title: Fully Connected Object Proposals for Video Segmentation

abstract: We present a novel approach to video segmentation using multiple object proposals. The problem is formulated as a minimization of a novel energy function defined over a fully connected graph of object proposals. Our model combines appearance with long-range point tracks, which is key to ensure robustness with respect to fast motion and occlusions over longer video sequences. As opposed to previous approaches based on object proposals, we do not seek the best per-frame object hypotheses to perform the segmentation. Instead, we combine multiple, potentially imperfect proposals to improve overall segmentation accuracy and ensure robustness to outliers. Overall, the basic algorithm consists of three steps. First, we generate a very large number of object proposals for each video frame using existing techniques. Next, we perform an SVM-based pruning step to retain only high quality proposals with sufficiently discriminative power. Finally, we determine the fore-and background classification by solving for the maximum a posteriori of a fully connected conditional random field, defined using our novel energy function. Experimental results on a well established dataset demonstrate that our method compares favorably to several recent state-of-the-art approaches.


title: Clever Search: A WordNet Based Wrapper for Internet Search Engines

abstract: This paper presents an approach to enhance search engines with information about word senses available in WordNet. The approach exploits information about the conceptual relations within the lexical-semantic net. In the wrapper for search engines presented, WordNet information is used to specify user's request or to classify the results of a publicly available web search engine, like google, yahoo, etc.


title: Hilbert-Huang Transform (HHT) Analysis of Human Activities Using Through-Wall Noise Radar

abstract: Various parts of the human body have different movements when a person is performing different physical activities. There is a need to remotely detect human heartbeat and breathing for applications involving anti-terrorism and search-and-rescue. Ultrawideband noise radar systems are attractive because they are covert and immune from interference. The conventional time-frequency analyses of human activity are not generally applicable to nonlinear and nonstationary signals. If one can decompose the noisy baseband reflected signal and extract only the human-induced Doppler from it, the identification of various human activities becomes easier. We propose a nonstationary model to describe human motion and apply the Hilbert-Huang transform (HHT), which is adaptive to nonlinear and nonstationary signals, in order to analyze frequency characteristics of the baseband signal. When used with noise-like radar data, it is useful covertly identify specific human movement.


title: Identifying Emotions in Social Media: Comparison of Word-Emotion Lexicons

abstract: In recent years, emotions expressed in social media messages have become a vivid research topic due to their influence on the spread of misinformation and online radicalization over online social networks. Thus, it is important to correctly identify emotions in order to make inferences from social media messages. In this paper, we report on the performance of three publicly available word-emotion lexicons (NRC, DepecheMood, EmoSenticNet) over a set of Facebook and Twitter messages. To this end, we designed and implemented an algorithm that applies natural language processing (NLP) techniques along with a number of heuristics that reflect the way humans naturally assess emotions in written texts. In order to evaluate the appropriateness of the obtained emotion scores, we conducted a questionnaire-based survey with human raters. Our results show that there are noticeable differences between the performance of the lexicons as well as with respect to emotion scores the human raters provided in our survey.


title: IoT based localization and tracking

abstract: Internet of Things (IoT) has several applications which directly affect our lives. IoT has been proposed to be part of the connected living paradigm. In this pool of applications, we also have localization based services. In the first one, localization of objects, animals and other living beings are determined using IoT. In the second one, the movement of the animals, kids, elderly and other moveable objects are tracked. Both localization and tracking are part of security and surveillance services in the connected living environments. In this paper, we provide the current state of the art for IoT based localization and tracking. We show the key technical aspects, compare the IoT based initiatives for localization with the non-IoT based services and finally show their utilities in different application domains. IoT based localization and tracking are found to be more pervasive and accurate than the previously used techniques.


title: Bonfire: a nomadic system for hybrid laptop-tabletop interaction

abstract: We present Bonfire, a self-contained mobile computing system that uses two laptop-mounted laser micro-projectors to project an interactive display space to either side of a laptop keyboard. Coupled with each micro-projector is a camera to enable hand gesture tracking, object recognition, and information transfer within the projected space. Thus, Bonfire is neither a pure laptop system nor a pure tabletop system, but an integration of the two into one new nomadic computing platform. This integration (1) enables observing the periphery and responding appropriately, e.g., to the casual placement of objects within its field of view, (2) enables integration between physical and digital objects via computer vision, (3) provides a horizontal surface in tandem with the usual vertical laptop display, allowing direct pointing and gestures, and (4) enlarges the input/output space to enrich existing applications. We describe Bonfire's architecture, and offer scenarios that highlight Bonfire's advantages. We also include lessons learned and insights for further development and use.


title: A Benchmark Dataset for Audio Classification and Clustering

abstract: We present a freely available benchmark dataset for audio classification and clustering. This dataset consists of 10 seconds samples of 1886 songs obtained from the Garageband site. Beside the audio clips themselves, textual meta data is provided for the individual songs. The songs are classified into 9 genres. In addition to the genre information, our dataset also consists of 24 hierarchical cluste r models created manually by a group of users. This enables a user centric evaluation of audio classification and clustering algorithms and gives researchers the opportunity to test the performance of their methods on heterogeneous data. We first give a motivation for assembling our benchmark dataset. Then we describe the dataset and its elements in more detail. Finally, we present some initial results using a set of audio features generated by a feature construction approach.


title: Some statistical issues in the comparison of speech recognition algorithms

abstract: The authors present two simple tests for deciding whether the difference in error rates between two algorithms tested on the same data set is statistically significant. The first (McNemar's test) requires the errors made by an algorithm to be independent events and is found to be most appropriate for isolated-word algorithms. The second (a matched-pairs test) can be used even when errors are not independent events and is more appropriate for connected speech. >


title: Application-aware traffic scheduling for workload offloading in mobile clouds

abstract: Mobile Cloud Computing (MCC) bridges the gap between limited capabilities of mobile devices and the increasing complexity of mobile applications, by offloading the computational workloads from local devices to the cloud. Current research supports workload offloading through appropriate application partitioning and remote method execution, but generally ignores the impact of wireless network characteristics on such offloading. Wireless data transmissions incurred by remote method execution consume a large amount of additional energy during transmission intervals when the network interface stays in the high-power state, and deferring these transmissions increases the response delay of mobile applications. In this paper, we adaptively balance the tradeoff between energy efficiency and responsiveness of mobile applications by developing application-aware wireless transmission scheduling algorithms. We take both causality and run-time dynamics of application method executions into account when deferring wireless transmissions, so as to minimize the wireless energy cost and satisfy the application delay constraint with respect to the practical system contexts. Systematic evaluations show that our scheme significantly improves the energy efficiency of workload offloading over realistic smartphone applications.


title: AutoVis: Automatic Visualization

abstract: AutoVis is a data viewer that responds to content - text, relational tables, hierarchies, streams, images - and displays the information appropriately (that is, as an expert would). Its design rests on the grammar of graphics, scagnostics and a modeler based on the logic of statistical analysis. We distinguish an automatic visualization system (AVS) from an automated visualization system. The former automatically makes decisions about what is to be visualized. The latter is a programming system for automating the production of charts, graphs and visualizations. An AVS is designed to provide a first glance at data before modeling and analysis are done. AVS is designed to protect researchers from ignoring missing data, outliers, miscodes and other anomalies that can violate statistical assumptions or otherwise jeopardize the validity of models. The design of this system incorporates several unique features: (1) a spare interface - analysts simply drag a data source into an empty window, (2) a graphics generator that requires no user definitions to produce graphs, (3) a statistical analyzer that protects users from false conclusions, and (4) a pattern recognizer that responds to the aspects (density, shape, trend, and so on) that professional statisticians notice when investigating data sets.


title: The Effects of Different Resistance Training Protocols on Muscular Strength and Endurance Development in Children

abstract: BACKGROUND ::: Previous research has shown that children can increase their muscular strength and muscular endurance as a result of regular participation in a progressive resistance training program. However, the most effective exercise prescription regarding the number of repetitions remains questionable. ::: ::: ::: OBJECTIVE ::: To compare the effects of a low repetition-heavy load resistance training program and a high repetition-moderate load resistance training program on the development of muscular strength and muscular endurance in children. Design. Prospective, controlled trial. ::: ::: ::: SETTING ::: Community-based youth fitness center. ::: ::: ::: SUBJECTS ::: Eleven girls and 32 boys between the ages of 5.2 and 11.8 years. ::: ::: ::: INTERVENTION ::: In twice-weekly sessions of resistance training for 8 weeks, children performed 1 set of 6 to 8 repetitions with a heavy load (n = 15) or 1 set of 13 to 15 repetitions with a moderate load (n = 16) on child-size exercise machines. Children in the control group (n = 12) did not resistance train. One repetition maximum (RM) strength and muscular endurance (repetitions performed posttraining with the pretraining 1-RM load) were determined on the leg extension and chest press exercises. ::: ::: ::: RESULTS ::: One RM leg extension strength significantly increased in both exercise groups compared with that in the control subjects. Increases of 31.0% and 40.9%, respectively, for the low repetition-heavy load and high repetition-moderate load groups were observed. Leg extension muscular endurance significantly increased in both exercise groups compared with that in the control subjects, although gains resulting from high repetition-moderate load training (13.1 +/- 6.2 repetitions) were significantly greater than those resulting from low repetition-heavy load training (8.7 +/- 2.9 repetitions). On the chest press exercise, only the high repetition-moderate load exercise group made gains in 1-RM strength (16.3%) and muscular endurance (5.2 +/- 3.6 repetitions) that were significantly greater than gains in the control subjects. ::: ::: ::: CONCLUSION ::: These findings support the concept that muscular strength and muscular endurance can be improved during the childhood years and favor the prescription of higher repetition-moderate load resistance training programs during the initial adaptation period.


Comparing Bayesian Network Classifiers


title: DM and CM Modeling Of Non-Isolated Buck Converters for EMI Filter Design

abstract: Non-isolated dc-dc converters play an important role in the electrification of automotive, aircraft and shipboard systems. Maximizing the power density/specific power of the EMI filter stage of these converters is critical to achieving the full benefits of electrification. To this end, lean yet accurate models are required. In this paper, a differential-mode/common-mode decomposition and equivalent modeling approach is applied to a non-isolated buck converter topology. Fully decomposed differential-mode and common-mode equivalent models are produced that yield insight into the significantimpact typical asymmetries in the buck converter and its parasitics have on its conducted emissions. A process is outlined for using the equivalent models to design optimal EMI filters for dc-dc converters with respect to system-level objectives.


title: An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU

abstract: Objectives:Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop


title: Joint Detection and Diagnosis of Prostate Cancer in Multi-parametric MRI Based on Multimodal Convolutional Neural Networks

abstract: This paper presents an automated method for jointly localizing prostate cancer (PCa) in multi-parametric MRI (mp-MRI) images and assessing the aggressiveness of detected lesions. Our method employs multimodal multi-label convolutional neural networks (CNNs), which are trained in a weakly-supervised manner by providing a set of prostate images with image-level labels without priors of lesions’ locations. By distinguishing images with different labels, discriminative visual patterns related to indolent PCa and clinically significant (CS) PCa are automatically learned from clutters of prostate tissues. Cancer response maps (CRMs) with each pixel indicating the likelihood of being part of indolent/CS are explicitly generated at the last convolutional layer. We define new back-propagate error of CNN to enforce both optimized classification results and consistent CRMs for different modalities. Our method enables the feature learning processes of different modalities to mutually influence each other and, in turn yield more representative features. Comprehensive evaluation based on 402 lesions demonstrates superior performance of our method to the state-of-the-art method [13].


title: Bioelectrics-new applications for pulsed power technology

abstract: Electric phenomena play an important role in biophysics. Bioelectric processes control the ion transport processes across membranes, and are the basis for information transfer along neurons. These electrical effects are generally triggered by chemical processes. However, it is also possible to control such cell functions and transport processes by applying pulsed electric fields. This area of bioengineering, bioelectrics, offers new applications for pulsed power technology. One such application is prevention of biofouling, an effect that is based on reversible electroporation of cell membranes. Pulsed electric fields of several kV/cm amplitude and submicrosecond duration have been found effective in preventing the growth of aquatic nuisance species on surfaces. Reversible electroporation is also used for medical applications, e.g. for delivery of chemotherapeutic drugs into tumor cells, for gene therapy, and for transdermal drug delivery. Higher electric fields cause irreversible membrane damage. Pulses in the microsecond range with electric field intensities in the tens of kV/cm are being used for bacterial decontamination of water and liquid food. A new type of field-cell interaction, "Intracellular Electromanipulation", by means of nanosecond pulses at electric fields exceeding 50 kV/cm has been recently added to known bioelectric effects. It is based on capacitive coupling to cell substructures, has therefore the potential to affect transport processes across subcellular membranes, and may be used for gene transfer into cell nuclei. There are also indications that it triggers intracellular processes, such as programmed cell death, an effect, which can be used for cancer treatment. In order to generate the required electric fields for these processes, high voltage, high current sources are required. The pulse duration needs to be short to prevent thermal effects. Pulse power technology is the enabling technology for bioelectrics. The field of bioelectrics, therefore opens up a new research area for pulse power engineers, with fascinating applications in biology and medicine.


Statistical Optimization of Ternary Blends of Poly(lactic acid)/Ethylene Acrylate Copolymer/Wood Flour Composites


title: Learning to Assign Orientations to Feature Points

abstract: We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point. Our method improves feature point matching upon the state-of-the art and can be used in conjunction with any existing rotation sensitive descriptors. To avoid the tedious and almost impossible task of finding a target orientation to learn, we propose to use Siamese networks which implicitly find the optimal orientations during training. We also propose a new type of activation function for Neural Networks that generalizes the popular ReLU, maxout, and PReLU activation functions. This novel activation performs better for our task. We validate the effectiveness of our method extensively with four existing datasets, including two non-planar datasets, as well as our own dataset. We show that we outperform the state-of-the-art without the need of retraining for each dataset.


title: Expectation-maximization for sparse and non-negative PCA

abstract: We study the problem of finding the dominant eigenvector of the sample covariance matrix, under additional constraints on the vector: a cardinality constraint limits the number of non-zero elements, and non-negativity forces the elements to have equal sign. This problem is known as sparse and non-negative principal component analysis (PCA), and has many applications including dimensionality reduction and feature selection. Based on expectation-maximization for probabilistic PCA, we present an algorithm for any combination of these constraints. Its complexity is at most quadratic in the number of dimensions of the data. We demonstrate significant improvements in performance and computational efficiency compared to other constrained PCA algorithms, on large data sets from biology and computer vision. Finally, we show the usefulness of non-negative sparse PCA for unsupervised feature selection in a gene clustering task.


Neural Network Approach to Predict Marine Traffic


title: Correlation between model observer and human observer performance in CT imaging when lesion location is uncertain

abstract: PURPOSE ::: The purpose of this study was to investigate the correlation between model observer and human observer performance in CT imaging for the task of lesion detection and localization when the lesion location is uncertain. ::: ::: ::: METHODS ::: Two cylindrical rods (3-mm and 5-mm diameters) were placed in a 35×26 cm torso-shaped water phantom to simulate lesions with -15 HU contrast at 120 kV. The phantom was scanned 100 times on a 128-slice CT scanner at each of four dose levels (CTDIvol=5.7, 11.4, 17.1, and 22.8 mGy). Regions of interest (ROIs) around each lesion were extracted to generate images with signal-present, with each ROI containing 128×128 pixels. Corresponding ROIs of signal-absent images were generated from images without lesion mimicking rods. The location of the lesion (rod) in each ROI was randomly distributed by moving the ROIs around each lesion. Human observer studies were performed by having three trained observers identify the presence or absence of lesions, indicating the lesion location in each image and scoring confidence for the detection task on a 6-point scale. The same image data were analyzed using a channelized Hotelling model observer (CHO) with Gabor channels. Internal noise was added to the decision variables for the model observer study. Area under the curve (AUC) of ROC and localization ROC (LROC) curves were calculated using a nonparametric approach. The Spearman's rank order correlation between the average performance of the human observers and the model observer performance was calculated for the AUC of both ROC and LROC curves for both the 3- and 5-mm diameter lesions. ::: ::: ::: RESULTS ::: In both ROC and LROC analyses, AUC values for the model observer agreed well with the average values across the three human observers. The Spearman's rank order correlation values for both ROC and LROC analyses for both the 3- and 5-mm diameter lesions were all 1.0, indicating perfect rank ordering agreement of the figures of merit (AUC) between the average performance of the human observers and the model observer performance. ::: ::: ::: CONCLUSIONS ::: In CT imaging of different sizes of low-contrast lesions (-15 HU), the performance of CHO with Gabor channels was highly correlated with human observer performance for the detection and localization tasks with uncertain lesion location in CT imaging at four clinically relevant dose levels. This suggests the ability of Gabor CHO model observers to meaningfully assess CT image quality for the purpose of optimizing scan protocols and radiation dose levels in detection and localization tasks for low-contrast lesions.


Classification of Image Steganography Techniques in Spatial Domain : A Study


title: One-Step Generation of Mice Carrying Mutations in Multiple Genes by CRISPR/Cas-Mediated Genome Engineering

abstract: Mice carrying mutations in multiple genes are traditionally generated by sequential recombination in embryonic stem cells and/or time-consuming intercrossing of mice with a single mutation. The CRISPR/Cas system has been adapted as an efficient gene-targeting technology with the potential for multiplexed genome editing. We demonstrate that CRISPR/Cas-mediated gene editing allows the simultaneous disruption of five genes (Tet1, 2, 3, Sry, Uty--8 alleles) in mouse embryonic stem (ES) cells with high efficiency. Coinjection of Cas9 mRNA and single-guide RNAs (sgRNAs) targeting Tet1 and Tet2 into zygotes generated mice with biallelic mutations in both genes with an efficiency of 80%. Finally, we show that coinjection of Cas9 mRNA/sgRNAs with mutant oligos generated precise point mutations simultaneously in two target genes. Thus, the CRISPR/Cas system allows the one-step generation of animals carrying mutations in multiple genes, an approach that will greatly accelerate the in vivo study of functionally redundant genes and of epistatic gene interactions.


title: Semi-Supervised On-line Boosting for Robust Tracking

abstract: Recently, on-line adaptation of binary classifiers for tracking have been investigated. On-line learning allows for simple classifiers since only the current view of the object from its surrounding background needs to be discriminiated. However, on-line adaption faces one key problem: Each update of the tracker may introduce an error which, finally, can lead to tracking failure (drifting). The contribution of this paper is a novel on-line semi-supervised boosting method which significantly alleviates the drifting problem in tracking applications. This allows to limit the drifting problem while still staying adaptive to appearance changes. The main idea is to formulate the update process in a semi-supervised fashion as combined decision of a given prior and an on-line classifier. This comes without any parameter tuning. In the experiments, we demonstrate real-time tracking of our SemiBoost tracker on several challenging test sequences where our tracker outperforms other on-line tracking methods.


title: Polyphenols: food sources and bioavailability

abstract: Polyphenols are abundant micronutrients in our diet, and evidence for their role in the prevention of degenerative diseases such as cancer and cardiovascular diseases is emerging. The health effects of polyphenols depend on the amount consumed and on their bioavailability. In this article, the nature and contents of the various polyphenols present in food sources and the influence of agricultural practices and industrial processes are reviewed. Estimates of dietary intakes are given for each class of polyphenols. The bioavailability of polyphenols is also reviewed, with particular focus on intestinal absorption and the influence of chemical structure (eg, glycosylation, esterification, and polymerization), food matrix, and excretion back into the intestinal lumen. Information on the role of microflora in the catabolism of polyphenols and the production of some active metabolites is presented. Mechanisms of intestinal and hepatic conjugation (methylation, glucuronidation, sulfation), plasma transport, and elimination in bile and urine are also described. Pharmacokinetic data for the various polyphenols are compared. Studies on the identification of circulating metabolites, cellular uptake, intracellular metabolism with possible deconjugation, biological properties of the conjugated metabolites, and specific accumulation in some target tissues are discussed. Finally, bioavailability appears to differ greatly between the various polyphenols, and the most abundant polyphenols in our diet are not necessarily those that have the best bioavailability profile. A thorough knowledge of the bioavailability of the hundreds of dietary polyphenols will help us to identify those that are most likely to exert protective health effects.


title: The examination of factors affecting e-learning effectiveness

abstract: Technology information has increased dramatically in the last years and has contributed to the growth in technology delivered instruction as an important learning and education method. In this perspective, many academic researches considered the importance of e-learning effectiveness.Although the existing models of e-learning effectiveness has improved our understanding of how online training can support and enhance learning, most of published models do not take into account the importance of the relationship between social presence and interaction. Thus, this study develops preceding investigation by extending a model of e-learning effectiveness which adds social presence to other studied variables including computer self efficacy, perceived usefulness, perceived ease of use, interaction between trainer and trainees, and e-learning effectiveness. Moreover, the model includes the possible relationships between independent factors. In this case, the present research seek to identify the influence of computer self efficacy, ease of use, perceived usefulness, interaction, and social presence on e- learning effectiveness. Furthermore, this study considers the possible influences between individual characteristics, perceptual characteristics and environmental characteristics.Using data from 410 employees, the conceptual model was validated through a Tunisian context. Results indicate the importance of interaction, perceived usefulness, perceived ease of use, and social presence on e-learning achievement. E-learning achievement, in turn, influences e-learning transfer.


title: A Joint Model for Entity Analysis: Coreference, Typing, and Linking

abstract: We present a joint model of three core tasks in the entity analysis stack: coreference resolution (within-document clustering), named entity recognition (coarse semantic typing), and entity linking (matching to Wikipedia entities). Our model is formally a structured conditional random field. Unary factors encode local features from strong baselines for each task. We then add binary and ternary factors to capture cross-task interactions, such as the constraint that coreferent mentions have the same semantic type. On the ACE 2005 and OntoNotes datasets, we achieve state-of-the-art results for all three tasks. Moreover, joint modeling improves performance on each task over strong independent baselines.


title: New geoelectrical characterisation of a continental collision zone in the West-Central Pyrenees: Constraints from long period and broadband magnetotellurics

abstract: Abstract Continental collision dominates the development of a large number of mountain ranges on Earth. Although the geological evolution of these regions is highly studied, lithospheric-scale physical processes are less well characterised and their interpretations frequently differ. A new magnetotelluric profile crossing the West-Central Pyrenees with broadband and long period magnetotelluric data constrains the geoelectrical features of this continental collision zone confining the geological and physical processes that take place at a lithospheric scale. Three geoelectrical structures associated with partial melting and its melt migration are imaged for the first time in the West-Central Pyrenees suggesting an Iberian subducted lower crust reaching the depth of 70 km. This result, supported by an analysis of the thermal evolution of the Iberian subducted lower crust, reinforces the hypothesis of partial melting in this region. From the long period magnetotelluric data, the asthenosphere has been imaged as a geoelectrical structure with electrical resistivity values between 20 Ω m and 70 Ω m. The lithosphere–asthenosphere boundary close to the collision zone has been constrained between 90 km and 110 km depth below the Iberian plate and between 120 km and 160 km depth below the transition zone between plates and the European plate, in agreement with the previous geophysical data. Additionally, other geoelectrical anomalies imaged have been associated with an unexpected major Variscan boundary located below the Ebro basin, Silurian sediments and free fluids in the Gavarnie thrust, and with a thick sequence of Jurassic and Early Cretaceous sediments situated below the North Pyrenean Thrust Sheet.


title: Clustering small-sized collections of short texts

abstract: The need to cluster small text corpora composed of a few hundreds of short texts rises in various applications; e.g., clustering top-retrieved documents based on their snippets. This clustering task is challenging due to the vocabulary mismatch between short texts and the insufficient corpus-based statistics (e.g., term co-occurrence statistics) due to the corpus size. We address this clustering challenge using a framework that utilizes a set of external knowledge resources that provide information about term relations. Specifically, we use information induced from the resources to estimate similarity between terms and produce term clusters. We also utilize the resources to expand the vocabulary used in the given corpus and thus enhance term clustering. We then project the texts in the corpus onto the term clusters to cluster the texts. We evaluate various instantiations of the proposed framework by varying the term clustering method used, the approach of projecting the texts onto the term clusters, and the way of applying external knowledge resources. Extensive empirical evaluation demonstrates the merits of our approach with respect to applying clustering algorithms directly on the text corpus, and using state-of-the-art co-clustering and topic modeling methods.


title: Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans

abstract: Brain-computer interfaces (BCIs) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user's commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains.


title: Failure of captive-born greater rheas (Rhea americana, Rheidae, Aves) to discriminate between predator and nonpredator models

abstract: The capacity to recognize and respond to predators can be lost by captive animals. Habituation to humans caused by the captive environment can be transferred to other stimuli, such as predators, a situation that conservation biologists wish to avoid. Greater rheas are threatened South American birds, for which there are plans for reintroduction programs in Brazil. The goal of this study was to evaluate the behavioral responses of captive-born greater rheas to different models of predators and nonpredators. Seventeen captive-born greater rheas, divided into four groups, from the Belo Horizonte Zoo, Brazil were studied. Three predator and three nonpredator models were presented to the birds and their behavioral responses recorded. Predators versus nonpredator models and the behavior of rheas in three experimental phases (baseline, models, and postmodels) were analyzed. Captive-born greater rheas modified their behaviors in the presence of both predator/nonpredator models, increasing alert and wary behaviors (alert, observing, and pacing behaviors: P 0.05). In conclusion, antipredator training should be implemented to develop the expression of this behavior before reintroductions are attempted.


title: Human Trajectory Prediction using Spatially aware Deep Attention Models

abstract: Trajectory Prediction of dynamic objects is a widely studied topic in the field of artificial intelligence. Thanks to a large number of applications like predicting abnormal events, navigation system for the blind, etc. there have been many approaches to attempt learning patterns of motion directly from data using a wide variety of techniques ranging from hand-crafted features to sophisticated deep learning models for unsupervised feature learning. All these approaches have been limited by problems like inefficient features in the case of hand crafted features, large error propagation across the predicted trajectory and no information of static artefacts around the dynamic moving objects. We propose an end to end deep learning model to learn the motion patterns of humans using different navigational modes directly from data using the much popular sequence to sequence model coupled with a soft attention mechanism. We also propose a novel approach to model the static artefacts in a scene and using these to predict the dynamic trajectories. The proposed method, tested on trajectories of pedestrians, consistently outperforms previously proposed state of the art approaches on a variety of large scale data sets. We also show how our architecture can be naturally extended to handle multiple modes of movement (say pedestrians, skaters, bikers and buses) simultaneously.


title: 100-GHz Transistors from Wafer-Scale Epitaxial Graphene

abstract: The high carrier mobility of graphene has been exploited in field-effect transistors that operate at high frequencies. Transistors were fabricated on epitaxial graphene synthesized on the silicon face of a silicon carbide wafer, achieving a cutoff frequency of 100 gigahertz for a gate length of 240 nanometers. The high-frequency performance of these epitaxial graphene transistors exceeds that of state-of-the-art silicon transistors of the same gate length.


title: Dermoscopy Findings of Pseudolymphomatous Folliculitis

abstract: Pseudolymphomatous folliculitis (PLF), which clinically mimicks cutaneous lymphoma, is a rare manifestation of cutaneous pseudolymphoma and cutaneous lymphoid hyperplasia. Here, we report on a 45-year-old Japanese woman with PLF. Dermoscopy findings revealed prominent arborizing vessels with small perifollicular and follicular yellowish spots and follicular red dots. A biopsy specimen also revealed dense lymphocytes, especially CD1a+ cells, infiltrated around the hair follicles. Without any additional treatment, the patient’s nodule rapidly decreased. The presented case suggests that typical dermoscopy findings could be a possible supportive tool for the diagnosis of PLF.


title: Hydromechanical investigation on 3 PWR upper plenum core structures

abstract: Abstract In this paper, experiments with three upper plenum core structures—the upper plenum core structure of active 300 MW PWR in the People's Republic of China and its two improved core structures, respectively—are carried out in a 1/4 scale transparent model of the active 300 MW PWR upper plenum with water. The horizontal across velocity in the upper plenum model is measured with an N-J type dynamic resistance strain foil velocity meter (N-J DRSFV). The axial velocity is obtained by using a laser Doppler velocity measuring system (LDV). The experimental results are compared and analyzed carefully. The complex flow velocity distribution is obtained in the experiment with each core structure. Finally, it is concluded that the improved upper plenum core structure can reduce the hydraulic load on the drop of control rods.


Effectiveness of Cellphone Banking on Service Quality in Commercial Banks in Botswana


title: PCA-aided fully convolutional networks for semantic segmentation of multi-channel fMRI

abstract: Semantic segmentation of functional magnetic resonance imaging (fMRI) makes great sense for pathology diagnosis and decision system of medical robots. The multi-channel fMRI data provide more information of the pathological features. But the increased amount of data causes complexity in feature detection. This paper proposes a principal component analysis (PCA)-aided fully convolutional network to particularly deal with multi-channel fMRI. We transfer the learned weights of contemporary classification networks to the segmentation task by fine-tuning. The experiments results are compared with various methods e.g. k-NN. A new labelling strategy is proposed to solve the semantic segmentation problem with unclear boundaries. Even with a small-sized training dataset, the test results demonstrate that our model outperforms other pathological feature detection methods. Besides, its forward inference only takes 90 milliseconds for a single set of fMRI data. To our knowledge, this is the first time to realize pixel-wise labeling of multi-channel magnetic resonance image using FCN.


title: An intelligent agent-assisted logistics exception management decision support system: a design-science approach

abstract: With the increased complexity and uncertainty in business operations, adaptive and collaborative business processes and exception management (EM) are gaining growing attention. In the logistics industry, the current logistics exceptions are managed using human resources together with the traditional workflow technology-based supply-chain management or other logistics tools. The traditional workflow technology models and manages business processes and anticipated exceptions based on predefined logical procedures of activities from a centralised perspective that offers inadequate decision support for flexibility and adaptability in EM. These procedures are limited when monitoring logistics activities in real time in order to detect and resolve the exceptions in a timely manner. In order to mitigate these problems, a design-science research approach-specifically an intelligent-agent decision support approach in logistics EM-has been proposed and investigated in this research. It contains three interrelated research phases. The first research phase focuses on the conceptualisation of the logistics EM. It consists of two parts. The first part is logistics exception classification, in order to enable more efficient decision support practices for logistics EM. The second part focuses on the development of the conceptual framework (an artefact) for design and development of logistics EM systems for decision making. The second research phase focuses on the formalisation of the conceptual framework. A multi-agent-based logistics EM system is designed based on the conceptual framework. The third research phase will focus on the development of the designed logistics EM artefact. It will include two stages. First, a prototype will be developed. To provide more adaptive, flexible and collaborative decision support, the intelligent agent technology will be used for implementation. Second, the prototype will be evaluated via social-science research methods: semi-structured interviews and laboratory experiment. It is proposed that this theory-driven agent-based logistics EM system will provide more efficient and timely decision-making support for managers in relation to logistics EM. The designed artefacts and the research design are the major contributions of this research, which add knowledge to design-science research theory and practice. The conceptualisation-formalisation-development research approach can be applied in other similar IS design-science research.


title: The strength-of-weak-ties perspective on creativity: A comprehensive examination and extension.

abstract: Disentangling the effects of weak ties on creativity, the present study separated, both theoretically and empirically, the effects of the size and strength of actors’ idea networks and examined their joint impact while simultaneously considering the separate, moderating role of network diversity. I hypothesized that idea networks of optimal size and weak strength were more likely to boost creativity when they afforded actors access to a wide range of different social circles. In addition, I examined whether the joint effects of network size, strength, and diversity on creativity were further qualified by the openness to experience personality dimension. As expected, results indicated that actors were most creative when they maintained idea networks of optimal size, weak strength, and high diversity and when they scored high on the openness dimension. The implications of these results are discussed.


title: Balancing Utility and Security: Securing Cloud Federations of Public Entities

abstract: Following their practical needs and legal constraints, recent application of the cloud paradigm among public administrations has been focused on the deployment of private clouds. Due to the increasing amount of data and processing requirements, many organizations are considering possibilities to additionally optimize their infrastructures and collaborative processes by employing private cloud federations.


title: Foreground object detection from videos containing complex background

abstract: This paper proposes a novel method for detection and segmentation of foreground objects from a video which contains both stationary and moving background objects and undergoes both gradual and sudden "once-off" changes. A Bayes decision rule for classification of background and foreground from selected feature vectors is formulated. Under this rule, different types of background objects will be classified from foreground objects by choosing a proper feature vector. The stationary background object is described by the color feature, and the moving background object is represented by the color co-occurrence feature. Foreground objects are extracted by fusing the classification results from both stationary and moving pixels. Learning strategies for the gradual and sudden "once-off" background changes are proposed to adapt to various changes in background through the video. The convergence of the learning process is proved and a formula to select a proper learning rate is also derived. Experiments have shown promising results in extracting foreground objects from many complex backgrounds including wavering tree branches, flickering screens and water surfaces, moving escalators, opening and closing doors, switching lights and shadows of moving objects.


title: Atmospheric Turbulence Effects on Wind-Turbine Wakes: An LES Study

abstract: A numerical study of atmospheric turbulence effects on wind-turbine wakes is presented. Large-eddy simulations of neutrally-stratified atmospheric boundary layer flows through stand-alone wind turbines were performed over homogeneous flat surfaces with four different aerodynamic roughness lengths. Emphasis is placed on the structure and characteristics of turbine wakes in the cases where the incident flows to the turbine have the same mean velocity at the hub height but different mean wind shears and turbulence intensity levels. The simulation results show that the different turbulence intensity levels of the incoming flow lead to considerable influence on the spatial distribution of the mean velocity deficit, turbulence intensity, and turbulent shear stress in the wake region. In particular, when the turbulence intensity level of the incoming flow is higher, the turbine-induced wake (velocity deficit) recovers faster, and the locations of the maximum turbulence intensity and turbulent stress are closer to the turbine. A detailed analysis of the turbulence kinetic energy budget in the wakes reveals also an important effect of the incoming flow turbulence level on the magnitude and spatial distribution of the shear production and transport terms.


title: Analysis of wind resistant capability of a HAV

abstract: A hybrid aerial vehicle (HAV), featured combination of a quadrotor system and a fixed-wing system, is one of the most promising configurations having both a vertical take-off and landing (VTOL) capability and high cruise performance. In this paper, a HAV has been designed and prototyped to analyze the performance of the HAV. The specifications of the HAV are summarized. The aerodynamic coefficients of the HAV derived from CFD data and the influence of axial flow on the maximum thrust of quad propellers that is calculated using QPROP are demonstrated, respectively. The wind resistant capability of the HAV is analyzed from two aspects according to different wind direction. The factors which have important influence on the wind resistant capability of the HAV are analyzed and the solutions to improve the wind resistant capability of the HAV are presented.


title: Decision analysis in Management Science

abstract: A part of the 50th anniversary of Management Science, the journal is publishing articles that reflect on the past, present, and future of the various subfields the journal represents. In this article, we consider decision analysis research as it has appeared in Management Science. After reviewing the foundations of decision analysis and the history of the journal’s decision analysis department, we review a number of key developments in decision analysis research that have appeared in Management Science and offer some comments on the current state of the field.


title: Segmental Recurrent Neural Networks for End-to-end Speech Recognition

abstract: We study the segmental recurrent neural network for end-to-end acoustic modelling. This model connects the segmental conditional random field (CRF) with a recurrent neural network (RNN) used for feature extraction. Compared to most previous CRF-based acoustic models, it does not rely on an external system to provide features or segmentation boundaries. Instead, this model marginalises out all the possible segmentations, and features are extracted from the RNN trained together with the segmental CRF. In essence, this model is self-contained and can be trained end-to-end. In this paper, we discuss practical training and decoding issues as well as the method to speed up the training in the context of speech recognition. We performed experiments on the TIMIT dataset. We achieved 17.3% phone error rate (PER) from the first-pass decoding — the best reported result using CRFs, despite the fact that we only used a zeroth-order CRF and without using any language model.


title: Ultra-Thin UHF Broadband Antenna on a Non-Uniform Aperiodic (NUA) MetaSurface

abstract: i»?A new concept to design ultrathin directional broadband antennas using a nonuniform aperiodic metasurface is introduced. The study and design of the NUA metasurface show that, by employing a decreasing taper for both the metasurface patch and their interelement spacing, broad impedance and pattern bandwidths can be attained. Experimental results show that, with a total thickness of 0.04 of the free space wavelength (corresponding to the lowest frequency of operation), an octave bandwidth can be attained, which is significantly larger compared with existing designs on uniform mushroom electromagnetic band-gap structures.


title: Modeling and Analysis of Synchronous Reluctance Machines With Circular Flux Barriers Through Conformal Mapping

abstract: Synchronous reluctance (SynRel) machines are gaining more and more importance in various fields of application thanks to their known merits like rugged construction, high efficiency, absence of field windings, and no or reduced need for permanent magnets. Out of the possible design variants, in this paper, SynRel motors with uniform mechanical air gap and circularly shaped flux barriers are considered and a conformal-mapping approach to their analytical modeling and simulation is proposed. A suitable conformal transformation is introduced to compute the reluctance of each rotor circularly shaped flux barrier and the result is then used to analytically determine the air-gap flux density distribution and the electromagnetic torque of the machine in arbitrary operating conditions. The accuracy of the methodology proposed is assessed against finite element analysis.


title: A failure mode avoidance approach to reliability

abstract: Failure modes are an inevitable consequence of the Product Development (PD) process. They are crucial in their impact on product design since they affect product quality and costs because of the requirement for counter measures, thus potentially imposing high risks on any organization engaged in pro duct development in terms of customer dissatisfaction, financial losses, potential safety litigation, loss of brand reputation etc. Failure Mode Avoidance is a pragmatic methodology to deal with failure modes through counter measures development in the early (that is, information based) phases of the PD process prior to any hardware existing, rather than waiting until the failure modes are naturally observed in the material state later in the process during the production preparation and launch phases, when there is typically an abundance of hardware available to expose the failure modes. This paper focuses to develop an approach to detect and mitigate failure modes systematically in the information based phases of the PD process via a conceptual framework built around Failure Mode and Effects Analysis (FMEA). The FMEA is an important methodology to identify failure modes during product development so that failure modes can be avoided through the development of countermeasures and provides a logical structure (“left to right”) to make the transition from failure mode to counter measure. However the FMEA lacks structure from “top to bottom” since the number of failure modes requiring attention at the outset is usually unknown. This lack of structure often leads to excessively large FM EA documentation with much time devoted to brainstorming a set of potential failure modes. The vision of this work is that the creativity of the engineering team can be devoted to developing counter measures for the failure modes rather than identifying them [1]. The purpose of the paper is to discuss the beginnings of a structured approach to identify failure modes in a systematic way (using the concept of taxonomies), so that the size of the FMEA can be managed.


title: Gait assessment system based on novel gait variability measures

abstract: In this paper, a novel gait assessment system based on measures of gait variability reflected through the variability of shapes of gait cycles trajectories is proposed. The presented gait assessment system is based on SVM (support vector machine) classifier and on gait variability-based features calculated from the hip and knee joint angle trajectories recorded using wearable IMUs during walking trials. A system classifier was trained to distinguish healthy gait patterns from the pathological ones. The features were extracted by calculating the distances between the joint trajectories of the individual gait cycles using 4 different distance functions. As result, the system is able to provide a Gait Variability Index (GVI), which is a numeric value that can be used as an indicator of a degree to which a pathological gait pattern is close to a healthy gait pattern. The system and GVI were tested in three experiments, involving subjects suffering from gait disorders caused by different neurological diseases. The results demonstrated that the proposed gait assessment system would be suitable for supporting clinicians in the evaluation of gait performances during the gait rehabilitation procedures.


title: Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data

abstract: By profiling the transcriptomes of individual cells, single-cell RNA sequencing provides unparalleled resolution to study cellular heterogeneity. However, this comes at the cost of high technical noise, including cell-specific biases in capture efficiency and library generation. One strategy for removing these biases is to add a constant amount of spike-in RNA to each cell and to scale the observed expression values so that the coverage of spike-in transcripts is constant across cells. This approach has previously been criticized as its accuracy depends on the precise addition of spike-in RNA to each sample. Here, we perform mixture experiments using two different sets of spike-in RNA to quantify the variance in the amount of spike-in RNA added to each well in a plate-based protocol. We also obtain an upper bound on the variance due to differences in behavior between the two spike-in sets. We demonstrate that both factors are small contributors to the total technical variance and have only minor effects on downstream analyses, such as detection of highly variable genes and clustering. Our results suggest that scaling normalization using spike-in transcripts is reliable enough for routine use in single-cell RNA sequencing data analyses.


title: Dynamic user profiling approach for services discovery in mobile environments

abstract: User profiling is commonly used to support personalization as well as to enhance usability of software systems by automatically adapting to the needs and preferences of their users. This paper presents an approach to dynamically update mobile users' profiles so that they can be provided with appropriate services while moving from one location to another. We propose an XML generic user profile structure that is dynamically updatable as the user experiences new services and that can be used by different software systems and applications. The proposed profile learning mechanism makes use of clustering users with a subset of similar attributes to predict appropriate services to a new user, and segmenting services with a subset of similar usage to discover and recommend new services that might be of interests to a mobile user. We demonstrated the feasibility of our approach by presenting a case study in tourism where tourists are assisted during their travels by providing them with services and information of interest. The tourist preferences and her/his current location are incorporated in the proactive formulation of suggestions on the tourist mobile devices about nearby points of interests (e.g. museums, restaurants...).


title: Access Control in the Era of Big Data: State of the Art and Research Directions

abstract: Data security and privacy issues are magnified by the volume, the variety, and the velocity of Big Data and by the lack, up to now, of a standard data model and related data manipulation language. In this paper, we focus on one of the key data security services, that is, access control, by highlighting the differences with traditional data management systems and describing a set of requirements that any access control solution for Big Data platforms may fulfill. We then describe the state of the art and discuss open research issues.


title: Inter-surface mapping

abstract: We consider the problem of creating a map between two arbitrary triangle meshes. Whereas previous approaches compose parametrizations over a simpler intermediate domain, we directly create and optimize a continuous map between the meshes. Map distortion is measured with a new symmetric metric, and is minimized during interleaved coarse-to-fine refinement of both meshes. By explicitly favoring low inter-surface distortion, we obtain maps that naturally align corresponding shape elements. Typically, the user need only specify a handful of feature correspondences for initial registration, and even these constraints can be removed during optimization. Our method robustly satisfies hard constraints if desired. Inter-surface mapping is shown using geometric and attribute morphs. Our general framework can also be applied to parametrize surfaces onto simplicial domains, such as coarse meshes (for semi-regular remeshing), and octahedron and toroidal domains (for geometry image remeshing). In these settings, we obtain better parametrizations than with previous specialized techniques, thanks to our fine-grain optimization.


title: Model-Based Simulation at Runtime for Self-Adaptive Systems

abstract: Modern software systems are subject to uncertainties, such as dynamics in the availability of resources or changes of system goals. Self-adaptation enables a system to reason about runtime models to adapt itself and realises its goals under uncertainties. Our focus is on providing guarantees for adaption goals. A prominent approach to provide such guarantees is automated verification of a stochastic model that encodes up-to-date knowledge of the system and relevant qualities. The verification results allow selecting an adaption option that satisfies the goals. There are two issues with this state of the art approach: i) changing goals at runtime (a challenging type of uncertainty) is difficult, and ii) exhaustive verification suffers from the state space explosion problem. In this paper, we propose a novel modular approach for decision making in self-adaptive systems that combines distinct models for each relevant quality with runtime simulation of the models. Distinct models support on the fly changes of goals. Simulation enables efficient decision making to select an adaptation option that satisfies the system goals. The tradeoff is that simulation results can only provide guarantees with a certain level of accuracy. We demonstrate the benefits and tradeoffs of the approach for a service-based telecare system.


Association of imperforate anus and congenital diaphragmatic hernia in one of a twins who conceived by Assisted Reproductive Technology (ART)


title: Viscosity minima in binary mixtures of ionic liquids + molecular solvents

abstract: The viscosity (η) of four binary mixtures (ionic liquids plus molecular solvents, ILs+MSs) was measured in the 283.15 < T/K < 363.15 temperature range. Different IL/MS combinations were selected in such a way that the corresponding η(T) functions exhibit crossover temperatures at which both pure components present identical viscosity values. Consequently, most of the obtained mixture isotherms, η(x), exhibit clear viscosity minima in the studied T-x range. The results are interpreted using auxiliary molecular dynamics (MD) simulation data in order to correlate the observed η(T,x) trends with the interactions in each mixture, including the balance between electrostatic forces and hydrogen bonding.


title: Advertising influences on young children's food choices and parental influence.

abstract: Objective: To evaluate whether advertising for food influences choices made by children, the strength of these influences, and whether they might be easily undone by parental influences.


Low Power High Speed CMOS current Comparator in 0 . 18 μ m and 0 . 13 μ m Technology


title: Validity of Antonovsky's sense of coherence scale: a systematic review.

abstract: STUDY OBJECTIVE ::: The aim of this paper is to systematically review and analyse the validity and reliability of Antonovsky's life orientation questionnaire/sense of coherence scale (SOC). ::: ::: ::: DESIGN ::: The study is descriptive and analytical with a systematic integration of the contemporary knowledge base on the salutogenic research published 1992-2003. The review includes 458 scientific publications and 13 doctoral theses. ::: ::: ::: SETTING ::: Worldwide, based on postgraduate scientific publications in eight authorized databases, doctoral theses, and available books. ::: ::: ::: MAIN RESULTS ::: The SOC questionnaire has been used in at least 33 languages in 32 countries with at least 15 different versions of the questionnaire. In 124 studies using SOC-29 the Cronbach's alpha ranges from 0.70 to 0.95. The alpha values in 127 studies using SOC-13 range from 0.70 to 0.92, and in 60 studies using a modified SOC scale range from 0.35 to 0.91. Test-retest correlation show stability and range from 0.69 to 0.78 (1 year), 0.64 (3 years), 0.42 to 0.45 (4 years), 0.59 to 0.67 (5 years) to 0.54 (10 years). The means of SOC-29 range 100.50 (SD 28.50) to 164.50 (SD 17.10) points and SOC-13 from 35.39 (SD 0.10) to 77.60 (SD 13.80) points. After 10 years SOC seems to be comparatively stable, but not as stable as Antonovsky initially assumed. SOC tends to increase with age. The factorial structure of SOC seems rather to be multidimensional than unidimensional. SOC predicts a positive outcome in a long term perspective, although there are divergent findings reported. The SOC scale seems to be a reliable, valid, and cross culturally applicable instrument measuring how people manage stressful situations and stay well.


title: Evaluation of Cost Estimation Metrics: Towards a Unified Terminology

abstract: Cost overrun of software projects is major cause of their failures. In order to facilitate accurate software cost estimation, there are several metrics, tools and datasets. In this paper, we evaluate and compare different metrics and datasets in terms of similarities and differences of involved software attributes. These metrics forecast project cost estimations based on different software attributes. Some of these metrics are public and standard while others are only employed in a particular metric tool/dataset. Sixteen public cost estimation datasets are collected and analyzed. Different perspectives are used to compare and classify those datasets. Tools for feature selection and classification are used to find the most important attributes in cost estimation datasets toward the goal of effort prediction. In order to have better estimation it is needed to correlate cost estimation from different resources, which requires a unified standard for software cost estimation metric tools and datasets. It is pertinent that a common cost estimation model may not work for each project due to diverse project size, application areas etc. We suggest having a standardized terminology of project attributes used for cost estimation. This would improve cost estimation as multiple metrics could be applied on a project without much additional effort.


title: A Matlab / Simulink Based Tool for Power Electronic Circuits

abstract: Transient simulation of power electronic circuits is of considerable interest to the designer. The switching nature of the devices used permits development of specialized algorithms which allow a considerable reduction in simulation time compared to general purpose simulation algorithms. This paper describes a method used to simulate a power electronic circuits using the SIMULINK toolbox within MATLAB software. Theoretical results are presented provides the basis of transient analysis of a power electronic circuits.


title: Simple accurate expressions for planar spiral inductances

abstract: We present several new simple and accurate expressions for the DC inductance of square, hexagonal, octagonal, and circular spiral inductors. We evaluate the accuracy of our expressions, as well as several previously published inductance expressions, in two ways: by comparison with three-dimensional field solver predictions and by comparison with our own measurements, and also previously published measurements. Our simple expression matches the field solver inductance values typically within around 3%, about an order of magnitude better than the previously published expressions, which have typical errors ground 20% (or more). Comparison with measured values gives similar results: our expressions (and, indeed, the field solver results) match within around 5%, compared to errors of around 20% for the previously published expressions. (We believe most of the additional errors in the comparison to published measured values is due to the variety of experimental conditions under which the inductance was measured.) Our simple expressions are accurate enough for design and optimization of inductors or of circuits incorporating inductors. Indeed, since inductor tolerance is typically on the order of several percent, "more accurate" expressions are not really needed in practice.


Enhancement of solubilization and bioavailability of poorly soluble drugs by physical and chemical modifications : A recent review


title: Choosing Multiple Parameters for Support Vector Machines

abstract: The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.


title: Segmentation-Aware Convolutional Networks Using Local Attention Masks

abstract: We introduce an approach to integrate segmentation information within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to smooth information across regions and increases their spatial precision. To obtain segmentation information, we set up a CNN to provide an embedding space where region co-membership can be estimated based on Euclidean distance. We use these embeddings to compute a local attention mask relative to every neuron position. We incorporate such masks in CNNs and replace the convolution operation with a "segmentation-aware" variant that allows a neuron to selectively attend to inputs coming from its own region. We call the resulting network a segmentation-aware CNN because it adapts its filters at each image point according to local segmentation cues. We demonstrate the merit of our method on two widely different dense prediction tasks, that involve classification (semantic segmentation) and regression (optical flow). Our results show that in semantic segmentation we can match the performance of DenseCRFs while being faster and simpler, and in optical flow we obtain clearly sharper responses than networks that do not use local attention masks. In both cases, segmentation-aware convolution yields systematic improvements over strong baselines. Source code for this work is available online at this http URL.


title: Electrochemical determinations of 6-mercaptopurine on the surface of a carbon nanotube-paste electrode modified with a cobalt salophen complex

abstract: A mixture of multi-walled carbon nanotube/graphite paste electrode modified with a salophen complex of cobalt was prepared and was applied for the study of the electrochemical behavior of 6-mercaptopurine (MP) using cyclic and differential pulse voltammetry (DPV). An excellent electrocatalytic activity toward the oxidation of MP was achieved, which led to a considerable lowering in the anodic overpotential and remarkable increase in the response sensitivity in comparison with unmodified electrode. Utilizing DPV method, a linear dynamic range of 1–100 μM with detection limit of 0.1 μM was obtained in phosphate buffer of pH 3.0. The electrochemical detection system was very stable, and the reproducibility of the electrode response, based on the six measurements during 1 month, was less than 3.0% for the slope of the calibration curves of MP. The electrochemical method as a simple, sensitive, and selective method was developed for the determination of MP in pharmaceutical dosage form and human plasma without any treatments.


title: Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding

abstract: Modeling textual or visual information with vector representations trained from large language or visual datasets has been successfully explored in recent years. However, tasks such as visual question answering require combining these vector representations with each other. Approaches to multimodal pooling include element-wise product or sum, as well as concatenation of the visual and textual representations. We hypothesize that these methods are not as expressive as an outer product of the visual and textual vectors. As the outer product is typically infeasible due to its high dimensionality, we instead propose utilizing Multimodal Compact Bilinear pooling (MCB) to efficiently and expressively combine multimodal features. We extensively evaluate MCB on the visual question answering and grounding tasks. We consistently show the benefit of MCB over ablations without MCB. For visual question answering, we present an architecture which uses MCB twice, once for predicting attention over spatial features and again to combine the attended representation with the question representation. This model outperforms the state-of-the-art on the Visual7W dataset and the VQA challenge.


title: Reliable Probability Estimates Based on Support Vector Machines for Large Multiclass Datasets

abstract: Venn Predictors (VPs) are machine learning algorithms that can provide well calibrated multiprobability outputs for their predictions. The only drawback of Venn Predictors is their computational inefficiency, especially in the case of large datasets. In this work, we propose an Inductive Venn Predictor (IVP) which overcomes the computational inefficiency problem of the original Venn Prediction framework. Each VP is defined by a taxonomy which separates the data into categories. We develop an IVP with a taxonomy derived from a multiclass Support Vector Machine (SVM), and we compare our method with other probabilistic methods for SVMs, namely Platt’s method, SVM Binning, and SVM with Isotonic Regression. We show that these methods do not always provide well calibrated outputs, while our IVP will always guarantee this property under the i.i.d. assumption.


title: Low Latency Datacenter Networking: A Short Survey

abstract: Datacenters are the cornerstone of the big data infrastructure supporting numerous online services. The demand for interactivity, which significantly impacts user experience and provider revenue, is translated into stringent timing requirements for flows in datacenter networks. Thus low latency networking is becoming a major concern of both industry and academia. ::: We provide a short survey of recent progress made by the networking community for low latency datacenter networks. We propose a taxonomy to categorize existing work based on four main techniques, reducing queue length, accelerating retransmissions, prioritizing mice flows, and exploiting multi-path. Then we review select papers, highlight the principal ideas, and discuss their pros and cons. We also present our perspectives of the research challenges and opportunities, hoping to aspire more future work in this space.


title: Frame Interpolation and Bidirectional Prediction of Video using Compactly-Encoded Optical Flow Fields and Label Fields

abstract: We consider the problems of motion-compensated frame interpolation (MCFI) and bidirectional prediction in a video coding environment. These applications generally require good motion estimates at the decoder. We use a multiscale optical-flow-based motion estimator that provides smooth, natural motion fields under bit-rate constraints. These motion estimates scale well with change in temporal resolution and provide considerable flexibility in the design and operation of coders and decoders. In the MCFI application, this estimator provides excellent interpolated frames that are superior to those of conventional motion estimators, both visually and in terms of peak signal-to-noise ratio (PSNR). We also consider the effect of occlusions in the bidirectional prediction application and introduce a dense label field that complements our motion estimator. This label field enables us to adaptively weight the forward and backward predictions and gives us substantial visual and PSNR improvements in the covered/uncovered regions of the sequence.


title: Is herniosis the single etiology of Saint’s triad?

abstract: Background ::: Sixty years ago, Saint’s triad (hiatus hernia, diverticulosis of the colon, and gallbladder disease) was first described in three patients. Since then, the association of these conditions has been questioned. We tested the hypothesis that these conditions are associated, and, based on recent research, propose a common etiology.


title: Use of the JVM at twitter: a bird's eye view

abstract: Specialties:15+ years of virtual machine implementation experience with special focus on memory management / garbage collection. Close to 20 years of C/C++ experience. 15+ years of Java experience. Expert in concurrent/parallel programming.


title: Challenges and success factors for large-scale agile transformations

abstract: Systematic review on large-scale agile transformations analyzing 52 papers.We identify 35 reported challenges in 9 categories, and 29 success factors in 11 categories.Few academic studies (6), mostly experience reports (46) identified.Challenges e.g. difficulty of implementing, integrating other functions, and change resistance.Success factors e.g. choosing and tailoring, management support, and mindset. Agile methods have become an appealing alternative for companies striving to improve their performance, but the methods were originally designed for small and individual teams. This creates unique challenges when introducing agile at scale, when development teams must synchronize their activities, and there might be a need to interface with other organizational units. In this paper we present a systematic literature review on how agile methods and lean software development has been adopted at scale, focusing on reported challenges and success factors in the transformation. We conducted a systematic literature review of industrial large-scale agile transformations. Our keyword search found 1875 papers. We included 52 publications describing 42 industrial cases presenting the process of taking large-scale agile development into use. Almost 90% of the included papers were experience reports, indicating a lack of sound academic research on the topic. We identified 35 reported challenges grouped into nine categories, and 29 success factors, grouped into eleven categories. The most salient success factor categories were management support, choosing and customizing the agile model, training and coaching, and mindset and alignment.


title: Special software for Automated Measuring Complex based on Rohde & Schwarz equipment

abstract: In the article the special software for automated measuring complex intended for research, adjustment and parametric synthesis of miniature high-selective band-pass microwave filters is offered. The architecture and algorithm of the automated measuring complex including the control computer and a set of control and measuring equipment (probe station, measuring signal generator, vector spectrum analyzer, vector network analyzer and oscilloscope) are briefly considered. The results of measurements of experimental samples of microwave filters obtained using the developed software of the measuring complex are presented.


title: Search Frictions and the Design of Online Marketplaces

abstract: Online marketplaces increasingly act as intermediaries in labor, housing, dating, and other markets where traders match with each other. These marketplaces use novel data generated by users’ activities on the website to design algorithms and products that inuence the search and matching process. I use internal data from Airbnb, a


title: Particle Swarm Optimization of the Multioscillatory LQR for a Three-Phase Four-Wire Voltage-Source Inverter With an $LC$ Output Filter

abstract: This paper presents evolutionary optimization of the linear quadratic regulator (LQR) for a voltage-source inverter with an LC output filter. The procedure involves particle-swarm-based search for the best weighting factors in the quadratic cost function. It is common practice that the weights in the cost function are set using the guess-and-check method. However, it becomes quite challenging, and usually very time-consuming, if there are many auxiliary states added to the system. In order to immunize the system against unbalanced and nonlinear loads, oscillatory terms are incorporated into the control scheme, and this significantly increases the number of weights to be guessed. All controller gains are determined altogether in one LQR procedure call, and the originality reported here refers to evolutionary tuning of the weighting matrix. There is only one penalty factor to be set by the designer during the controller synthesis procedure. This coefficient enables shaping the dynamics of the closed-loop system by penalizing the dynamics of control signals instead of selecting individual weighting factors for augmented state vector components. Simulational tuning and experimental verification (the physical converter at the level of 21 kVA) are included.


title: Online Active Learning Methods for Fast Label-Efficient Spam Filtering

abstract: Active learning methods seek to reduce the number of labeled examples needed to train an effective classifier, and have natural appeal in spam filtering applications where trustworthy labels for messages may be costly to acquire. Past investigations of active learning in spam filtering have focused on the pool-based scenario, where there is assumed to be a large, unlabeled data set and the goal is to iteratively identify the best subset of examples for which to request labels. However, even with optimizations this is a costly approach. We investigate an online active learning scenario where the filter is exposed to a stream of messages which must be classified one at a time. The filter may only request a label for a given message immediately after it has been classified. The goal is to achieve strong online classification performance with few label requests. This is a novel scenario for low-cost active spam filtering, fitting for application in large-scale systems. We draw from the label efficient machine learning literature to investigate several approaches to selective sampling in this scenario using linear classifiers. We show that online active learning can dramatically reduce labeling and training costs with negligible additional overhead while maintaining high levels of classification performance.


title: Multi-Scale Context Aggregation by Dilated Convolutions

abstract: State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction and image classification are structurally different. In this work, we develop a new convolutional network module that is specifically designed for dense prediction. The presented module uses dilated convolutions to systematically aggregate multi-scale contextual information without losing resolution. The architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage. We show that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. In addition, we examine the adaptation of image classification networks to dense prediction and show that simplifying the adapted network can increase accuracy.


title: Correlating Structural Changes and Gas Evolution during the Thermal Decomposition of Charged LixNi0.8Co0.15Al0.05O2 Cathode Materials

abstract: In this work, we present results from the application of a new in situ technique that combines time-resolved synchrotron X-ray diffraction and mass spectroscopy. We exploit this approach to provide direct correlation between structural changes and the evolution of gas that occurs during the thermal decomposition of (over)charged cathode materials used in lithium-ion batteries. Results from charged LixNi0.8Co0.15Al0.05O2 cathode materials indicate that the evolution of both O2 and CO2 gases are strongly related to phase transitions that occur during thermal decomposition, specifically from the layered structure (space group R3m) to the disordered spinel structure (Fd3m), and finally to the rock-salt structure (Fm3m). The state of charge also significantly affects both the structural changes and the evolution of oxygen as the temperature increases: the more extensive the charge, the lower the temperature of the phase transitions and the larger the oxygen release. Ex situ X-ray absorption spectroscopy (XA...


title: The phylogenetic position of the budding bacteria Blastobacter aggregatus and Gemmobacter aquatilis gen., nov. sp. nov.

abstract: Blastobacter aggregatus and a Blastobacter-like isolate (IFAM 1031) were analysed by the 16S ribosomal RNA cataloguing approach in order to determine their phylogenetic position. Both phenotypical similar organisms are members of the alpha-subdivision of purple phototrophic bacteria and their non-phototrophic relatives but they are not closely related: B. aggregatus clusters with Agrobacterium tumefaciens and Rhizobium species; the unnamed strain displays a moderate relationship to members of Rhodobacter and Paracoccus denitrificans, with which is shares the character of a nicked 23S rRNA. Although the budding isolate IFAM 1031 resembles members of Blastobacter phenotypically, in the broad DNA G+C content and in the fatty acid pattern, a unique set of characters was found which allows description of the isolate as the typus of a new genus for which Gemmobacter gen. nov. is proposed, with G. aquatilis sp. nov. as the type species. G. aquatilis harbors at least two plasmids of different size and unknown function.


Customer Relationship Management : Related Theories , Challenges and Application in Banking Sector


title: The Past, Present, and Future of Cognitive Architectures

abstract: Cognitive architectures are theories of cognition that try to capture the essential representations and mechanisms that underlie cognition. Research in cognitive architectures has gradually moved from a focus on the functional capabilities of architectures to the ability to model the details of human behavior, and, more recently, brain activity. Although there are many different architectures, they share many identical or similar mechanisms, permitting possible future convergence. In judging the quality of a particular cognitive model, it is pertinent to not just judge its fit to the experimental data but also its simplicity and ability to make predictions.


title: Nearest-Neighbour Classifiers in Natural Scene Analysis

abstract: It is now well-established that k nearest-neighbour classi"ers o!er a quick and reliable method of data classi"cation. In this paper we extend the basic de"nition of the standard k nearest-neighbour algorithm to include the ability to resolve con#icts when the highest number of nearest neighbours are found for more than one training class (model-1). We also propose model-2 of nearest-neighbour algorithm that is based on "nding the nearest average distance rather than nearest maximum number of neighbours. These new models are explored using image understanding data. The models are evaluated on pattern recognition accuracy for correctly recognising image texture data of "ve natural classes: grass, trees, sky, river re#ecting sky and river re#ecting trees. On noise contaminated test data, the new nearest neighbour models show very promising results for further studies. We evaluate their performance with increasing values of neighbours (k) and discuss their future in scene analysis research. Crown Copyright 2001 Published by Elsevier Science Ltd. on behalf of Pattern Recognition Society. All rights reserved.


title: Learner and Instructional Factors Influencing Learning Outcomes within a Blended Learning Environment

abstract: Among the many studies focusing on the effect of learner and instructional variables on learning outcomes, few studies have investigated the effect of these variables and their mediating mechanisms influencing students’ learning within a blended learning environment. This study examined the influence of instructional and learner variables on learning outcomes for a blended instruction course offered for undergraduate students. Data analysis indicated that age, prior experiences with distance learning opportunities, preference in delivery format, and average study time are those learner antecedents differentiating learning outcomes among groups of college students. From a regression analysis, the influence of learner, instructional, and motivational variables on learning outcomes found to be consolidated around one variable in learning application.


title: DesignScape: Design with Interactive Layout Suggestions

abstract: Creating graphic designs can be challenging for novice users. This paper presents DesignScape, a system which aids the design process by making interactive layout suggestions, i.e., changes in the position, scale, and alignment of elements. The system uses two distinct but complementary types of suggestions: refinement suggestions, which improve the current layout, and brainstorming suggestions, which change the style. We investigate two interfaces for interacting with suggestions. First, we develop a suggestive interface, where suggestions are previewed and can be accepted. Second, we develop an adaptive interface where elements move automatically to improve the layout. We compare both interfaces with a baseline without suggestions, and show that for novice designers, both interfaces produce significantly better layouts, as evaluated by other novices.


title: Chemical Structures and Bioactivities of Sulfated Polysaccharides from Marine Algae

abstract: Sulfated polysaccharides and their lower molecular weight oligosaccharide derivatives from marine macroalgae have been shown to possess a variety of biological activities. The present paper will review the recent progress in research on the structural chemistry and the bioactivities of these marine algal biomaterials. In particular, it will provide an update on the structural chemistry of the major sulfated polysaccharides synthesized by seaweeds including the galactans (e.g., agarans and carrageenans), ulvans, and fucans. It will then review the recent findings on the anticoagulant/antithrombotic, antiviral, immuno-inflammatory, antilipidemic and antioxidant activities of sulfated polysaccharides and their potential for therapeutic application.


title: Deep LSTM for Large Vocabulary Continuous Speech Recognition

abstract: Recurrent neural networks (RNNs), especially long short-term memory (LSTM) RNNs, are effective network for sequential task like speech recognition. Deeper LSTM models perform well on large vocabulary continuous speech recognition, because of their impressive learning ability. However, it is more difficult to train a deeper network. We introduce a training framework with layer-wise training and exponential moving average methods for deeper LSTM models. It is a competitive framework that LSTM models of more than 7 layers are successfully trained on Shenma voice search data in Mandarin and they outperform the deep LSTM models trained by conventional approach. Moreover, in order for online streaming speech recognition applications, the shallow model with low real time factor is distilled from the very deep model. The recognition accuracy have little loss in the distillation process. Therefore, the model trained with the proposed training framework reduces relative 14\% character error rate, compared to original model which has the similar real-time capability. Furthermore, the novel transfer learning strategy with segmental Minimum Bayes-Risk is also introduced in the framework. The strategy makes it possible that training with only a small part of dataset could outperform full dataset training from the beginning.


title: Temporal Localization of Fine-Grained Actions in Videos by Domain Transfer from Web Images

abstract: We address the problem of fine-grained action localization from temporally untrimmed web videos. We assume that only weak video-level annotations are available for training. The goal is to use these weak labels to identify temporal segments corresponding to the actions, and learn models that generalize to unconstrained web videos. We find that web images queried by action names serve as well-localized highlights for many actions, but are noisily labeled. To solve this problem, we propose a simple yet effective method that takes weak video labels and noisy image labels as input, and generates localized action frames as output. This is achieved by cross-domain transfer between video frames and web images, using pre-trained deep convolutional neural networks. We then use the localized action frames to train action recognition models with long short-term memory networks. We collect a fine-grained sports action data set FGA-240 of more than 130,000 YouTube videos. It has 240 fine-grained actions under 85 sports activities. Convincing results are shown on the FGA-240 data set, as well as the THUMOS 2014 localization data set with untrimmed training videos.


title: Enhanced barrier functions and anti-inflammatory effect of cultured coconut extract on human skin

abstract: Abstract Natural plant oils have been used as a translational alternative to modern medicine. Particularly, virgin coconut oil (VCO) has gained popularity because of its potential benefits in pharmaceutical, nutritional, and cosmetic applications. Cultured coconut extract (CCE) is an alternative end product of VCO, which undergoes a further bacterial fermentation process. This study aimed to investigate the effects of CCE on human skin. We analyzed the expression of skin barrier molecules and collagens after applying CCE on human explanted skin. To evaluate the anti-inflammatory properties of CCE, the expression of inflammatory markers was analyzed after ultraviolet B (UVB) irradiation. The CCE-treated group showed increased expression of cornified cell envelope components, which contribute to protective barrier functions of the stratum corneum. Further, the expression of inflammatory markers was lower in the CCE-treated group after exposure to UVB radiation. These results suggest an anti-inflammatory effect of CCE against UVB irradiation-induced inflammation. Additionally, the CCE-treated group showed increased collagen and hyaluronan synthase-3 expression. In our study, CCE showed a barrier-enhancing effect and anti-inflammatory properties against ex vivo UVB irradiation-induced inflammation. The promising effect of CCE may be attributed to its high levels of polyphenols and fatty acid components.


title: A 0.6–6.2 GHz wideband LNA using resistive feedback and gate inductive peaking techniques for multiple standards application

abstract: This paper presents a 0.6-6.2 GHz wideband CMOS low noise amplifier (LNA) for multi-band application. The LNA design is based on a common source (CS) cascade amplifier with resistive feedback, gate inductive peaking, and source degeneration inductive topologies to realize the matching networks for broadband operation. The authors also derived the formula of the input and output impedances those help to select proper values of biasing and matching elements. The LNA achieves a peak gain S21 of 11.61 dB, a noise figure (NF) of 5.28 to 5.82 dB at frequency from 0.6 to 6.2 GHz, and an input third-intercept point (IIP3) of -2.55 dBm at 2.5 GHz under DC power of 8.45 mW from a 1.5 V supply voltage. The figure-of-merit (FOM) for wideband LNA is up to 0.51 at 2.5 GHz. The LNA is fabricated in tsmc™ 0.18-μm RF CMOS technology with a chip area of 0.8 mm2.


title: Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks

abstract: We provide theoretical investigation of curriculum learning in the context of stochastic gradient descent when optimizing the convex linear regression loss. We prove that the rate of convergence of an ideal curriculum learning method is monotonically increasing with the difficulty of the examples. Moreover, among all equally difficult points, convergence is faster when using points which incur higher loss with respect to the current hypothesis. We then analyze curriculum learning in the context of training a CNN. We describe a method which infers the curriculum by way of transfer learning from another network, pre-trained on a different task. While this approach can only approximate the ideal curriculum, we observe empirically similar behavior to the one predicted by the theory, namely, a significant boost in convergence speed at the beginning of training. When the task is made more difficult, improvement in generalization performance is also observed. Finally, curriculum learning exhibits robustness against unfavorable conditions such as excessive regularization.


title: Nanoimprinted Polymer Solar Cell

abstract: Among the various organic photovoltaic devices, the conjugated polymer/fullerene approach has drawn the most research interest. The performance of these types of solar cells is greatly determined by the nanoscale morphology of the two components (donor/acceptor) and the molecular orientation/crystallinity in the photoactive layer. A vertically bicontinuous and interdigitized heterojunction between donor and acceptor has been regarded as one of the ideal structures to enable both efficient charge separation and transport. Synergistic control of polymer orientation in the nanostructured heterojunction is also critical to improve the performance of polymer solar cells. Nanoimprint lithography has emerged as a new approach to simultaneously control both the heterojunction morphology and polymer chains in organic photovoltaics. Currently, in the area of nanoimprinted polymer solar cells, much progress has been achieved in the fabrication of nanostructured morphology, control of molecular orientation/crystallin...


title: Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications

abstract: Sparse coding exhibits good performance in many computer vision applications. However, due to the overcomplete codebook and the independent coding process, the locality and the similarity among the instances to be encoded are lost. To preserve such locality and similarity information, we propose a Laplacian sparse coding (LSc) framework. By incorporating the similarity preserving term into the objective of sparse coding, our proposed Laplacian sparse coding can alleviate the instability of sparse codes. Furthermore, we propose a Hypergraph Laplacian sparse coding (HLSc), which extends our Laplacian sparse coding to the case where the similarity among the instances defined by a hypergraph. Specifically, this HLSc captures the similarity among the instances within the same hyperedge simultaneously, and also makes the sparse codes of them be similar to each other. Both Laplacian sparse coding and Hypergraph Laplacian sparse coding enhance the robustness of sparse coding. We apply the Laplacian sparse coding to feature quantization in Bag-of-Words image representation, and it outperforms sparse coding and achieves good performance in solving the image classification problem. The Hypergraph Laplacian sparse coding is also successfully used to solve the semi-auto image tagging problem. The good performance of these applications demonstrates the effectiveness of our proposed formulations in locality and similarity preservation.