title: The Tire as an Intelligent Sensor

abstract: Active safety systems are based upon the accurate and fast estimation of the value of important dynamical variables such as forces, load transfer, actual tire-road friction (kinetic friction) muk, and maximum tire-road friction available (potential friction) mup. Measuring these parameters directly from tires offers the potential for improving significantly the performance of active safety systems. We present a distributed architecture for a data-acquisition system that is based on a number of complex intelligent sensors inside the tire that form a wireless sensor network with coordination nodes placed on the body of the car. The design of this system has been extremely challenging due to the very limited available energy combined with strict application requirements for data rate, delay, size, weight, and reliability in a highly dynamical environment. Moreover, it required expertise in multiple engineering disciplines, including control-system design, signal processing, integrated-circuit design, communications, real-time software design, antenna design, energy scavenging, and system assembly.


title: Margin based permutation variable importance: A stable importance measure for random forest

abstract: Permutation based variable importance measure (VIM) has been widely used in various research fields. For example, in gene expression studies, it has been regarded as a screening tool to select a subset of relevant genes for subsequent analysis or better predictive performance. However, little effort has been devoted to the stability of variable importance measures. In this paper, margin based permutation variable importance measures (VIM-MDs) are proposed, which utilize the similarity between margin distribution before and after random permutation to evaluate the importance of variables. Experiments on six benchmark datasets show that the VIM-MDs outperform permutation based variable importance measure in terms of both global stability and predictive accuracy, which indicates that the proposed method could be used as an effective and stable variable importance measure for random forest.


ERP Assimilation Challenge: An Integrative Framework for a Better Post-Implementation Assimilation


title: KrishnaCam: Using a longitudinal, single-person, egocentric dataset for scene understanding tasks

abstract: We record, and analyze, and present to the community, KrishnaCam, a large (7.6 million frames, 70 hours) egocentric video stream along with GPS position, acceleration and body orientation data spanning nine months of the life of a computer vision graduate student. We explore and exploit the inherent redundancies in this rich visual data stream to answer simple scene understanding questions such as: How much novel visual information does the student see each day? Given a single egocentric photograph of a scene, can we predict where the student might walk next? We find that given our large video database, simple, nearest-neighbor methods are surprisingly adept baselines for these tasks, even in scenes and scenarios where the camera wearer has never been before. For example, we demonstrate the ability to predict the near-future trajectory of the student in broad set of outdoor situations that includes following sidewalks, stopping to wait for a bus, taking a daily path to work, and the lack of movement while eating food.


title: Developing a Generic Framework for E-Government

abstract: Electronic government (e-government) initiatives are pervasive and form a significant part of government investment portfolio in almost all countries around the world. However, understanding of what is meant by e-government is still nascent and becomes complicated because the construct means different things to different people. Consequently, the conceptualization and implementation of e-government programs are diverse and are often difficult to assess and compare across different contexts of application. This paper addresses the following key question: Given the wide variety of visions, strategic agendas, and contexts of application, how may we assess, categorize, classify, compare, and discuss the e-government efforts of various government administrations? In answering this question, we propose a generic e-government framework that will allow for the identification of e-government strategic agendas and key application initiatives that transcend country-specific requirements. In developing the framework, a number of requirements are first outlined. The framework is proposed and described; it is then illustrated using brief case studies from three countries. Finally, findings and limitations are discussed.


Tunneling theory without the transfer-Hamiltonian formalism. I.


title: Aggregated Momentum: Stability Through Passive Damping

abstract: Momentum is a simple and widely used trick which allows gradient-based optimizers to pick up speed along low curvature directions. Its performance depends crucially on a damping coefficient $\beta$. Large $\beta$ values can potentially deliver much larger speedups, but are prone to oscillations and instability; hence one typically resorts to small values such as 0.5 or 0.9. We propose Aggregated Momentum (AggMo), a variant of momentum which combines multiple velocity vectors with different $\beta$ parameters. AggMo is trivial to implement, but significantly dampens oscillations, enabling it to remain stable even for aggressive $\beta$ values such as 0.999. We reinterpret Nesterov's accelerated gradient descent as a special case of AggMo and analyze rates of convergence for quadratic objectives. Empirically, we find that AggMo is a suitable drop-in replacement for other momentum methods, and frequently delivers faster convergence.


title: Boosting Entity Linking Performance by Leveraging Unlabeled Documents

abstract: Modern entity linking systems rely on large collections of documents specifically annotated for the task (e.g., AIDA CoNLL). In contrast, we propose an approach which exploits only naturally occurring information: unlabeled documents and Wikipedia. Our approach consists of two stages. First, we construct a high recall list of candidate entities for each mention in an unlabeled document. Second, we use the candidate lists as weak supervision to constrain our document-level entity linking model. The model treats entities as latent variables and, when estimated on a collection of unlabelled texts, learns to choose entities relying both on local context of each mention and on coherence with other entities in the document. The resulting approach rivals fully-supervised state-of-the-art systems on standard test sets. It also approaches their performance in the very challenging setting: when tested on a test set sampled from the data used to estimate the supervised systems. By comparing to Wikipedia-only training of our model, we demonstrate that modeling unlabeled documents is beneficial.


title: “Finding my own motivation” — A Mixed Methods Study of Exercise and Behaviour Change Support During Oncological Treatment

abstract: BACKGROUND ::: Exercising during oncological treatment is beneficial but challenging for persons with cancer and may require strategies to increase motivation. Behaviour change support, including specific behaviour change techniques (BCTs), have been used to facilitate exercise in persons undergoing oncological treatment, but more detailed knowledge from an individual perspective is needed to inform clinical practice. The aims were to explore the motivational experiences of exercise combined with behaviour change support, and to describe how specific BCTs were valued among persons exercising during oncological treatment. ::: ::: ::: METHODS ::: A mixed-methods study was conducted using semi-structured interviews (n = 18) and a questionnaire (n = 229). Participants with breast, colorectal or prostate cancer who completed or dropped out of a six-month exercise programme during oncological treatment were included. The interviews were analysed with thematic analysis and the questionnaire with descriptive statistics (median and interquartile range). ::: ::: ::: RESULTS ::: The participants underwent a motivational process through the exercise programme. By experiencing 'Health gains and mastery', 'Learning', 'Affinity', 'Commitment', and 'Managing challenges', they found incentives that fostered feelings of autonomy, competence and relatedness, leading to an increased motivation to exercise. Social support from coaches, structuring the physical environment with scheduled sessions, self-monitoring with resistance training log, and feedback based on heart rate monitor and fitness tests were the most valued BCTs. ::: ::: ::: CONCLUSIONS ::: The results indicate the importance of finding incentives and creating an environment that fosters autonomy, competence and relatedness to motivate persons to exercise during oncological treatment. Some BCTs appear particularly useful and may be used by health professionals to increase patients' motivation to exercise.


title: Towards Answering Opinion Questions: Separating Facts From Opinions And Identifying The Polarity Of Opinion Sentences

abstract: Opinion question answering is a challenging task for natural language processing. In this paper, we discuss a necessary component for an opinion question answering system: separating opinions from fact, at both the document and sentence level. We present a Bayesian classifier for discriminating between documents with a preponderance of opinions such as editorials from regular news stories, and describe three unsupervised, statistical techniques for the significantly harder task of detecting opinions at the sentence level. We also present a first model for classifying opinion sentences as positive or negative in terms of the main perspective being expressed in the opinion. Results from a large collection of news stories and a human evaluation of 400 sentences are reported, indicating that we achieve very high performance in document classification (upwards of 97% precision and recall), and respectable performance in detecting opinions and classifying them at the sentence level as positive, negative, or neutral (up to 91% accuracy).


title: Learning Fingerprint Minutiae Location and Type

abstract: For simplicity of pattern recognition system design, a sequential approach consisting of sensing, feature extraction and classification/matching is conventionally adopted, where each stage transforms its input relatively independently. In practice, the interaction between these modules is limited. Some of the errors in this end-to-end sequential processing can be eliminated, especially for the feature extraction stage, by revisiting the input pattern. We propose a feedforward of the original grayscale image data to a feature (minutiae) verification stage in the context of a minutiae-based fingerprint verification system. This minutiae verification stage is based on reexamining the grayscale profile in a detected minutia's spatial neighborhood in the sensed image. We also show that a feature refinement (minutiae classification) stage that assigns one of two class labels to each detected minutia (ridge ending and ridge bifurcation) can improve the matching accuracy by ∼1% and when combined with the proposed minutiae verification stage, the matching accuracy can be improved by ∼3.2% on our fingerprint database.


S-WAVE MASSES AND DECAY PROPERTIES OF D AND DS MESONS


title: Solid-state transformer interfaced PMSG wind energy conversion system

abstract: The solid-state transformer (SST) has been regarded as an emerging technology where emphasis is mainly on the design of the device. To explore its system integration opportunities, this paper proposes and demonstrates a SST interfaced permanent magnet synchronous generator (PMSG) wind energy conversion system. The system integration issues along with wind turbine level control methods have been presented and simulated for power management. Moreover, the lab hardware prototype has been set up, which consists of an induction motor based wind turbine emulator (WTE), PMSG, pulse-width modulation (PWM) rectifier, SST, and resistive load bank. Experiments have been carried out to validate the proposed system and control strategy.


An overview of Natural Language Inference Data Collection : The way forward ?


title: The saving and spending habits of young people

abstract: Over 250 British children and adolescents completed a questionnaire on their sources of personal income (pocket money/allowance, part-time job, gifts), as well as how much they had saved, where it was stored, and for what purpose it was intended. Particular attention was paid to bank accounts. The participants also responded to various attitude statements about money and the economic situation in general. Results showed numerous sex and age, but few class differences. Males received more pocket money and presents than females, and older children more than younger children. Over 80% of the children claimed their parents would not give them extra money if they had spent it all. Regressional analyses showed that the best predictors of regularity of saving, as well as the proportion of money saved, were the more money received; the less money spent in the previous week and total amount of money saved in the previous week. The results are discussed in terms of the limited empirical literature on children's pocket money allowances, particularly with respect to demographic differences. Limitations of the methodology are also reported.


title: Measuring team effectiveness in cyber-defense exercises: a cross-disciplinary case study

abstract: In 2010, IT-security experts from northern European governments and organizations gathered to conduct the first of a series of NATO-led cyber-defense exercises in a pilot attempt of training cyber defense. To gain knowledge on how to assess team effectiveness in cyber-defense exercises, this case study investigates the role of behavioral assessment techniques as a complement to task-based performance measurement. The collected data resulted in a massive data set including system logs, observer reports, and surveys. Six different methods were compared for feasibility in assessing the teams’ performance, including automated availability check, exploratory sequential data analysis, and network intrusion detection system attack analysis. In addition, observer reports and surveys were used to collect aspects relating to team structures and processes, aiming to discover whether these aspects can explain differences in effectiveness. The cross-disciplinary approach and multiple metrics create possibilities to study not only the performance-related outcome of the exercise, but also why this result is obtained. The main conclusions found are (1) a combination of technical performance measurements and behavioral assessment techniques are needed to assess team effectiveness, and (2) cyber situation awareness is required not only for the defending teams, but also for the observers and the game control.


title: Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods

abstract: Preface 1. Introduction 2. Some modern image analysis tools 3. Image modeling and representation 4. Image denoising 5. Image deblurring 6. Image inpainting 7. Image processing: segmentation Bibliography Index.


title: Automatic dynamic web service composition: A survey and problem formalization

abstract: The aim of Web service composition is to arrange multiple services into workflows supplying complex user needs. Due to the huge amount of Web services and the need to supply dynamically varying user goals, it is necessary to perform the composition automatically. The objective of this article is to overview the issues of automatic dynamic Web service composition. We discuss the issues related to the semantics of services, which is important for automatic Web ser- vice composition. We propose a problem formalization contributing to the formal definition of the pre-/post-conditions, with possible value restrictions, and their re- lation to the semantics of services. We also provide an overview of several existing approaches dealing with the problem of Web service composition and discuss the current achievements in the field and depict some open research areas.


title: Measuring cognitive and psychological engagement: Validation of the Student Engagement Instrument

abstract: Abstract A review of relevant literatures led to the construction of a self-report instrument designed to measure two subtypes of student engagement with school: cognitive and psychological engagement. The psychometric properties of this measure, the Student Engagement Instrument (SEI), were assessed based on responses of an ethnically and economically diverse urban sample of 1931 ninth grade students. Factor structures were obtained using exploratory factor analyses (EFAs) on half of the dataset, with model fit examined using confirmatory factor analyses (CFAs) on the other half of the dataset. The model displaying the best empirical fit consisted of six factors, and these factors correlated with expected educational outcomes. Further research is suggested in the iterative process of developing the SEI, and the implications of these findings are discussed.


A Fully Convolutional Neural Network for Cardiac Segmentation Input DICOM Image Output Segmentation Mask


title: Towards a Benchmark for ETL Workflows

abstract: Extraction–Transform–Load (ETL) processes comprise complex data workflows, which are responsible for the maintenance of a Data Warehouse. Their practical importance is denoted by the fact that a plethora of ETL tools currently constitutes a multi-million dollars market. However, each one of them follows a different design and modeling technique and internal language. So far, the research community has not agreed upon the basic characteristics of ETL tools. Hence, there is a necessity for a unified way to assess ETL workflows. In this paper, we investigate the main characteristics and peculiarities of ETL processes and we propose a principled organization of test suites for the problem of experimenting with ETL scenarios.


title: Liveness Detection for Face Recognition 9

abstract: Biometrics is an emerging technology that enables uniquely recognizing humans based upon one or more intrinsic physiological or behavioral characteristics, such as faces, fingerprints, irises, voices (Ross et al., 2006). However, spoofing attack (or copy attack) is still a fatal threat for biometric authentication systems (Schukers, 2002). Liveness detection, which aims at recognition of human physiological activities as the liveness indicator to prevent spoofing attack, is becoming a very active topic in field of fingerprint recognition and iris recognition (Schuckers, 2002; Bigun et al., 2004; Parthasaradhi et al., 2005; Antonelli et al., 2006). In face recognition community, although numerous recognition approaches have been presented, the effort on anti-spoofing is still very limited (Zhao et al., 2003). The most common faking way is to use a facial photograph of a valid user to spoof face recognition systems. Nowadays, video of a valid user can also be easily captured by needle camera for spoofing. Therefore anti-spoof problem should be well solved before face recognition could be widely applied in our life. Most of the current face recognition works with excellent performance, are based on intensity images and equipped with a generic camera. Thus, an anti-spoofing method without additional device will be preferable, since it could be easily integrated into the existing face recognition systems. In Section 2, we give a brief review of spoofing ways in face recognition and some related work. The potential clues will be also presented and commented. In Section 3, a real-time liveness detection approach is presented against photograph spoofing in a non-intrusive manner for face recognition, which does not require any additional hardware except for a generic webcamera. In Section 4, databases are introduced for eyeblink-based anti-spoofing. Section 5 presents an extensive set of experiments to show effectiveness of our approach. Discussions are in Section 6.


title: Developing language in a developing body: the relationship between motor development and language development *

abstract: During the first eighteen months of life, infants acquire and refine a whole set of new motor skills that significantly change the ways in which the body moves in and interacts with the environment. In this review article, I argue that motor acquisitions provide infants with an opportunity to practice skills relevant to language acquisition before they are needed for that purpose ; and that the emergence of new motor skills changes infants’ experience with objects and people in ways that are relevant for both general communicative development and the acquisition of language. Implications of this perspective for current views of co-occurring language and motor impairments and for methodology in the field of child language research are also considered. The emergence of language is one of the crowning achievements of the first two years of life. We now know a great deal about developmental precursors to language and its timetable of emergence in both typically and atypically developing children ; and it is widely acknowledged that there are links between achievements in the cognitive and social communication domains and the emerging language system. During the first eighteen months of life, however, infants also acquire and refine a whole set of new motor skills that fundamentally transform their experiences with objects and people. Indeed, independent locomotion is one of, if not the developmental event most eagerly anticipated by proud


title: Improving pedestrian detection

abstract: Pedestrian detection is an active problem in computer vision research, with applications in robotics, self-driving cars and surveillance. It involves generating bounding boxes to indicate the location of every pedestrian in an input image. This paper proposes a method to augment a basic pedestrian detector with a Convolutional Neural Network. An implementation of the proposed algorithm was trained and tested on an ensemble of widely used pedestrian detection datasets, and achieved a 30% decrease in the FPPI (false positives per image) metric over an unaugmented HOG detector. However, it also increased the miss rate by 3.21%.


COMMUNICATION SCIENCES AND ENGINEERING IX. SPEECH COMMUNICATION Academic and Research Staff


Temporal Random Indexing: a Tool for Analysing Word Meaning Variations in News


title: Implementation of Low Area and High Data Throughput CRC Design on FPGA

abstract: This paper introduces an alternative way to implement CRC hardware on FPGA to speed up the CRC calculation while maintaining a verylow area. The traditional implementations with high data throughput have very large area. In Ourdesign weused the CRC Reduced Table Lookup Algorithm (RTLA) for achieving very low area, while using pipelined architecture for having high data throughput.In our implementation we have reached a data throughput of more than 100 Gbps when the data input width is 200 bits or more, and relatively fixed maximum frequency which make doubling the data width approximately doubles the data throughput. The proposed design will be suitable candidate for manycommunication protocols such as 100 Gbps Ethernet.


title: Media Influence on Sexual Activity and Contraceptive Use: A Cross Sectional Survey among Young Women in Urban Nigeria.

abstract: This study assessed the relationship between recent exposure to family planning (FP) messages in the media (newspaper, radio, television, and mobile phones) and use of modern contraceptive methods among women aged 15-24 years living in six cities in Nigeria. Logistic regression models were used to predict recent media exposure to FP messages and its association with sexual experience and modern contraceptive method use. About 45% of our sample had ever had sex with only a quarter of them using a modern contraceptive method at the time of survey. Approximately 71% of our sample was exposed to FP messages in the media within the three months preceding the survey. The main sources of media exposure were mobile phones (48%), radio (37%), and television (29%). Controlling for relevant factors, recent media exposure to FP messages predicted both sexual experience and use of modern contraceptive methods, although there were city-level differences.


title: Object Tracking in Wireless Sensor Networks: Challenges and Solutions

abstract: Wireless Sensor Networks (WSNs) are small, inexpensive and battery-operated sensor nodes that are deployed over a geographical area. WSNs are used in many applications such border patrolling, military intrusion detection, wildlife animal monitoring, surveillance of natural disasters and healthcare systems. Mobile object tracking is a vital task in all these applications. The goal of this work is to highlight the most important challenges in the field of object tracking and provide a survey of the WSN architectural design and implementation approaches for tackling this problem. To that end, we analyze how each approach responds to each challenge and where it falls short. This analysis should provide researchers with a state-of-the-art review and inspire them to propose novel solutions.


title: Predicting Student Performance by Using Data Mining Methods for Classification

abstract: Data mining methods are often implemented at advanced universities today for analyzing available data and extracting information and knowledge to support decision-making. This paper presents the initial results from a data mining research project implemented at a Bulgarian university, aimed at revealing the high potential of data mining applications for university management.


title: Performance evaluation of 1-point-RANSAC visual odometry

abstract: Monocular visual odometry is the process of computing the egomotion of a vehicle purely from images of a single camera. This process involves extracting salient points from consecutive image pairs, matching them, and computing the motion using standard algorithms. This paper analyzes one of the most important steps toward accurate motion computation, which is outlier removal. The random sample consensus (RANSAC) has been established as the standard method for model estimation in the presence of outliers. RANSAC is an iterative method, and the number of iterations necessary to find a correct solution is exponential in the minimum number of data points needed to estimate the model. It is therefore of utmost importance to find the minimal parameterization of the model to estimate. For unconstrained motion [six degrees of freedom (DoF)] of a calibrated camera, this would be five correspondences. In the case of planar motion, the motion model complexity is reduced (three DoF) and can be parameterized with two points. In this paper we show that when the camera is installed on a nonholonomic wheeled vehicle, the model complexity reduces to two DoF and therefore the motion can be parameterized with a single-point correspondence. Using a single-feature correspondence for motion estimation is the lowest model parameterization possible and results in the most efficient algorithm for removing outliers, which we call 1-point RANSAC. To support our method, we run many experiments on both synthetic and real data and compare the performance with state-of-the-art approaches and with different vehicles, both indoors and outdoors. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.


title: Demand-adaptive Clothing Image Retrieval Using Hybrid Topic Model

abstract: This paper proposes a novel approach to meet users' multi-dimensional requirements in clothing image retrieval. It enables users to add search conditions by modifying the color, texture, shape and attribute descriptors of the query images to further refine their requirements. We propose the Hybrid Topic (HT) model to learn the intricate semantic representation of the descriptors above. The model provides an effective multi-dimensional representation of clothes and is able to perform automatic image annotation by probabilistic reasoning from image search. Furthermore, we develop a demand-adaptive retrieval strategy which refines users' specific requirements and removes users' unwanted features. Our experiments show that the HT method significantly outperforms the deep neural network methods. The accuracy could be further improved in cooperation with image annotation and demand-adaptive retrieval strategy.


title: Galectin Expression Profiling Identifies Galectin-1 and Galectin-9Δ5 as Prognostic Factors in Stage I/II Non-Small Cell Lung Cancer

abstract: Approximately 30–40% of the patients with early stage non-small cell lung cancer (NSCLC) will present with recurrent disease within two years of resection. Here, we performed extensive galectin expression profiling in a retrospective study using frozen and paraffin embedded tumor tissues from 87 stage I/II NSCLC patients. Our data show that galectin mRNA expression in NSCLC is confined to galectin-1, -3, -4, -7, -8, and -9. Next to stage, univariable Cox regression analysis identified galectin-1, galectin-9FL and galectin-9Δ5 as possible prognostic markers. Kaplan-Meier survival estimates revealed that overall survival was significantly shorter in patients that express galectin-1 above median levels, i.e., 23.0 (2.9–43.1) vs. 59.9 (47.7–72.1) months (p = 0.020) as well as in patients that express galectin-9Δ5 or galectin-9FL below the median, resp. 59.9 (41.9–75.9) vs. 32.8 (8.7–56.9) months (p = 0.014) or 23.2 (−0.4–46.8) vs. 58.9 (42.9–74.9) months (p = 0.042). All three galectins were also prognostic for disease free survival. Multivariable Cox regression analysis showed that for OS, the most significant prognostic model included stage, age, gal-1 and gal-9Δ5 while the model for DFS included stage, age and gal-9Δ5. In conclusion, the current study confirms the prognostic value of galectin-1 and identifies galectin-9Δ5 as novel potential prognostic markers in early stage NSCLC. These findings could help to identify early stage NSCLC patients that might benefit most from adjuvant chemotherapy.


title: Engaging learners in cybersecurity careers: lessons from the launch of the national cyber league

abstract: Educators and sponsors endorse competitions as a strong, positive influence on career choice. However, empirical studies of cybersecurity competitions are lacking, and evidence from computer science and mathematics competitions has been mixed. Here we report initial results from an ongoing study of the National Cyber League to provide a glimpse of the role of competitions in fostering cybersecurity career engagement. Preliminary results suggest that cyber competitions attract experienced individuals who will remain in the profession for the long-term, but future research is needed to understand how cyber competitions may engage women and those new to the field.


title: Antitrust, the Internet, and the Economics of Networks

abstract: Network industries, including the Internet, have shown significant growth, substantial competition, and rapid innovation. This Chapter examines antitrust policy towards network industries. The discussion considers the policy implications of various concepts in the economics of networks: natural monopoly, network economic effects, vertical exclusion, and dynamic efficiency. Our analysis finds that antitrust policy makers should not presume that network industries are more subject to monopolization than other industries. We find that deregulation and the strength of competition in network industries have removed justifications for structural separation as a remedy. Also, we argue that that deregulation and competition have effectively eliminated support for application of the essential facilities doctrine. Antitrust policy in network industries should be guided by considerations of dynamic efficiency.


title: Implementation of Industry 4.0 Technologies: What Can We Learn from the Past?

abstract: The fourth industrial revolution (“Industry 4.0”) promises a multifaceted paradigm shift in manufacturing. This study aims to gain an in-depth understanding of what the transition to Industry 4.0 may involve. We do so by looking for, and learning from, experiences of similar shifts in the past. Specifically, we conduct a structured literature review of the rich operations management literature on Advanced Manufacturing Technologies (AMTs). AMTs were arguably central in the shift from the second to the third revolution in the second half of the 20th century. A review of the existing AMT literature allows us to infer relevant observations, theories, and findings for the emerging shift into Industry 4.0. We employ the review process defined by Tranfield et al. (2003).


title: Effects of Coffee on Ambulatory Blood Pressure in Older Men and Women : A Randomized Controlled Trial

abstract: Abstract —This study assessed the effects of regular coffee drinking on 24-hour ambulatory blood pressure (ABP) in normotensive and hypertensive older men and women. Twenty-two normotensive and 26 hypertensive, nonsmoking men and women, with a mean age of 72.1 years (range, 54 to 89 years), took part in the study. After 2 weeks of a caffeine-free diet, subjects were randomized to continue with the caffeine-free diet and abstain from caffeine-containing drinks or drink instant coffee (5 cups per day, equivalent to 300 mg caffeine per day) in addition to the caffeine-free diet for a further 2 weeks. Change in systolic and diastolic blood pressures (SBP, DBP) determined by 24-hour ambulatory BP monitoring showed significant interactions between coffee drinking and hypertension status. In the hypertensive group, rise in mean 24-hour SBP was greater by 4.8 (SEM, 1.3) mm Hg ( P =0.031) and increase in mean 24-hour DBP was higher by 3.0 (1.0) mm Hg ( P =0.010) in coffee drinkers than in abstainers. There were no significant differences between abstainers and coffee drinkers in the normotensive group for 24-hour, daytime, or nighttime SBP or DBP. In older men and women with treated or untreated hypertension, ABP increased in coffee drinkers and decreased in abstainers. Restriction of coffee intake may be beneficial in older hypertensive individuals.


title: Agromorphological diversity of local Bambara groundnut (Vigna subterranea (L.) Verdc.) collected in Benin

abstract: Bambara groundnut (Vigna subterranea (L.) Verdc.) is a neglected and less exploited species in Benin. However, the rational use of the different landraces available involves an appropriate knowledge of their characteristics. This study examined the characteristics of 52 local landraces of Bambara groundnut traditionally cultivated in Benin based on 12 qualitative and 11 quantitative variables, in order to expose those of good qualities. After a classification based on the morphological variables of the seeds, the experimental field was installed in a completely randomized device of blocks repeated three times. The collected data was analyzed with Statistica 7.1 and NTSYS PC 2.2. softwares using qualitative characteristics (Growth’s type, Stem’s hairiness, Flower’s colour, Leave’s shape etc.). As result, the number of morphotypes increased from 18 (out of classification) to 47. Therefore, it appears that the Benin collection of Bambara groundnut is broad in morphological variability. The analysis of the principal component of the quantitative variables revealed three specific classes within the collection. The most efficient variety which belonged to the second class, had a high field yield per hectare (> 1 t/ha). The Souhouloumankpa variety yielded the earliest (103 days after sowing) in addition to the high field yield. The study also showed a positive relationship between 100 seed’s weight, their length, the number of seeds per plant and the seed’s field yield in kg/ha. For a sustainable preservation and an improvement of the available resources, a molecular characterization of the Bambara groundnut landraces in Benin Republic needs to be done.


title: Query processing on smart SSDs: opportunities and challenges

abstract: Data storage devices are getting "smarter." Smart Flash storage devices (a.k.a. "Smart SSD") are on the horizon and will package CPU processing and DRAM storage inside a Smart SSD, and make that available to run user programs inside a Smart SSD. The focus of this paper is on exploring the opportunities and challenges associated with exploiting this functionality of Smart SSDs for relational analytic query processing. We have implemented an initial prototype of Microsoft SQL Server running on a Samsung Smart SSD. Our results demonstrate that significant performance and energy gains can be achieved by pushing selected query processing components inside the Smart SSDs. We also identify various changes that SSD device manufacturers can make to increase the benefits of using Smart SSDs for data processing applications, and also suggest possible research opportunities for the database community.


title: Efficient and extensible algorithms for multi query optimization

abstract: Complex queries are becoming commonplace, with the growing use of decision support systems. These complex queries often have a lot of common sub-expressions, either within a single query, or across multiple such queries run as a batch. Multi-query optimization aims at exploiting common sub-expressions to reduce evaluation cost. Multi-query optimization has hither-to been viewed as impractical, since earlier algorithms were exhaustive, and explore a doubly exponential search space. In this paper we demonstrate that multi-query optimization using heuristics is practical, and provides significant benefits. We propose three cost-based heuristic algorithms: Volcano-SH and Volcano-RU, which are based on simple modifications to the Volcano search strategy, and a greedy heuristic. Our greedy heuristic incorporates novel optimizations that improve efficiency greatly. Our algorithms are designed to be easily added to existing optimizers. We present a performance study comparing the algorithms, using workloads consisting of queries from the TPC-D benchmark. The study shows that our algorithms provide significant benefits over traditional optimization, at a very acceptable overhead in optimization time.


title: Learning from lived experience for the improvement of health-supportive built environment practice

abstract: ABSTRACTThis paper presents “lessons” for the planning, design, construction and management of built environments to be supportive of the health of residents and other users. These lessons are an o...


title: Independent motion detection with event-driven cameras

abstract: Unlike standard cameras that send intensity images at a constant frame rate, event-driven cameras asynchronously report pixel-level brightness changes, offering low latency and high temporal resolution (both in the order of micro-seconds). As such, they have great potential for fast and low power vision algorithms for robots. Visual tracking, for example, is easily achieved even for very fast stimuli, as only moving objects cause brightness changes. However, cameras mounted on a moving robot are typically non-stationary and the same tracking problem becomes confounded by background clutter events due to the robot ego-motion. In this paper, we propose a method for segmenting the motion of an independently moving object for event-driven cameras. Our method detects and tracks corners in the event stream and learns the statistics of their motion as a function of the robot's joint velocities when no independently moving objects are present. During robot operation, independently moving objects are identified by discrepancies between the predicted corner velocities from ego-motion and the measured corner velocities. We validate the algorithm on data collected from the neuromorphic iCub robot. We achieve a precision of ~ 90 % and show that the method is robust to changes in speed of both the head and the target.


title: Transition-Aware Human Activity Recognition Using Smartphones

abstract: This work presents the Transition-Aware Human Activity Recognition (TAHAR) system architecture for the recognition of physical activities using smartphones. It targets real-time classification with a collection of inertial sensors while addressing issues regarding the occurrence of transitions between activities and unknown activities to the learning algorithm. We propose two implementations of the architecture which differ in their prediction technique as they deal with transitions either by directly learning them or by considering them as unknown activities. This is accomplished by combining the probabilistic output of consecutive activity predictions of a Support Vector Machine (SVM) with a heuristic filtering approach. The architecture is validated over three case studies that involve data from people performing a broad spectrum of activities (up to 33), while carrying smartphones or wearable sensors. Results show that TAHAR outperforms state-of-the-art baseline works and reveal the main advantages of the architecture.


title: Positive influence maximization in signed social networks based on simulated annealing

abstract: Abstract Current studies of influence maximization focus almost exclusively on unsigned social networks ignoring the polarities of the relationships between users. Influence maximization in signed social networks containing both positive relationships ( e.g. , friend or like) and negative relationships ( e.g. , enemy or dislike) is still a challenging problem which remains much open. A few studies made use of greedy algorithms to solve the problem of positive influence or negative influence maximization in signed social networks. Although greedy algorithm is able to achieve a good approximation, it is computational expensive and not efficient enough. Aiming at this drawback, we propose an alternative method based on Simulated Annealing (SA) for the positive influence maximization problem in this paper. Additionally, we also propose two heuristics to speed up the convergence process of the proposed method. Comprehensive experiments results on three signed social network datasets, Epinions, Slashdot and Wikipedia, demonstrate that our method can yield similar or better performance than the greedy algorithms in terms of positive influence spread but run faster.


title: How do farmers make decisions in a land degradation context ? : a case study from northern Vietnam

abstract: Stemming from a three villages case study in Northern Vietnam, this paper examines how farmers have coped with and adapted to land degradation in the uplands. It assesses how policies and research programs implemented in the area have affected local determinants, namely farmers’ perception of the environment and local rules, and how in turn the latter impacted individual and community behaviour. Following a qualitative approach that draws from ethnography and institutional analysis, it hopes to contribute to the current literature by testing the effectiveness of this approach in understanding farmers’ decisions in a land degradation context and by highlighting new determinants that have not been so far much considered in the analysis of land use change. Land degradation has attracted the attention of donors, researchers and policy-makers in Vietnam over the past two decades. Deforestation and soil erosion were identified as the key issues to address, and officially justified the design of new national policies and research programmes. Since the 90s, specific policies have targeted uplands management in Vietnam, including forest land allocation, sedentarisation and reforestation programmes. Improving (or substituting) ethnic minorities’ land management systems and reforesting barren hills have been two major goals for these government initiatives. If some of these objectives (such as reducing significantly shifting cultivation practices) have been reached, their success in improving people’s livelihoods and uplands’ environmental attributes has been largely challenged. On the contrary, social and economic inequities between mountainous and deltaic areas have increased over the last decade. At the same time, considerable research effort has focused on developing soil conservation and sustainable land management practices. However, although some results were technically promising, very few of them have been adopted by farmers. Recognition for this shortcoming emerged within the scholars’ community in the late 90s. Main causes were identified as a non integration of socio-economic determinants within the development of technical solutions and a focus on the plot level with no consideration of how upscaling results. Stemming from this recognition, a new research paradigm emerged, guiding new research and development projects on land degradation in tropical countries. The latter have adopted a participatory, interdisciplinary, communityand catchment-based framework. Several research projects have been implemented in Vietnam following this new line of scientific enquiry. However, research techniques are still hardly transformed into farmers’ practices. This paper aims to highlight new key determinants for successful policies and research projects addressing land management issues by analysing farmers decisions regarding land use in three villages of Northern Vietnam, including one village in which research programmes on soil erosion have been implemented. In these three villages, farmers have practised shifting cultivation in the uplands for a few decades, cultivation annual crops such as cassava, arrowroot and taro. Annual cropping has been pointed out both by local authorities and researchers as highly soil erosive. Policies modifying land tenure, restricting land use and encouraging reforestation have been implemented nationwide since the late 90s. At the same time, researchers have organised in one village farmers’ field schools and have proposed soil conservation practices to limit land degradation. Recently, all farmers stopped annual cropping and have planted monoculture tree plantations or let land under fallow. This research work analyses to which extent policies and research works have contributed to land use change in the area, by examining farmers’ perception of land degradation and farmers decision-making process. It is not a research project or policy evaluation, in the sense that it doesn’t seek to systematically identify the necessary and sufficient factors for the adoption of sustainable land management practices. It rather seeks to link observed farmers’ decisions with a range of factors and assess the relative contribution of these factors in farmers’ behaviour. Data collection and analysis rely on an approach drawing from ethnography and institutional analysis. Semantic realism is used as an ontological basis to address farmers’ perception of uplands and soil erosion. It distinguishes ‘brute facts’ and ‘institutional facts’ to examine the social function of the term ‘land degradation’ and study how these meanings are shared by different social groups. Then institutional analysis is applied to decrypt farmers’ decisions and behaviour, using a refined version of the Institutional Analysis and Development (IAD) framework developed by Ostrom. The refined version proposed by the present research work integrates an historical perspective and pays a special attention to the social constructions of land degradation by local actors. Results suggest that the research projects have had little impact on farmers’ perception of uplands and land degradation in the study area. Narratives brought up by policies were much more powerful in impacting farmers’ beliefs. However, institutional analysis showed that the shift from annual cropping to tree plantations in the area was rather an accident than the result of farmers’ new beliefs on forests and reforestation incentives provided by national policies. Narratives on forest benefits carried out along with policies, even if assimilated into individual’s imagination have had little impact on final farmers’ decision. The collapse of local rules, due to a combination of soil fertility decrease and change in land tenure, was the decisive factor in land use change. This study defends that, when natural resources are managed by local users, local studies integrating acute models of individual behaviour have marked assets over meso or macro scale studies. Though powerful to analyse the contribution of aggregated or macro-scale factors at the regional or national level, the latter might miss decisive factors that are only observable at the community level. It argues that, when seeking to explain human behaviour, using a socially constructed view of nature is a necessary approach to assess the relative impact of external forces on individual decisions. Lastly, it showed that when examining commons management, individual decisions should be analysed together with community dynamics. In the present case study, collective determinants were as much responsible for land use change as individual strategy.


On the Connection of Deep Fusion to Ensembling


title: High-speed hyperspectral video acquisition with a dual-camera architecture

abstract: We propose a novel dual-camera design to acquire 4D high-speed hyperspectral (HSHS) videos with high spatial and spectral resolution. Our work has two key technical contributions. First, we build a dual-camera system that simultaneously captures a panchromatic video at a high frame rate and a hyperspectral video at a low frame rate, which jointly provide reliable projections for the underlying HSHS video. Second, we exploit the panchromatic video to learn an over-complete 3D dictionary to represent each band-wise video sparsely, and a robust computational reconstruction is then employed to recover the HSHS video based on the joint videos and the self-learned dictionary. Experimental results demonstrate that, for the first time to our knowledge, the hyperspectral video frame rate reaches up to 100fps with decent quality, even when the incident light is not strong.


Computational Stereo


title: Enhancing the Input Representation: From Complexity to Simplicity

abstract: We introduce an efficient algorithm for mining informative combinations of attribute-values for a given task. We use informative attribute-values to enhance the input representation of data. We apply our approach to coreference resolution using a simple set of attributes like syntactic roles and string match. With the enhanced representation, a simple coreference model outperforms more complex state-of-the-art models by a large margin. The use of the enhanced representation results in robust improvements in both in-domain and out-of-domain evaluations.


title: Regressing Heatmaps for Multiple Landmark Localization Using CNNs

abstract: We explore the applicability of deep convolutional neural networks (CNNs) for multiple landmark localization in medical image data. Exploiting the idea of regressing heatmaps for individual landmark locations, we investigate several fully convolutional 2D and 3D CNN architectures by training them in an end-to-end manner. We further propose a novel SpatialConfiguration-Net architecture that effectively combines accurate local appearance responses with spatial landmark configurations that model anatomical variation. Evaluation of our different architectures on 2D and 3D hand image datasets show that heatmap regression based on CNNs achieves state-of-the-art landmark localization performance, with SpatialConfiguration-Net being robust even in case of limited amounts of training data.


title: Plant-growth-promoting compounds produced by two agronomically important strains of Azospirillum brasilense, and implications for inoculant formulation

abstract: We evaluated phytohormone and polyamine biosynthesis, siderophore production, and phosphate solubilization in two strains (Cd and Az39) of Azospirillum brasilense used for inoculant formulation in Argentina during the last 20 years. Siderophore production and phosphate solubilization were evaluated in a chemically defined medium, with negative results. Indole 3-acetic acid (IAA), gibberellic acid (GA3), and abscisic acid (ABA) production were analyzed by gas chromatography-mass spectrometry. Ethylene, polyamine, and zeatin (Z) biosynthesis were determined by gas chromatography-flame ionization detector and high performance liquid chromatography (HPLC-fluorescence and -UV), respectively. Phytohormones IAA, Z, GA3, ABA, ethylene, and growth regulators putrescine, spermine, spermidine, and cadaverine (CAD) were found in culture supernatant of both strains. IAA, Z, and GA3 were found in all two strains; however, their levels were significantly higher (p < 0.01) in Cd (10.8, 2.32, 0.66 μg ml−1). ABA biosynthesis was significantly higher (p < 0.01) in Az39 (0.077 μg ml−1). Ethylene and polyamine CAD were found in all two strains, with highest production in Cd cultured in NFb plus l-methionine (3.94 ng ml−1 h−1) and Az39 cultured in NFb plus l-lysine (36.55 ng ml−1 h−1). This is the first report on the evaluation of important bioactive molecules in strains of A. brasilense as potentially capable of direct plant growth promotion or agronomic yield increase. Az39 and Cd showed differential capability to produce the five major phytohormones and CAD in chemically defined medium. This fact has important technological implications for inoculant formulation as different concentrations of growth regulators are produced by different strains or culture conditions.


title: Road Segmentation Using CNN with GRU

abstract: This paper presents an accurate and fast algorithm for road segmentation using convolutional neural network (CNN) and gated recurrent units (GRU). For autonomous vehicles, road segmentation is a fundamental task that can provide the drivable area for path planning. The existing deep neural network based segmentation algorithms usually take a very deep encoder-decoder structure to fuse pixels, which requires heavy computations, large memory and long processing time. Hereby, a CNN-GRU network model is proposed and trained to perform road segmentation using data captured by the front camera of a vehicle. GRU network obtains a long spatial sequence with lower computational complexity, comparing to traditional encoder-decoder architecture. The proposed road detector is evaluated on the KITTI road benchmark and achieves high accuracy for road segmentation at real-time processing speed.


title: Via-in-Trench: A Revolutionary Panel-Based Package RDL Configuration Capable of 200-450 IO/mm/Layer, an Innovation for More-Than-Moore System Integration

abstract: This paper presents, for the first time, a novel silicon damascene like via-in-trench (ViT) interconnect for panel-scale package redistribution layer (RDL) configuration. The panel scale damascene RDL in this paper comprises of ultra-fine copper embedded trenches and microvias with diameter equal to the width of trenches using a 5 µm thick dry film photosensitive dielectric. A 140 µm thick glass substrate is used as the core material. The new panel scalable ViT interconnect is targeted for low cost, next generation 2D and 2.5D interposers and high density packages. The ViT RDL is integrated with 2 µm diameter microvias with 2.5 µm half-line pitch copper traces embedded in a 5 µm thick dry film photo-imageable dielectric (PID) polymer. This RDL integration directly translates to IO density of 200 IO/mm/layer. IO/mm/layer, as defined by Intel, is the number of wires routed per mm of die edge on each layer of package substrate. There is no capture pad required for ViT interconnect demonstrated in this paper. The routing Cu trace is aligned directly on top of microvia instead of the conventional via-capture pad-trace interconnect configuration. The fabrication of such a high density RDL is achieved by patterning a trench over via and then fully filling with copper. Conventional i-line (365 nm) photolithography, widely used for patterning PWB and package substrates, was employed for fine trenches formation as well as small microvias in the PID. An advanced 5 µm thick PID film IF4605 was selected for build-up layers. Experimental results showed that microvias with diameters of 2 µm and trenches with half-line pitch of 2.5 µm were achieved in 5 µm thick IF dry film. Traces with half-line pitch of 1 µm were demonstrated in a 3 µm thick liquid photo resist film. The aspect ratios were 2.5 for dry film PID and 3 for liquid photo-resist respectively. The best interconnection density in terms of IO/mm/layer was calculated to be 200 using dry film PID and can be extended to 450 using thinner PIDs. For comparison, the IO density for state-of-the-art organic interposer was 40 by using semi-additive process (SAP). The embedded trench technology breaks through the limit of SAP and achieves 5-10X interconnect density compared to SAP. The ViT interconnect is a revolutionary package RDL configuration to meet the requirements of future package substrates for high performance computing, high bandwidth memory and micro-miniaturized system applications. The demonstration of ViT RDL configuration on thin glass substrate with L/S/Via/Pitch of 2.5/2.5/2/20 µm using embedded trench approach will be presented and the fabrication processes will be described in detail.


title: Binarization of degraded document image based on feature space partitioning and classification

abstract: In this paper, we propose a new algorithm for the binarization of degraded document images. We map the image into a 2D feature space in which the text and background pixels are separable, and then we partition this feature space into small regions. These regions are labeled as text or background using the result of a basic binarization algorithm applied on the original image. Finally, each pixel of the image is classified as either text or background based on the label of its corresponding region in the feature space. Our algorithm splits the feature space into text and background regions without using any training dataset. In addition, this algorithm does not need any parameter setting by the user and is appropriate for various types of degraded document images. The proposed algorithm demonstrated superior performance against six well-known algorithms on three datasets.


title: Software cost estimation using computational intelligence techniques

abstract: This paper presents computational intelligence techniques for software cost estimation. We proposed a new recurrent architecture for Genetic Programming (GP) in the process. Three linear ensembles based on (i) arithmetic mean (ii) geometric mean and (iii) harmonic mean are implemented. We also performed GP based feature selection. The efficacy of these techniques viz Multiple Linear Regression, Polynomial Regression, Support Vector Regression, Classification and Regression Tree, Multivariate Adaptive Regression Splines, Multilayer FeedForward Neural Network, Radial Basis Function Neural Network, Counter Propagation Neural Network, Dynamic Evolving Neuro-Fuzzy Inference System, Tree Net, Group Method of Data Handling and Genetic Programming has been tested on the International Software Benchmarking Standards Group (ISBSG) release 10 dataset. Ten-fold cross validation is performed throughout the study. The results obtained from our experiments indicate that new recurrent architecture for Genetic Programming outperformed all the other techniques.


title: Word Emdeddings through Hellinger PCA

abstract: Word embeddings resulting from neural language models have been shown to be a great asset for a large variety of NLP tasks. However, such architecture might be difficult and time-consuming to train. Instead, we propose to drastically simplify the word embeddings computation through a Hellinger PCA of the word co- occurence matrix. We compare those new word embeddings with some well-known embeddings on named entity recognition and movie review tasks and show that we can reach similar or even better performance. Although deep learning is not really necessary for generating good word embeddings, we show that it can provide an easy way to adapt embeddings to specific tasks.


title: Trait salience in the spontaneous self-concept.

abstract: A discrimination theory of selective perception was used to predict that a given trait would be spontaneously salient in a person's self-concept to the exten that this trait was distinctive for the person within her or his social groups. Sixth-grade students' general and physical spontaneous self-concepts were elicited in their classroom settings. The distinctiveness within the classroom of each student's characteristics on each of a variety of dimensions was determined, and it was found that in a majority of cases the dimension was significantly more salient in the spontaneous self-concepts of those students whose characteristic on thedimension was more distinctive. Also reported are incidental findings which include a description of the contents of spontaneous self-comcepts as well as determinants of their length and of the spontaneous mention of one's sex as part of one's self-concept.


title: Learning Clip Representations for Skeleton-Based 3D Action Recognition

abstract: This paper presents a new representation of skeleton sequences for 3D action recognition. Existing methods based on hand-crafted features or recurrent neural networks cannot adequately capture the complex spatial structures and the long-term temporal dynamics of the skeleton sequences, which are very important to recognize the actions. In this paper, we propose to transform each channel of the 3D coordinates of a skeleton sequence into a clip. Each frame of the generated clip represents the temporal information of the entire skeleton sequence and one particular spatial relationship between the skeleton joints. The entire clip incorporates multiple frames with different spatial relationships, which provide useful spatial structural information of the human skeleton. We also propose a multitask convolutional neural network (MTCNN) to learn the generated clips for action recognition. The proposed MTCNN processes all the frames of the generated clips in parallel to explore the spatial and temporal information of the skeleton sequences. The proposed method has been extensively tested on six challenging benchmark datasets. Experimental results consistently demonstrate the superiority of the proposed clip representation and the feature learning method for 3D action recognition compared to the existing techniques.


title: Modeling of attraction force generated by interdigital electrodes for electroadhesive robots

abstract: A new direct method for calculating the electrostatic force of electroadhesive robots generated by interdigital electrodes is presented. Here, series expansion is employed to express the spatial potential, and point matching method is used in dealing with some boundary conditions. The attraction force is calculated using the Maxwell stress tensor formula. The accuracy of this method is verified through comparing our results with that of simulation work as well as reported experimental data, the agreement is found to be very good.


Exploring Hierarchical User Feedback in Email Clustering


title: Rear-View Vehicle Detection and Tracking by Combining Multiple Parts for Complex Urban Surveillance

abstract: Traffic surveillance is an important topic in intelligent transportation systems. Robust vehicle detection and tracking is one challenging problem for complex urban traffic surveillance. This paper proposes a rear-view vehicle detection and tracking method based on multiple vehicle salient parts using a stationary camera. We show that spatial modeling of these vehicle parts is crucial for overall performance. First, the vehicle is treated as an object composed of multiple salient parts, including the license plate and rear lamps. These parts are localized using their distinctive color, texture, and region feature. Furthermore, the detected parts are treated as graph nodes to construct a probabilistic graph using a Markov random field model. After that, the marginal posterior of each part is inferred using loopy belief propagation to get final vehicle detection. Finally, the vehicles' trajectories are estimated using a Kalman filter, and a tracking-based detection technique is realized. Experiments in practical urban scenarios are carried out under various weather conditions. It can be shown that our method adapts to partial occlusion and various lighting conditions. Experiments also show that our method can achieve real-time performance.


title: Craniofacial reconstruction using a combined statistical model of face shape and soft tissue depths: methodology and validation.

abstract: Abstract Forensic facial reconstruction aims at estimating the facial outlook associated with an unidentified skull specimen. Estimation is generally based on tabulated average values of soft tissue thicknesses measured at a sparse set of landmarks on the skull. Traditional ‘plastic’ methods apply modeling clay or plasticine on a cast of the skull, approximating the estimated tissue depths at the landmarks and interpolating in between. Current computerized techniques mimic this landmark interpolation procedure using a single static facial surface template. However, the resulting reconstruction is biased by the specific choice of the template and no face-specific regularization is used during the interpolation process. We reduce the template bias by using a flexible statistical model of a dense set of facial surface points, combined with an associated sparse set of skull-based landmarks. This statistical model is constructed from a facial database of ( N = 118) individuals and limits the reconstructions to statistically plausible outlooks. The actual reconstruction is obtained by fitting the skull-based landmarks of the template model to the corresponding landmarks indicated on a digital copy of the skull to be reconstructed. The fitting process changes the face-specific statistical model parameters in a regularized way and interpolates the remaining landmark fit error using a minimal bending thin-plate spline (TPS)-based deformation. Furthermore, estimated properties of the skull specimen (BMI, age and gender, e.g.) can be incorporated as conditions on the reconstruction by removing property-related shape variation from the statistical model description before the fitting process. The proposed statistical method is validated, both in terms of accuracy and identification success rate, based on leave-one-out cross-validation tests applied on the facial database. Accuracy results are obtained by statistically analyzing the local 3D facial surface differences of the reconstructions and their corresponding ground truth. Identification success rate is obtained by comparing, based on correlation, Euclidean distance matrix (EDM) signatures of the reconstructed and the original 3D facial surfaces in the database. A subjective identification success rate is quantified based on face-pool tests. Finally a qualitative comparison is made between facial reconstructions of a real-case skull, based on two typical static face models and our statistical model, showing the shortcomings of current face models and the improved performance of the statistical model.


title: Studying Weak Central Coherence at Low Levels: Children with Autism do not Succumb to Visual Illusions. A Research Note

abstract: While anecdotal reports of abnormal perceptual experiences in autism abound, there have been to date no experimental studies showing fundamental perceptual peculiarities. The present paper reports results from a first study of low-level visual integration in autism. Twenty-five subjects with autism, 21 normal 7- and 8-year-olds, and 26 children with learning difficulties were asked to make simple judgements about six well-known visual illusions. Two conditions were used, in an attempt to explore group differences; standard two-dimensional black and white line drawings, and the same figures augmented with raised coloured lines. The subjects with autism were less likely to succumb to the two-dimensional illusions than were the other groups, and were less aided by the three-dimensional disembedded’ condition. These striking results are discussed with reference to the ‘central coherence’ account of autism.


title: Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets

abstract: Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.


title: Functional Gradient Boosting based on Residual Network Perception

abstract: Residual Networks (ResNets) have become state-of-the-art models in deep learning and several theoretical studies have been devoted to understanding why ResNet works so well. One attractive viewpoint on ResNet is that it is optimizing the risk in a functional space by combining an ensemble of effective features. In this paper, we adopt this viewpoint to construct a new gradient boosting method, which is known to be very powerful in data analysis. To do so, we formalize the gradient boosting perspective of ResNet mathematically using the notion of functional gradients and propose a new method called ResFGB for classification tasks by leveraging ResNet perception. Two types of generalization guarantees are provided from the optimization perspective: one is the margin bound and the other is the expected risk bound by the sample-splitting technique. Experimental results show superior performance of the proposed method over state-of-the-art methods such as LightGBM.


title: Protective Humoral Immunity Induced by Lipopolysaccharide Incorporated Liposome in Mice Against Shigella flexneri Infection

abstract: This study was conducted to determine the role of lipopolysaccahride-liposome conjugate (LPS-LIP) as a potential vaccine against shigellosis in mice by determining the IgG titer. One hundred stool samples were collected from patients with diarrhea from different hospitals in Baghdad city during the period December 2011-May 2012. Thirty isolates were suspected to be Shigella. Four isolates of Shigella flexneri were obtained after performing some biochemical tests and Api System. Antibiotic sensitivity test was carried out and results revealed that all isolates were resist to some antibiotics used in this study. One isolate was selected for LPS extraction by phenol hot water extraction method. Chemical characterization of the extracted LPS revealed that the carbohydrate content was 2.34 mg/ml, while the protein concentration was 0.52µg/ml. Partial purification of the extracted LPS was carried out by using gel- filtration chromatography on Sephacryl S-200 and results showed that three peaks were obtained and protein, carbohydrate concentration were estimated for each peak. The second peak observed to have the highest carbohydrate content was (25%). and the lowest contaminated protein was (0.001%). The partial purified LPS was analyzed by Sodium Dodecyl Sulphate Gel Electrophoresis (SDS-PAGE) and results reveled that two bands with molecular weights 100 and 150 KDa were present. Band with MW 100 kDa represented LPS. IgG titer was estimated by Elisa technique. A significant increase in IgG titer was recorded in mice treated with conjugate after infection with S. flexneri.


title: Metaphor: A Computational Perspective

abstract: The literary imagination may take flight on the wings of metaphor, but hard-headed scientists are just as likely as doe-eyed poets to reach for a metaphor when the descriptive need arises. Metaphor is a pervasive aspect of every genre of text and every register of speech, and is as useful for describing the inner workings of a "black hole" (itself a metaphor) as it is the affairs of the human heart. The ubiquity of metaphor in natural language thus poses a significant challenge for Natural Language Processing (NLP) systems and their builders, who cannot afford to wait until the problems of literal language have been solved before turning their attention to figurative phenomena. This book offers a comprehensive approach to the computational treatment of metaphor and its figurative brethren-including simile, analogy, and conceptual blending-that does not shy away from their important cognitive and philosophical dimensions. Veale, Shutova, and Beigman Klebanov approach metaphor from multiple computational perspectives, providing coverage of both symbolic and statistical approaches to interpretation and paraphrase generation, while also considering key contributions from philosophy on what constitutes the "meaning" of a metaphor. This book also surveys available metaphor corpora and discusses protocols for metaphor annotation. Any reader with an interest in metaphor, from beginning researchers to seasoned scholars, will find this book to be an invaluable guide to what is a fascinating linguistic phenomenon.


title: Analytical Modeling of Armature Reaction Air-Gap Flux Density Considering the Non-Homogeneously Saturated Rotor in a Fractional-Slot Concentrated-Wound IPM Machine

abstract: The flux of the embedded magnets in the rotor of an interior permanent magnet (IPM) synchronous machine causes a nonhomogeneously saturated rotor iron. This alters the reluctance of the stator flux path due to the nonlinear B-H curve of the rotor iron and should be considered in the analytical field calculations; however, it is often neglected in the prior-art methods leading to inaccurate results. Moreover, the effect of a fractional-slot concentrated-wound (FSCW) stator on the analytical field calculations is not addressed in the existing literature. The magnetic field characteristics of IPM synchronous machines are commonly formulated using an equivalent air-gap function. In order to account for the aforementioned shortcomings in the existing methods for the analytical field calculations, an equivalent air-gap function is proposed in this paper that considers the particular properties of an IPM rotor and an FSCW stator. To this aim, based on the rotor geometry and the nonlinear B-H curve of the iron core, a method is first proposed to model the saturation level and the relative permeability at different regions of the rotor iron. This model is then used in the proposed equivalent air-gap function to compute the armature reaction air-gap flux density. The proposed theory is validated through finite element analysis and experimental results on a prototype FSCW IPM machine in the laboratory.


title: The development of face expertise

abstract: Abstract Recent neuroimaging studies in adults indicate that visual areas selective for recognition of faces can be recruited through expertise for nonface objects. This reflects a new emphasis on experience in theories of visual specialization. In addition, novel work infers differences between categories of nonface objects, allowing a re-interpretation of differences seen between recognition of faces and objects. Whether there are experience-independent precursors of face expertise remains unclear; indeed, parallels between literature for infants and adults suggest that methodological issues need to be addressed before strong conclusions can be drawn regarding the origins of face recognition.


Shortest Path Based Column Generation on Large Networks with Many Resource Constraints


title: Central Role of the Early Colonizer Veillonella sp. in Establishing Multispecies Biofilm Communities with Initial, Middle, and Late Colonizers of Enamel

abstract: Human dental biofilm communities comprise several species, which can interact cooperatively or competitively. Bacterial interactions influence biofilm formation, metabolic changes, and physiological function of the community. Lactic acid, a common metabolite of oral bacteria, was measured in the flow cell effluent of one-, two- and three-species communities growing on saliva as the sole nutritional source. We investigated single-species and multispecies colonization by using known initial, early, middle, and late colonizers of enamel. Fluorescent-antibody staining and image analysis were used to quantify the biomass in saliva-fed flow cells. Of six species tested, only the initial colonizer Actinomyces oris exhibited significant growth. The initial colonizer Streptococcus oralis produced lactic acid but showed no significant growth. The early colonizer Veillonella sp. utilized lactic acid in two- and three-species biofilm communities. The biovolumes of all two-species biofilms increased when Veillonella sp. was present as one of the partners, indicating that this early colonizer promotes mutualistic community development. All three-species combinations exhibited enhanced growth except one, i.e., A. oris, Veillonella sp., and the middle colonizer Porphyromonas gingivalis, indicating specificity among three-species communities. Further specificity was seen when Fusobacterium nucleatum (a middle colonizer), Aggregatibacter actinomycetemcomitans (a late colonizer), and P. gingivalis did not grow with S. oralis in two-species biofilms, but inclusion of Veillonella sp. resulted in growth of all three-species combinations. We propose that commensal veillonellae use lactic acid for growth in saliva and that they communicate metabolically with initial, early, middle, and late colonizers to establish multispecies communities on enamel.


title: MADAM: a multi-level anomaly detector for android malware

abstract: Currently, in the smartphone market, Android is the platform with the highest share. Due to this popularity and also to its open source nature, Android-based smartphones are now an ideal target for attackers. Since the number of malware designed for Android devices is increasing fast, Android users are looking for security solutions aimed at preventing malicious actions from damaging their smartphones. ::: ::: In this paper, we describe MADAM, a Multi-level Anomaly Detector for Android Malware. MADAM concurrently monitors Android at the kernel-level and user-level to detect real malware infections using machine learning techniques to distinguish between standard behaviors and malicious ones. The first prototype of MADAM is able to detect several real malware found in the wild. The device usability is not affected by MADAM due to the low number of false positives generated after the learning phase.


title: Exploring Invariances in Deep Convolutional Neural Networks Using Synthetic Images

abstract: Crowdsourced 3D CAD models are becoming easily accessible online, and can potentially generate an infinite number of training images for almost any object category. We show that adapting contemporary Deep Convolutional Neural Net (DCNN) models to such data can be effective, especially in the few-shot regime where none or only a few annotated real images are available, or where the images are not well matched to the target domain. Little is known about the degree of realism necessary to train models with deep features on CAD data. In a detailed analysis, we use synthetic images to probe DCNN invariance to object-class variations caused by 3D shape, pose, and photorealism, with surprising findings. In particular, we show that DCNNs used as a fixed representation exhibit a large amount of invariance to these factors, but, if allowed to adapt, can still learn effectively from synthetic data. These findings guide us in designing a method for adaptive training of DCNNs using real and synthetic data. We show that our approach significantly outperforms previous methods on the benchmark PASCAL VOC2007 dataset when learning in the fewshot scenario, and outperform training with real data in a domain shift scenario on the Office benchmark.


title: Characteristics of human perception and their relevance when studying information behavior

abstract: – The purpose of this paper is to highlight findings regarding human perception in allied disciplines and to argue that information behaviour research needs to find ways to address human characteristics that imply that: first, subjects are likely to fail to recognize information that is present in an environment and potentially relevant to a task at hand; and second, subjects would not be able to report on the fact that they failed to recognize the information. The authors also discuss as to how information behaviour research can address the aforementioned challenges resulting from human movement and perception. , – The author draws on the literature primarily in cognitive science and psychology to highlight the findings that are most relevant to the scientific study of information behaviour, to develop a model of the information environment in which information behaviour is situated, and to critically examine how data is collected in information behaviour research. Ways to provide more comprehensive information about information behaviour are also discussed. , – The literature in cognitive science and psychology suggests that failing to notice information relevant to a task at hand may not be the exception but to be expected, and needs to be taken into account by information behaviour researchers. Popular data collection methods including questionnaires and interviews do not pick that up because subjects would not be aware of the fact which means in turn that they cannot articulate the fact either. This suggests that: first, current models of information behaviour focus too much on one side of the coin; and second, information behaviour researchers may need to complement their data collection methods with data collection methods such as gaze tracking. , – This is a conceptual paper based on the careful analysis of relevant research primarily in cognitive science and psychology. Relating theory to practice provides a strong indication of the general validity of the findings but there may be other aspects that have not been covered as yet. , – The paper is unique in that it critically reviews information behaviour research from a human perception and movement point of view. There have been papers criticizing information behaviour research from a methodological point of view. This paper adds to that body of work and proposes a way forward.


title: Single bit full adder design using 8 transistors with novel 3 transistors XNOR gate

abstract: In present work a new XNOR gate using three transistors has been presented, which shows power dissipation of 550.7272µW in 0.35 µm technology with supply voltage of 3.3V. Minimum level for high output of 2.05V and maximum level for low output of 0.084V have been obtained. A single bit full adder using eight transistors has been designed using proposed XNOR cell, which shows power dissipation of 581.542µW. Minimum level for high output of 1.97V and maximum level for low output of 0.24V is obtained for sum output signal. For carry signal maximum level for low output of 0.32V and minimum level for high output of 3.2V have been achieved. Simulations have been performed by using SPICE based on TSMC 0.35µm CMOS technology. Power consumption of proposed XNOR gate and full adder has been compared with earlier reported circuits and proposed circuit’s shows better performance in terms of power consumption and transistor count.


title: Predictive modeling of plant messenger RNA polyadenylation sites

abstract: BackgroundOne of the essential processing events during pre-mRNA maturation is the post-transcriptional addition of a polyadenine [poly(A)] tail. The 3'-end poly(A) track protects mRNA from unregulated degradation, and indicates the integrity of mRNA through recognition by mRNA export and translation machinery. The position of a poly(A) site is predetermined by signals in the pre-mRNA sequence that are recognized by a complex of polyadenylation factors. These signals are generally tri-part sequence patterns around the cleavage site that serves as the future poly(A) site. In plants, there is little sequence conservation among these signal elements, which makes it difficult to develop an accurate algorithm to predict the poly(A) site of a given gene. We attempted to solve this problem.ResultsBased on our current working model and the profile of nucleotide sequence distribution of the poly(A) signals and around poly(A) sites in Arabidopsis, we have devised a Generalized Hidden Markov Model based algorithm to predict potential poly(A) sites. The high specificity and sensitivity of the algorithm were demonstrated by testing several datasets, and at the best combinations, both reach 97%. The accuracy of the program, called p oly(A) s ite s leuth or PASS, has been demonstrated by the prediction of many validated poly(A) sites. PASS also predicted the changes of poly(A) site efficiency in poly(A) signal mutants that were constructed and characterized by traditional genetic experiments. The efficacy of PASS was demonstrated by predicting poly(A) sites within long genomic sequences.ConclusionBased on the features of plant poly(A) signals, a computational model was built to effectively predict the poly(A) sites in Arabidopsis genes. The algorithm will be useful in gene annotation because a poly(A) site signifies the end of the transcript. This algorithm can also be used to predict alternative poly(A) sites in known genes, and will be useful in the design of transgenes for crop genetic engineering by predicting and eliminating undesirable poly(A) sites.


title: Efficacy and Long-Term Safety of a Dengue Vaccine in Regions of Endemic Disease

abstract: BACKGROUND A candidate tetravalent dengue vaccine is being assessed in three clinical trials involving more than 35,000 children between the ages of 2 and 16 years in Asian– Pacific and Latin American countries. We report the results of long-term follow-up interim analyses and integrated efficacy analyses. METHODS We are assessing the incidence of hospitalization for virologically confirmed dengue as a surrogate safety end point during follow-up in years 3 to 6 of two phase 3 trials, CYD14 and CYD15, and a phase 2b trial, CYD23/57. We estimated vaccine efficacy using pooled data from the first 25 months of CYD14 and CYD15. RESULTS Follow-up data were available for 10,165 of 10,275 participants (99%) in CYD14 and 19,898 of 20,869 participants (95%) in CYD15. Data were available for 3203 of the 4002 participants (80%) in the CYD23 trial included in CYD57. During year 3 in the CYD14, CYD15, and CYD57 trials combined, hospitalization for virologically confirmed dengue occurred in 65 of 22,177 participants in the vaccine group and 39 of 11,089 participants in the control group. Pooled relative risks of hospitalization for dengue were 0.84 (95% confidence interval [CI], 0.56 to 1.24) among all participants, 1.58 (95% CI, 0.83 to 3.02) among those under the age of 9 years, and 0.50 (95% CI, 0.29 to 0.86) among those 9 years of age or older. During year 3, hospitalization for severe dengue, as defined by the independent data monitoring committee criteria, occurred in 18 of 22,177 participants in the vaccine group and 6 of 11,089 participants in the control group. Pooled rates of efficacy for symptomatic dengue during the first 25 months were 60.3% (95% CI, 55.7 to 64.5) for all participants, 65.6% (95% CI, 60.7 to 69.9) for those 9 years of age or older, and 44.6% (95% CI, 31.6 to 55.0) for those younger than 9 years of age. CONCLUSIONS Although the unexplained higher incidence of hospitalization for dengue in year 3 among children younger than 9 years of age needs to be carefully monitored during long-term follow-up, the risk among children 2 to 16 years of age was lower in the vaccine group than in the control group. (Funded by Sanofi Pasteur; ClinicalTrials .gov numbers, NCT00842530, NCT01983553, NCT01373281, and NCT01374516.)


title: ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography

abstract: Computer-aided techniques may lead to more accurate and more acces-sible diagnosis of thorax diseases on chest radiography. Despite the success of deep learning-based solutions, this task remains a major challenge in smart healthcare, since it is intrinsically a weakly supervised learning problem. In this paper, we incorporate the attention mechanism into a deep convolutional neural network, and thus propose the ChestNet model to address effective diagnosis of thorax diseases on chest radiography. This model consists of two branches: a classification branch serves as a uniform feature extraction-classification network to free users from troublesome handcrafted feature extraction, and an attention branch exploits the correlation between class labels and the locations of patholog-ical abnormalities and allows the model to concentrate adaptively on the patholog-ically abnormal regions. We evaluated our model against three state-of-the-art deep learning models on the Chest X-ray 14 dataset using the official patient-wise split. The results indicate that our model outperforms other methods, which use no extra training data, in diagnosing 14 thorax diseases on chest radiography.


title: Robust and Scalable Differentiable Neural Computer for Question Answering

abstract: Deep learning models are often not easily adaptable to new tasks and require task-specific adjustments. The differentiable neural computer (DNC), a memory-augmented neural network, is designed as a general problem solver which can be used in a wide range of tasks. But in reality, it is hard to apply this model to new tasks. We analyze the DNC and identify possible improvements within the application of question answering. This motivates a more robust and scalable DNC (rsDNC). The objective precondition is to keep the general character of this model intact while making its application more reliable and speeding up its required training time. The rsDNC is distinguished by a more robust training, a slim memory unit and a bidirectional architecture. We not only achieve new state-of-the-art performance on the bAbI task, but also minimize the performance variance between different initializations. Furthermore, we demonstrate the simplified applicability of the rsDNC to new tasks with passable results on the CNN RC task without adaptions.


title: Eraser: Your Data Won't Be Back

abstract: Secure deletion of data from non-volatile storage is a well-recognized problem. While numerous solutions have been proposed, advances in storage technologies have stymied efforts to solve the problem. For instance, SSDs make use of techniques such as wear leveling that involve replication of data; this is in direct opposition to efforts to securely delete sensitive data from storage. We present a technique to provide secure deletion guarantees at file granularity, independent of the characteristics of the underlying storage medium. The approach builds on prior seminal work on cryptographic erasure, encrypting every file on an insecure medium with a unique key that can later be discarded to cryptographically render the data irrecoverable. To make the approach scalable and, therefore, usable on commodity systems, keys are organized in an efficient tree structure where a single master key is confined to a secure store. We describe an implementation of this scheme as a fileaware stackable block device, deployed as a standalone Linux kernel module that does not require modifications to the operating system. Our prototype demonstrates that secure deletion independent of the underlying storage medium can be achieved with comparable overhead to existing full disk encryption implementations.


title: Multifractal analysis of axial maps applied to the study of urban morphology

abstract: Street layout is an important element of urban morphology that informs on urban patterns influenced by city growing through the years under different planning regulations and different socio-economic contexts. It is assumed by several authors that urban morphology has a fractal or monofractal nature. However, not all the urban patterns can be considered as monofractal because of the presence of different morphologies. Therefore, a single fractal dimension may not always be enough to describe urban morphology. In this sense, a multifractal approach serves to tackle this problem by describing urban areas in terms of a set of fractal dimensions. With this aim in mind, two different neighbourhoods of the city of Cordoba, in Andalusia (Spain), are analysed by using the Sandbox multifractal method and lacunarity. We analyse the street patterns represented by axial maps and obtained from the Space Syntax algorithm. The results suggest that the Renyi dimension spectrum is superior to a single fractal dimension to describe the urban morphology of Cordoba, given the presence of regular and irregular street layouts established under different planning and socio-economic regimes.


title: A simple transmit diversity technique for wireless communications

abstract: This paper presents a simple two-branch transmit diversity scheme. Using two transmit antennas and one receive antenna the scheme provides the same diversity order as maximal-ratio receiver combining (MRRC) with one transmit antenna, and two receive antennas. It is also shown that the scheme may easily be generalized to two transmit antennas and M receive antennas to provide a diversity order of 2M. The new scheme does not require any bandwidth expansion or any feedback from the receiver to the transmitter and its computation complexity is similar to MRRC.


title: Laser-based detection and depth estimation of dry and water-filled potholes: A geometric approach

abstract: In secondary Indian roads, one often encounters potholes which can be either dry or water-filled. Accordingly, to ensure safe driving, it is imperative to detect potholes and estimate their depths in either condition. In this paper, we develop a physics-based geometric framework, where such detection and depth-estimation can be accomplished using suitable laser. Specifically, we relate dry pothole depth to measured optical deviation using simple ray optics. Further, we use Snell's law of refraction to obtain a quartic equation, and its appropriate real root to relate water-filled pothole depth to the corresponding optical deviation. Here we take into account diminishing resolu- tion with increasing distance from the camera. We conclude by experimentally validating our method.


title: Virtual negative coupling in folded waveguide cavity filter for space applications

abstract: This paper describes the design of a folded rectangular waveguide cavity band pass filter, incorporating virtual negative coupling (VNC) by means of circular iris, at X-band. Thus, the filter is robust from mechanical aspects, and hence apt for space application. The filter realized at 8310 MHz with 1.2 percent fractional bandwidth, has a sharp roll-off due to VNC. As a result, rejection better than 40 dBc is achieved at just 100 MHz offset from band centre. Compared to traditional all pole filter, introduction of VNC has resulted in around 20% size reduction.


title: A New Perspective on Uncalibrated Photometric Stereo

abstract: We investigate the problem of reconstructing normals, albedo and lights of Lambertian surfaces in uncalibrated photometric stereo under the perspective projection model. Our analysis is based on establishing the integrability constraint. In the orthographic projection case, it is well-known that when such constraint is imposed, a solution can be identified only up to 3 parameters, the so-called generalized bas-relief (GBR) ambiguity. We show that in the perspective projection case the solution is unique. We also propose a closed-form solution which is simple, efficient and robust. We test our algorithm on synthetic data and publicly available real data. Our quantitative tests show that our method outperforms all prior work of uncalibrated photometric stereo under orthographic projection.


title: Modeling user expertise for choosing levels of shared autonomy

abstract: In shared autonomy, a robot and human user both have some level of control in order to achieve a shared goal. Choosing the balance of control given to the user and the robot can be a challenging problem since different users have different preferences and vary in skill levels when operating a robot. We propose using a novel formulation of Partially Observable Markov Decision Process (POMDP) to represent a model of the user's expertise in controlling the robot. The POMDP uses observations from the user's actions and from the environment to update the belief of the user's skill and chooses a level of control between the robot and the user. The level of control given between the user and the robot is encapsulated in macro-action controllers. A user study was run to test the performance of our formulation. Users drive a simulated robot through an obstacle-filled map while the POMDP model chooses appropriate macro-action controllers based on the belief state of the user's skill level. The results of the user study show that our model can encapsulate user skill. The results also show that using the controller with greater robot autonomy helped users of low skill avoid obstacles more than it helped users of high skill.