different methods. One solution is to convert the outlier score to probabilities. These probability estimates can provide understandable and meaningful results for assessing outlying values. Moreover, the probabilities can be combined to produce an ensemble of outlier detection methods, further enhancing the detection of outliers. In this paper, we propose a unique approach leveraging probabilistic programming to fit the original outlier score distributions to a 3-parameter Lognormal distribution. We provide empirical evidence for the use of this distribution, compare the probability estimates with the outlier scores, discuss confidence in these estimates, evaluate detection performance via the probabilities, and provide an ensemble detection example. Our research indicates this approach reasonably models the original outlier scores, resulting in meaningful outlier probability estimates.


title: Cost Complexity Pruning of Ensemble Classifiers

abstract: In this paper we study methods that combine multiple clas- sification models learned over separate data sets in a dis- tributed database setting. Numerous studies posit that such approaches provide the means to efficiently scale learning to large datasets, while also boosting the accuracy of individ- ual classifiers. These gains, however, come at the expense of an increased demand for run-time system resources. The fi- nal ensemble meta-classifier may consist of a large collection of base classifiers that require increased memory resources while also slowing down classification throughput. Here, we present a technique for measuring the tradeoff between pre- dictive performance and available run time system resources and we describe an algorithm for pruning (i.e. discarding a subset of the available base classifiers) the ensemble meta- classifier as a means to reduce its size while preserving its accuracy. The algorithm is independent of the method used initially when computing the meta-classifier. It is based on decision tree pruning methods and relies on the map- ping of an arbitrary ensemble meta-classifier to a decision tree model. Through an extensive empirical study on meta- classifiers computed over two real data sets, we illustrate our pruning algorithm to be a robust approach to discarding classification models without degrading the overall predic- tive performance of an ensemble computed over those that remain after pruning.


title: A general framework for blockchain analytics

abstract: Modern cryptocurrencies exploit decentralised blockchains to record a public and unalterable history of transactions. Besides transactions, further information is stored for different, and often undisclosed, purposes, making the blockchains a rich and increasingly growing source of valuable information, in part of difficult interpretation. Many data analytics have been developed, mostly based on specifically designed and ad-hoc engineered approaches. We propose a general-purpose framework, seamlessly supporting data analytics on both Bitcoin and Ethereum --- currently the two most prominent cryptocurrencies. Such a framework allows us to integrate relevant blockchain data with data from other sources, and to organise them in a database, either SQL or NoSQL. Our framework is released as an open-source Scala library. We illustrate the distinguishing features of our approach on a set of significant use cases, which allow us to empirically compare ours to other competing proposals, and evaluate the impact of the database choice on scalability.


title: Predictive Analysis on Twitter: Techniques and Applications

abstract: Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches, and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories.


title: Three-Phase Weinberg Isolated DC–DC Converter: Analysis, Design, and Experimentation

abstract: This paper presents the three-phase version of the isolated dc-dc Weinberg converter. The main characteristics of the proposed converter are as follows: small volume, low weight, reduced number of components, buck operation for a duty cycle of less than 1/3, and input and output current ripple cancellation for a duty cycle of 1/3 or 2/3. Typical applications include telecommunication, electric vehicles, renewable energy conversion systems, and batteries. This paper gives theoretical analysis, design example, and experimental data on a 735-W 120-Vdc-input 75-Vdc-output 42-kHz switching frequency laboratory prototype. The prototype performance shows good agreement with the theoretical predictions.


title: A Working Spatio-Temporal Model Of The Human Visual System For Image Restoration And Quality Assessment Applications

abstract: This paper describes a spatio-temporal model of the human visual system (HVS) for video imaging applications, predicting the response of the neurons of the primary visual cortex. The model simulates the behavior of the HVS with a three-dimensional filter bank which decomposes the data into perceptual channels, each one being tuned to a specific spatial frequency, orientation and temporal frequency. It further accounts for contrast sensitivity, inter-stimuli masking and spatio-temporal interaction. The free parameters of the model have been estimated by psychophysics. The model can then be used as the basis for many applications. As an example, a quality metric for coded video sequences is presented.


title: Computer-Aided Staging of Lymphoma Patients With FDG PET/CT Imaging Based on Textural Information

abstract: We have designed a computer-aided diagnosis system to discriminate between hypermetabolic cancer lesions and hypermetabolic inflammatory or physiological but noncancerous processes in FDG PET/CT exams of lymphoma patients. Detection performance of the support vector machine (SVM) classifier was assessed based on feature sets including 105 positron emission tomography (PET) and Computed tomography (CT) characteristics derived from the clinical practice and from more sophisticated texture analysis. An original feature selection method based on combining different filter methods was proposed. The evaluation database consisted of 156 lymphomatous and 32 suspicious but nonlymphomatous regions of interest. Different types of training databases including either the PET and CT features or the PET features only, with or without feature selection, were evaluated to assess the added value of multimodality and texture information on classification performance. An optimization study was conducted for each classifier separately to select the best combination of parameters. Promising classification performance was achieved by the SVM classifier combined with the 12 most discriminant PET and CT features with a value of the area under the receiver operating curve of 0.91.


title: Social recommendation: a review

abstract: Recommender systems play an important role in helping online users find relevant information by suggesting information of potential interest to them. Due to the potential value of social relations in recommender systems, social recommendation has attracted increasing attention in recent years. In this paper, we present a review of existing recommender systems and discuss some research directions. We begin by giving formal definitions of social recommendation and discuss the unique property of social recommendation and its implications compared with those of traditional recommender systems. Then, we classify existing social recommender systems into memory-based social recommender systems and model-based social recommender systems, according to the basic models adopted to build the systems, and review representative systems for each category. We also present some key findings from both positive and negative experiences in building social recommender systems, and research directions to improve social recommendation capabilities.


title: Anti-proliferative and potential anti-diabetic effects of phenolic-rich extracts from edible marine algae

abstract: Phenolic-rich extracts from four edible marine macroalgae commonly found in UK waters were tested for their potential biological effects towards cultured colon cancer cells and for their ability to inhibit digestive enzymes to achieve potential anti-diabetic effects. Extracts from Palmaria, Ascophyllum and Alaria, but not Ulva, gave reasonable recoveries of phenolics and inhibited the proliferation of colon cancer cells in a dose-responsive manner. Alaria extracts were more effective than Palmaria or Ascophyllum extracts, but Palmaria and Ascophyllum would provide greater amounts of phenolics per gram intake. Extracts from Palmaria, Ascophyllum and Alaria all inhibited a-amylase activity to some extent, but Ascophyllum extracts were very effective with an IC50 of � 0.1 lg/ml GAE. The Ascophyllum extracts also inhibited a-glucosidase, the other key enzyme involved in starch digestion and blood glucose regulation, at low levels (e.g. IC50 � 20 lg/ml GAE). After fractionation on Sephadex LH-20, the inhibitory activity from Ascophyllum was concentrated in the fraction which, from mass spectrometric evidence, was enriched in phlorotannins. These components have the capacity to inhibit a-amylase and a-glucosidase activities at lM levels, which are easily achievable in the gut. This may explain the anti-diabetic properties associated with algal extracts and algal phenolics in various in vivo studies.


title: Real time road traffic event detection using Twitter and spark

abstract: Because of its real-time nature Twitter is a powerful source of information for detecting real time event happening in the surrounding. People make tweets on whatever events they are seen in their day to day life. In this paper, we take advantage of this for detecting road traffic by using tweets related to traffic and accidents. The goal of this system is to assign an appropriate class label by using classification techniques. For classification of tweets, we employed logistic regression and SVM classifier. We also used some text mining, machine learning, statistics and natural language processing techniques to extract meaningful information from raw tweets. As there are 6,000 tweets are twitted per second on twitter. So Apache spark is used to handle such large amount of data.


title: A comparative study of object proposals re-ranking methods for object detection

abstract: Object detection is one of the most essential problems in computer vision and has made great progress in recent years, which is mainly contributed by powerful CNN and accurate object proposals. However, most of the existing proposal generation methods suffer from strong localization bias, and achieve high recall by outputting large amount bounding boxes (e.g. 2000). Thus, an effective proposal re-ranking approach becomes crucial to obtain high quality object proposals with few bounding box numbers. In this paper, we make a comparative study of the existing unsupervised objectness measuring approaches to testify their effectiveness and generalization abilities. Experiments on PASCAL VOC 2007 dataset demonstrates that contour is not an adequate objectness cue to pop out high quality proposals, because an object usually has a closed contour, while a good proposal (with high IoU) may not, and considering more objectness cues can get better performance. Thus, more effective objectness cues should be explored and combined together for proposals re-ranking, such more accurate saliency maps, which can greatly benefit subsequent object detection task and is also our future work.


title: The validity of two versions of the GHQ in the WHO study of mental illness in general health care

abstract: Background . In recent years the 12-item General Health Questionnaire (GHQ-12) has been extensively used as a short screening instrument, producing results that are comparable to longer versions of the GHQ. Methods . The validity of the GHQ-12 was compared with the GHQ-28 in a World Health Organization study of psychological disorders in general health care. Results are presented for 5438 patients interviewed in 15 centres using the primary care version of the Composite International Diagnostic Instrument, or CIDI-PC. Results . Results were uniformly good, with the average area under the ROC curve 88, range from 83 to 95. Minor variations in the criteria used for defining a case made little difference to the validity of the GHQ, and complex scoring methods offered no advantages over simpler ones. The GHQ was translated into 10 other languages for the purposes of this study, and validity coefficients were almost as high as in the original language. There was no tendency for the GHQ to work less efficiently in developing countries. Finally gender, age and educational level are shown to have no significant effect on the validity of the GHQ. Conclusions . If investigators wish to use a screening instrument as a case detector, the shorter GHQ is remarkably robust and works as well as the longer instrument. The latter should only be preferred if there is an interest in the scaled scores provided in addition to the total score.


title: Forecasting Volatility in Indian Stock Market using Artificial Neural Network with Multiple Inputs and Outputs

abstract: Volatility in stock markets has been extensively studied in the applied finance literature. In this paper, Artificial Neural Network models based on various back propagation algorithms have been constructed to predict volatility in the Indian stock market through volatility of NIFTY returns and volatility of gold returns. This model considers India VIX, CBOE VIX, volatility of crude oil returns (CRUDESDR), volatility of DJIA returns (DJIASDR), volatility of DAX returns (DAXSDR), volatility of Hang Seng returns (HANGSDR) and volatility of Nikkei returns (NIKKEISDR) as predictor variables. Three sets of experiments have been performed over three time periods to judge the effectiveness of the approach. General Terms Artificial Neural Network, Volatility


title: Extracellular matrix of newly forming vessels—An immunohistochemical study

abstract: Abstract During wound healing, embryological development, and solid tumor growth, the established vasculature gives rise to large numbers of new blood vessels. This neovascular response occurs at the level of the capillary bed, where endothelial cells divide rapidly, locally remodel the surrounding stroma, and migrate away from existing vessels to form capillary sprouts. In order to examine the environment of these newly forming vessels, actively growing blood vessels in neovascularized rabbit and guinea pig corneas were examined immunohistochemically using antibodies against laminin, type IV collagen, heparan sulfate proteoglycans, entactin, and factor VIII-related antigen. Sequential serial 5-μm sections taken from the unfixed frozen corneas in a plane perpendicular to the direction of vessel growth were stained with these antibodies. It was possible to follow well-defined lumenized vessels out through sequential sections to the point where they became single factor VIII-R positive cells in the region of the capillary sprout. Examination of these stained sections has shown the presence of four important basement membrane components—laminin, type IV collagen, heparan sulfate proteoglycan, and entactin—associated with actively migrating and invading capillary sprouts. These results suggest that the extracellular matrix of the actively invading capillary sprouts does not differ qualitatively from that of the established vasculature.


Laboratory Handling and Storage of Peroxy Compounds