title: How Does Social Capital Affect Retweets

abstract: In Twitter, a retweet happens when a user copies another user's tweet and indicates that the content is originally from someone else, and is a common way to share information. This paper explores user’s perceptions of the way social capital influences retweet practices. We discuss how Twitter users perceive values and how they create benefits for the social network and themselves from three points of view: referrals, information access and timing. We do so through a qualitative analysis of questionnaires answered by Twitter users and four quantitative case studies of retweets. We analyze users' motivations, map the spread of retweets and their content, and discuss how referrals, information access and timing can play important roles in what and how users will retweet. Our findings lead to the conclusion that users seek individual benefits but the competition for social capital allows for collective benefits to emerge.


title: Scene Flow Estimation: A Survey

abstract: This paper is the first to review the scene flow estimation field, which analyzes and compares methods, technical challenges, evaluation methodologies and performance of scene flow estimation. Existing algorithms are categorized in terms of scene representation, data source, and calculation scheme, and the pros and cons in each category are compared briefly. The datasets and evaluation protocols are enumerated, and the performance of the most representative methods is presented. A future vision is illustrated with few questions arisen for discussion. This survey presents a general introduction and analysis of scene flow estimation.


title: Dielectric Rod Antenna Array With Substrate Integrated Waveguide Planar Feed Network for Wideband Applications

abstract: Dielectric rod antenna (DRA) elements have numerous advantages over conventional waveguide and horn antenna elements. They have low insertion loss, broadband input match, high mutual decoupling efficiency, and low-cost manufacturing. Additionally, the radiation pattern of the dielectric rod antenna is almost frequency-independent. However, its feeding is generally a challenge. This paper demonstrates a novel ultrawideband (UWB) eight-element DRA fed by a wideband substrate integrated waveguide (SIW) structure. The eight-way feed net work has less than 1.1 dB insertion loss and only ±0.9 dB and ±4° amplitude and phase imbalance, respectively. The developed low-cost and compact dual-layer DRA design covers over 40% bandwidth at X-band. It has a good input match, almost constant radiation pattern with a gain of 14.5 ± 2 dB over the frequency band, low dispersion, low squint performance, and almost flat group delay, which are essential requirements for many UWB ap plications. The developed array antenna is compact and occupies only 10 × 9 cm2.


Discovery, Analysis, and Presentation of Strong Rules


title: The use of indicators to assess the sustainability of farms converting to organic production.

abstract: A key feature of farm sustainability is the need to protect and make optimum use of limited natural resources within an economically efficient and socially acceptable agricultural system. Organic farming is often presented as a sustainable solution for agriculture. It is a challenge to measure sustainability in a practical way. This paper is based on a project which aimed to discover and test a suitable method for assessing changes in sustainability at the farm level. It studied the changes in sustainability on three conventional farms that had converted to organic production systems in the UK. A set of indicators for evaluating farm sustainability was devised, based on the French ‘Method IDEA’ (Indicators of Sustainability in Agriculture), which works with the three dimensions of sustainability: environmental, social and economic. The main conclusions from this project were that conversion to organic production increased the social sustainability of the all the farms studied. The environmental dimension has tended to either increase, or stay at a high level. In contrast to this, the economic dimension has decreased or increased slightly from a low level. The technique has proved a useful way of obtaining a broad ranging snapshot of a farm’s sustainability. The project was limited by the small sample of three farms, and applying the technique to a larger sample of farms would certainly strengthen the validity of the conclusions. INTRODUCTION Sustainability in relation to agriculture is becoming increasingly important as the linkages between the economy, society and the environment are more widely recognized (Van der Werf and Petit, 2002). Sustainability is a term with diverse interpretation; however, it is commonly applied to ecological or environmental, social and economic aspects of farming systems. Sustainable agriculture is essentially concerned with the ability of agroecosystems to remain productive in the longer term. In order to judge whether an agriculture system or farm is sustainable or not, an easy to use tool or method is required that can provide information understandable to practitioners such as farmers and to other stakeholders such as policy makers. The aims of this MSc project were to discover and to test a suitable method of assessing changes in sustainability. The method was applied to a group of farms that had recently converted to organic production in the UK during 1996-2001, in order to look for changes in sustainability that had occurred due to conversion. MATERIALS AND METHODS After examining a number of techniques used to measure sustainability, it was decided to adapt and test the Method IDEA (Indicators of Sustainability of Agriculture) (Vilain, Indicators to Assess Sustainability of Farms Converting to Organic Production 32 2000). This is a quantitative evaluation system devised for assessing sustainability at the farm level in France. In the system, the three main dimensions of sustainability are; agroecological, socio-territorial and economic. Each of these is subdivided into three or four main components (Table 1) and these are further sub divided into indicators, which are based on measurable data or values. During this project 37 different measures were made to score each farm on a wide range of farming practices, land organization, product quality, social interactions on and off the farm, economic performance and efficiency. Table 1. Sustainability dimensions and components Dimensions Components Agro-ecological Diversity Land organization Agricultural practice Socio-territorial Quality of the products and territory Employment and services Ethic and human development Economic Viability Independence Ownership Efficiency Three different farms were chosen from a DEFRA-funded ‘Conversion to organic vegetable production’ project. This project has studied the agronomic and economic performance of a group of ten farms, which have converted to organic production. The farms chosen represent different farm types from the group; mixed, arable and intensive vegetable farms. For each of the three farms, data were assembled for the year prior to conversion, and then again 3 to 5 years later, to determine the value of the indicators for each of these years. The data were obtained from farm records and accounts, and were supplemented by a questionnaire and interview with each farmer and by observations undertaken when visiting the farm. This enabled a snapshot of the sustainability of the farming system, preand post-conversion to organic production. Table 2. Descriptions of the farms FARM I FARM II FARM III Location Warwickshire Lincolnshire Bedfordshire Type of business Family Company Family Farm size (ha) 36 1956 24 Farm type Mixed General cropping Horticulture Type of conversion Single step (1999) Phased over 7 yr Phased over 6 yr Data based from 1996-2001 1996-200


title: Non-Singular Assembly-mode Changing Motions for 3-RPR Parallel Manipulators

abstract: When moving from one arbitrary location at another, a parallel manipulator may change its assembly-mode without crossing a singularity. Because the non-singular change of assembly-mode cannot be simply detected, the actual assembly-mode during motion is difficult to track. This paper proposes a global explanatory approach to help better understand non-singular assembly-mode changing motions for 3-RPR planar parallel manipulators. The approach consists in fixing one of the actuated joints and analyzing the configuration-space as a surface in a 3-dimensional space. Such a global description makes it possible to display all possible non-singular assembly-mode changing trajectories.


Information Retrieval


title: Expert Systems: Design and Development

abstract: From the Publisher: ::: Presents a step-by-step methodology for designing expert systems. Each chapter on design methodology starts with a problem and leads the reader through the design of a system which solves that problem.


Successful therapy of chronic renal allograft failure by enhanced immunosuppression.


title: Should Algorithms for Random SAT and Max-SAT be Different?

abstract: We analyze to what extent the random SAT and Max-SAT problems differ in their properties. Our findings suggest that for random $k$-CNF with ratio in a certain range, Max-SAT can be solved by any SAT algorithm with subexponential slowdown, while for formulae with ratios greater than some constant, algorithms under the random walk framework require substantially different heuristics. In light of these results, we propose a novel probabilistic approach for random Max-SAT called ProMS. Experimental results illustrate that ProMS outperforms many state-of-the-art local search solvers on random Max-SAT benchmarks.


title: Image segmentation using fuzzy clustering: A survey

abstract: This paper presents a survey of latest image segmentation techniques using fuzzy clustering. Fuzzy C-Means (FCM) Clustering is the most wide spread clustering approach for image segmentation because of its robust characteristics for data classification. In this paper, four image segmentation algorithms using clustering, taken from the literature are reviewed. To address the drawbacks of conventional FCM, all these approaches have modified the objective function of conventional FCM and have incorporated spatial information in the objective function of the standard FCM. The techniques that have been reviewed in this survey are Segmentation for noisy medical images with spatial probability, Novel Fuzzy C-Means Clustering (NFCM), Fuzzy Local Information C-Means (FLICM) Clustering Algorithm and Improved Spatial Fuzzy C-Means Clustering (ISFCM) algorithm.


title: Language Models with Pre-Trained (GloVe) Word Embeddings

abstract: In this work we implement a training of a Language Model (LM), using Recurrent Neural Network (RNN) and GloVe word embeddings, introduced by Pennigton et al. in [1]. The implementation is following the general idea of training RNNs for LM tasks presented in [2], but is rather using Gated Recurrent Unit (GRU) [3] for a memory cell, and not the more commonly used LSTM [4].


title: Basic senior personas: a representative design tool covering the spectrum of European older adults

abstract: The persona method is a powerful approach to focus on needs and characteristics of target users, keeping complex user data,numbers and diagrams alive during the whole design cycle.However, the development of prosperous personas requires a considerable amount of time, effort and specific skills. This paper introduces the development of a set of 30 basic senior personas, covering a broad range of characteristics of European older adults, following a quantitative development approach. The aim of this tool is to support researchers and developers in extending empathy for their target users when developing ICT solutions for the benefit of older adults. The main innovation lies in the representativeness of the basic senior personas. The personas build on multifaceted quantitative data from a single source including micro-level information from roughly 12,500 older individuals living in different European countries. The resulting personas may be applied in their basic form but are extendable to specific contexts. Also, the suggested tool addresses the drawbacks of current existing personas describing older adults: being representative and cost-efficient. The basic senior personas, a filter tool, a manual and templates for "persona marketing" articles are available for free online under http://elderlypersonas.cure.at.


title: Transformer-Less Grid Feeding Current Source Inverter for Solar Photovoltaic System

abstract: High efficiency and operating life of grid feeding solar photovoltaic (PV) inverters are demanded. Due to reduced dc-link capacitor requirement, current source inverter (CSI) offers higher reliability than the voltage source based solar inverter. However, conventional three-phase pulse width modulated (PWM) current source based solar inverter injects high earth leakage current into the grid. In order to suppress this current, an isolation transformer can be used. Use of this transformer increases the cost and size, and decreases overall efficiency. In order to address the aforementioned limitations, a modified CSI is proposed in this paper. The proposed inverter suppresses the earth leakage current without using an isolation transformer, thereby increasing the efficiency and reducing cost as compared to conventional current source based solar inverters. A mathematical model of the system is derived based on which controller for the operation of the inverter is designed. The effectiveness of the scheme is verified through detailed simulation study. To confirm the viability of the scheme, experimental studies are carried out on a scaled-down laboratory prototype.


title: Deformable part models are convolutional neural networks

abstract: Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition. They are typically viewed as distinct approaches: DPMs are graphical models (Markov random fields), while CNNs are “black-box” non-linear classifiers. In this paper, we show that a DPM can be formulated as a CNN, thus providing a synthesis of the two ideas. Our construction involves unrolling the DPM inference algorithm and mapping each step to an equivalent CNN layer. From this perspective, it is natural to replace the standard image features used in DPMs with a learned feature extractor. We call the resulting model a DeepPyramid DPM and experimentally validate it on PASCAL VOC object detection. We find that DeepPyramid DPMs significantly outperform DPMs based on histograms of oriented gradients features (HOG) and slightly outperforms a comparable version of the recently introduced R-CNN detection system, while running significantly faster.


title: Crime pattern detection, analysis & prediction

abstract: Crimes are a social irritation and cost our society deeply in several ways. Any research that can help in solving crimes quickly will pay for itself. About 10% of the criminals commit about 50% of the crimes [9]. The system is trained by feeding previous years record of crimes taken from legitimate online portal of India listing various crimes such as murder, kidnapping and abduction, dacoits, robbery, burglary, rape and other such crimes. As per data of Indian statistics, which gives data of various crime of past 14 years (2001–2014) a regression model is created and the crime rate for the following years in various states can be predicted [8]. We have used supervised, semi-supervised and unsupervised learning technique [4] on the crime records for knowledge discovery and to help in increasing the predictive accuracy of the crime. This work will be helpful to the local police stations in crime suppression.


Blue-Collar Discourses of Workplace Dignity : Using Outgroup Comparisons to Construct Positive Identities


title: Large Scale Purchase Prediction with Historical User Actions on B2C Online Retail Platform

abstract: This paper describes the solution of Bazinga Team for Tmall Recommendation Prize 2014. With real-world user action data provided by Tmall, one of the largest B2C online retail platforms in China, this competition requires to predict future user purchases on Tmall website. Predictions are judged on F1Score, which considers both precision and recall for fair evaluation. The data set provided by Tmall contains more than half billion action records from over ten million distinct users. Such massive data volume poses a big challenge, and drives competitors to write every single program in MapReduce fashion and run it on distributed cluster. We model the purchase prediction problem as standard machine learning problem, and mainly employ regression and classification methods as single models. Individual models are then aggregated in a two-stage approach, using linear regression for blending, and finally a linear ensemble of blended models. The competition is approaching the end but still in running during writing this paper. In the end, our team achieves F1Score 6.11 and ranks 7th (out of 7,276 teams in total).


title: DDoS: Survey of Traceback Methods

abstract: The problem of identifying Distributed Denial of Service (DDoS) is one of the hardest threats in the internet security. It is important to protect the resource and trace from the Denial of Service (DoS) attack, but it is difficult to distinguish normal traffic and DoS attack traffic because the DoS generally hide their identities/origins. Especially the attackers often use incorrect or spoofed source IP address, so tracing the source of the denial of service is hardest in internet. Lot of techniques and methodologies are used to trace the DDoS attacks. This paper presents some of the mostly used predicting traceback techniques to solve the problem. The main goal of this paper is appraise the different traceback techniques of the DDoS. This paper evaluates the different traceback methods of the classification.


title: The Sling Lift: An Ancillary Procedure for Malar Rejuvenation

abstract: Background ::: The malar region has been a crucial target in many facial rejuvenation techniques because the beauty and youthful contour of a convex midface and a smooth eyelid–cheek sulcus are key features of a pleasing face-lift result. The full midface subperiosteal lift has helped to address these issues. However, the desire of patients currently for a rapid recovery and return to work with a natural-looking result has influenced procedural selection. Another concern is for safer procedures with reduced potential risk. Progressively fewer invasive techniques, such as the minimal access cranial suspension (MACS) lift, have been a response to these core concerns.


title: Efficient Parallel Ear Decomposition of Graphs with Application to Betweenness-Centrality

abstract: Parallel graph algorithms continue to attract a lotof research attention given their applications to several fields ofsciences and engineering. Efficient design and implementation ofgraph algorithms on modern manycore accelerators has to how-ever contend with a host of challenges including not being able toreach full memory system throughput and irregularity. Of late, focusing on real-world graphs, researchers are addressing thesechallenges by using decomposition and preprocessing techniquesguided by the structural properties of such graphs. In this direction, we present a new GPU algorithm forobtaining an ear decomposition of a graph. Our implementationof the proposed algorithm on an NVidia Tesla K40c improvesthe state-of-the-art by a factor of 2.3x on average on a collectionof real-world and synthetic graphs. The improved performanceof our algorithm is due to our proposed characterization thatidentifies edges of the graph as redundant for the purposes of anear decomposition. We then study an application of the ear decomposition of agraph in computing the betweenness-centrality values of nodesin the graph. We use an ear decomposition of the input graph tosystematically remove nodes of degree two. The actual computation of betweenness-centrality is done on the remaining nodesand the results are extended to nodes removed in the previousstep. We show that this approach improves the state-of-the-artfor computing betweenness-centrality on an NVidia K40c GPUby a factor of 1.9x on average over a collection of real-world graphs.


title: An inhibitor of ornithine decarboxylase in the thymus and spleen of dexamethasone-treated rats.

abstract: A marked decrease in activity of ornithine decarboxylase in thymus and spleen occurs soon after treatment of rats with a glucocorticoid. In the present study, evidence was obtained that extracts of these tissues prepared 5 h after administration of dexamethasone, when the enzyme activity is very low, contain an inhibitor of ornithine decarboxylase. The inhibitor is also present at 12 h after treatment and, in lesser amount, at 2.5 h, but was not evident at 24 h. The inhibitory activity was destroyed by treatment with heat or with trypsin, and was not lost on dialysis of the extract. Preliminary experiments indicate that the Mr of the inhibitor is greater than 50 000, which differentiates it from antizyme, an inhibitor of ornithine decarboxylase found in several other cell types. The inhibitor seems to act by a non-catalytic and non-competitive mechanism. The inhibition is dependent on the amount of inhibitor and does not change with time. Since inhibition is not changed by dialysis of the inhibitory extract, its activity apparently does not require small-Mr substances. This differentiates it from inhibitors which inactivate ornithine decarboxylase by covalent modification, such as the polyamine-dependent protein kinase or transglutaminase. The formation of this inhibitor is an early event in lymphoid tissues in response to dexamethasone and may be important in causing the inhibition of cell division which precedes the destruction of lymphocytes.


title: A Guide to Fully Homomorphic Encryption

abstract: Fully homomorphic encryption (FHE) has been dubbed the holy grail of cryptography, an elusive goal which could solve the IT world’s problems of security and trust. Research in the area exploded after 2009 when Craig Gentry showed that FHE can be realised in principle. Since that time considerable progress has been made in finding more practical and more efficient solutions. Whilst research quickly developed, terminology and concepts became diverse and confusing so that today it can be difficult to understand what the achievements of different works actually are. The purpose of this paper is to address three fundamental questions: What is FHE? What can FHE be used for? What is the state of FHE today? As well as surveying the field, we clarify different terminology in use and prove connections between different FHE notions.


Paving the Road to Exascale with Many-Task Computing


title: Triathlete Risk of Pelvic Floor Disorders, Pelvic Girdle Pain, and Female Athlete Triad.

abstract: OBJECTIVES ::: Our primary objective was to describe the prevalence of pelvic floor disorders (PFDs), pelvic girdle pain (PGP), and the female athlete triad (disordered eating, menstrual irregularities, and osteoporosis) in a female triathlete population. We also evaluated for an association between these conditions. ::: ::: ::: METHODS ::: We administered an online survey to women who self-identified as female triathletes. Using validated questionnaires, Epidemiology of Prolapse and Incontinence Questionnaire and Pelvic Girdle Questionnaire, along with the female athlete triad questionnaire, we identified the prevalence of PFDs, PGP, and female athlete triad in this specific population. Demographic and exercise intensity were also queried to characterize these female triathletes. ::: ::: ::: RESULTS ::: Three hundred eleven female triathletes responded to the Internet survey. There was a significant prevalence of PFDs with stress urinary incontinence and anal incontinence symptoms being most common (37.4% and 28%, respectively). Urgency urinary incontinence and pelvic organ prolapse were less common (16% and 5%, respectively). Stress urinary incontinence and pelvic organ prolapse were more common in parous triathletes (P = 0.001, P = 0.05). Pelvic girdle pain was noted in 18% of these triathletes but was not disabling. Twenty-four percent of participants screened positive for at least 1 arm of the female athlete triad. No association was found between the female athlete triad and PFDs. ::: ::: ::: CONCLUSIONS ::: Pelvic floor disorders are common and bothersome in female triathletes. Pelvic girdle pain, while present, does not limit these athletes from their training. These female triathletes may be at risk for the female athlete triad. Female triathletes may be at risk for both metabolic and PFDs and should be screened when identified.


The limbic basal-ganglia-thalamocortical circuit and goal-directed behavior


title: Deep learning predicts hip fracture using confounding patient and healthcare variables

abstract: Hip fractures are a leading cause of death and disability among older adults. Hip fractures are also the most commonly missed diagnosis on pelvic radiographs, and delayed diagnosis leads to higher cost and worse outcomes. Computer-aided diagnosis (CAD) algorithms have shown promise for helping radiologists detect fractures, but the image features underpinning their predictions are notoriously difficult to understand. In this study, we trained deep-learning models on 17,587 radiographs to classify fracture, 5 patient traits, and 14 hospital process variables. All 20 variables could be individually predicted from a radiograph, with the best performances on scanner model (AUC = 1.00), scanner brand (AUC = 0.98), and whether the order was marked “priority” (AUC = 0.79). Fracture was predicted moderately well from the image (AUC = 0.78) and better when combining image features with patient data (AUC = 0.86, DeLong paired AUC comparison, p = 2e-9) or patient data plus hospital process features (AUC = 0.91, p = 1e-21). Fracture prediction on a test set that balanced fracture risk across patient variables was significantly lower than a random test set (AUC = 0.67, DeLong unpaired AUC comparison, p = 0.003); and on a test set with fracture risk balanced across patient and hospital process variables, the model performed randomly (AUC = 0.52, 95% CI 0.46–0.58), indicating that these variables were the main source of the model’s fracture predictions. A single model that directly combines image features, patient, and hospital process data outperforms a Naive Bayes ensemble of an image-only model prediction, patient, and hospital process data. If CAD algorithms are inexplicably leveraging patient and process variables in their predictions, it is unclear how radiologists should interpret their predictions in the context of other known patient data. Further research is needed to illuminate deep-learning decision processes so that computers and clinicians can effectively cooperate.


title: A Modular Software Framework for Eye–Hand Coordination in Humanoid Robots

abstract: We describe our software system enabling a tight integration between vision and control modules on complex, high-DOF humanoid robots. This is demonstrated with the iCub humanoid robot performing visual object detection, reaching and grasping actions. A key capability of this system is reactive avoidance of obstacle objects detected from the video stream while carrying out reach-and-grasp tasks. The subsystems of our architecture can independently be improved and updated, for example, we show that by using machine learning techniques we can improve visual perception by collecting images during the robot’s interaction with the environment. We describe the task and software design constraints that led to the layered modular system architecture.


title: DeepMatching: A Structural Seed Identification Framework for Social Network Alignment

abstract: Network alignment aims at finding a bijective mapping between nodes of two networks. Due to its wide application in various fields (e.g., Computer Vision, Data Management, Bioinformatics, and Privacy Protection), researchers have proposed many network alignment algorithms, most of which rely on a set of pre-mapped seeds. However, it is challenging to identify an initial credible set of seeds solely with structural information. In this paper, by exploiting the observation that a true mapping leads to a large portion of consistent edges among the mapped nodes, we formally define the credibility of a mapping as its deviation from a random one. This enables us to measure the credibility of an initial set of seeds. We also present DeepMatching which is a seed identification framework for social network alignment. First, we represent the nodes of the two mapping networks with their structural feature vectors by employing graph embedding techniques. Second, we obtain an initial mapping of the nodes based on the obtained vectors by leveraging point set registration methods. Third, we develop a heuristic algorithm to extract a credible set of seed from the initial mapping. Finally, we utilize the extracted seed set as input of an efficient propagation-based algorithm for large scale network alignment. We conduct extensive experiments to evaluate the performance of DeepMatching, and the results clearly demonstrate its effectiveness and the efficiency.


title: How accurate must an in-car information system be?: consequences of accurate and inaccurate information in cars

abstract: Driving requires focused attention and timely decision making for appropriate maneuvers. This relies on well-timed and accurate information. Designing an in-vehicle information system it is important to ensure that the information for the driver does not negatively affect cognitive processing and driving performance. This study investigates levels of information accuracy necessary in in-vehicle information systems to elicit positive behavioral and attitudinal responses from the driver. In a 2 (gender) by 5 (accuracy: 100%, 88%, 76%, 64% and no system) between-participants study, 100 participants drove in a driving simulator for 25 minutes with an in-vehicle information system designed to inform the driver of hazard and traffic events. Results show that decreasing the accuracy of the system decreased both driving performance and trust and liking of car and in-vehicle system. Female drivers in particular benefit from the in-vehicle system and show higher tolerance of inaccuracies. Design implications for in-vehicle systems are discussed.


Curve Evolution Schemes for Parquet Deformations


Modeling Non-Functional Requirements


title: Analysis of multi-core architecture for automotive applications

abstract: Transportation is the artery of India's economy and also the back bone of cultural exchange, national unity and integrity. Nearly 60% of the world's population has vehicles. This vital increase in vehicles is due to the rapid development of electronics. In a car nearly 30% of the weight goes to electronics which symbolizes the major role of electronics. These advancements led to the increase in the number of electronic control units (ECUs) in modern vehicles. Higher safety concern and good comfort in turn puts higher computational demand on ECUs, which further increases the design complexity of automotive control systems. Multi-core processors, whose usage in the general purpose machines have now extended its hand to support the additional computation needs of the automotive industry. This paper illustrates the multicore processor based ECU system and states its advantages compared to the current single core based ECU. In this paper, the timing analysis is made by comparing both the systems. The timing analysis is based on the number of instructions capable of running in the processors and time taken to execute a number of tasks. This timing analysis is made using RTaW — ECU and SNIPER. The former is scheduling software and the latter is the multicore based simulator of tasks. Also in this paper, the power consumed in both the systems is manipulated using SNIPER power estimation tool. This proves that the number of ECU's get reduced which in turn reduces the cost and power consumed thus paving way to the modern transportation system.


title: A low-power all-digital GFSK demodulator with robust clock data recovery

abstract: This paper presents an all-digital Gaussian frequency shift keying (GFSK) demodulator with robust clock data recovery (CDR) for low-intermediate-frequency (low-IF) receivers in wireless sensor networks (WSN). The proposed demodulator can detect and adapt to the intermediate frequency of the received signal automatically. In addition, the CDR can tolerate the frequency deviation of the input clock. An implementation of the demodulator with CDR is realized with HJTC 0.18 ¼m CMOS technology. The chip is designed for GFSK signals with a center frequency of 200 kHz, a modulation index of 1 and a data rate of 100 kbps. Experimental results show that the chip consumes 0.53 mA from a 1.8 V power supply, and only a 11 dB input signal to noise ratio (SNR) is required for 10-3 bit error rate (BER). The tolerance range for IF offset is \pm12.5% at 11 dB input SNR, and the CDR can tolerate frequency deviation of the input clock of \pm0.1%.


title: Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

abstract: Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB) (e.g., Wikipedia). In this paper, we propose a novel embedding method specifically designed for NED. The proposed method jointly maps words and entities into the same continuous vector space. We extend the skip-gram model by using two models. The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words. By combining contexts based on the proposed embedding with standard NED features, we achieved state-of-the-art accuracy of 93.1% on the standard CoNLL dataset and 85.2% on the TAC 2010 dataset.


title: Fast Software Polar Decoders

abstract: Among error-correcting codes, polar codes are the first to provably achieve channel capacity with an explicit construction. In this work, we present software implementations of a polar decoder that leverage the capabilities of modern general-purpose processors to achieve an information throughput in excess of 200 Mbps, a throughput well suited for software-defined-radio applications. We also show that, for a similar error-correction performance, the throughput of polar decoders both surpasses that of LDPC decoders targeting general-purpose processors and is competitive with that of state-of-the-art software LDPC decoders running on graphic processing units.


title: "The speaking celt": augmented reality avatars guide through a museum -- case study

abstract: "The Speaking Celt" app is an Augmented Reality companion app for museum visitors to the Keltenmuseum Hallein (Museum of Celtic Heritage) in Hallein, Pflegerplatz 5, Salzburg, Austria. Three Avatars guide the visitors through the museum. The app has been developed using Wikitude's Augmented Reality SDK (Image Recognition and 2D Tracking technology). In this case-study, I would like to talk about the user acceptance of this form of story-telling.


title: Treating Cockroach Phobia With Augmented Reality

abstract: Abstract In vivo exposure is the recommended treatment of choice for specific phobias; however, it demonstrates a high attrition rate and is not effective in all instances. The use of virtual reality (VR) has improved the acceptance of exposure treatments to some individuals. Augmented reality (AR) is a variation of VR wherein the user sees the real world augmented by virtual elements. The present study tests an AR system in the short (posttreatment) and long term (3, 6, and 12 months) for the treatment of cockroach phobia using a multiple baseline design across individuals (with 6 participants). The AR exposure therapy was applied using the “one-session treatment” guidelines developed by Ost, Salkovskis, and Hellstrom (1991). Results showed that AR was effective at treating cockroach phobia. All participants improved significantly in all outcome measures after treatment; furthermore, the treatment gains were maintained at 3, 6, and 12-month follow-up periods. This study discusses the advantages of AR as well as its potential applications.


title: Bone graphs: medial shape parsing . . .

abstract: The recognition of 3-D objects from their silhouettes demands a shape representation which is stable with respect to minor changes in viewpoint and articulation. This can be achieved by parsing a silhouette into parts and relationships that do not change across similar object views. Medial descriptions, such as skeletons and shock graphs, provide part-based decompositions but suffer from instabilities. As a result, similar shapes may be represented by dissimilar part sets. We propose a novel shape parsing approach which is based on identifying and regularizing the ligature structure of a medial axis, leading to a bone graph, a medial abstraction which captures a more stable notion of an object's parts. Our experiments show that it offers improved recognition and pose estimation performance in the presence of within-class deformation over the shock graph.


title: Hole Filling Through Photomontage

abstract: To fill holes in photographs of structured, man made environments, we propose a technique which automatically adjusts and clones large image patches that have similar structure. These source patches can come from elsewhere in the same image, or from other images shot from different perspectives. Two significant developments of this work are the ability to automatically detect and adjust source patches whose macrostructure is compatible with the hole region, and alternately, to interactively specify a user's desired search regions. In contrast to existing photomontage algorithms which either synthesize microstructure or require careful user interaction to fill holes, our approach handles macrostructure with an adjustable degree of automation.


title: Stress-Induced Phosphorylation of S. pombe Atf1 Abrogates Its Interaction with F Box Protein Fbh1

abstract: The Atf1 transcription factor is critical for directing stress-induced gene expression in fission yeast [1]. Upon exposure to stress, Atf1 is hyperphosphorylated by the mitogen-activated protein kinase (MAPK) Sty1 [2, 3], which results in its stabilization [4]. The resulting increase in Atf1 is vital for a robust response to certain stresses [4]. Here we investigated the mechanism by which phosphorylation stabilizes Atf1. We show that Atf1 is a target for the ubiquitin-proteasome system and that its degradation is dependent upon an SCF E3 ligase containing the F box protein Fbh1. Turnover of Atf1 requires an intact F box, but not DNA helicase activity of Fbh1. Accordingly, disruption of Fbh1 F box function suppresses phenotypes associated with loss of Atf1 phosphorylation. Atf1 and Fbh1 interact under basal conditions, but this binding is lost upon stress. In contrast, a version of Atf1 lacking all intact MAPK sites still interacts with Fbh1 upon stress, indicating that the association between the F box protein and substrate is disrupted by stress-induced phosphorylation. Most F box protein-substrate interactions described to date are mediated positively by phosphorylation [5]. Thus, our findings represent a novel means of regulating the interaction between an F box protein and its substrate. Moreover, Atf1 is the first target described in any organism for the Fbh1 F box protein.


title: Path-guided artificial potential fields with stochastic reachable sets for motion planning in highly dynamic environments

abstract: Highly dynamic environments pose a particular challenge for motion planning due to the need for constant evaluation or validation of plans. However, due to the wide range of applications, an algorithm to safely plan in the presence of moving obstacles is required. In this paper, we propose a novel technique that provides computationally efficient planning solutions in environments with static obstacles and several dynamic obstacles with stochastic motions. Path-Guided APF-SR works by first applying a sampling-based technique to identify a valid, collision-free path in the presence of static obstacles. Then, an artificial potential field planning method is used to safely navigate through the moving obstacles using the path as an attractive intermediate goal bias. In order to improve the safety of the artificial potential field, repulsive potential fields around moving obstacles are calculated with stochastic reachable sets, a method previously shown to significantly improve planning success in highly dynamic environments. We show that Path-Guided APF-SR outperforms other methods that have high planning success in environments with 300 stochastically moving obstacles. Furthermore, planning is achievable in environments in which previously developed methods have failed.


title: Review spotlight: a user interface for summarizing user-generated reviews using adjective-noun word pairs

abstract: Many people read online reviews written by other users to learn more about a product or venue. However, the overwhelming amount of user-generated reviews and variance in length, detail and quality across the reviews make it difficult to glean useful information. In this paper, we present the iterative design of our system, called Review Spotlight. It provides a brief overview of reviews using adjective-noun word pairs, and allows the user to quickly explore the reviews in greater detail. Through a laboratory user study which required participants to perform decision making tasks, we showed that participants could form detailed impressions about restaurants and decide between two options significantly faster with Review Spotlight than with traditional review webpages.


Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter.


title: Procrastination and Impatience ∗

abstract: There is a large body of literature documenting both a preference for immediacy and a tendency to procrastinate. O'Donoghue and Rabin (1999a,b, 2001) and Choi et al. (2005) model these behaviors as the two faces of the same phenomenon. In this paper, we use a combination of lab, field, and survey evidence to study whether these two types of behavior are indeed linked. To measure immediacy we had subjects choose between a series of smaller-sooner and larger-later rewards. Both rewards were paid with a check in order to control for transaction costs. To measure procrastination we use the subjects' actual behavior in cashing the check and completing tasks on time. Our results lend support to the hypothesis that subjects who have a preference for immediacy are indeed more likely to procrastinate.


title: Augmented creativity: bridging the real and virtual worlds to enhance creative play

abstract: Augmented Reality (AR) holds unique and promising potential to bridge between real-world activities and digital experiences, allowing users to engage their imagination and boost their creativity. We propose the concept of Augmented Creativity as employing ar on modern mobile devices to enhance real-world creative activities, support education, and open new interaction possibilities. We present six prototype applications that explore and develop Augmented Creativity in different ways, cultivating creativity through ar interactivity. Our coloring book app bridges coloring and computer-generated animation by allowing children to create their own character design in an ar setting. Our music apps provide a tangible way for children to explore different music styles and instruments in order to arrange their own version of popular songs. In the gaming domain, we show how to transform passive game interaction into active real-world movement that requires coordination and cooperation between players, and how ar can be applied to city-wide gaming concepts. We employ the concept of Augmented Creativity to authoring interactive narratives with an interactive storytelling framework. Finally, we examine how Augmented Creativity can provide a more compelling way to understand complex concepts, such as computer programming.


title: Unified theory of Alzheimer’s disease (UTAD): implications for prevention and curative therapy

abstract: The aim of this review is to propose a Unified Theory of Alzheimer’s disease (UTAD) that integrates all key behavioural, genetic and environmental risk factors in a causal chain of etiological and pathogenetic events. It is based on three concepts that emanate from human’s evolutionary history: (1) The grandmother-hypothesis (GMH), which explains human longevity due to an evolutionary advantage in reproduction by trans-generational transfer of acquired knowledge. Consequently it is argued that mental health at old-age must be the default pathway of humans’ genetic program and not development of AD. (2) Therefore, mechanism like neuronal rejuvenation (NRJ) and adult hippocampal neurogenesis (AHN) that still function efficiently even at old age provide the required lifelong ability to memorize personal experiences important for survival. Cumulative evidence from a multitude of experimental and epidemiological studies indicate that behavioural and environmental risk factors, which impair productive AHN, result in reduced episodic memory performance and in reduced psychological resilience. This leads to avoidance of novelty, dysregulation of the hypothalamic–pituitary–adrenal (HPA)-axis and cortisol hypersecretion, which drives key pathogenic mechanisms of AD like the accumulation and oligomerization of synaptotoxic amyloid beta, chronic neuroinflammation and neuronal insulin resistance. (3) By applying to AHN the law of the minimum (LOM), which defines the basic requirements of biological growth processes, the UTAD explains why and how different lifestyle deficiencies initiate the AD process by impairing AHN and causing dysregulation of the HPA-axis, and how environmental and genetic risk factors such as toxins or ApoE4, respectively, turn into disease accelerators under these unnatural conditions. Consequently, the UTAD provides a rational strategy for the prevention of mental decline and a system-biological approach for the causal treatment of AD, which might even be curative if the systemic intervention is initiated early enough in the disease process. Hence an individualized system-biological treatment of patients with early AD is proposed as a test for the validity of UTAD and outlined in this review.


Ego Identity Status, Adjustment, and Academic Achievement.


title: Approaches for teaching computational thinking strategies in an educational game: A position paper

abstract: Computer science is expanding into K12 education and numerous educational games and systems have been created to teach programming skills, including many block-based programming environments. Teaching computational thinking has received particular attention, and more research is needed on using educational games to directly teach computational thinking skills. We propose to investigate this using Dragon Architect, an educational block-based programming game we are developing. Specifically, we wish to study ways of directly teaching computational thinking strategies such as divide and conquer in an educational game, as well as ways to evaluate our approaches.


Post-glacial colonization, demographic history, and selection in Arabidopsis lyrata : genome-wide and candidate gene based approach


title: A general framework for statistical performance comparison of evolutionary computation algorithms

abstract: This paper proposes a statistical methodology for comparing the performance of evolutionary computation algorithms. A two-fold sampling scheme for collecting performance data is introduced, and this data is assessed using a multiple hypothesis testing framework relying on a bootstrap resampling procedure. The proposed method offers a convenient, flexible, and reliable approach to comparing algorithms in a wide variety of applications.


title: Variational Bayesian Super Resolution

abstract: In this paper, we address the super resolution (SR) problem from a set of degraded low resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the sub-pixel motion between the LR images significantly affects the performance of the reconstructed HR image. In this paper, we propose novel super resolution methods where the HR image and the motion parameters are estimated simultaneously. Utilizing a Bayesian formulation, we model the unknown HR image, the acquisition process, the motion parameters and the unknown model parameters in a stochastic sense. Employing a variational Bayesian analysis, we develop two novel algorithms which jointly estimate the distributions of all unknowns. The proposed framework has the following advantages: 1) Through the incorporation of uncertainty of the estimates, the algorithms prevent the propagation of errors between the estimates of the various unknowns; 2) the algorithms are robust to errors in the estimation of the motion parameters; and 3) using a fully Bayesian formulation, the developed algorithms simultaneously estimate all algorithmic parameters along with the HR image and motion parameters, and therefore they are fully-automated and do not require parameter tuning. We also show that the proposed motion estimation method is a stochastic generalization of the classical Lucas-Kanade registration algorithm. Experimental results demonstrate that the proposed approaches are very effective and compare favorably to state-of-the-art SR algorithms.


title: Unanimity games and Pareto optimality

abstract: A central question in group decision theory is the existence of a simple mechanism that necessarily leads to Pareto optimal outcomes despite noncooperative behavior of the participants. It is shown that the multistage unanimity game is such a mechanism if we assume that the non-cooperative players end at an equilibria which is symmetric and persistent.


Folktale similarity based on ontological abstraction Marijn Schraagen Digital


title: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)

abstract: The article mainly described the Web number of pages according to scoop out of basic mission, include a contents, structure, use etc. Aim at the complexity of the Web data with the special, the Web data mining t daily record's waiting a small part can mining a method with the in common use data, besides which, have to do the data processing of the necessity to the Web page, make it attain the excavation request that the structure turns a data, or use the XML technique to construct the half structure data mode to carry on again data excavation.


title: Optimal Personal Comfort Management Using SPOT+

abstract: We present SPOT+, a system that allows office workers to optimally balance between heating energy consumption and personal thermal comfort. In prior work, we described SPOT: a smart personal thermal control system based on reactive control [8]. In contrast, the SPOT+ system performs predictive control. Specifically, SPOT+ uses the k-nearest-neighbour algorithm to predict room occupancy and learning-based model predictive control (LBMPC) to predict future room temperature and to compute the optimal sequence of control inputs. This allows the system to schedule future temperature setpoints to optimize an objective function expressed as a linear combination of thermal comfort and energy consumption. We have deployed SPOT+ as well as four other alternative control schemes in an office workspace. We find that SPOT+ reduces energy usage by 60% compared to a fixed-temperature setpoint and reduces personal thermal discomfort from 0.36 to 0.02 (in the ASHRAE comfort scale) compared to SPOT.


title: Enabling technology for multilingual natural language generation: the KPML development environment

abstract: Natural language generation is now moving away from research prototypes into more practical applications. Generation functionality is also being asked to play a more significant role in established applications such as machine translation. In both cases, multilingual generation techniques have much to offer. However, the take-up of multilingual generation is being restricted by a critical lack both of large-scale linguistic resources suited to the generation task and of appropriate development environments. This paper describes KPML, a multilingual development environment that offers one possible solution to these problems. KPML aims to provide generation projects with standardized, broad-coverage, reusable resources and a basic engine for using such resources for generation. A variety of focused debugging aids ensure efficient maintenance, while supporting multilingual work such as contrastive language development and automatic merging of independently developed resources. KPML is based on a new, generic approach to multilinguality in resource description that extends significantly beyond previous approaches. The system has already been used in a number of large generation projects and is freely available to the generation community.


title: Robust Detection of Premature Ventricular Contractions Using a Wave-Based Bayesian Framework

abstract: Detection and classification of ventricular complexes from the ECG is of considerable importance in Holter and critical care patient monitoring, being essential for the timely diagnosis of dangerous heart conditions. Accurate detection of premature ventricular contractions (PVCs) is particularly important in rela- tion to life-threatening arrhythmias. In this paper, we introduce a model-based dynamic algorithm for tracking the ECG characteris- tic waveforms using an extended Kalman filter. The algorithm can work on single or multiple leads. A "polargram"—a polar repre- sentation of the signal—is introduced, which is constructed using the Bayesian estimations of the state variables. The polargram allows the specification of a polar envelope for normal rhythms. Moreover, we propose a novel measure of signal fidelity by monitor- ing the covariance matrix of the innovation signals throughout the filtering procedure. PVCs are detected by simultaneous tracking the signal fidelity and the polar envelope. Five databases, including 40 records from MIT-BIH arrhythmia database, are used for differ- entiating normal, PVC, and other beats. Performance evaluation results show that the proposed method has an average detection accuracy of 99.10%, aggregate sensitivity of 98.77%, and aggre- gate positive predictivity of 97.47%. Furthermore, the method is capable of 100% accuracy for records that contain only PVCs and normal sinus beats. The results illustrate that the method can con- tribute to, and enhance the performance of clinical PVC detection. Index Terms—Characteristic waves, ECG, extended Kalman filter (EKF), premature ventricular contraction (PVC), signal fi- delity, signal quality, wave-based dynamical model.


Gurus, Theory, and Practice: A Reply to Catlaw and Chapman


title: Dual Attention Networks for Multimodal Reasoning and Matching

abstract: We propose Dual Attention Networks (DANs) which jointly leverage visual and textual attention mechanisms to capture fine-grained interplay between vision and language. DANs attend to specific regions in images and words in text through multiple steps and gather essential information from both modalities. Based on this framework, we introduce two types of DANs for multimodal reasoning and matching, respectively. The reasoning model allows visual and textual attentions to steer each other during collaborative inference, which is useful for tasks such as Visual Question Answering (VQA). In addition, the matching model exploits the two attention mechanisms to estimate the similarity between images and sentences by focusing on their shared semantics. Our extensive experiments validate the effectiveness of DANs in combining vision and language, achieving the state-of-the-art performance on public benchmarks for VQA and image-text matching.


title: Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks

abstract: Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-words representation as a surrogate for the content information but they are neither effective nor natural enough. To address this problem, we develop a collaborative recurrent autoencoder (CRAE) which is a denoising recurrent autoencoder (DRAE) that models the generation of content sequences in the collaborative filtering (CF) setting. The model generalizes recent advances in recurrent deep learning from i.i.d. input to non-i.i.d. (CF-based) input and provides a new denoising scheme along with a novel learnable pooling scheme for the recurrent autoencoder. To do this, we first develop a hierarchical Bayesian model for the DRAE and then generalize it to the CF setting. The synergy between denoising and CF enables CRAE to make accurate recommendations while learning to fill in the blanks in sequences. Experiments on real-world datasets from different domains (CiteULike and Netflix) show that, by jointly modeling the order-aware generation of sequences for the content information and performing CF for the ratings, CRAE is able to significantly outperform the state of the art on both the recommendation task based on ratings and the sequence generation task based on content information.


title: The internet of things in agriculture for sustainable rural development

abstract: Rural areas in South Africa and Zambia face a number of similar issues in the domains of agriculture, connectivity, water, transport, health and education etc., which calls for potentially similar solutions to be directed towards solving these issues. The intention of this research is to investigate the potential contributions of internet of things technologies (IoT) towards poverty reduction in these rural areas, in line with the needs identified in these communities and with emphasis on agriculture. The paper identifies examples of IoTs to mitigate the agricultural needs of these communities for the domains of crop farming, weather forecasting, wildlife management, forestry, livestock farming, market identification and rural financing.


title: Keywords Applying Stakeholder Theory to E-Government: Benefits and Limits

abstract: According to most scholars in the field, stakeholder theory is not a special theory on a firm’s constituencies but sets out to replace today’s prevailing neoclassical economic concept of the firm. Though stakeholder theory explicitly is a theory on a private sector entity, some scholars apply it to public sector organizations. This paper summarizes stakeholder theory, discusses its premises and justifications, compares its tracks, sheds light on recent attempts to join the two tracks, and discusses the benefits and limits of its practical applicability to the public sector using the case of a recent New York State eGovernment initiative.


title: IV Estimation of a Panel Threshold Model of Tourism Specialization and Economic Development

abstract: The significant impact of international tourism in stimulating economic growth is especially important from a policy perspective. For this reason, the relationship between international tourism and economic growth would seem to be an interesting and topical empirical issue. The purpose of this paper is to investigate whether tourism specialization is important for economic development in 159 countries over the period 1989-2008. The results from panel threshold regressions show a positive relationship between economic growth and tourism. Instrumental variable estimation of a threshold regression is used to quantify the contributions of tourism specialization to economic growth, while correcting for endogeneity between the regressors and error term. The significant impact of tourism specialization on economic growth in most regressions is robust to different specifications of tourism specialization, as well as to differences in real GDP measurement. However, the coefficients of the tourism specialization variables in the two regimes are significantly different, with a higher impact of tourism on economic growth found in the low regime. These findings do not change with changes in the threshold variables. The empirical results suggest that tourism growth does not always lead to substantial economic growth.


title: Distance protection of transmission line with infeed based on real-time simulator

abstract: In this paper real-time simulation, analysis, and validation of the conventional distance relay based on MATLAB/simulink and Real-Time LABoratory (RT-LAB) is presented. In addition to the detailed model of six impedance measuring units of distance relay, power system model is implemented in Opal-RT's RT-LAB simulator. Some cases are highlighted to illustrate the modelling performance. Moreover, the effect of infeed current, which is an emerging issue for power system protection, using conventional distance relay in real-time environment is evaluated.


title: How do deep convolutional neural networks learn from raw audio waveforms

abstract: Prior work on speech and audio processing has demonstrated the ability to obtain excellent performance when learning directly from raw audio waveforms using convolutional neural networks (CNNs). However, the exact inner workings of a CNN remain unclear, which hinders further developments and improvements into this direction. In this paper, we theoretically analyze and explain how deep CNNs learn from raw audio waveforms and identify potential limitations of existing network structures. Based on this analysis, we further propose a new network architecture (called SimpleNet), which offers a very simple but concise structure and high model interpretability.


title: Mechanics and energetics of level walking with powered ankle exoskeletons

abstract: SUMMARY Robotic lower limb exoskeletons that can alter joint mechanical power ::: output are novel tools for studying the relationship between the mechanics and ::: energetics of human locomotion. We built pneumatically powered ankle ::: exoskeletons controlled by the user9s own soleus electromyography (i.e. ::: proportional myoelectric control) to determine whether mechanical assistance ::: at the ankle joint could reduce the metabolic cost of level, steady-speed ::: human walking. We hypothesized that subjects would reduce their net metabolic ::: power in proportion to the average positive mechanical power delivered by the ::: bilateral ankle exoskeletons. Nine healthy individuals completed three 30 min ::: sessions walking at 1.25 m s –1 while wearing the ::: exoskeletons. Over the three sessions, subjects9 net metabolic energy ::: expenditure during powered walking progressed from +7% to –10% of that ::: during unpowered walking. With practice, subjects significantly reduced soleus ::: muscle activity (by ∼28% root mean square EMG, P <0.0001) and ::: negative exoskeleton mechanical power (–0.09 W kg –1 at ::: the beginning of session 1 and –0.03 W kg –1 at the end ::: of session 3; P =0.005). Ankle joint kinematics returned to similar ::: patterns to those observed during unpowered walking. At the end of the third ::: session, the powered exoskeletons delivered ∼63% of the average ankle ::: joint positive mechanical power and ∼22% of the total positive mechanical ::: power generated by all of the joints summed (ankle, knee and hip) during ::: unpowered walking. Decreases in total joint positive mechanical power due to ::: powered ankle assistance (∼22%) were not proportional to reductions in net ::: metabolic power (∼10%). The `apparent efficiency9 of the ankle joint ::: muscle–tendon system during human walking (∼0.61) was much greater ::: than reported values of the `muscular efficiency9 of positive mechanical work ::: for human muscle (∼0.10–0.34). High ankle joint `apparent ::: efficiency9 suggests that recoiling Achilles9 tendon contributes a significant ::: amount of ankle joint positive power during the push-off phase of walking in ::: humans.


title: Electrical performance of vertical natural capacitor for RF system-on-chip in 32-nm technology

abstract: Radio frequency system-on-chips (RF SoC) require high quality passive devices such as capacitors. We comprehensively studied the vertical natural capacitors (VNCAP) made of CMOS back-end-of-lines (BEOL) in 32-nm technology. We used electromagnetic simulation and a Pi-type equivalent circuit model for the study of the VNCAP, and reported its electrical characteristics including the scattering parameter, effective capacitance, self-resonant frequency and quality factor up to 20 GHz. We also briefly discussed the performance scaling trend of VNCAPs from 65-nm to 32-nm technology.


title: An evaluation of test structures for measuring the contact resistance of 3-D bonded interconnects

abstract: This paper evaluates test structures designed to characterise electrical contacts between interconnect on bonded wafers. Both simulation and experimental measurements are used to explore the capability of a stacked Greek cross type test structure to extract the contact resistivity (pc) between two bonded conductive layers. It is concluded from the simulations and actual electrical measurements of the benchmark Kelvin structures that the stacked Greek cross can only be used where there is a relatively high specific contact resistivity. For the structures evaluated in this study, this was found to be greater than pc > 9.0 x 10-7 Omega-cm2.


title: AutoWitness: Locating and tracking stolen property while tolerating GPS and radio outages

abstract: We present AutoWitness, a system to deter, detect, and track personal property theft, improve historically dismal stolen property recovery rates, and disrupt stolen property distribution networks. A property owner embeds a small tag inside the asset to be protected, where the tag lies dormant until it detects vehicular movement. Once moved, the tag uses inertial sensor-based dead reckoning to estimate position changes, but to reduce integration errors, the relative position is reset whenever the sensors indicate the vehicle has stopped. The sequence of movements, stops, and turns are logged in compact form and eventually transferred to a server using a cellular modem after both sufficient time has passed (to avoid detection) and RF power is detectable (hinting cellular access may be available). Eventually, the trajectory data are sent to a server which attempts to match a path to the observations. The algorithm uses a Hidden Markov Model of city streets and Viterbi decoding to estimate the most likely path. The proposed design leverages low-power radios and inertial sensors, is immune to intransit cloaking, and supports post hoc path reconstruction. Our prototype demonstrates technical viability of the design; the volume market forces driving machine-to-machine communications will soon make the design economically viable.


title: Detecting anomalous records in categorical datasets

abstract: We consider the problem of detecting anomalies in high aritycategorical datasets. In most applications, anomalies are defined as datapoints that are "abnormal". Quite often we have access to data which consists mostly of normal records, a long with a small percentage of unlabelled anomalous records. We are interested in the problem of unsupervised anomaly detection, where we use the unlabelled data for training, and detect records that do not follow the definition of normality. A standard approach is to create a model of normal data, and compare test records against it. A probabilistic approach builds a likelihood model from the training data. Records are tested for anomalies based on the complete record likelihood given the probability model. For categorical attributes, bayes nets give a standard representation of the likelihood. While this approach is good at finding outliers in the dataset, it often tends to detect records with attribute values that are rare. Sometimes, just detecting rare values of an attribute is not desired and such outliers are not considered as anomalies in that context. We present an alternative definition of anomalies, and propose an approach of comparing against marginal distribution of attribute subsets. We show that this is a more meaningful way of detecting anomalies, and has a better performance over semi-synthetic as well as real world datasets.


title: Opportunities and obstacles for deep learning in biology and medicine

abstract: Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results...


title: Synthesis of bireciprocal wave digital filters with equiripple amplitude and phase

abstract: In this contribution, robust simultaneous amplitude and phase approximations are delivered for bireciprocal wave digital filters exhibiting arbitrary amplitude and linear phase responses. Moreover, the resulting amplitude and phase characteristics are equiripple. The approximation is relying on translating the given amplitude and phase specifications into corresponding specifications for the difference and sum phase functions of the two branch bireciprocal polynomials. Consequently, these difference and sum functions are approximated in an alternative and iterative procedure such that they converge into their optimal behavior. Accordingly, the two branch polynomials are finally obtained and the synthesis of the filter structure in the wave digital domain is determined.


title: Degrees of freedom of three-user SISO interference channels under hybrid CSI at the transmitters

abstract: In this paper, we consider the degrees of freedom (DoF) of the three-user single-input-single-output interference channel (SISO-IC) with independent and identically distributed (i.i.d.) fading across antennas and channel uses and under hybrid channel state information (CSI) at the transmitters. Hybrid CSI implies that delayed CSI of the whole network is available to all the transmitters whereas only local instantaneous CSI is available to each transmitter which is obtained through feedback from its own receiver. We show that a DoF of 6 can be attained for this scenario which is greater than the existing results on completely delayed CSI feedback scenario. Further, the local instantaneous CSIT needs to be available only for 20% of the total transmission time. In other words, the CSIT in our scheme can be delayed up to 80% of the channel coherence time. Also, the message for every user is completely private, i.e., any part of it cannot be decoded by any unintended user.


title: Reviewing the Design of DAML+OIL: An Ontology Language for the Semantic Web

abstract: In the current "Syntactic Web", uninterpreted syntactic constructs are given meaning only by private off-line agreements that are inaccessible to computers. In the Semantic Web vision, this is replaced by a web where both data and its semantic definition are accessible and manipulable by computer software. DAML+OIL is an ontology language specifically designed for this use in the Web; it exploits existing Web standards (XML and RDF), adding the familiar ontological primitives of object oriented and frame based systems, and the formal rigor of a very expressive description logic. The definition of DAML+OIL is now over a year old, and the language has been in fairly widespread use. In this paper, we review DAML+OIL's relation with its key ingredients (XML, RDF, OIL, DAML-ONT, Description Logics), we discuss the design decisions and trade-offs that were the basis for the language definition, and identify a number of implementation challenges posed by the current language. These issues are important for designers of other representation languages for the Semantic Web, be they competitors or successors of DAML+OIL, such as the language currently under definition by W3C.


title: An unknown key-share attack on the MQV key agreement protocol

abstract: The MQV key agreement protocol, a technique included in recent standards, is shown in its basic form to be vulnerable to an unknown key-share attack. Although the attack's practical impact on security is minimal---a key confirmation step easily prevents it---the attack is noteworthy in the principles it illustrates about protocol design. First, minor qefficiency improvementsq can significantly alter the security properties of a protocol. Second, protocol analysis must consider potential interactions with all parties, not just those that are normally online. Finally, attacks must be assessed in terms of system requirements, not just in isolation.


title: A graph-based approach to Web services composition

abstract: Automatic composition of Web services has drawn a great deal of attention recently. By composition, we mean taking advantage of currently existing Web services to provide a new service that does not exist on its own. Therefore, in order to have a more complex service, we can use some semantically related simpler Web services and execute them in such a way that the whole set provides the desired service. There are Web service specification languages that specify semantic properties of Web services. These languages are helpful in searching for those Web services that can participate in a composition. This work is aimed at searching among Web services in order to find those whose composition provides a specific behavior. Those Web services found after this search are incrementally composed together to build a new service that realizes that behavior. Our technique takes advantage of graph structures and also a particular formalism called interface automata.


title: Attention-Based Bi-LSTM Model for Anomalous HTTP Traffic Detection

abstract: Recently, cyber-attacks with complex types have occurred more frequently than before, while communication traffic provides a clue to probe anomalous network behaviors. Therefore, how to detect malicious network attacks from large scale communication traffic in a timely manner and grasp their attack characteristics is a major challenge for website security. Since the content of the network traffic complies with strict writing specifications and structural standards, it is usually modeled and analyzed as natural language. In this paper, we propose a deep neural network model utilizing Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism to model HTTP traffic as a natural language sequence. The application of attention mechanism can assist in detecting anomalous traffic and discovering critical parts of anomalous traffic. Extensive experiments over large traffic data have illustrated that the proposed model has outstanding performance in malicious HTTP traffic detection.


title: Immunophenotyping of HPV Types 16 and 18 among Sudanese Patients with Oral Lesions

abstract: OBJECTIVE ::: The aim of this study was to screen patients with oral lesions for the presence of Human Papilloma Virus (HPV) types 16 and 18. ::: ::: ::: METHODS ::: Sixty patients aged between 11-80 years with a mean age of 46 years were examined using immunohistological techniques. All samples were retrieved from RICK during the period from August 2009 to August 2010. Out of 60 patients, 50 had Oral Squamous Cell Carcinomas (OSCCs) and the remaining ten had benign oral lesions, included as internal control. ::: ::: ::: RESULTS ::: Of the 50 patients with OSCCs, 10 (20%) showed positive immunohistochemical results for HPV types 16 and 18 of which 50% were detected among males and 50% were demonstrated among females. The ten positive findings were Immunophenotyped as follows: five were positive with HPV type 16, four with type 18 and one was positive for HPV types 16 and18. All patients with benign oral lesions were negative for HPV immunohistochemistry. ::: ::: ::: CONCLUSION ::: The study suggests the role of HPV 16 and 18 in the etiology of oral cancers in different parts of Sudan. However, the use of molecular techniques such as PCR are needed to confirm the results of immunohistochemistry in the role of the HPV in developing of OSCC in Sudan.


title: Functional imaging of hippocampal place cells at cellular resolution during virtual navigation

abstract: Dombeck and colleagues describe a method for two-photon calcium imaging using a genetically encoded indicator in the hippocampus of awake, behaving mice. This powerful approach permits the recording of multiple hippocampal place cells' activity with subcellular resolution as the mice run on a track in a virtual reality environment.


title: Functional Roles of Alpha-Band Phase Synchronization in Local and Large-Scale Cortical Networks

abstract: Alpha-frequency band (8-14 Hz) oscillations are among the most salient phenomena in human electroencephalography (EEG) recordings and yet their functional roles have remained unclear. Much of research on alpha oscillations in human EEG has focused on peri-stimulus amplitude dynamics, which phenomenologically support an idea of alpha oscillations being negatively correlated with local cortical excitability and having a role in the suppression of task-irrelevant neuronal processing. This kind of an inhibitory role for alpha oscillations is also supported by several functional magnetic resonance imaging (fMRI) and trans-cranial magnetic stimulation (TMS) studies. Nevertheless, investigations of local and inter-areal alpha phase dynamics suggest that the alpha-frequency band rhythmicity may play a role also in active task-relevant neuronal processing. These data imply that inter-areal alpha phase synchronization could support attentional, executive, and contextual functions. In this review, we outline evidence supporting different views on the roles of alpha oscillations in cortical networks and unresolved issues that should be addressed to resolve or reconcile these apparently contrasting hypotheses.


title: Validation of the Instructional Materials Motivation Survey (IMMS) in a self-directed instructional setting aimed at working with technology

abstract: The ARCS Model of Motivational Design has been used myriad times to design motivational instructions that focus on attention, relevance, confidence and satisfaction in order to motivate students. The Instructional Materials Motivation Survey (IMMS) is a 36-item situational measure of people's reactions to instructional materials in the light of the ARCS model. Although the IMMS has been used often, both as a pretest and a posttest tool serving as either a motivational needs assessment prior to instruction or a measure of people's reactions to instructional materials afterward, the IMMS so far has not been validated extensively, taking statistical and theoretical aspects of the survey into account. This paper describes such an extensive validation study, for which the IMMS was used in a self-directed instructional setting aimed at working with technology (a cellular telephone). Results of structural equation modeling show that the IMMS can be reduced to 12 items. This Reduced Instructional Materials Motivation Survey IMMS (RIMMS) is preferred over the original IMMS. The parsimonious RIMMS measures the four constructs attention, relevance, confidence and satisfaction of the ARCS model well, and reflects its conditional nature.


title: Towards a Theoretical Understanding of Hashing-Based Neural Nets

abstract: Parameter reduction has been a popular topic in deep learning due to the ever- ::: increasing size of deep neural network models and the need to train and run deep neural nets on resource limited machines. Despite many efforts in this area, there were no rigorous theoretical guarantees on why existing neural net compression methods should work. In this paper, we provide provable guarantees on some hashing-based parameter reduction methods in neural nets. First, we introduce a neural net compression scheme based on random linear sketching (which is usually implemented efficiently via hashing), and show that the sketched (smaller) network is able to approximate the original network on all ::: input data coming from any smooth well-conditioned low-dimensional manifold. The sketched network can also be trained directly via back-propagation. Next, we study the previously proposed HashedNets architecture and show that the optimization landscape of one-hidden-layer HashedNets has a local strong convexity property similar to a normal fully connected neural network. Together ::: with the initialization algorithm developed in [51], this implies that the parameters in HashedNets can be provably recovered. We complement our theoretical results with some empirical verification.


title: Analysis and Simulation of Delay and Buffer Requirements of satellite-ATM Networks for TCP/IP Traffic

abstract: In this paper we present a model to study the end-to-end delay performance of a satellite-ATM netowrk. We describe a satellite-ATM network architecture. The architecture presents a trade-off between the on-board switching/processing features and the complexity of the satellite communication systems. The end-to-end delay of a connection passing through a satellite constellation consists of the transmission delay, the uplink and downlink ground terminal-satellite propagation delay, the inter-satellite link delays, the on-board switching, processing and buffering delays. In a broadband satellite network, the propagation and the buffering delays have the most impact on the overall delay. We present an analysis of the propagation and buffering delay components for GEO and LEO systems. We model LEO constellations as satellites evenly spaced in circular orbits around the earth. A simple routing algorithm for LEO systems calculates locally optimal paths for the end-to-end connection. This is used to calculate the end-to-end propagation delays for LEO networks. We present a simulation model to calculate the buffering delay for TCP/IP traffic over ATM ABR and UBR service categories. We apply this model to calculate total end-to-end delays for TCP/IP over satellite-ATM networks.


title: Design-oriented compact models for CNTFETs

abstract: This paper deals with the compact modeling of an emerging technology: the carbon nanotube field-effect transistor (CNTFET). The paper proposed two design-oriented compact models, the first one for CNTFET with a classical behavior (MOSFET-like CNTFET), and the second one for CNTFET with an ambipolar behavior (Schottky-barrier CNTFET). Both models have been compared with exact numerical simulations and then implemented in VHDL-AMS


title: Eye colour: portals into pigmentation genes and ancestry.

abstract: Several recent papers have tried to address the genetic determination of eye colour via microsatellite linkage, testing of pigmentation candidate gene polymorphisms and the genome wide analysis of SNP markers that are informative for ancestry. These studies show that the OCA2 gene on chromosome 15 is the major determinant of brown and/or blue eye colour but also indicate that other loci will be involved in the broad range of hues seen in this trait in Europeans.


title: Comparison of the influence of PM drive system with voltage adaptation or machine winding reconfiguration on HEV/EV applications

abstract: This paper examines the performance benefits of four IPM drive system configurations for HEV/EV applications with the following features for controlling the machine voltage: 1) boosted inverter, 2) Y/Δ, 3) series/parallel (S/P) and 4) tapped reconfigurable windings. The four systems are modeled and their impact on drive system performance is investigated. Additionally, comparisons in terms of flux weakening capability, torque, output power, losses, number of machine winding leads and number of added components are studied in detail. Detailed set of simulation and experiments results were used for assessing the performance benefits. The trade-off study shows that the drive system with tapped reconfigurable winding results in the favorable cost/benefit ratio with similar inverter voltage/current limits.


title: Landing a VTOL Unmanned Aerial Vehicle on a Moving Platform Using Optical Flow

abstract: This paper presents a nonlinear controller for a vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) that exploits a measurement optical flow to enable hover and landing control on a moving platform, such as, for example, the deck of a sea-going vessel. The VTOL vehicle is assumed to be equipped with a minimum sensor suite [i.e., a camera and an inertial measurement unit (IMU)], manoeuvring over a textured flat target plane. Two different tasks are considered in this paper. The first concerns the stabilization of the vehicle relative to the moving platform that maintains a constant offset from a moving reference. The second concerns regulation of automatic vertical landing onto a moving platform. Rigorous analysis of system stability is provided, and simulations are presented. Experimental results are provided for a quadrotor UAV to demonstrate the performance of the proposed control strategy.


title: Comparison between microprocessor-controlled ankle/foot and conventional prosthetic feet during stair negotiation in people with unilateral transtibial amputation

abstract: INTRODUCTION The kinematics and kinetics of lower-limb joints during stair ambulation are different from those during level walking [1]. The majority of prosthetic feet are designed with an emphasis on level-ground gait and do not consider the biomechanics of functional activities such as ascending and descending stairs. While the physiological ankle can actively dorsiflex up to 27[degrees] during movement on stairs [1-2], prosthetic feet with solid ankle designs are fixed at 90[degrees] and those with articulated ankles and J-shaped ankle springs vary the amount of functional dorsiflexion based on their design. The Proprio foot (Ossur Inc; Reykjavik, Iceland) consists of a microprocessor-controlled ankle that can dorsiflex up to 4[degrees] during stair ascent and descent. Alimusaj et al. recently reported that the active dorsiflexion of the Proprio foot/ankle during the swing phase of stair negotiation improved knee kinematics and kinetics compared with a fixed ankle that allowed no active motion [3]. However, it is unknown whether the swing-phase dorsiflexion feature of the Proprio foot offers any advantage to users with unilateral transtibial amputation (TTA) over conventional prosthetic feet with varying degrees of stance-phase dorsiflexion during stair ascent and descent. Few studies comparing the effects of prosthetic feet on stair mobility have been published. Torburn et al. calculated the stride characteristics of 10 subjects with traumatic amputation using five prosthetic feet during stair ascent and descent [4]. The stride characteristics were calculated by means of foot switches as subjects ascended and descended a 4-step portable staircase. During ascent, the Flex Foot (Ossur Inc) and Carbon Copy Foot (The Ohio Willow Wood Co; Mt. Sterling, Ohio) resulted in a more symmetrical gait (ratio: 1.03 and 1.05, respectively) only during the initial double-limb support phase and were significantly different than the solid ankle cushion heel (SACH) foot (ratio: 1.3). During stair descent, no significant differences in stride characteristics were found among prosthetic feet. Torburn et al. concluded that none of the five test feet was clinically more advantageous for stair ambulation. Yack et al. studied the lower-limb joint moments and powers during stair ascent with three different prosthetic feet [5]. They concluded that subjects with amputation use a hip-extensor dominant strategy on the amputated side during stair ascent, and dynamic response (DR) feet (Flex Foot and Re-Flex VSP [Ossur Inc]) are advantageous over a SACH foot because they reduce the moments and power generation at the hip joint. The only study using a microprocessor-controlled foot for stair negotiation was published by Alimusaj et al., who tested the Proprio foot with its active dorsiflexion feature, the "stair mode," turned on and turned off [3]. They reported a significant change in the hip and knee moments between the two conditions for both stair ascent and descent. They concluded that the active dorsiflexion feature improves the knee kinetics and kinematics on the amputated side during stair ambulation. While comparisons between DR and non-DR feet have been reported, no studies have investigated the differences in stair ambulation between a microprocessor-controlled foot and conventional prosthetic feet in those with TTA at two distinct functional levels. In the United States, the Medicare Functional Classification Level (MFCL) classifies mobility of people with lower-limb loss into five functional levels, primarily based on their potential to ambulate with a prosthesis (Table 1) [6-7]. Subsequent to recommendations by manufacturer representatives and invited advisors, the Centers for Medicare and Medicaid Services committee assigned prosthetic feet to a specific MFCL K-Level (categories K1-K4 feet) based on their mechanical properties (Table 1). In the majority of cases, a person with amputation's functional classification dictates the respective category of prosthetic foot prescribed and worn. …


title: Attitudes toward online availability of US public records

abstract: Many have enthusiastically greeted the ability to search and view public records online as a great advance for transparency and accountability. Such ability, however, also creates value tensions with privacy and other important human values. In this paper, we report findings from a survey of 134 residents of the US Pacific Northwest on their awareness of and attitudes towards online access to political campaign records and real estate transaction histories, bringing to light some of the social implications of technological changes that increase ease of access to public records. We show that, while respondents often understood the reason behind making these records public, considerable concern about the current accessibility of these records exists, along with a precautionary indication that such open access may reduce public participation for some individuals.


title: TruSense: Information Leakage from TrustZone

abstract: With the emergence of Internet of Things, mobile devices are generating more network traffic than ever. TrustZone is a hardware-enabled trusted execution environment for ARM processors. While TrustZone is effective in providing the much-needed memory isolation, we observe that it is possible to derive secret information from secure world using the cache contention, due to its high-performance cache sharing design. In this work, we propose TruSense to study the timing-based cache side-channel information leakage of TrustZone. TruSense can be launched from not only the normal world operating system but also a non-privileged user application. Without access to virtual-to-physical address mapping in user applications, we devise a novel method that uses the expected channel statistics to allocate memory for cache probing. We also show how an attacker might use the less accurate performance event interface as a timer. Using the T-table based AES implementation in OpenSSL 1.0.1f as an example, we demonstrate how a normal world attacker can steal fine-grained secret in the secure world. We also discuss possible mitigations for the information leakage.


title: Sourdough Technology-A Traditional Way for Wholesome Foods: A Review

abstract: Abstract: In the present era, consumers wish to have a wide range of foods that are nutritious and flavorful and have long shelf life without added preservatives. Sourdough is an important modern fermentation of cereal flours and water based upon an earlier spontaneous process. The sourdough microflora is dominated by lactic acid bacteria and, along with yeast, they play a key role in the fermentation of bread dough. Factors that affect the quality of sourdough are dough yield, temperature, type of starter culture, acidity of the medium, and the substrate. Sourdough is classified into 3 types (Types I, II, and III); the most widely used for commercial production is Type III. The sourdough fermentation has a number of beneficial effects that include prolonged shelf life, accelerated volume gain, delayed staling, improved bread flavor, and good nutritional value. Sourdough also improves sensory characteristics such as loaf volume, evenness of baking, color, aroma, taste, and texture of breads. Sourdough has been reported to contribute to extended shelf life by inhibiting spoilage bacteria and mold growth.


title: Factors that Significantly Impact the Implementation of an Agile Software Development Methodology

abstract: The Internet economy has altered the current rules of software engineering. Traditional development methodologies have proven too cumbersome to meet the rapidly changing requirements and short product cycles demanded by business today. To meet these rapidly changing requirements, software developers have developed agile software development methodologies (SDMs) utilizing iterative development, prototyping, templates, and minimal documentation requirements. This research project investigated agile SDM implementation using an online survey sent to software development practitioners worldwide. This survey data was used to identify factors related to agile SDM implementation. The factors that significantly impacted agile methodology implementations included training, management involvement, access to external resources, and corporation size. Other factors such as using models, having an implementation plan, collocating the development team, and developing software for Internet or intranet use did not significantly impact the implementation of an agile software development methodology. A number of the factors that impact the implementation of an agile development methodology are completely under the control of management. Organizations that are considering implement ting an agile methodology are able to manipulate some of these factors to increase the opportunities for success of their methodology.


title: Towards Universal Paraphrastic Sentence Embeddings

abstract: We consider the problem of learning general-purpose, paraphrastic sentence embeddings based on supervision from the Paraphrase Database (Ganitkevitch et al., 2013). We compare six compositional architectures, evaluating them on annotated textual similarity datasets drawn both from the same distribution as the training data and from a wide range of other domains. We find that the most complex architectures, such as long short-term memory (LSTM) recurrent neural networks, perform best on the in-domain data. However, in out-of-domain scenarios, simple architectures such as word averaging vastly outperform LSTMs. Our simplest averaging model is even competitive with systems tuned for the particular tasks while also being extremely efficient and easy to use. ::: In order to better understand how these architectures compare, we conduct further experiments on three supervised NLP tasks: sentence similarity, entailment, and sentiment classification. We again find that the word averaging models perform well for sentence similarity and entailment, outperforming LSTMs. However, on sentiment classification, we find that the LSTM performs very strongly-even recording new state-of-the-art performance on the Stanford Sentiment Treebank. ::: We then demonstrate how to combine our pretrained sentence embeddings with these supervised tasks, using them both as a prior and as a black box feature extractor. This leads to performance rivaling the state of the art on the SICK similarity and entailment tasks. We release all of our resources to the research community with the hope that they can serve as the new baseline for further work on universal sentence embeddings.


title: Efficacy and safety of a hyaluronic acid filler in subjects treated for correction of midface volume deficiency: a 24 month study

abstract: Background ::: Hyaluronic acid (HA) fillers are an established intervention for correcting facial volume deficiency. Few studies have evaluated treatment outcomes for longer than 6 months. The purpose of this study was to determine the durability of an HA filler in the correction of midface volume deficiency over 24 months, as independently evaluated by physician investigators and subjects.


title: Video pose estimation via medium granularity graphical model with spatial-temporal symmetric constraint part model

abstract: We address the problem of full body human pose estimation in video. Most previous work consider body part, pose or trajectory of body part as basic unit to compose the pose sequence. In contrast, we consider tracklet of body part as the basic unit. Based on this medium granularity representation we develop a spatio-temporal graphical model to select an optimal tracklet for each part in each video segment. In our model, tracklet nodes of symmetric parts are coupled to one node to overcome the double counting problem. Through iterative spatial and temporal parsing, optimal solution is achieved in polynomial time. We apply our model on three publicly available datasets and show remarkable quantitative and qualitative improvements over the state-of-the-art approaches.


Chemometrics and intelligent laboratory systems Tutorial Introduction to multi-layer feed-forward neural networks