title: Design and testing of a spherical parallel mini manipulator

abstract: The article describes the design, prototyping and preliminary testing of a miniaturized spherical parallel manipulator (SPM) with 2 DOFs of rotation, conceived to handle the orientation of a mobile platform during the assembly of very small components. The paper summarizes the basic equations of position and differential kinematics, and describes all the project phases for the realization of the first prototype. Furthermore, experimental data of preliminary tests are presented.


title: A Supervised Patch-Based Approach for Human Brain Labeling

abstract: We propose in this work a patch-based image labeling method relying on a label propagation framework. Based on image intensity similarities between the input image and an anatomy textbook, an original strategy which does not require any nonrigid registration is presented. Following recent developments in nonlocal image denoising, the similarity between images is represented by a weighted graph computed from an intensity-based distance between patches. Experiments on simulated and in vivo magnetic resonance images show that the proposed method is very successful in providing automated human brain labeling.


title: A new interpretation of nonlinear energy operator and its efficacy in spike detection

abstract: A nonlinear energy operator (NEO) gives an estimate of the energy content of a linear oscillator. This has been used to quantify the AM-FM modulating signals present in a sinusoid. Here, the authors give a new interpretation of NEO and extend its use in stochastic signals. They show that NEO accentuates the high-frequency content. This instantaneous nature of NEO and its very low computational burden make it an ideal tool for spike detection. The efficacy of the proposed method has been tested with simulated signals as well as with real electroencephalograms (EEGs).


title: A compact dual-mand metamaterial-based rat-race coupler for a MIMO system application

abstract: A compact dual-band rat-race coupler is proposed in this paper. By using composite right/left-handed (CRLH) transmission lines (TLs) and investigating all possible combinations of phase responses of the individual TLs in the coupler, a dual-band and miniaturized rat-race coupler was implemented. The dual-band rat-race coupler shows a 55% size reduction. This CRLH-based coupler is used as a mode-decoupling network in a dual-band front-end MIMO system, along with a planar antenna array, to split two orthogonal radiation modes from the connected array. A pair of compact dual-band antennas were built and closely spaced to demonstrate pattern diversity by in-phase or out-of-phase excitations from the coupler. Good experimental isolation of the system, −29 dB at 2.4 GHz and −34.2 dB at 5.2 GHz, is exhibited verifying the desired decoupling property. Furthermore, pattern diversity in MIMO communications was observed by two measured orthogonal radiation patterns.


title: Developing pervasive multi-agent systems with nature-inspired coordination

abstract: Pervasive computing systems can be modelled effectively as populations of interacting autonomous components. The key challenge to realizing such models is in getting separately-specified and -developed sub-systems to discover and interoperate with each other in an open and extensible way, supported by appropriate middleware services. In this paper, we argue that nature-inspired coordination models offer a promising way of addressing this challenge. We first frame the various dimensions along which nature-inspired coordination models can be defined, and survey the most relevant proposals in the area. We describe the nature-inspired coordination model developed within the SAPERE project as a synthesis of existing approaches, and show how it can effectively support the multifold requirements of modern and emerging pervasive services. We conclude by identifying what we think are the open research challenges in this area, and identify some research directions that we believe are promising.


title: Coronary Centerline Extraction via Optimal Flow Paths and CNN Path Pruning

abstract: We present a novel method for the automated extraction of blood vessel centerlines. There are two major contributions. First, in order to avoid the shortcuts to which minimal path methods are prone, we find optimal paths in a computed flow field. We solve for a steady state porous media flow inside a region of interest and trace centerlines as maximum flow paths. We explain how to estimate anisotropic orientation tensors which are used as permeability tensors in our flow field computation. Second, we introduce a convolutional neural network (CNN) classifier for removing extraneous paths in the detected centerlines. We apply our method to the extraction of coronary artery centerlines found in Computed Tomography Angiography (CTA). The robustness and stability of our method are enhanced by using a model-based detection of coronary specific territories and main branches to constrain the search space [15]. Validation against 20 comprehensively annotated datasets had a sensitivity and specificity at or above 90 %. Validation against 106 clinically annotated coronary arteries showed a sensitivity above 97 %.


title: What a to-do: studies of task management towards the design of a personal task list manager

abstract: This paper reports on the results of studies of task management to support the design of a task list manager. We examined the media used to record and organize to-dos and tracked how tasks are completed over time. Our work shows that, contrary to popular wisdom, people are not poor at prioritizing. Rather, they have well-honed strategies for tackling particular task management challenges. By illustrating what factors influence task completion and how representations function to support task management, we hope to provide a strong foundation for the design of a personal to-do list manager. We also present some preliminary efforts in this direction.


Unsupervised and Semi-supervised Clustering: a Brief Survey


title: Online Action Detection in Untrimmed, Streaming Videos - Modeling and Evaluation.

abstract: The goal of Online Action Detection (OAD) is to detect action in a timely manner and to recognize its action category. Early works focused on early action detection, which is effectively formulated as a classification problem instead of online detection in streaming videos, because these works used partially seen short video clip that begins at the start of action. Recently, researchers started to tackle the OAD problem in the challenging setting of untrimmed, streaming videos that contain substantial background shots. However, they evaluate OAD in terms of per-frame labeling, which does not require detection at the instance-level and does not evaluate the timeliness of the online detection process. In this paper, we design new protocols and metrics. Further, to specifically address challenges of OAD in untrimmed, streaming videos, we propose three novel methods: (1) we design a hard negative samples generation module based on Generative Adversarial Network (GAN) framework to better distinguish ambiguous background shots that share similar scenes but lack true characteristics of action start; (2) during training we impose a temporal consistency constraint between data around action start and data succeeding action start to model their similarity; (3) we introduce an adaptive sampling strategy to handle the scarcity of the important training data around action start. We conduct extensive experiments using THUMOS'14 and ActivityNet. We show that our proposed strategies lead to significant performance gains and improve state-of-the-art results. A systematic ablation study also confirms the effectiveness of each proposed method.


title: Generating Believable Stories in Large Domains

abstract: Planning-based techniques are a very powerful tool for automated story generation. However, as the number of possible actions increases, traditional planning techniques suffer from a combinatorial explosion due to large branching factors. In this work, we apply Monte Carlo Tree Search (MCTS) techniques to generate stories in domains with large numbers of possible actions (100+). Our approach employs a Bayesian story evaluation method to guide the planning towards believable stories that reach a user defined goal. We generate stories in a novel domain with different type of story goals. Our approach shows an order of magnitude improvement in performance over traditional search techniques.


title: User Profile Modeling and Applications to Digital Libraries

abstract: The ultimate goal of an information provider is to satisfy the user information needs. That is, to provide the user with the right information, at the right time, through the right means. A prerequisite for developing personalised services is to rely on user profiles representing users' information needs. In this paper we will first address the issue of presenting a general user profile model. Then, the general user profile model will be customised for digital libraries users.


title: Two rhamnetin digalactosides and an oleanolic acid digalactoside from the flowers of Cassia laevigata

abstract: Abstract From the flowers of Cassia laevigata , two new rhamnetin glycosides, the 3-galactosyl(1→4)- galactopyranoside and the related 3-galactosyl(1→6)galactopyranoside, and oleanolic acid 3-galactosyl(1→4)- galactopyranoside have been isolated. These three glycosides have not been isolated earlier from any plant source. The known compounds quercetin, docosyl alcohol, carnaubyl alcohol, ceryl alcohol and octacosanol have also been obtained.


title: Dynamic Space Efficient Hashing

abstract: We consider space efficient hash tables that can grow and shrink dynamically and are always highly space efficient, i.e., their space consumption is always close to the lower bound even while growing and when taking into account storage that is only needed temporarily. None of the traditionally used hash tables have this property. We show how known approaches like linear probing and bucket cuckoo hashing can be adapted to this scenario by subdividing them into many subtables or using virtual memory overcommitting. However, these rather straightforward solutions suffer from slow amortized insertion times due to frequent reallocation in small increments. Our main result is DySECT ({\bf Dy}namic {\bf S}pace {\bf E}fficient {\bf C}uckoo {\bf T}able) which avoids these problems. DySECT consists of many subtables which grow by doubling their size. The resulting inhomogeneity in subtable sizes is equalized by the flexibility available in bucket cuckoo hashing where each element can go to several buckets each of which containing several cells. Experiments indicate that DySECT works well with load factors up to 98\%. With up to 2.7 times better performance than the next best solution.


title: Solving Infinite Kolam in Knot Theory

abstract: In south India, there are traditional patterns of line-drawings encircling dots, called ``Kolam'', among which one-line drawings or the ``infinite Kolam'' provide very interesting questions in mathematics. For example, we address the following simple question: how many patterns of infinite Kolam can we draw for a given grid pattern of dots? The simplest way is to draw possible patterns of Kolam while judging if it is infinite Kolam. Such a search problem seems to be NP complete. However, it is certainly not. In this paper, we focus on diamond-shaped grid patterns of dots, (1-3-5-3-1) and (1-3-5-7-5-3-1) in particular. By using the knot-theory description of the infinite Kolam, we show how to find the solution, which inevitably gives a sketch of the proof for the statement ``infinite Kolam is not NP complete.'' Its further discussion will be given in the final section.


title: Inducing Features of Random Fields

abstract: We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the field and an iterative scaling algorithm is used to estimate the optimal values of the weights. The random field models and techniques introduced in this paper differ from those common to much of the computer vision literature in that the underlying random fields are non-Markovian and have a large number of parameters that must be estimated. Relations to other learning approaches, including decision trees, are given. As a demonstration of the method, we describe its application to the problem of automatic word classification in natural language processing.


title: Traumatic Penile Injury: From Circumcision Injury to Penile Amputation

abstract: The treatment of external genitalia trauma is diverse according to the nature of trauma and injured anatomic site. The classification of trauma is important to establish a strategy of treatment; however, to date there has been less effort to make a classification for trauma of external genitalia. The classification of external trauma in male could be established by the nature of injury mechanism or anatomic site: accidental versus self-mutilation injury and penis versus penis plus scrotum or perineum. Accidental injury covers large portion of external genitalia trauma because of high prevalence and severity of this disease. The aim of this study is to summarize the mechanism and treatment of the traumatic injury of penis. This study is the first review describing the issue.


title: Enterococcus faecalis: its role in root canal treatment failure and current concepts in retreatment.

abstract: Enterococcus faecalis is a microorganism commonly detected in asymptomatic, persistent endodontic infections. Its prevalence in such infections ranges from 24% to 77%. This finding can be explained by various survival and virulence factors possessed by E. faecalis, including its ability to compete with other microorganisms, invade dentinal tubules, and resist nutritional deprivation. Use of good aseptic technique, increased apical preparation sizes, and inclusion of 2% chlorhexidine in combination with sodium hypochlorite are currently the most effective methods to combat E. faecalis within the root canal systems of teeth. In the changing face of dental care, continued research on E. faecalis and its elimination from the dental apparatus may well define the future of the endodontic specialty.


title: Virtual Museums and Virtual Realities

abstract: First, the artifacts on display are supposed to be real. For example, a real amphora is displayed and not its photograph or some other facsimile. There are many disadvantages to this practice: Some artifacts are too large, or may be incomplete, making their display difficult; expensive measures have to be taken to assure security for the artifacts; and the displays need environmental conditions which preserve the artifacts and make it comfortable for people to view them.


title: Pleural Fluid pH as a Predictor of Pleurodesis Failure

abstract: STUDY OBJECTIVES ::: To determine the predictive accuracy of pH for identifying patients with malignant pleural effusions who will fail pleurodesis. ::: ::: ::: DESIGN ::: Analysis of published and unpublished individual patient-level data retrieved from a MEDLINE search and correspondence with primary investigators. ::: ::: ::: STUDY SELECTION ::: Studies that reported pleural fluid pH values and outcomes of pleurodesis for patients with malignant pleural effusions. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: Primary investigators supplied data for 433 patients. Receiver operating characteristic analysis and logistic regression estimated the predictive accuracy, decision thresholds, and value of pleural fluid pH compared with other clinical factors. The primary investigations were graded for study design. ::: ::: ::: RESULTS ::: Pleural fluid pH was the only independent predictor of pleurodesis failure (odds ratio, 4.46; 95% confidence interval [CI], 2.69 to 7.45; p < 0.0001) and had an area under the receiver operating characteristic curve (decision threshold, < or = 7.28) of 0.671 (95% CI, 0.624 to 0.715). The pH model fit the data well (p = 0.48) with the probability of pleurodesis failure increasing as pH decreased; specificity and negative predictive values for pleurodesis failure exceeded 90% and 80%, respectively, with a positive predictive value of 45.7% at pH values < or = 7.15. The primary studies had several important design limitations. ::: ::: ::: CONCLUSIONS ::: Using patient-level data, this study showed that pleural fluid pH has only modest predictive value for predicting symptomatic failure and should be used with caution, if at all, in selecting patients for pleurodesis. The limitations of the primary studies and low predictive accuracy should be considered when using pleural fluid pH for patient care.


title: The Changing Role of the Enterprise Architect

abstract: Enterprise architecture is practiced in different ways, and there are different types of enterprise architects with quite different roles. This paper looks closer at the role of enterprise architects and the importance of the enterprise architects' understanding of boundary issues in their practice. The paper suggests that enterprise architects must focus on problem-finding more than problem-solving, and should develop not just more dialectic skills, but also dialogic skills. The paper also argues that the enterprise architects must gain a deeper understanding of the enterprise, and need to start working with other enterprise disciplines.


Fast text searching: allowing errors


title: A real-time 8000 bps ACELP coder with low complexity and high quality

abstract: A kind of real time speech coder with low complexity and high quality at a bit rate of 8.0 kbps is presented. The encoding algorithm is based on algebraic code excited linear prediction (ACELP). Efficient pitch and codebook search strategies along with efficient quantization procedures, have been developed to achieve high quality encoded speech with a complexity implementable on current low price fixed point DSP chips such as the TMS320C25. Through subjective tests we have verified that the proposed algorithm is comparable to the CVSD coder at 32 kbps.


title: Highly Sensitive, Flexible, and Wearable Pressure Sensor Based on a Giant Piezocapacitive Effect of Three-Dimensional Microporous Elastomeric Dielectric Layer.

abstract: We report a flexible and wearable pressure sensor based on the giant piezocapacitive effect of a three-dimensional (3-D) microporous dielectric elastomer, which is capable of highly sensitive and stable pressure sensing over a large tactile pressure range. Due to the presence of micropores within the elastomeric dielectric layer, our piezocapacitive pressure sensor is highly deformable by even very small amounts of pressure, leading to a dramatic increase in its sensitivity. Moreover, the gradual closure of micropores under compression increases the effective dielectric constant, thereby further enhancing the sensitivity of the sensor. The 3-D microporous dielectric layer with serially stacked springs of elastomer bridges can cover a much wider pressure range than those of previously reported micro-/nanostructured sensing materials. We also investigate the applicability of our sensor to wearable pressure-sensing devices as an electronic pressure-sensing skin in robotic fingers as well as a bandage-type pr...


title: Impact of Approximate Multipliers on VGG Deep Learning Network

abstract: This paper presents a study on the applicability of using approximate multipliers to enhance the performance of the VGGNet deep learning network. Approximate multipliers are known to have reduced power, area, and delay with the cost of an inaccuracy in output. Improving the performance of the VGGNet in terms of power, area, and speed can be achieved by replacing exact multipliers with approximate multipliers as demonstrated in this paper. The simulation results show that approximate multiplication has a very little impact on the accuracy of VGGNet. However, using approximate multipliers can achieve significant performance gains. The simulation was completed using different generated error matrices that mimic the inaccuracy that approximate multipliers introduce to the data. The impact of various ranges of the mean relative error and the standard deviation was tested. The well-known data sets CIFAR-10 and CIFAR-100 were used for testing the network’s classification accuracy. The impact on the accuracy was assessed by simulating approximate multiplication in all the layers in the first set of tests, and in selective layers in the second set of tests. Using approximate multipliers in all the layers leads to very little impact on the network’s accuracy. In addition, an alternative approach is to use a hybrid of exact and approximate multipliers. In the hybrid approach, 39.14% of the deeper layer’s multiplications can be approximate while having a reduced negligible impact on the network’s accuracy.


title: Certified Defenses for Data Poisoning Attacks

abstract: Machine learning systems trained on user-provided data are susceptible to data poisoning attacks, whereby malicious users inject false training data with the aim of corrupting the learned model. While recent work has proposed a number of attacks and defenses, little is understood about the worst-case loss of a defense in the face of a determined attacker. We address this by constructing approximate upper bounds on the loss across a broad family of attacks, for defenders that first perform outlier removal followed by empirical risk minimization. Our bound comes paired with a candidate attack that nearly realizes the bound, giving us a powerful tool for quickly assessing defenses on a given dataset. Empirically, we find that even under a simple defense, the MNIST-1-7 and Dogfish datasets are resilient to attack, while in contrast the IMDB sentiment dataset can be driven from 12% to 23% test error by adding only 3% poisoned data.


title: Screening of Detergents for Stabilization of Functional Membrane Proteins.

abstract: Membrane protein studies usually require use of detergents to extract and isolate proteins from membranes and manipulate them in a soluble context for their functional or structural characterization. However, solubilization with detergent may interfere with MP stability and may directly affect MP function or structure. Moreover, detergent properties can be affected such as critical micellar concentration (CMC) can be affected by the experimental conditions. Consequently, the experimenter must pay attention to both the protein and the behavior of the detergent. This article provides a convenient protocol for estimating the CMC of detergents in given experimental conditions. Then, it presents two protocols aimed at monitoring the function of a membrane protein in the presence of detergent. Such experiments may help to test various detergents for their inactivating or stabilizing effects on long incubation times, ranging from few hours to some days. © 2018 by John Wiley & Sons, Inc.


title: Overview of on-chip electrostatic discharge protection design with SCR-based devices in CMOS integrated circuits

abstract: An overview on the electrostatic discharge (ESD) protection circuits by using the silicon controlled rectifier (SCR)-based devices in CMOS ICs is presented. The history and evolution of SCR device used for on-chip ESD protection is introduced. Moreover, two practical problems (higher switching voltage and transient-induced latchup issue) limiting the use of SCR-based devices in on-chip ESD protection are reported. Some modified device structures and trigger-assist circuit techniques to reduce the switching voltage of SCR-based devices are discussed. The solutions to overcome latchup issue in the SCR-based devices are also discussed to safely apply the SCR-based devices for on-chip ESD protection in CMOS IC products.


title: Unsupervised Domain Adaptation in the Wild: Dealing with Asymmetric Label Sets

abstract: The goal of domain adaptation is to adapt models learned on a source domain to a particular target domain. Most methods for unsupervised domain adaptation proposed in the literature to date, assume that the set of classes present in the target domain is identical to the set of classes present in the source domain. This is a restrictive assumption that limits the practical applicability of unsupervised domain adaptation techniques in real world settings ("in the wild"). Therefore, we relax this constraint and propose a technique that allows the set of target classes to be a subset of the source classes. This way, large publicly available annotated datasets with a wide variety of classes can be used as source, even if the actual set of classes in target can be more limited and, maybe most importantly, unknown beforehand. ::: To this end, we propose an algorithm that orders a set of source subspaces that are relevant to the target classification problem. Our method then chooses a restricted set from this ordered set of source subspaces. As an extension, even starting from multiple source datasets with varied sets of categories, this method automatically selects an appropriate subset of source categories relevant to a target dataset. Empirical analysis on a number of source and target domain datasets shows that restricting the source subspace to only a subset of categories does indeed substantially improve the eventual target classification accuracy over the baseline that considers all source classes.


title: AC VOLTAGE REGULATION BY SWITCH MODE BUCK-BOOST VOLTAGE CONTROLLER

abstract: Voltage sag is an important power quality problem, which may affect domestic, industrial and commercial customers. Voltage sags may either be decrease or increase in the magnitude of system voltage due to faults or change in loads. In this paper a switch mode AC Buck- Boost regulator is proposed to maintain constant voltage across a medium size domestic or commercial appliance.


title: Provably secure user authentication and key agreement scheme for wireless sensor networks

abstract: In recent years, user authentication has emerged as an interesting field of research in wireless sensor networks. Most recently, in 2016, Chang and Le presented a scheme to authenticate the users in wireless sensor network using a password and smart card. They proposed two protocols P1 and P2. P1 is based on exclusive or XOR and hash functions, while P2 deploys elliptic curve cryptography in addition to the two functions used in P1. Although their protocols are efficient, we point out that both P1 and P2 are vulnerable to session specific temporary information attack and offline password guessing attack, while P1 is also vulnerable to session key breach attack. In addition, we show that both the protocols P1 and P2 are inefficient in authentication and password change phases. To withstand these weaknesses found in their protocols, we aim to design a new authentication and key agreement scheme using elliptic curve cryptography. Rigorous formal security proofs using the broadly accepted, the random oracle models, and the Burrows-Abadi-Needham logic and verification using the well-known Automated Validation of Internet Security Protocols and Applications tool are preformed on our scheme. The analysis shows that our designed scheme has the ability to resist a number of known attacks comprising those found in both Chang-Le's protocols. Copyright © 2016 John Wiley & Sons, Ltd.


The dolls revolution: Australian theatre and cultural imagination


title: Research on keyword extraction based on Word2Vec weighted TextRank

abstract: In this paper, we do a research on the keyword extraction method of news articles. We build a candidate keywords graph model based on the basic idea of TextRank, use Word2Vec to calculate the similarity between words as transition probability of nodes' weight, calculate the word score by iterative method and pick the top N of the candidate keywords as the final results. Experimental results show that the weighted TextRank algorithm with correlation of words can improve performance of keyword extraction generally.


title: AN ANALYSIS FOR CREDIT RATING AND MOMENTUM STRATEGY

abstract: In recent years, investors are increasingly concerned about whether the credit risk will affect the return on investment. This paper discusses the credit rating and momentum investment strategy relationship. The research period is from January 2005 to December 2010, and the sample is the ordinary shares of companies listed on Taiwan Stock Exchange (TSE). By calculating the cumulative returns of the investment portfolio of the holding period, and grouping the research samples by credit rating, this paper tests the relationship between credit rating and momentum investment strategy in Taiwan?s stock market. Second, in the exploration of the factors affecting credit rating and stock returns, this paper uses variables including firm size, financial leverage, turnover rate, company age and industry to analyze the impact of factors including information asymmetry and industry on the investment strategy. Moreover, this paper probes into the impact of January Effect and business cycle on credit rating. The empirical results reveal that Taiwan?s stock market does not have the momentum effect, although there is reverse investment strategy. In other words, the returns of stocks of investment portfolio of better credit rating are higher than those of poorer credit rating; and the results are reverse if the reverse investment strategy is applied. The empirical results are not affected by adding variables such as firm size, financial leverage, turnover rate, company age and industry. Hence, momentum investment strategy of Taiwan?s stock market is not affected by credit rating.


title: Enabling IoT Ecosystems through Platform Interoperability

abstract: Today, the Internet of Things (IoT) comprises vertically oriented platforms for things. Developers who want to use them need to negotiate access individually and adapt to the platform-specific API and information models. Having to perform these actions for each platform often outweighs the possible gains from adapting applications to multiple platforms. This fragmentation of the IoT and the missing interoperability result in high entry barriers for developers and prevent the emergence of broadly accepted IoT ecosystems. The BIG IoT (Bridging the Interoperability Gap of the IoT) project aims to ignite an IoT ecosystem as part of the European Platforms Initiative. As part of the project, researchers have devised an IoT ecosystem architecture. It employs five interoperability patterns that enable cross-platform interoperability and can help establish successful IoT ecosystems.


title: Colocalization of GWAS and eQTL Signals Detects Target Genes

abstract: The vast majority of genome-wide association study (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual's disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue could play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWASs and expression quantitative trail locus (eQTL) studies is challenging because of the uncertainty induced by linkage disequilibrium and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present eCAVIAR, a probabilistic method that has several key advantages over existing methods. First, our method can account for more than one causal variant in any given locus. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Using publicly available eQTL data on 45 different tissues, we demonstrate that eCAVIAR can prioritize likely relevant tissues and target genes for a set of glucose- and insulin-related trait loci.


title: Electronmicroscopical observations on yolk and yolk formation in Ophryotrocha labronica LaGreca and Bacci

abstract: In Ophryotrocha labronica LaGreca & Bacci mature yolk granules are found only in the ovocyte. Other typical yolk elements are lipid droplets, small vesicular bodies, multivesicular bodies and dense bodies. The two last-mentioned also appear in the accompanying nurse cell and from there obviously pass over unchanged into the ovocyte through a specific intercellular bridge, the fusome.


title: Mutations in chromatin regulators functionally link Cornelia de Lange syndrome and clinically overlapping phenotypes

abstract: The coordinated tissue-specific regulation of gene expression is essential for the proper development of all organisms. Mutations in multiple transcriptional regulators cause a group of neurodevelopmental disorders termed “transcriptomopathies” that share core phenotypical features including growth retardation, developmental delay, intellectual disability and facial dysmorphism. Cornelia de Lange syndrome (CdLS) belongs to this class of disorders and is caused by mutations in different subunits or regulators of the cohesin complex. Herein, we report on the clinical and molecular characterization of seven patients with features overlapping with CdLS who were found to carry mutations in chromatin regulators previously associated to other neurodevelopmental disorders that are frequently considered in the differential diagnosis of CdLS. The identified mutations affect the methyltransferase-encoding genes KMT2A and SETD5 and different subunits of the SWI/SNF chromatin-remodeling complex. Complementary to this, a patient with Coffin–Siris syndrome was found to carry a missense substitution in NIPBL. Our findings indicate that mutations in a variety of chromatin-associated factors result in overlapping clinical phenotypes, underscoring the genetic heterogeneity that should be considered when assessing the clinical and molecular diagnosis of neurodevelopmental syndromes. It is clear that emerging molecular mechanisms of chromatin dysregulation are central to understanding the pathogenesis of these clinically overlapping genetic disorders.


title: Addressing bias in machine learning algorithms: A pilot study on emotion recognition for intelligent systems

abstract: Recently, there has been an explosion of cloud-based services that enable developers to include a spectrum of recognition services, such as emotion recognition, in their applications. The recognition of emotions is a challenging problem, and research has been done on building classifiers to recognize emotion in the open world. Often, learned emotion models are trained on data sets that may not sufficiently represent a target population of interest. For example, many of these on-line services have focused on training and testing using a majority representation of adults and thus are tuned to the dynamics of mature faces. For applications designed to serve an older or younger age demographic, using the outputs from these pre-defined models may result in lower performance rates than when using a specialized classifier. Similar challenges with biases in performance arise in other situations where datasets in these large-scale on-line services have a non-representative ratio of the desired class of interest. We consider the challenge of providing application developers with the power to utilize pre-constructed cloud-based services in their applications while still ensuring satisfactory performance for their unique workload of cases. We focus on biases in emotion recognition as a representative scenario to evaluate an approach to improving recognition rates when an on-line pre-trained classifier is used for recognition of a class that may have a minority representation in the training set. We discuss a hierarchical classification approach to address this challenge and show that the average recognition rate associated with the most difficult emotion for the minority class increases by 41.5% and the overall recognition rate for all classes increases by 17.3% when using this approach.


title: Infant Mortality and Socioeconomic Status : New Bottle , Same Old Wine By

abstract: This paper presents the results of an ecological analysis of the relationship between infant mortality and economic status in metropolitan Ohio for the period 1960–2000. The data examined are centered on the five censuses undertaken during this 40-year period. The basic unit of analysis is the census tract of mother’s usual residence, with economic status being determined by the percentage of low income families living in each tract. For each of the five periods covered, census tracts were aggregated into broad income areas and three-year average infant mortality rates were computed for each area, by age, sex, race and exogenous-endogenous causes of death. The most important conclusion to be drawn from the data is that in spite of some very remarkable declines in infant mortality at all class levels since 1960, there continues to be a very clear and pronounced inverse association between income status and infant mortality. Indeed, the evidence indicates that the relationship has become stronger over the years. These observations are applicable for both sexes, for whites and nonwhites, for neonatal and postneonatal deaths, and for both major cause of death groups. It is concluded that while public health programs are important, any progress in narrowing this long-standing differential is unlikely unless ways can be found to enhance the economic well-being of the lower socioeconomic groups.


title: DL-SFA: Deeply-Learned Slow Feature Analysis for Action Recognition

abstract: Most of the previous work on video action recognition use complex hand-designed local features, such as SIFT, HOG and SURF, but these approaches are implemented sophisticatedly and difficult to be extended to other sensor modalities. Recent studies discover that there are no universally best hand-engineered features for all datasets, and learning features directly from the data may be more advantageous. One such endeavor is Slow Feature Analysis (SFA) proposed by Wiskott and Sejnowski [33]. SFA can learn the invariant and slowly varying features from input signals and has been proved to be valuable in human action recognition [34]. It is also observed that the multi-layer feature representation has succeeded remarkably in widespread machine learning applications. In this paper, we propose to combine SFA with deep learning techniques to learn hierarchical representations from the video data itself. Specifically, we use a two-layered SFA learning structure with 3D convolution and max pooling operations to scale up the method to large inputs and capture abstract and structural features from the video. Thus, the proposed method is suitable for action recognition. At the same time, sharing the same merits of deep learning, the proposed method is generic and fully automated. Our classification results on Hollywood2, KTH and UCF Sports are competitive with previously published results. To highlight some, on the KTH dataset, our recognition rate shows approximately 1% improvement in comparison to state-of-the-art methods even without supervision or dense sampling.


title: Pedagogy with information and communications technologies in transition

abstract: This paper presents an analysis of ways in which pedagogy with information and communications technologies (ICTs) may need to adapt to accommodate to a major shift in our conceptions of knowledge and learning. A holistic approach to this analysis based on Checkland's "systems thinking" suggested changes in pedagogy needed for 21st century learning and suggested ways of managing the complexity in order to support teachers in developing their pedagogical practices. The examination of how learning is conceptualised while learners are in contact with vast arrays of knowledge through Internet access and how this understanding can be reconciled with current views of knowledge acquisition in formal education suggests a need for rebalancing in most phases of education between individual work and group participation. Furthermore, opportunities need to be increased for learners to develop expertise in their chosen domains and to make links between their formal and informal learning. Examination of scenarios in which people learn through peer interaction rather than any formal teaching suggests a need to recognise and not underestimate young people's capabilities. The paper proposes incorporating opportunities for students to engage with self-organizing social systems into pedagogy. This would complement an emphasis on develosping and understanding both individual and shared expertise.


abstract: To predict a set of diverse and informative proposals with enriched representations, this paper introduces a differentiable Determinantal Point Process (DPP) layer that is able to augment the object detection architectures. Most modern object detection architectures, such as Faster R-CNN, learn to localize objects by minimizing deviations from the ground truth, but ignore correlation between multiple proposals and object categories. Non-Maximum Suppression (NMS) as a widely used proposal pruning scheme ignores label-and instance-level relations between object candidates resulting in multi-labeled detections. In the multi-class case, NMS selects boxes with the largest prediction scores ignoring the semantic relation between categories of potential election. In contrast, our trainable DPP layer, allowing for Learning Detection with Diverse Proposals (LDDP), considers both label-level contextual information and spatial layout relationships between proposals without increasing the number of parameters of the network, and thus improves location and category specifications of final detected bounding boxes substantially during both training and inference schemes. Furthermore, we show that LDDP keeps it superiority over Faster R-CNN even if the number of proposals generated by LDPP is only ~30% as many as those for Faster R-CNN.


title: Gamification and Human Factors in Quality Management Systems: Mapping from Octalysis Framework to ISO 10018

abstract: Human factors are important in order to achieve outcomes which are consistent and aligned with organizational strategies and values. However, understanding how to successfully deal with human factors involved in a Quality Management System is a challenging issue. Therefore, there is a need to move beyond traditional mechanisms to manage human aspects. While much attention has focused on the motivation of people though gamification in recent years our mapping found that others human factors described in ISO 10018 such as communication, education, engagement and teamwork could be achieved. Nevertheless, getting the best out of people is not always easy and it is a challenge that cannot be ignored.


title: Autoimmune diseases, bipolar disorder, and non-affective psychosis

abstract: This paper extends earlier work on autoimmune diseases and schizophrenia (1) to bipolar disorder and disorders with features similar to schizophrenia, sometimes termed non-affective psychosis. This new analysis, with more than twice as many observations, allows comparison of bipolar disorder to schizophrenia, as well as facilitating more precise study of the temporal relationship of autoimmune disease and the first diagnosis of bipolar disorder, schizophrenia, and non-affective psychosis. The presentation of these disorders in parallel fashion facilitates assessment of the value of the nosologic distinctions between them. ::: ::: The autoimmune diseases which have been studied most thoroughly in their relationship to bipolar disorder are the autoimmune thyroid diseases and multiple sclerosis (MS). The relationship between affective psychosis and thyroid disorder was noticed as early as 1888 (2). The metabolic slowing related to thyroid hormone deficiency could possibly be related to symptoms of depression (e.g., 3), but there is also a consistent association with psychosis, including a particular association, not always confirmed, with rapid-cycling bipolar disorder (4–6). The serotonin and thyroid systems are connected (7), and there are putative but unproven associations between antithyroid antibodies and mood disorders (8, 9). The effect of lithium on thyroid function was noticed years ago (10), but the observation may result in part from a constitutional factor which predisposes to bipolar disorder (11, 12). ::: ::: The relationship of affective disorders to MS has been studied for decades (e.g., 13). To some extent, MS presents with affective symptoms, but there is comorbidity of the diagnosed cases as well as symptom overlap (14, 15). Three small family studies suggest a genetic relationship between the two disorders (16–18). As with bipolar disorder, the relationship is clouded by the possibility that treatment for MS is connected to affective state (19). ::: ::: The literature on autoimmune diseases and schizophrenia has been reviewed previously (1). It is particularly rich for celiac disease (20), autoimmune thyroid disease (21), and rheumatoid arthritis (22). In our earlier work we showed that thyrotoxicosis, celiac disease, autoimmune hemolytic anemia, and Sjogren’s syndrome were more common in the family members of persons diagnosed with schizophrenia, prior to the diagnosis of schizophrenia in the case, and were more common in the cases of schizophrenia themselves as well, prior to the diagnosis of schizophrenia (1). A later study from Sweden showed that the risk of both schizophrenia and non-affective psychosis was raised by about 50% after the diagnosis of celiac disease, although the relationship was statistically significant only for the broader category of non-affective psychosis (23).


title: Why medicine still needs a scientific foundation: restating the hypotheticodeductive model - part one.

abstract: Medical science stands accused of providing an incomplete understanding of health because it is supposedly founded on linearity, reductionism, and positivism. These criticisms misrepresent the scientific method. The alternatives offered by complexity theory, postmodernism, and qualitative research risk falling into the traps that the scientific method avoids. The hypotheticodeductive model of science provides both a coherent description of the growth of scientific knowledge and a prescription for the conduct of good science.


title: Enhancement of Power System Stability Using Adaptive Combinational Load Shedding Methods

abstract: In the modern power systems operating at lower stability margins, conventional non-adaptive schemes cannot offer adequate protection for securing the power system, especially against combinational events. For major disturbances, the active power deficit is usually accompanied by reactive power deficit and frequency stability and voltage stability of the system are jeopardized simultaneously. In this paper, three adaptive combinational load shedding methods are proposed to improve operation of the conventional underfrequency load shedding scheme in order to enhance power system stability following severe disturbances. The proposed methods use locally measured frequency and voltage signals to counteract such events. In the proposed algorithms, load shedding is started from the locations which have higher voltage decay for longer period of time. The speed, location, and amount of load shedding are changed adaptively depending on the disturbance location, voltage status of the system, and the rate of frequency decline. Operation of the conventional and the proposed load shedding methods have been simulated in an actual large network. Obtained simulation results confirm that the proposed methods provide considerable enhancement in the power system voltage stability margin, and by using the proposed algorithms, various power system blackouts could be prevented.


title: Prediction and Simulation of Human Mobility Following Natural Disasters

abstract: In recent decades, the frequency and intensity of natural disasters has increased significantly, and this trend is expected to continue. Therefore, understanding and predicting human behavior and mobility during a disaster will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, such research is very difficult to perform owing to the uniqueness of various disasters and the unavailability of reliable and large-scale human mobility data. In this study, we collect big and heterogeneous data (e.g., GPS records of 1.6 million users1 over 3 years, data on earthquakes that have occurred in Japan over 4 years, news report data, and transportation network data) to study human mobility following natural disasters. An empirical analysis is conducted to explore the basic laws governing human mobility following disasters, and an effective human mobility model is developed to predict and simulate population movements. The experimental results demonstrate the efficiency of our model, and they suggest that human mobility following disasters can be significantly more predictable and be more easily simulated than previously thought.


title: A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models

abstract: In 2008, financial tsunami started to impair the economic development of many countries, including Taiwan. The prediction of financial crisis turns to be much more important and doubtlessly holds public attention when the world economy goes to depression. This study examined the predictive ability of the four most commonly used financial distress prediction models and thus constructed reliable failure prediction models for public industrial firms in Taiwan. Multiple discriminate analysis (MDA), logit, probit, and artificial neural networks (ANNs) methodology were employed to a dataset of matched sample of failed and non-failed Taiwan public industrial firms during 1998-2005. The final models are validated using within sample test and out-of-the-sample test, respectively. The results indicated that the probit, logit, and ANN models which used in this study achieve higher prediction accuracy and possess the ability of generalization. The probit model possesses the best and stable performance. However, if the data does not satisfy the assumptions of the statistical approach, then the ANN approach would demonstrate its advantage and achieve higher prediction accuracy. In addition, the models which used in this study achieve higher prediction accuracy and possess the ability of generalization than those of [Altman, Financial ratios-discriminant analysis and the prediction of corporate bankruptcy using capital market data, Journal of Finance 23 (4) (1968) 589-609, Ohlson, Financial ratios and the probability prediction of bankruptcy, Journal of Accounting Research 18 (1) (1980) 109-131, and Zmijewski, Methodological issues related to the estimation of financial distress prediction models, Journal of Accounting Research 22 (1984) 59-82]. In summary, the models used in this study can be used to assist investors, creditors, managers, auditors, and regulatory agencies in Taiwan to predict the probability of business failure.


title: Anonymous voting by two-round public discussion

abstract: In 2006, Hao and Zielinski proposed a two-round anonymous veto protocol (called AV-net), which provided exceptional efficiency compared to related techniques. In this study, the authors add a self-tallying function to the AV-net, making it a general-purpose voting protocol. The new protocol works in the same setting as the AV-net – it requires no trusted third parties or private channels, and participants execute the protocol by sending two-round public messages. Compared with related voting protocols in past work, this is significantly more efficient in terms of the number of rounds, computational cost and bandwidth usage.


title: "Why Should I Trust You?": Explaining the Predictions of Any Classifier

abstract: Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one. In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally varound the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted.


title: Therapeutic radiology: the modalities and their effects on oral tissues

abstract: In recent years, therapeutic radiology has been used extensively in the management of head and neck malignancies. An increasing number of the population who have been exposed to such therapy are being seen by the dentist for dental treatment. It is recognized that radiation therapy may cause temporary and permanent alterations in tissue. A discussion of the various therapeutic modalities and their side effects on the oral tissues has been presented to aid the dental practitioner in understanding the problems and care of such patients. (auth)


title: The Digital Hadrian's Villa Project: Using virtual worlds to control suspected solar alignments

abstract: This paper discusses how the Digital Hadrian's Villa Project has used virtual world technology to control the validity of a claimed alignment between the sun's position at sunset on the summer solstice during the reign of Hadrian (117–138 CE) and the tower structure at Hadrian's Villa known as Roccabruna. The conclusion is reached that virtual world technology is usefully applied in investigations of this kind.


title: Appropriateness of foot interaction for non-accurate spatial tasks

abstract: This paper describes alternative methods for manipulating graphical user interfaces with a foot. Feet are used in many real world tasks together with the rest of the body, but in computer environments they are almost completely put aside as an interaction possibility. One of the major problems in choosing input methods for different tasks in user interfaces is determining what kind of method is appropriate for a certain task. Feet could easily be used as a supportive input method in interaction with computers together with the traditional mouse. In this paper, we discuss the possibility of using foot input in different non-accurate spatial tasks, and the efficiency and usability experience the users have of foot interaction compared with a traditional hand-based interface with the same input device. The aim is to find out how well foot interaction suits for non-accurate spatial tasks.


title: Evaluation of Deep Learning based Pose Estimation for Sign Language Recognition

abstract: Human body pose estimation and hand detection are two important tasks for systems that perform computer vision-based sign language recognition(SLR). However, both tasks are challenging, especially when the input is color videos, with no depth information. Many algorithms have been proposed in the literature for these tasks, and some of the most successful recent algorithms are based on deep learning. In this paper, we introduce a dataset for human pose estimation for SLR domain. We evaluate the performance of two deep learning based pose estimation methods, by performing user-independent experiments on our dataset. We also perform transfer learning, and we obtain results that demonstrate that transfer learning can improve pose estimation accuracy. The dataset and results from these methods can create a useful baseline for future works.


title: A hybrid method consisting of GA and SVM for intrusion detection system

abstract: In this paper, a hybrid method of support vector machine and genetic algorithm (GA) is proposed and its implementation in intrusion detection problem is explained. The proposed hybrid algorithm is employed in reducing the number of features from 45 to 10. The features are categorized into three priorities using GA algorithm as the highest important is the first priority and the lowest important is placed in the third priority. The feature distribution is done in a way that 4 features are placed in the first priority, 4 features in the second, and 2 features in the third priority. The results reveal that the proposed hybrid algorithm is capable of achieving a true-positive value of 0.973, while the false-positive value is 0.017.


title: Temporal multimodal fusion for video emotion classification in the wild

abstract: This paper addresses the question of emotion classification. The task consists in predicting emotion labels (taken among a set of possible labels) best describing the emotions contained in short video clips. Building on a standard framework – lying in describing videos by audio and visual features used by a supervised classifier to infer the labels – this paper investigates several novel directions. First of all, improved face descriptors based on 2D and 3D Convolutional Neural Networks are proposed. Second, the paper explores several fusion methods, temporal and multimodal, including a novel hierarchical method combining features and scores. In addition, we carefully reviewed the different stages of the pipeline and designed a CNN architecture adapted to the task; this is important as the size of the training set is small compared to the difficulty of the problem, making generalization difficult. The so-obtained model ranked 4th at the 2017 Emotion in the Wild challenge with the accuracy of 58.8 %.


title: Applying PCA for Traffic Anomaly Detection: Problems and Solutions

abstract: Spatial Principal Component Analysis (PCA) has been proposed for network-wide anomaly detection. A recent work has shown that PCA is very sensitive to calibration settings. Unfortunately, the authors did not provide further explanations for this observation. In this paper, we fill this gap and provide the reasoning behind the found discrepancies. We revisit PCA for anomaly detection and evaluate its performance on our data. We develop a slightly modified version of PCA that uses only data from a single router. Instead of correlating data across different spatial measurement points, we correlate the data across different metrics. With the help of the analyzed data, we explain the pitfalls of PCA and underline our argumentation with measurement results. We show that the main problem is that PCA fails to capture temporal correlation. We propose a solution to deal with this problem by replacing PCA with the Karhunen-Loeve transform. We find that when we consider temporal correlation, anomaly detection results are significantly improved.


title: Regionalization of Drought across South Korea Using Multivariate Methods

abstract: Topographic and hydro-climatic features of South Korea are highly heterogeneous and able to influence the drought phenomena in the region. The complex topographical and hydro-climatic features of South Korea need a statistically accurate method to find homogeneous regions. Regionalization of drought in a bivariate framework has scarcely been applied in South Korea before. Hierarchical Classification on Principal Components (HCPC) algorithm together with Principal Component Analysis (PCA) method and cluster validation indices were investigated and used for the regionalization of drought across the South Korean region. Statistical homogeneity and discordancy of the region was tested on univariate and bivariate frameworks. HCPC indicate that South Korea should be divided into four regions which are closer to being homogeneous. Univariate and bivariate homogeneity and discordancy tests showed the significant difference in their results due to the inability of univariate homogeneity and discordancy measures to consider the joint behavior of duration and severity. Regionalization of drought for SPI time scale of 1, 3, 6, 12, and 24 months showed significant variation in discordancy and homogeneity of the region with the change in SPI time scale. The results of this study can be used as basic data required to establish a drought mitigation plan on regional scales.


title: A Data Science Approach to Understanding Residential Water Contamination in Flint

abstract: When the residents of Flint learned that lead had contaminated their water system, the local government made water-testing kits available to them free of charge. The city government published the results of these tests, creating a valuable dataset that is key to understanding the causes and extent of the lead contamination event in Flint. This is the nation's largest dataset on lead in a municipal water system. In this paper, we predict the lead contamination for each household's water supply, and we study several related aspects of Flint's water troubles, many of which generalize well beyond this one city. For example, we show that elevated lead risks can be (weakly) predicted from observable home attributes. Then we explore the factors associated with elevated lead. These risk assessments were developed in part via a crowd sourced prediction challenge at the University of Michigan. To inform Flint residents of these assessments, they have been incorporated into a web and mobile application funded by Google.org. We also explore questions of self-selection in the residential testing program, examining which factors are linked to when and how frequently residents voluntarily sample their water.


title: Building a Neural Machine Translation System Using Only Synthetic Parallel Data

abstract: Recent works have shown that synthetic parallel data automatically generated by translation models can be effective for various neural machine translation (NMT) issues. In this study, we build NMT systems using only synthetic parallel data. As an efficient alternative to real parallel data, we also present a new type of synthetic parallel corpus. The proposed pseudo parallel data are distinct from previous works in that ground truth and synthetic examples are mixed on both sides of sentence pairs. Experiments on Czech-German and French-German translations demonstrate the efficacy of the proposed pseudo parallel corpus, which shows not only enhanced results for bidirectional translation tasks but also substantial improvement with the aid of a ground truth real parallel corpus.


title: ReFinder: A Context-Based Information Refinding System

abstract: In this paper, we present a context-based information refinding system called ReFinder. It leverages human's natural recall characteristics and allows users to refind files and Web pages according to the previous access context. ReFinder refinds information based on a query-by-context model over a context memory snapshot, linking to the accessed information contents. Context instances in the memory snapshot are organized in a clustered and associated manner, and dynamically evolve in life cycles to mimic brain memory's decay and reinforcement phenomena. We evaluate the scalability of ReFinder on a large synthetic data set. The experimental results show that consistent degradation of context instances in the context memory and the ones in user's refinding requests can lead to the best refinding precision and recall. An 8-week user study is also conducted to examine the applicability of ReFinder. Initial findings show that time, place, and activity could serve as useful recall clues. On average, 15.53 seconds are needed to complete a refinding request with ReFinder and 84.42 seconds with other existing methods. Some further possible improvement of ReFinder is also discussed at the end of the paper.


title: State machine replication in containers managed by Kubernetes

abstract: Computer virtualization brought fast resource provisioning to data centers and the deployment of pay-per-use cost models. The system virtualization provided by containers like Docker has improved this flexibility of resource provisioning. Applications that require more restrictive agreement and ordering guarantees can also benefit from operating inside containers. This paper proposes the integration of coordination services in a container management system called Kubernetes (k8s), seeking to restrict the containers size and to offer automatic state replication. A protocol that uses shared memory available in Kubernetes was developed, and an evaluation was conducted to show the viability of the proposal.


title: Optimal Matched Rectifying Surface for Space Solar Power Satellite Applications

abstract: In this paper, we propose a microwave energy reception approach for space solar power satellite applications based on the concept of artificial perfectly matched layer. By embedding rectifying diodes into well-designed metamaterial cells, the obtained rectifying surface simultaneously exhibits a nearly perfect impedance matching to the air and the rectifying circuits, and a strong impedance mismatching to the air at harmonic frequencies, leading to a simple structure that can be implemented using commercial multi-layer printed circuit board technology. The fabricated sample shows that, with a thickness of nearly 1/40 of the free-space wavelength, this rectifying surface has an experimental power absorption rate of 99.92% under normal incident power density of 0.1 mW/cm2, and a 56.16-dB suppression to the second-order harmonic. The measured rectifying efficiency complies well with the theoretical expectation. By introducing an additional layer of passive power combiner network, the proposed approach can also be used in applications of harvesting weak ambient RF and microwave energy. We expect a wide range of applications to emerge from this novel concept in the future.


title: Full Duplex Switched Ethernet for Next Generation "1553B"-Based Applications

abstract: Over the last thirty years, the MIL-STD 1553B data bus has been used in many embedded systems, like aircrafts, ships, missiles and satellites. However, the increasing number and complexity of interconnected subsystems lead to emerging needs for more communication bandwidth. Therefore, a new interconnection system is needed to overcome the limitations of the MIL-STD 1553B data bus. Among several high speed networks, full duplex switched Ethernet is put forward here as an attractive candidate to replace the MIL-STD 1553B data bus. However, the key argument against switched Ethernet lies in its non-deterministic behavior that makes it inadequate to deliver hard time-constrained communications. Hence, our primary objective in this paper is to achieve an accepted QoS level offered by switched Ethernet, to support diverse "1553B"-based applications requirements. We evaluate the performance of traffic shaping techniques on full duplex switched Ethernet with an adequate choice of service strategy in the switch, to guarantee the real-time constraints required by these specific 1553B-based applications. An analytic study is conducted, using the network calculus formalism, to evaluate the deterministic guarantees offered by our approach. Theoretical analysis are then investigated in the case of a realistic "1553B"-based application extracted from a real military aircraft network. The results herein show the ability of profiled full duplex switched Ethernet to satisfy 1553B-like real-time constraints


title: Analytical Determination of the Capacitor Voltage Balancing Dynamics for Three-Phase Flying Capacitor Converters

abstract: This paper presents a new strategy for the analytical determination of the natural voltage balancing dynamics of three-phase flying capacitor converters. The approach substitutes double Fourier series representations of the pulsewidth modulation switching signals into a nonlinear transient circuit model of the three-phase converter. This results in a linearized state space model with the Fourier coefficients of the modulation strategy defining the state space matrix terms. The state space model can be readily developed for converters of any level and allows for the rapid analytical investigation of the dynamic (and static) balancing behavior over a wide range of operating conditions. Furthermore, the approach allows powerful linear analysis strategies such as root-locus methods to be used to investigate the converter performance as a function of changes in parameters, such as modulation index and load. The analysis approach has been fully verified by comparison with experimental results obtained on a low-voltage three-phase prototype converter.


title: Comparing grounded theory and topic modeling: Extreme divergence or unlikely convergence?

abstract: Researchers in information science and related areas have developed various methods for analyzing textual data, such as survey responses. This article describes the application of analysis methods from two distinct fields, one method from interpretive social science and one method from statistical machine learning, to the same survey data. The results show that the two analyses produce some similar and some complementary insights about the phenomenon of interest, in this case, nonuse of social media. We compare both the processes of conducting these analyses and the results they produce to derive insights about each method's unique advantages and drawbacks, as well as the broader roles that these methods play in the respective fields where they are often used. These insights allow us to make more informed decisions about the tradeoffs in choosing different methods for analyzing textual data. Furthermore, this comparison suggests ways that such methods might be combined in novel and compelling ways.


title: Decision-Theory For Crisis Management.

abstract: Abstract : The Rockwell International Science Center's main contribution to the ARPA/Rome Laboratory Planning Initiative has been the development and application of decision theoretic concepts and tools for the problem of military transportation and crisis planning. Real world planning, like most complete problems, involves multiple competing objectives and uncertainty about actual outcomes. Thus effective operational tools for planning must be able to efficiently manage uncertainty and trade-offs between competing objectives. Rockwell's contributions span; three main areas of the planning process: plan generation, plan evaluation, and plan simulation. Contributions range from basic research results to application-oriented software tools for military transportation planning.


title: iSTART 1 iSTART: Interactive Strategy Training for Active Reading and Thinking

abstract: Interactive Strategy Training for Active Reading and Thinking (iSTART) is a Web-based application that provides young adolescent to college-age students with high-level reading strategy training to improve comprehension of science texts. iSTART is modeled after an effective, human-delivered intervention called self-explanation reading training (SERT), which trains readers to use active reading strategies to self-explain difficult texts more effectively. To make the training more widely available, the Web-based trainer has been developed. Transforming the training from a human-delivered application to a computer-based one has resulted in a highly interactive trainer that adapts its methods to the performance of the students. The iSTART trainer introduces the strategies in a simulated classroom setting with interaction between three animated characters—an instructor character and two student characters— and the human trainee. Thereafter, the trainee identifies the strategies in the explanations of a student character who is guided by an instructor character. Finally, the trainee practices self-explanation under the guidance of an instructor character. We describe this system and discuss how appropriate feedback is generated.


title: EVALUATION OF EFFECTIVENESS IN A NOVEL WOUND HEALING OINTMENT-CROCODILE OIL BURN OINTMENT

abstract: Background: Crocodile oil and its products are used as ointments for burns and scalds in traditional medicines. A new ::: ointment formulation - crocodile oil burn ointment (COBO) was developed to provide more efficient wound healing ::: activity. The purpose of the study was to evaluate the burn healing efficacy of this new formulation by employing deep ::: second-degree burns in a Wistar rat model. The analgesic and anti-inflammatory activities of COBO were also studied ::: to provide some evidences for its further use. ::: Materials and methods: The wound healing potential of this formulation was evaluated by employing a deep ::: second-degree burn rat model and the efficiency was comparatively assessed against a reference ointment – (1% wt ⁄ wt) ::: silver sulfadiazine (SSD). After 28 days, the animals were euthanized and the wounds were removed for transversal ::: and longitudinal histological studies. Acetic acid-induced writhing in mice was used to evaluate the analgesic activity ::: and its anti-inflammatory activity was observed in xylene -induced edema in mice. ::: Results: COBO enhanced the burn wound healing (20.5±1.3 d) as indicated by significant decrease in wound closure ::: time compared with the burn control (25.0±2.16 d) (P<0.01). Hair follicles played an importance role in the ::: physiological functions of the skin, and their growth in the wound could be revealed for the skin regeneration situation. ::: Histological results showed that the hair follicles were well-distributed in the post-burn skin of COBO treatment group, ::: and the amounts of total, active, primary and secondary hair follicles in post-burn 28-day skin of COBO treatment ::: groups were more than those in burn control and SSD groups. On the other hand, the analgesic and anti-inflammatory ::: activity of COBO were much better than those of control group, while they were very close to those of moist exposed ::: burn ointment (MEBO). ::: Conclusions: COBO accelerated wound closure, reduced inflammation, and had analgesic effects compared with SSD ::: in deep second degree rat burn model. These findings suggest that COBO would be a potential therapy for treating ::: human burns.


title: Convergence Analysis of Gradient Descent Algorithms with Proportional Updates

abstract: The rise of deep learning in recent years has brought with it increasingly clever optimization methods to deal with complex, non-linear loss functions. These methods are often designed with convex optimization in mind, but have been shown to work well in practice even for the highly non-convex optimization associated with neural networks. However, one significant drawback of these methods when they are applied to deep learning is that the magnitude of the update step is sometimes disproportionate to the magnitude of the weights (much smaller or larger), leading to training instabilities such as vanishing and exploding gradients. An idea to combat this issue is gradient descent with proportional updates. Gradient descent with proportional updates was introduced in 2017. It was independently developed by You et al (Layer-wise Adaptive Rate Scaling (LARS) algorithm) and by Abu-El-Haija (PercentDelta algorithm). The basic idea of both of these algorithms is to make each step of the gradient descent proportional to the current weight norm and independent of the gradient magnitude. It is common in the context of new optimization methods to prove convergence or derive regret bounds under the assumption of Lipschitz continuity and convexity. However, even though LARS and PercentDelta were shown to work well in practice, there is no theoretical analysis of the convergence properties of these algorithms. Thus it is not clear if the idea of gradient descent with proportional updates is used in the optimal way, or if it could be improved by using a different norm or specific learning rate schedule, for example. Moreover, it is not clear if these algorithms can be extended to other problems, besides neural networks. We attempt to answer these questions by establishing the theoretical analysis of gradient descent with proportional updates, and verifying this analysis with empirical examples.


Optimal control problem for the BCF model describing crystal surface growth


title: New brushless alternator for automotive applications

abstract: The main contribution of this paper is the description, design and experimental testing of a new brushless synchronous generator with no permanent magnets. Such an alternator is particularly attractive for automotive applications where brushes create liabilities such as reliability, limited lifetime and low field current through brushes. Due to the increasing power demand, there has been a growing interest in new alternator technologies that can replace the Lundell (or claw-pole) alternator. The alternator presented here has the potential to overcome the efficiency and power rating limits associated with the Lundell alternator without added manufacturing cost. This paper presents the design, control and electrical performance of such an alternator including analysis and experimental results.


RADIO OVER FIBER FOR WIRELESS COMMUNICATION


title: Graph-based color Gamut Mapping using neighbor metric

abstract: Colors are displayed in different ways on various devices, such as cameras, screens and printers. In order to achieve consistent appearance on these devices, color management is usually used, where the core part is Gamut Mapping Algorithm (GMA). However, the widely adopted Point-wise Gamut Mapping Algorithms (PGMAs) have been restricted to compromise between color accuracy and details. In this work, we firstly split color space into small cubes through sampling colors from it. Then, we built a 26-neighbored graph with the sample colors as vertexes and perceptual color differences between adjacent vertexes as weights. Based on the above graph, a new Multi-source Shortest Paths Algorithm (MSS-PA) is proposed to establish color mapping relationships between out-of-gamut colors and colors in gamut boundary. In the MSSPA, distance of shortest path between nonadjacent vertexes are used to replace those calculated directly using CIEDE2000, which is useful to measure large color difference. Experimental results show that our method achieves superior performance on the aspect of keeping accuracy and preserving details compared with HPMinDE and SGCK.


title: A new efficient ellipse detection method

abstract: In this paper, we introduce a new method for ellipse detection. This method takes the advantages of major axis of an ellipse to find ellipse parameter fast and efficiently. It only needs a one-dimensional accumulator array to accumulate the length information for minor axis of the ellipse. This method does not require the evaluation of the tangents or curvatures of the edge contours, which are generally very sensitive to noisy working conditions. No complicated mathematical computation is involved in the implementation and the required computational storage space is much cheaper compared to the current methods. Experiments with both synthetic and real images indicate the effectiveness of the proposed method.


title: Automatic essay grading system for short answers in English language

abstract: Automatic Essay Grading (AEG) system is defined as the computer technology that evaluates and grades written prose. The short essay answer, where the es say is written in short sentences where it has two types the open ended short answer and the close ended sho rt answer where it is our research domain based on the computer subject. The Marking of short essay answers automatically is one of the most complicated domains because it is relying heavily on the semant ic similarity in meaning refers to the degree to wh ich two sentences are similar in the meaning where both used similar words in the meaning, in this case Humans are able to easily judge if a concepts are r elated to each other, there for is a problem when S tudent use a synonym words during the answer in case they forget the target answer and they use their alterna tive words in the answer which will be different from th e Model answer that prepared by the structure. The Standard text similarity measures perform poorly on such tasks. Short answer only provides a limited content, because the length of the text is typicall y short, ranging from a single word to a dozen word s. This research has two propose; the first propose is Alternative Sentence Generator Method in order to generate the alternative model answer by connecting the method with the synonym dictionary. The second proposed three algorithms combined together in matching phase, Commons Words (COW), Longest Common Subsequence (LCS) and Semantic Distance (SD), these algorithms have been successfully used in many Natural Language Processi ng systems and have yielded efficient results. The system was manually tested on 40 questions answered by three students and evaluated by teacher in class. The proposed system has yielded %82 corre lation-style with human grading, which has made the system significantly better than the other stat e of the art systems.


Effects of Anxiety on Athletic Performance


title: Broadband spatially combined amplifier array using tapered slot transitions in waveguide

abstract: Most reported spatially combined or quasioptical amplifier arrays exhibit resonant narrowband performance (<10%) and have not addressed thermal management issues. We report a waveguide-based spatial combining scheme using broadband tapered-slot transitions, capable of realizing full waveguide band coverage (40% fractional bandwidth) with good thermal properties. An X-band prototype using eight medium-power GaAs monolithic microwave integrated circuits (MMICs) produced an output power of 2.4 W and 9-dB power gain at 1-dB compression, with a combining efficiency of 68% and </spl plusmn/1-dB gain variation over the full waveguide band (8-12 GHz).


title: Estimating Depth From Monocular Images as Classification Using Deep Fully Convolutional Residual Networks

abstract: Depth estimation from single monocular images is a key component of scene understanding and has benefited largely from deep convolutional neural networks (CNN) recently. In this article, we take advantage of the recent deep residual networks and propose a simple yet effective approach to this problem. We formulate depth estimation as a pixel-wise classification task. Specifically, we first discretize the continuous depth values into multiple bins and label the bins according to their depth range. Then we train fully convolutional deep residual networks to predict the depth label of each pixel. Performing discrete depth label classification instead of continuous depth value regression allows us to predict a confidence in the form of probability distribution. We further apply fully-connected conditional random fields (CRF) as a post processing step to enforce local smoothness interactions, which improves the results. We evaluate our approach on both indoor and outdoor datasets and achieve state-of-the-art performance.


A Survey on Facial Expression Recognition Techniques


title: Evolution of antifungal-drug resistance: mechanisms and pathogen fitness

abstract: Like other microorganisms, fungi exist in populations that are adaptable. Under the selection imposed by antifungal drugs, drug-sensitive fungal pathogens frequently evolve resistance. Although the molecular mechanisms of resistance are well-characterized, there are few measurements of the impact of these mechanisms on pathogen fitness in different environments. To predict resistance before a new drug is prescribed in the clinic, the full spectrum of potential resistance mutations and the interactions among combinations of divergent mechanisms can be determined in evolution experiments. In the search for new strategies to manage drug resistance, measuring the limits of adaptation might reveal methods for trapping fungal pathogens in evolutionary dead ends.


title: Frontal Cortex and Reward-Guided Learning and Decision-Making

abstract: Reward-guided decision-making and learning depends on distributed neural circuits with many components. Here we focus on recent evidence that suggests four frontal lobe regions make distinct contributions to reward-guided learning and decision-making: the lateral orbitofrontal cortex, the ventromedial prefrontal cortex and adjacent medial orbitofrontal cortex, anterior cingulate cortex, and the anterior lateral prefrontal cortex. We attempt to identify common themes in experiments with human participants and with animal models, which suggest roles that the areas play in learning about reward associations, selecting reward goals, choosing actions to obtain reward, and monitoring the potential value of switching to alternative courses of action.


Reflections on non-heartbeating organ donation: how 3 years of experience affected the University of Pittsburgh's Ethics Committee's actions.


title: Similarity Search on Time Series based on Threshold Queries

abstract: Similarity search in time series data is required in many application fields. The most prominent work has focused on similarity search considering either complete time series or similarity according to subsequences of time series. For many domains like financial analysis, medicine, environmental meteorology, or environmental observation, the detection of temporal dependencies between different time series is very important. In contrast to traditional approaches which consider the course of the time series for the purpose of matching, coarse trend information about the time series could be sufficient to solve the above mentioned problem. In particular, temporal dependencies in time series can be detected by determining the points of time at which the time series exceeds a specific threshold. In this paper, we introduce the novel concept of threshold queries in time series databases which report those time series exceeding a user-defined query threshold at similar time frames compared to the query time series. We present a new efficient access method which uses the fact that only partial information of the time series is required at query time. The performance of our solution is demonstrated by an extensive experimental evaluation on real world and artificial time series data.


title: A study of normalization and embedding in MongoDB

abstract: With the advancement in the database technology NoSQL databases are becoming more and more popular now a days. The cloud-based NoSQL database MongoDB is one of them. As one can know the data modeling strategies for relational and non-relational databases differ drastically. Modeling of data in MongoDB database depends on the data and characteristics of MongoDB. Due to variation in data models MongoDB based application performance gets affected. In the present paper we have applied two different modeling styles as embedding of documents and normalization on collections. With the embedding feature we may face situation where documents grow in size after creation which may degrade the performance of database. The maximum document size allowed in MongoDB is limited. With references we get maximum flexibility than embedding but client-side applications must issue follow-up queries to resolve the references. The joins in this case cannot be effectively used. Hence there is need for defining the strategy of extent of normalization and embedding to get better performance in the mixed situation. The paper discussed here shows the variation in the performance along with the change in the modeling style with reference to normalization and embedding and it gives the base to find the extent of normalization and embedding for reducing query execution time.


title: Motif-based Classification of Time Series with Bayesian Networks and SVMs

abstract: Classification of time series is an important task with many challenging applications like brain wave (EEG) analysis, signature verification or speech recognition. In this paper we show how characteristic local patterns (motifs) can improve the classification accuracy. We introduce a new motif class, generalized semi-continuous motifs. To allow flexibility and noise robustness, these motifs may include gaps of various lengths, generic and more specific wildcards. We propose an efficient algorithm for mining generalized sequential motifs. In experiments on real medical data, we show how generalized semi-continuous motifs improve the accuracy of SVMs and Bayesian Networks for time series classification.


title: Information comics: An overview

abstract: The analysis of the structure of comics is increasing but only recently have “information comics” been analyzed. These are a subset of comics whose purpose is not to tell a story or entertain but to transfer information or communicate concepts, even though that narrative is often considered an essential element of comics. Information transfer is considered an essential element of many texts, especially educational ones. The effective use of comics in education settings has been discussed since at least 1944. While “information comics” do differ in some aspects, this study will show via Peircean Visual Design that “information comics” are fundamentally the same as other comics, and while comics as a whole may be effective in knowledge transfer there appears to be little data supporting this claim.


title: Contrasting neuronal activity in the ipsilateral and contralateral supplementary motor areas in relation to a movement of monkey's distal hindlimb

abstract: Recordings were made from the supplementary motor areas in both cortical hemispheres of a monkey trained to press a foot pedal. As a result of appropriate limb fixation, the movement was performed with activity predominantly in distal muscles of the right hindlimb. Forty-three percent of contralateral neurons showed movement-related activity. In contrast, only 9% of ipsilateral neurons were active and magnitudes of their activity were smaller.


title: Renewable Diesel Production from the Hydrotreating of Rapeseed Oil with Pt/Zeolite and NiMo/Al2O3 Catalysts

abstract: As an alternative way to produce diesel hydrocarbons, the hydrocracking of rapeseed oil was studied on three different types of bifunctional catalysts: Pt/H-Y, Pt/H-ZSM-5, and sulfided NiMo/γ-Al2O3. Experiments were carried out in a batch reactor over a temperature range of 300−400 °C and initial hydrogen pressures from 5 to 11 MPa. The reaction time was limited to 3 h to prevent a high degree of cracking. The Pt-zeolite catalysts had a strong catalytic activity for both cracking and hydrogenation reactions, and therefore a higher severity was required to reach a relatively high oil conversion into liquid hydrocarbons. With dependence on the activity of the acid sites of the catalysts, the results show a trade-off between the yield of green diesel and the degree of isomerization, which had a direct effect on the cold properties of the diesel. Among the three catalysts, hydrocracking on Ni-Mo/γ-Al2O3 gave the highest yield of liquid hydrocarbons in the boiling range of the diesel fraction, i.e., green dies...


title: Brain tumor segmentation using thresholding, morphological operations and extraction of features of tumor

abstract: Brain is the most important and vital organ of the human body. The control and coordination of all the other vital structures is carried out by the brain. The tumor is formed by the uncontrolled multiplication of cell division. Numerous techniques were developed to detect and segment the brain tumor. Using thresholding and morphological operations efficient brain tumor segmentation is carried out. This is the efficient algorithm where segmentation of tumor is carried out and its features such as centroid, perimeter and area are calculated from the segmented tumor. To detect the brain tumor, scanned MRI images are given as the input. The work involved here helps in medical field to detect tumor and its features helps in giving the treatment plan to the patient. The entire paper is divided in to seven sections which are described in detailed in the following sections.


title: A permutation approach to validation

abstract: We give a permutation approach to validation (estimation of out‐sample error). One typical use of validation is model selection. We establish the legitimacy of the proposed permutation complexity by proving a uniform bound on the out‐sample error, similar to a Vapnik‐Chervonenkis (VC)‐style bound. We extensively demonstrate this approach experimentally on synthetic data, standard data sets from the UCI‐repository, and a novel diffusion data set. The out‐of‐sample error estimates are comparable to cross validation (CV); yet, the method is more efficient and robust, being less susceptible to overfitting during model selection. Copyright © 2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 3: 361‐380 2010 © 2010 Wiley Periodicals, Inc. ::: ::: A preliminary version of this paper appears in the Siam Data Mining Conference, 2010.


title: Determination of ripeness and grading of tomato using image analysis on Raspberry Pi

abstract: Fruits and vegetables market is subject of choice. Thus, it is important for the suppliers to label the quality of goods before dealing out. Some commercially available fruit sorting and grading system have been introduced, but they are almost expensive for small and medium fruit processing industry. Presently, human experts grade the agriculture goods based on its vision based features that cause the inaccuracy, inconsistency and inefficiency on defining the quality of agriculture goods. In this paper, we are inspecting the quality of tomato based on shape, size and degree of ripeness. An edge detection algorithm is used to estimate the shape and size of tomato and color detecting algorithm is used for the ripeness determination. All these algorithms are implemented on Raspberry Pi development board which will become independent and cost effective system. Our system includes Raspberry Pi development board, conveyor belt, motors, Pi camera. All the interfacing of the above devices will be carried out and will make a cost effective embedded system for the determination of shape, size and degree of ripeness of tomato. Same system can be utilized for other fruits and vegetables also.