title: Review of the Satisfaction with Life Scale

abstract: The Satisfaction With Life Scale (SWLS) was developed to assess satis-faction with the respondent’s life as a whole. The scale does not assess satisfaction with life domains such as health or finances but allows subjects to integrate and weight these domains in whatever way they choose. Normative data are presented for the scale, which shows good convergent validity with other scales and with other types of assessments of subjective well-being. Life satisfaction as assessed by the SWLS shows a degree of temporal stability (e.g., 0.54 for 4 years), yet the SWLS has shown sufficient sensitivity to be potentially valuable to detect change in life satis-faction during the course of clinical intervention. Further, the scale shows discrim-inant validity from emotional well-being measures. The SWLS is recommended as a complement to scales that focus on psychopathology or emotional well-being because it assesses an individuals’ conscious evaluative judgment of his or her life by using the person’s own criteria.


title: The Built Environment and Obesity

abstract: Obesity results from a complex interaction between diet, physical activity, and the environment. The built environment encompasses a range of physical and social elements that make up the structure of a community and may influence obesity. This review summarizes existing empirical research relating the built environment to obesity. The Medline, PsychInfo, and Web of Science databases were searched using the keywords ‘‘obesity’’ or ‘‘overweight’’ and ‘‘neighborhood’’ or ‘‘built environment’’ or ‘‘environment.’’ The search was restricted to Englishlanguage articles conducted in human populations between 1966 and 2007. To meet inclusion criteria, articles had to 1) have a direct measure of body weight and 2) have an objective measure of the built environment. A total of 1,506 abstracts were obtained, and 20 articles met the inclusion criteria. Most articles (84%) reported a statistically significant positive association between some aspect of the built environment and obesity. Several methodological issues were of concern, including the inconsistency of measurements of the built environment across studies, the cross-sectional design of most investigations, and the focus on aspects of either diet or physical activity but not both. Given the importance of the physical and social contexts of individual behavior and the limited success of individual-based interventions in long-term obesity prevention, more research on the impact of the built environment on obesity is needed.


title: Unveiling facebook: a measurement study of social network based applications

abstract: Online social networking sites such as Facebook and MySpace have become increasingly popular, with close to 500 million users as of August 2008. The introduction of the Facebook Developer Platform and OpenSocial allows third-party developers to launch their own applications for the existing massive user base. The viral growth of these social applications can potentially influence how content is produced and consumed in the future Internet. To gain a better understanding, we conducted a large-scale measurement study of the usage characteristics of online social network based applications. In particular, we developed and launched three Facebook applications, which have achieved a combined subscription base of over 8 million users. Using the rich dataset gathered through these applications, we analyze the aggregate workload characteristics (including temporal and geographical distributions) as well as the structure of user interactions. We explore the existence of 'communities', with high degree of interaction within a community and limited interaction outside the community. We find that a small fraction of users account for the majority of activity within the context of our Facebook applications and a small number of applications account for the majority of users on Facebook. Furthermore, user response times for Facebook applications are independent of source/destination user locality. We also investigate distinguishing characteristics of social gaming applications. To the best of our knowledge, this is the first study analyzing user activities on online social applications.


title: Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons

abstract: Suicide is among the leading causes of death in China. However, technical approaches toward preventing suicide are challenging and remaining under development. Recently, several actual suicidal cases were preceded by users who posted micro blogs with suicidal ideation to Sina Weibo, a Chinese social media network akin to Twitter. It would therefore be desirable to detect suicidal ideations from micro blogs in real-time, and immediately alert appropriate support groups, which may lead to successful prevention. In this paper, we propose a real-time suicidal ideation detection system deployed over Weibo, using machine learning and known psychological techniques. Currently, we have identified 53 known suicidal cases who posted suicide notes on Weibo prior to their deaths. We explore linguistic features of these known cases using a psychological lexicon dictionary, and train an effective suicidal Weibo post detection model. 6714 tagged posts and several classifiers are used to verify the model. By combining both machine learning and psychological knowledge, SVM classifier has the best performance of different classifiers, yielding an F-measure of 68:3%, a Precision of 78:9%, and a Recall of 60:3%.


title: Factors influencing enzymatic estimates of infarct size

abstract: Because creatine kinase (CK)* in the heart is found primarily in myocardial cells themselves, it is not surprising that cardiac CK depletion after coronary occlusion correlates with gross, 1 microscopic and ultrastructural criteria of infarction, 2 regional ischemia detected with microspheres, 1,3 depressed free fatty acid accumulation 4 and altered attenuation of ultrasound. 5 Accordingly, estimation of CK depletion from plasma CK time-activity curves appeared to be potentially useful, and a simple model was developed in which serial plasma CK changes were attributed to release of CK from the heart and first order disappearance from blood.6. 7 Despite obvious imperfections, enzymatic estimates of infarct size in studies from several centers have correlated with biochemical and morphologic analyses of the heart in experimental animals 6-9 and morbidity, mortality 1°-12 and histochemical assessment of necrosis in patients. 13 Estimates have been improved by quantifying MB CK, TM an isoenzyme in man virtually confined to myocardium. Further improvements should result from characterization of mechanisms responsible for kinetics of CK in the circulation. 15 Although plasma time-activity curves have been considered from a theoretical point of view, TM inadequate attention has been devoted to the quality of the data. One important source of error is sampling technique. Since skeletal muscle contains more than 3,000 IU/g, and plasma CK activity is approximately 0.1 to 1.5 IU/ml, even minor trauma to muscle near the sampling site will greatly distort results. 17 Accordingly, plasma CK time-activity curves should not be performed after surgery in experimental animals until values have returned to control levels, repetitive venipuncture should be avoided and plasma samples should be collected after thorough washout of dead space "in catheters to assure reproducibility. Neglect of these precautions leads to


title: Cross-Layer Antenna Selection and Channel Allocation for MIMO Cognitive Radios

abstract: We propose algorithms to address the spectrum efficiency and fairness issues of multi band multiuser Multiple-Input and Multiple-Output (MIMO) cognitive ad-hoc networks. To improve the transmission efficiency of the MIMO system, a cross layer antenna selection algorithm is proposed. Using the transmission efficiency results, user data rate of the cognitive ad-hoc network is determined. Objective function for the average data rate of the multi band multiuser cognitive MIMO ad-hoc network is also defined. For the average data rate objective function, primary users interference is considered as performance constraint. Furthermore, using the user data rate results, a learning-based channel allocation algorithm is proposed. Finally, numerical results are presented for performance evaluation of the proposed antenna selection and channel allocation algorithms.


title: A novel algorithm for detecting bacterial leaf scorch (BLS) of shade trees using image processing

abstract: Bacterial leaf scorch(BLS) is a common infection seen in different shade tree species such as American elm, red maple, sweet gum, sycamore and London plane, and a number of species of oak. The disease is caused by the bacterial pathogen Xylella fastidiosa. Spread of the disease may lead to early fall and determinate growth of the trees. The symptoms of the disease may first appear on branches and leaves and it spreads to other parts of the plant on a later stage. So detection of the infected areas of the plant leads to early analysis and elimination of disease spread and saves the plant from disease spread. Proposed scheme uses a novel approach to detect the infected areas of the plant by segmenting the plant leaves by clustering. K-means clustering is performed for different cluster centres to obtain different cluster groups of the region of interest (ROI).


title: Domain Induced Dirichlet Mixture of Gaussian Processes: An Application to Predicting Disease Progression in Multiple Sclerosis Patients

abstract: Predicting disease course is critical in chronic progressive diseases such as multiple sclerosis (MS) for determining treatment. Forming an accurate predictive model based on clinical data is particularly challenging when data is gathered from multiple clinics/physicians as the labels vary with physicians' subjective judgment about clinical tests and further we have no a priori knowledge of the various types of physician subjectivity. At the same time, we often have some (limited) domain knowledge on how to group patients into disease progression subgroups. In this paper, we first present our rationale for choosing a Dirichlet mixture of Gaussian processes (DPMGP) model to address the subjectivity in our data. We then introduce a new approach to incorporating domain knowledge into the non-parametric mixture model. We demonstrate the efficacy of our model by applying it to two medical datasets to predict disease progression in MS patients and disability levels in early Parkinson's patients.


title: Enhanced Catalytic Four-Electron Dioxygen (O2) and Two-Electron Hydrogen Peroxide (H2O2) Reduction with a Copper(II) Complex Possessing a Pendant Ligand Pivalamido Group

abstract: A copper complex, [(PV-tmpa)Cu(II)](ClO4)2 (1) [PV-tmpa = bis(pyrid-2-ylmethyl){[6-(pivalamido)pyrid-2-yl]methyl}amine], acts as a more efficient catalyst for the four-electron reduction of O2 by decamethylferrocene (Fc*) in the presence of trifluoroacetic acid (CF3COOH) in acetone as compared with the corresponding copper complex without a pivalamido group, [(tmpa)Cu(II)](ClO4)2 (2) (tmpa = tris(2-pyridylmethyl)amine). The rate constant (k(obs)) of formation of decamethylferrocenium ion (Fc*(+)) in the catalytic four-electron reduction of O2 by Fc* in the presence of a large excess CF3COOH and O2 obeyed first-order kinetics. The k(obs) value was proportional to the concentration of catalyst 1 or 2, whereas the k(obs) value remained constant irrespective of the concentration of CF3COOH or O2. This indicates that electron transfer from Fc* to 1 or 2 is the rate-determining step in the catalytic cycle of the four-electron reduction of O2 by Fc* in the presence of CF3COOH. The second-order catalytic rate constant (k(cat)) for 1 is 4 times larger than the corresponding value determined for 2. With the pivalamido group in 1 compared to 2, the Cu(II)/Cu(I) potentials are -0.23 and -0.05 V vs SCE, respectively. However, during catalytic turnover, the CF3COO(-) anion present readily binds to 2 shifting the resulting complex's redox potential to -0.35 V. The pivalamido group in 1 is found to inhibit anion binding. The overall effect is to make 1 easier to reduce (relative to 2) during catalysis, accounting for the relative k(cat) values observed. 1 is also an excellent catalyst for the two-electron two-proton reduction of H2O2 to water and is also more efficient than is 2. For both complexes, reaction rates are greater than for the overall four-electron O2-reduction to water, an important asset in the design of catalysts for the latter.


Success factors for Augmented Reality Business Models Chapter 1 Success factors for Augmented Reality Business Models


title: Predicting Pre-click Quality for Native Advertisements

abstract: Native advertising is a specific form of online advertising where ads replicate the look-and-feel of their serving platform. In such context, providing a good user experience with the served ads is crucial to ensure long-term user engagement. In this work, we explore the notion of ad quality, namely the effectiveness of advertising from a user experience perspective. We design a learning framework to predict the pre-click quality of native ads. More specifically, we look at detecting offensive native ads, showing that, to quantify ad quality, ad offensive user feedback rates are more reliable than the commonly used click-through rate metrics. We then conduct a crowd-sourcing study to identify which criteria drive user preferences in native advertising. We translate these criteria into a set of ad quality features that we extract from the ad text, image and advertiser, and then use them to train a model able to identify offensive ads. We show that our model is very effective in detecting offensive ads, and provide in-depth insights on how different features affect ad quality. Finally, we deploy a preliminary version of such model and show its effectiveness in the reduction of the offensive ad feedback rate.


title: Serial#Given by KSAE ROBUST LANE MARKINGS DETECTION AND ROAD GEOMETRY COMPUTATION

abstract: Detection of lane markings based on a camera sensor can be a low-cost solution to lane departure and curve-over-speed warnings. A number of methods and implementations have been reported in the literature. However, reliable detection is still an issue because of cast shadows, worn and occluded markings, variable ambient lighting conditions, for example. We focus on increasing detection reliability in two ways. First, we employed an image feature other than the commonly used edges: ridges, which we claim addresses this problem better. Second, we adapted RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane lines to the image features, based on both ridgeness and ridge orientation. In addition, the model was fitted for the left and right lane lines simultaneously to enforce a consistent result. Four measures of interest for driver assistance applications were directly computed from the fitted parametric model at each frame: lane width, lane curvature, and vehicle yaw angle and lateral offset with regard the lane medial axis. We qualitatively assessed our method in video sequences captured on several road types and under very different lighting conditions. We also quantitatively assessed it on synthetic but realistic video sequences for which road geometry and vehicle trajectory ground truth are known.


title: Meaningful Alignments

abstract: We propose a method for detecting geometric structures in an image, without any a priori information. Roughly speaking, we say that an observed geometric event is “meaningful” if the expectation of its occurences would be very small in a random image. We discuss the apories of this definition, solve several of them by introducing “maximal meaningful events” and analyzing their structure. This methodology is applied to the detection of alignments in images.


title: Guest Editorial: A Review of Worst-Case Execution-Time Analysis

abstract: A development process for safety-critical real-time computer systems has to emphasize the importance of time. On the one hand, such a development process has to be based on hardware and software technology that supports predictability in the time domain. On the other hand, the development process has to provide tools for assessing and verifying the correctness of the timing of both the hardware and the software components of the real-time systems being developed. Together with schedulability analysis, Worst-case execution time analysis (WCET analysis) forms the basis for establishing confidence into the timely operation of a real-time system. WCET analysis does so by computing (upper) bounds for the execution times of the tasks in the system. These bounds are needed for allocating the correct CPU time to the tasks of an application. They form the inputs for schedulability tools, which test whether a given task set is schedulable (and will thus meet the timing requirements of the application) on a given target system. While schedulability analysis is one of the traditional fields of investigation in real-time systems research, WCET analysis caught the attention of the research community only about ten years ago (Kligerman and Stoyenko, 1986; Mok et al., 1989; Puschner and Koza, 1989; Shaw, 1989). In the last decade, however, more and more research groups started to put a focus on WCET analysis. As a result, substantial progress has been made in this area in a relatively short time. After ten years of research in the field, it is appropriate to have a special issue on WCET analysis. It is the goal of this special issue to review the achievements in WCET analysis and to report about the recent advances in this field. In the following section we will define the problem area of WCET analysis and thus clarify the issue WCET analysis is dealing with— still many people mix up execution-time analysis and response-time analysis. We will then summarize the subproblems of WCET analysis and provide an overview of previous contributions to the state of the art in this field. At the end of the introduction we will give an overview to the research papers that have been selected for this special issue.


title: Microbial life in the phyllosphere

abstract: Our knowledge of the microbiology of the phyllosphere, or the aerial parts of plants, has historically lagged behind our knowledge of the microbiology of the rhizosphere, or the below-ground habitat of plants, particularly with respect to fundamental questions such as which microorganisms are present and what they do there. In recent years, however, this has begun to change. Cultivation-independent studies have revealed that a few bacterial phyla predominate in the phyllosphere of different plants and that plant factors are involved in shaping these phyllosphere communities, which feature specific adaptations and exhibit multipartite relationships both with host plants and among community members. Insights into the underlying structural principles of indigenous microbial phyllosphere populations will help us to develop a deeper understanding of the phyllosphere microbiota and will have applications in the promotion of plant growth and plant protection.


title: Modeling and analysis of electric field and electrostatic adhesion force generated by interdigital electrodes for wall climbing robots

abstract: A model is presented for the analysis of the electric field and electrostatic adhesion force produced by interdigital electrodes. Assuming that the potential varies linearly with distance in inter-electrode gaps, the potential distribution on the electrode plane is obtained by taking the first-order Taylor series approximation. The expressions of electric field components are then derived by solving the Laplace equation for the electrical potential in each subregion. The electrostatic adhesion force is calculated using the Maxwell stress tensor formulation. The dynamic properties of the electric field and electrostatic adhesion force are assessed by evaluating the transient response of the field and force under a step in applied voltages. To verify the model developed, an experimental study is carried out in conjunction with the theoretical analysis to evaluate the adhesion performance of an electrode panel on a glass pane. A double tracked wall climbing robot is designed and tested on various wall surfaces. The limit of the approximation method of the inter-electrode potential is discussed. It is found that vacuum suction force is involved in the adhesion. The influence of this vacuum suction force on electrostatic adhesion is also discussed. The results of this work would provide support for theoretical guidelines and system optimization for the electrostatic adhesion technology applied to wall climbing robots.


title: Using first-order logic to compress sentences

abstract: Sentence compression is one of the most challenging tasks in natural language processing, which may be of increasing interest to many applications such as abstractive summarization and text simplification for mobile devices. In this paper, we present a novel sentence compression model based on first-order logic, using Markov Logic Network. Sentence compression is formulated as a word/phrase deletion problem in this model. By taking advantage of first-order logic, the proposed method is able to incorporate local linguistic features and to capture global dependencies between word deletion operations. Experiments on both written and spoken corpora show that our approach produces competitive performance against the state-of-the-art methods in terms of manual evaluation measures such as importance, grammaticality, and overall quality.


title: Metric Learning by Collapsing Classes

abstract: We present an algorithm for learning a quadratic Gaussian metric (Mahalanobis distance) for use in classification tasks. Our method relies on the simple geometric intuition that a good metric is one under which points in the same class are simultaneously near each other and far from points in the other classes. We construct a convex optimization problem whose solution generates such a metric by trying to collapse all examples in the same class to a single point and push examples in other classes infinitely far away. We show that when the metric we learn is used in simple classifiers, it yields substantial improvements over standard alternatives on a variety of problems. We also discuss how the learned metric may be used to obtain a compact low dimensional feature representation of the original input space, allowing more efficient classification with very little reduction in performance.


title: Balancing push and pull for data broadcast

abstract: The increasing ability to interconnect computers through internet-working, wireless networks, high-bandwidth satellite, and cable networks has spawned a new class of information-centered applications based on data dissemination . These applications employ broadcast to deliver data to very large client populations. We have proposed the Broadcast Disks paradigm [Zdon94, Acha95b] for organizing the contents of a data broadcast program and for managing client resources in response to such a program. Our previous work on Broadcast Disks focused exclusively on the “push-based” approach, where data is sent out on the broadcast channel according to a periodic schedule, in anticipation of client requests. In this paper, we study how to augment the push-only model with a “pull-based” approach of using a backchannel to allow clients to send explicit requests for data to the server. We analyze the scalability and performance of a broadcast-based system that integrates push and pull and study the impact of this integration on both the steady state and warm-up performance of clients. Our results show that a client backchannel can provide significant performance improvement in the broadcast environment, but that unconstrained use of the backchannel can result in scalability problems due to server saturation. We propose and investigate a set of three techniques that can delay the onset of saturation and thus, enhance the performance and scalability of the system.


title: Reorientation of the stripe Phase of 2D Electrons by a Minute Density Modulation

abstract: Interacting two-dimensional electrons confined in a GaAs quantum well exhibit isotropic transport when the Fermi level resides in the first excited (N=1) Landau level. Adding an in-plane magnetic field (B_{||}) typically leads to an anisotropic, stripelike (nematic) phase of electrons with the stripes oriented perpendicular to the B_{||} direction. Our experimental data reveal how a periodic density modulation, induced by a surface strain grating from strips of negative electron-beam resist, competes against the B_{||}-induced orientational order of the stripe phase. Even a minute (<0.25%) density modulation is sufficient to reorient the stripes along the direction of the surface grating.


Three Types of Chief Digital Officers and the Reasons Organizations Adopt the Role


title: Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification

abstract: Recently, neural networks have achieved great success on sentiment classification due to their ability to alleviate feature engineering. However, one of the remaining challenges is to model long texts in document-level sentiment classification under a recurrent architecture because of the deficiency of the memory unit. To address this problem, we present a Cached Long Short-Term Memory neural networks (CLSTM) to capture the overall semantic information in long texts. CLSTM introduces a cache mechanism, which divides memory into several groups with different forgetting rates and thus enables the network to keep sentiment information better within a recurrent unit. The proposed CLSTM outperforms the state-of-the-art models on three publicly available document-level sentiment analysis datasets.


title: The design of a throwable two-wheeled reconnaissance robot

abstract: Miniature throwable robots are suited for urban war, anti-terrorism, rescue and some other emergency case for its light, low cost, small and convenient to deploy. This paper designs a throwable two-wheeled reconnaissance robot BHTBOT-3. The key structure of BHTBOT-3 is based on co-centric shaft structure which solves the restriction of drive shaft diameter. The bamboo-inspired outer shell makes its strength increased by 300% while the weight increased only by 20%. The process of non-linear drop impact is simulated by LS-DYNA based on BLATZ-KO material model. The tire is improved with the analysis of the simulation result. The final experiments show that BHTBOT-3 can survive after the throw to the ceramic tile flooring from 10 meters high stair.


title: Clustering Indian stock market data for portfolio management

abstract: In this paper a data mining approach for classification of stocks into clusters is presented. After classification, the stocks could be selected from these groups for building a portfolio. It meets the criterion of minimizing the risk by diversification of a portfolio. The clustering approach categorizes stocks on certain investment criteria. We have used stock returns at different times along with their valuation ratios from the stocks of Bombay Stock Exchange for the fiscal year 2007-2008. Results of our analysis show that K-means cluster analysis builds the most compact clusters as compared to SOM and Fuzzy C-means for stock classification data. We then select stocks from the clusters to build a portfolio, minimizing portfolio risk and compare the returns with that of the benchmark index, i.e. Sensex.


title: DeepFocus: learned image synthesis for computational display

abstract: Reproducing accurate retinal defocus blur is important to correctly drive accommodation and address vergence-accommodation conflict in head-mounted displays (HMDs). Numerous accommodation-supporting HMDs have been proposed. Three architectures have received particular attention: varifocal, multifocal, and light field displays. These designs all extend depth of focus, but rely on computationally expensive rendering and optimization algorithms to reproduce accurate retinal blur (often limiting content complexity and interactive applications). To date, no unified computational framework has been proposed to support driving these emerging HMDs using commodity content. In this paper, we introduce Deep-Focus, a generic, end-to-end trainable convolutional neural network designed to efficiently solve the full range of computational tasks for accommodation-supporting HMDs. This network is demonstrated to accurately synthesize defocus blur, focal stacks, multilayer decompositions, and multiview imagery using commonly available RGB-D images. Leveraging recent advances in GPU hardware and best practices for image synthesis networks, DeepFocus enables real-time, near-correct depictions of retinal blur with a broad set of accommodation-supporting HMDs.


title: A Single-Stage LED Driver Based on Double LLC Resonant Tanks for Automobile Headlight With Digital Control

abstract: A novel single-stage light-emitting diode (LED) driver with high voltage gain for LED automobile headlight is proposed in the paper. The converter is made up of two parts, which are the boost cell and double LLC cell, and the two parts are integrated by sharing the switch. Since the number of the system components is reduced by integrating the switches, the cost decreases. The boost circuit works in the continuous conduction mode with double voltage gain, and the double LLC cell connected in series can decrease the peak current and increase the efficiency of the system. A 30-W prototype is proposed in the lab, with 30 high-brightness LEDs connected in series. The experimental results well confirm the theoretical prediction, and the efficiency of the system for the full load is higher than 92%.


title: Robust Interpolation of Correspondences for Large Displacement Optical Flow

abstract: The interpolation of correspondences (EpicFlow) was widely used for optical flow estimation in most-recent works. It has the advantage of edge-preserving and efficiency. However, it is vulnerable to input matching noise, which is inevitable in modern matching techniques. In this paper, we present a Robust Interpolation method of Correspondences (called RicFlow) to overcome the weakness. First, the scene is over-segmented into superpixels to revitalize an early idea of piecewise flow model. Then, each model is estimated robustly from its support neighbors based on a graph constructed on superpixels. We propose a propagation mechanism among the pieces in the estimation of models. The propagation of models is significantly more efficient than the independent estimation of each model, yet retains the accuracy. Extensive experiments on three public datasets demonstrate that RicFlow is more robust than EpicFlow, and it outperforms state-of-the-art methods.


title: STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling

abstract: We propose a novel superpixel-based multi-view convolutional neural network for semantic image segmentation. The proposed network produces a high quality segmentation of a single image by leveraging information from additional views of the same scene. Particularly in indoor videos such as captured by robotic platforms or handheld and bodyworn RGBD cameras, nearby video frames provide diverse viewpoints and additional context of objects and scenes. To leverage such information, we first compute region correspondences by optical flow and image boundary-based superpixels. Given these region correspondences, we propose a novel spatio-temporal pooling layer to aggregate information over space and time. We evaluate our approach on the NYU-Depth-V2 and the SUN3D datasets and compare it to various state-of-the-art single-view and multi-view approaches. Besides a general improvement over the state-of-the-art, we also show the benefits of making use of unlabeled frames during training for multi-view as well as single-view prediction.


title: Exosomes surf on filopodia to enter cells at endocytic hot spots, traffic within endosomes, and are targeted to the ER

abstract: Exosomes are nanovesicles released by virtually all cells, which act as intercellular messengers by transfer of protein, lipid, and RNA cargo. Their quantitative efficiency, routes of cell uptake, and subcellular fate within recipient cells remain elusive. We quantitatively characterize exosome cell uptake, which saturates with dose and time and reaches near 100% transduction efficiency at picomolar concentrations. Highly reminiscent of pathogenic bacteria and viruses, exosomes are recruited as single vesicles to the cell body by surfing on filopodia as well as filopodia grabbing and pulling motions to reach endocytic hot spots at the filopodial base. After internalization, exosomes shuttle within endocytic vesicles to scan the endoplasmic reticulum before being sorted into the lysosome as their final intracellular destination. Our data quantify and explain the efficiency of exosome internalization by recipient cells, establish a new parallel between exosome and virus host cell interaction, and suggest unanticipated routes of subcellular cargo delivery.


title: Learning Long Term Dependencies via Fourier Recurrent Units

abstract: It is a known fact that training recurrent neural networks for tasks that have long term dependencies is challenging. One of the main reasons is the vanishing or exploding gradient problem, which prevents gradient information from propagating to early layers. In this paper we propose a simple recurrent architecture, the Fourier Recurrent Unit (FRU), that stabilizes the gradients that arise in its training while giving us stronger expressive power. Specifically, FRU summarizes the hidden states $h^{(t)}$ along the temporal dimension with Fourier basis functions. This allows gradients to easily reach any layer due to FRU's residual learning structure and the global support of trigonometric functions. We show that FRU has gradient lower and upper bounds independent of temporal dimension. We also show the strong expressivity of sparse Fourier basis, from which FRU obtains its strong expressive power. Our experimental study also demonstrates that with fewer parameters the proposed architecture outperforms other recurrent architectures on many tasks.


title: Performance of a High-Speed Motor Drive System Using a Novel Multilevel Inverter Topology

abstract: This paper examines the performance of a novel multilevel six-switch three phase inverter drive for low-voltage high speed motor applications. Hard switched voltage source inverter topologies are limited in terms of switching frequency and therefore as the frequency modulation ratio decreases, the resulting increase in harmonic currents may cause excessive rotor heating or lead to larger than desired output reactors with a series voltage drop. The multi-level inverter structure offers an increased number of voltage levels, reduced dead-time effects and a significant reduction in harmonic content, resulting in a reduction of total losses compared to the standard six-switch three phase inverter. The harmonic reduction provided by the multilevel topology relative to the standard inverter is experimentally demonstrated with a 15 HP 18,000 rpm induction machine.


title: Rendering specular microgeometry with wave optics

abstract: We design a rendering algorithm based on wave optics, able to compute spatially-varying specular highlights from micron-scale surface heightfields with high-resolution detail. Our solution approximates the heightfields using Gabor kernels.


title: A cloud computing based telemedicine service

abstract: “Health is the greatest possession”. Various techniques are drawn for the better treatment of health of society. As the technology is reaching its peak, it is emerging in medical treatments quickly and fruitfully giving birth to “telemedicine” - invention of technology in medicine. Earlier slow and erroneous processes are replaced by precise and faultless methods involving fast internet services. These techniques allow real-time data accessibility with proper authentication. The idea is based on cloud-computing and real time streaming of videos. The information is made available on the WEB in a suitable format, from where, it can be accessed by authorized medical staff. Cloud computing has a revolutionary effect on telemedicine. Many medical professionals are already using advance telehealth application of cloud computing. According to various specialists and researchers, cloud computing can improve healthcare services to an undoubtedly large extent. This paper discusses the advancement in utilization of cloud computing in field of telehealth. It can contribute to improve health scenario all over the world.


title: Entity-Based Query Recommendation for Long-Tail Queries

abstract: Query recommendation, which suggests related queries to search engine users, has attracted a lot of attention in recent years. Most of the existing solutions, which perform analysis of users’ search history (or query logs), are often insufficient for long-tail queries that rarely appear in query logs. To handle such queries, we study the use of entities found in queries to provide recommendations. Specifically, we extract entities from a query, and use these entities to explore new ones by consulting an information source. The discovered entities are then used to suggest new queries to the user. In this article, we examine two information sources: (1) a knowledge base (or KB), such as YAGO and Freebase; and (2) a click log, which contains the URLs accessed by a query user. We study how to use these sources to find new entities useful for query recommendation. We further study a hybrid framework that integrates different query recommendation methods effectively. As shown in the experiments, our proposed approaches provide better recommendations than existing solutions for long-tail queries. In addition, our query recommendation process takes less than 100ms to complete. Thus, our solution is suitable for providing online query recommendation services for search engines.


title: Death in the "microwave oven": A form of execution by carbonization.

abstract: Death in the "microwave oven" has nothing to do with microwaves energy. It is the jargon name given to a criminal form of execution by carbonization that has been adopted by drug dealers in Rio de Janeiro (Brazil). The goal is to torture and intimidate victims, in an attempt of corpse occultation and to make identification harder or impossible. This paper brings to attention of the forensic international community an unusual and very cruel form of execution as a way to document these situations.


title: Sports injuries and illnesses during the London Summer Olympic Games 2012

abstract: Background The Olympic Movement Medical Code encourages all stakeholders to ensure that sport is practised without danger to the health of the athletes. Systematic surveillance of injuries and illnesses is the foundation for developing preventive measures in sport. Aim To analyse the injuries and illnesses that occurred during the Games of the XXX Olympiad, held in London in 2012. Methods We recorded the daily occurrence (or nonoccurrence) of injuries and illnesses (1) through the reporting of all National Olympic Committee (NOC) medical teams and (2) in the polyclinic and medical venues by the London Organising Committee of the Olympic and Paralympic Games’ (LOCOG) medical staff. Results In total, 10 568 athletes (4676 women and 5892 men) from 204 NOCs participated in the study. NOC and LOCOG medical staff reported 1361 injuries and 758 illnesses, equalling incidences of 128.8 injuries and 71.7 illnesses per 1000 athletes. Altogether, 11% and 7% of the athletes incurred at least one injury or illness, respectively. The risk of an athlete being injured was the highest in taekwondo, football, BMX, handball, mountain bike, athletics, weightlifting, hockey and badminton, and the lowest in archery, canoe slalom and sprint, track cycling, rowing, shooting and equestrian. 35% of the injuries were expected to prevent the athlete from participating during competition or training. Women suffered 60% more illnesses than men (86.0 vs 53.3 illnesses per 1000 athletes). The rate of illness was the highest in athletics, beach volleyball, football, sailing, synchronised swimming and taekwondo. A total of 310 illnesses (41%) affected the respiratory system and the most common cause of illness was infection (n=347, 46%). Conclusions At least 11% of the athletes incurred an injury during the games and 7% of the athletes’ an illness. The incidence of injuries and illnesses varied substantially among sports. Future initiatives should include the development of preventive measures tailored for each specific sport and the continued focus among sport bodies to institute and further develop scientific injury and illness surveillance systems.


title: Contextual Query Intent Extraction for Paid Search Selection

abstract: Paid Search algorithms play an important role in online advertising where a set of related ads is returned based on a searched query. The Paid Search algorithms mostly consist of two main steps. First, a given searched query is converted to different sub-queries or similar phrases which preserve the core intent of the query. Second, the generated sub-queries are matched to the ads bidded keywords in the data set, and a set of ads with highest utility measuring relevance to the original query are returned. The focus of this paper is optimizing the first step by proposing a contextual query intent extraction algorithm to generate sub-queries online which preserve the intent of the original query the best. Experimental results over a very large real-world data set demonstrate the superb performance of proposed approach in optimizing both relevance and monetization metrics compared with one of the existing successful algorithms in our system.


Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification


title: Ecphory of autobiographical memories: An fMRI study of recent and remote memory retrieval

abstract: Ecphory occurs when one recollects a past event cued by a trigger, such as a picture, odor, or name. It is a central component of autobiographical memory, which allows us to “travel mentally back in time” and re-experience specific events from our personal past. Using fMRI and focusing on the role of medial temporal lobe (MTL) structures, we investigated the brain bases of autobiographical memory and whether they change with the age of memories. Importantly, we used an ecphory task in which the remote character of the memories was ensured. The results showed that a large bilateral network supports autobiographical memory: temporal lobe, temporo-occipito-parietal junction, dorsal prefrontal cortex, medial frontal cortex, retrosplenial cortex and surrounding areas, and MTL structures. This network, including MTL structures, changed little with the age of the memories.


title: ATTRACTING WHOM ? - MANAGING USER -GENERATED - CONTENT COMMUNITIES FOR MONETIZATION

abstract: Successful monetization of user-generated-content (UGC) business calls for attracting enough users, and the right users. The defining characteristic of UGC is users are also content contributors. In this study, we analyze the impact of a UGC firm’s quality control decision on user community composition. We model two UGC firms in competition, with one permitting only high quality content while the other not controlling quality. Users differ in their valuations and the content quality they contribute. Through analyzing various equilibrium situations, we find that higher reward value generally benefits the firm without quality control. However, when the intrinsic value of contribution is low, higher reward value may surprisingly drive high valuation users away from that firm. Also somewhat interestingly, we find that higher cost of contribution may benefit the firm that does not control quality. Our work is among the first to study the business impact of quality control of UGC.


On the Individuality of Fingerprints: Models and Methods


title: You shouldn't collect my secrets: thwarting sensitive keystroke leakage in mobile IME apps

abstract: IME (input method editor) apps are the primary means of interaction on mobile touch screen devices and thus are usually granted with access to a wealth of private user input. In order to understand the (in)security of mobile IME apps, this paper first performs a systematic study and uncovers that many IME apps may (intentionally or unintentionally) leak users' sensitive data to the outside world (mainly due to the incentives of improving the user's experience). To thwart the threat of sensitive information leakage while retaining the benefits of an improved user experience, this paper then proposes I-BOX, an app-transparent oblivious sandbox that minimizes sensitive input leakage by confining untrusted IME apps to predefined security policies. Several key challenges have to be addressed due to the proprietary and closed-source nature of most IME apps and the fact that an IME app can arbitrarily store and transform user input before sending it out. By designing system-level transactional execution, I-BOX works seamlessly and transparently with IME apps. Specifically, I-BOX first checkpoints an IME app's state before the first keystroke of an input, monitors and analyzes the user's input, and rolls back the state to the checkpoint if it detects the potential danger that sensitive input may be leaked. A proof of concept I-BOX prototype has been built for Android and tested with a set of popular IME apps. Experimental results show that I-BOX is able to thwart the leakage of sensitive input for untrusted IME apps, while incurring very small runtime overhead and little impact on user experience.


Physician Adoption of Technology Linked to Providing Benefits


title: Predicting churn in mobile free-to-play games

abstract: In the mobile game industry, Free-to-Play games are dominantly released, and therefore player retention and purchases have become important issues. In this paper, we propose a game player model for predicting when players will leave a game. Firstly, we define player churn in the game and extract features that contain the properties of the player churn from the player logs. And then we tackle the problem of imbalanced datasets. Finally, we exploit classification algorithms from machine learning and evaluate the performance of the proposed prediction model using cross-validation. Experimental results show that the proposed model has high accuracy enough to predict churn for real-world application.


title: A time-dependent proportional hazards survival model for credit risk analysis

abstract: In the consumer credit industry, assessment of default risk is critically important for the financial health of both the lender and the borrower. Methods for predicting risk for an applicant using credit bureau and application data, typically based on logistic regression or survival analysis, are universally employed by credit card companies. Because of the manner in which the predictive models are fit using large historical sets of existing customer data that extend over many years, default trends, anomalies, and other temporal phenomena that result from dynamic economic conditions are not brought to light. We introduce a modification of the proportional hazards survival model that includes a time-dependency mechanism for capturing temporal phenomena, and we develop a maximum likelihood algorithm for fitting the model. Using a very large, real data set, we demonstrate that incorporating the time dependency can provide more accurate risk scoring, as well as important insight into dynamic market effects that can inform and enhance related decision making.


title: ANEMA: Autonomic network management architecture to support self-configuration and self-optimization in IP networks

abstract: The increasing scale, technology advances and services of modern networks have dramatically complicated their management such that in the near future it will be almost impossible for human administrators to monitor them. To control this complexity, IBM has introduced a promising approach aiming to create self-managed systems. This approach, called Autonomic Computing, aims to design computing equipment able to self-adapt its configuration and to self-optimize its performance depending on its situation in order to fulfill high-level objectives defined by the human operator. In this paper, we present our autonomic network management architecture (ANEMA) that implements several policy forms to achieve autonomic behaviors in the network equipments. In ANEMA, the high-level objectives of the human administrators and the users are captured and expressed in terms of 'Utility Function' policies. The 'Goal' policies describe the high-level management directives needed to guide the network to achieve the previous utility functions. Finally, the 'behavioral' policies describe the behaviors that should be followed by network equipments to react to changes in their context and to achieve the given 'Goal' policies. In order to highlight the benefits of ANEMA architecture and the continuum of policies to introduce autonomic management in a multiservice IP network, a testbed has been implemented and several scenarios have been executed.


title: All about activity injection: Threats, semantics, and detection

abstract: Android supports seamless user experience by maintaining activities from different apps in the same activity stack. While such close inter-app communication is essential in the Android framework, the powerful inter-app communication contains vulnerabilities that can inject malicious activities into a victim app's activity stack to hijack user interaction flows. In this paper, we demonstrate activity injection attacks with a simple malware, and formally specify the activity activation mechanism using operational semantics. Based on the operational semantics, we develop a static analysis tool, which analyzes Android apps to detect activity injection attacks. Our tool is fast enough to analyze real-world Android apps in 6 seconds on average, and our experiments found that 1,761 apps out of 129,756 real-world Android apps inject their activities into other apps' tasks.


title: Exploring the motivations of social commerce: A perspective of consumer shopping value

abstract: Recently, due to the social networking sites (SNS) users continue to grow, social commerce has been addressed as an important issue by scholars and practitioners. However, the motivations of social commerce intention are unclear. This study aimed to explore the motivations of social commerce intention based on the perspective of consumer shopping value and the model of goal-directed behavior. Given that social commerce has the feature of social and commercial, this study firstly investigated the influences of social, hedonic, and utilitarian motivations on social and commercial desires, respectively. Then, the impacts of both desires on social commerce intention were examined. This study was conducted by survey method. The results revealed that social and hedonic motivations could arouse SNS users' social desire; whereas utilitarian motivation could evoke SNS users' commercial desire. Both commercial and social desires would lead to social commerce intention. Based on the research findings, this study finally provided some discussions and suggestions for firms and SNS service providers to enhance SNS users' social commerce intention.


title: SIMD defragmenter: efficient ILP realization on data-parallel architectures

abstract: Single-instruction multiple-data (SIMD) accelerators provide an energy-efficient platform to scale the performance of mobile systems while still retaining post-programmability. The central challenge is translating the parallel resources of the SIMD hardware into real application performance. In scientific applications, automatic vectorization techniques have proven quite effective at extracting large levels of data-level parallelism (DLP). However, vectorization is often much less effective for media applications due to low trip count loops, complex control flow, and non-uniform execution behavior. As a result, SIMD lanes remain idle due to insufficient DLP. To attack this problem, this paper proposes a new vectorization pass called SIMD Defragmenter to uncover hidden DLP that lurks below the surface in the form of instruction-level parallelism (ILP). The difficulty is managing the data packing/unpacking overhead that can easily exceed the benefits gained through SIMD execution. The SIMD degragmenter overcomes this problem by identifying groups of compatible instructions (subgraphs) that can be executed in parallel across the SIMD lanes. By SIMDizing in bulk at the subgraph level, packing/unpacking overhead is minimized. On a 16-lane SIMD processor, experimental results show that SIMD defragmentation achieves a mean 1.6x speedup over traditional loop vectorization and a 31% gain over prior research approaches for converting ILP to DLP.


title: Modeling error-based Adaptive User Interfaces

abstract: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi CHAPTER 1. OVERVIEW AND MOTIVATION . . . . . . . . . . . . . . . . 1 1.


title: Distance-based k-nearest neighbors outlier detection method in large-scale traffic data

abstract: This paper presents a k-nearest neighbors (kNN) method to detect outliers in large-scale traffic data collected daily in every modern city. Outliers include hardware and data errors as well as abnormal traffic behaviors. The proposed kNN method detects outliers by exploiting the relationship among neighborhoods in data points. The farther a data point is beyond its neighbors, the more possible the data is an outlier. Traffic data here was recorded in a video format, and converted to spatial-temporal (ST) traffic signals by statistics. The ST signals are then transformed to a two-dimensional (2D) (x, y) -coordinate plane by Principal Component Analysis (PCA) for dimension reduction. The distance-based kNN method is evaluated by unsupervised and semi-supervised approaches. The semi-supervised approach reaches 96.19% accuracy.


title: QEx: robust quad mesh extraction

abstract: The most popular and actively researched class of quad remeshing techniques is the family of parametrization based quad meshing methods. They all strive to generate an integer-grid map, i.e. a parametrization of the input surface into R2 such that the canonical grid of integer iso-lines forms a quad mesh when mapped back onto the surface in R3. An essential, albeit broadly neglected aspect of these methods is the quad extraction step, i.e. the materialization of an actual quad mesh from the mere "quad texture". Quad (mesh) extraction is often believed to be a trivial matter but quite the opposite is true: numerous special cases, ambiguities induced by numerical inaccuracies and limited solver precision, as well as imperfections in the maps produced by most methods (unless costly countermeasures are taken) pose significant challenges to the quad extractor. We present a method to sanitize a provided parametrization such that it becomes numerically consistent even in a limited precision floating point representation. Based on this we are able to provide a comprehensive and sound description of how to perform quad extraction robustly and without the need for any complex tolerance thresholds or disambiguation rules. On top of that we develop a novel strategy to cope with common local fold-overs in the parametrization. This allows our method, dubbed QEx, to generate all-quadrilateral meshes where otherwise holes, non-quad polygons or no output at all would have been produced. We thus enable the practical use of an entire class of maps that was previously considered defective. Since state of the art quad meshing methods spend a significant share of their run time solely to prevent local fold-overs, using our method it is now possible to obtain quad meshes significantly quicker than before. We also provide libQEx, an open source C++ reference implementation of our method and thus significantly lower the bar to enter the field of quad meshing.


title: Emergent Coordination Through Competition

abstract: We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics. We demonstrate that decentralized, population-based training with co-play can lead to a progression in agents' behaviors: from random, to simple ball chasing, and finally showing evidence of cooperation. Our study highlights several of the challenges encountered in large scale multi-agent training in continuous control. In particular, we demonstrate that the automatic optimization of simple shaping rewards, not themselves conducive to co-operative behavior, can lead to long-horizon team behavior. We further apply an evaluation scheme, grounded by game theoretic principals, that can assess agent performance in the absence of pre-defined evaluation tasks or human baselines.


title: A review of dengue research in malaysia.

abstract: Dengue infection is a major cause of morbidity and mortality in Malaysia. To date, much research on dengue infection conducted in Malaysia have been published. One hundred and sixty six articles related to dengue in Malaysia were found from a search through a database dedicated to indexing all original data relevant to medicine published between the years 2000-2013. Ninety articles with clinical relevance and future research implications were selected and reviewed. These papers showed evidence of an exponential increase in the disease epidemic and a varying pattern of prevalent dengue serotypes at different times. The early febrile phase of dengue infection consist of an undifferentiated fever. Clinical suspicion and ability to identify patients at risk of severe dengue infection is important. Treatment of dengue infection involves judicious use of volume expander and supportive care. Potential future research areas are discussed to narrow our current knowledge gaps on dengue infection.


title: Adaptive offset min-sum algorithm for low-density parity check codes

abstract: An improvement is presented to the offset min-sum decoding algorithm for low-density parity check codes. The proposed algorithm introduces a more efficient adjustment for check-node update computation in view of different minimum values. According to the optimal correction factor of normalized min-sum algorithm, we can determine the adaptive offset item. The improved algorithm achieves a noticeable performance gain with only a modest increase in computation complexity


title: Two Variants of Anti-Slavery: Religious Organization and Social Mobilization in Britain and France, 1780–1870 (1980)

abstract: Dealing with its international aspects, histories of abolition tend to concentrate primarily on its Anglo-American dimension as a mutually reinforcing ‘connection’, rather than as a subject for comparative national analysis.1 In this context other European colonial powers are treated as peripheral. French anti-slavery is duly accorded recognition in the intellectual history of pre-abolitionism, and a brief dramatic role in the French Revolutionary era. Thereafter French abolitionists disappear into oblivion, or remain in the unfolding drama as modest members of the chorus.


title: A 1Tb 4b/cell 64-stacked-WL 3D NAND flash memory with 12MB/s program throughput

abstract: Since the first demonstration of a production quality three-dimensional (3D) stacked-word-line NAND Flash memory [1], the 3b/cell 3D NAND Flash memory has seen areal density increases of more than 50% per year due to the aggressive development of 3D-wordline-stacking technology. This trend has been consistent for the last three consecutive years [2-4], however the storage market still requires higher density for diverse digital applications. A 4b/cell technology is one promising solution to increase bit density [5]. In this paper, we propose a 4b/cell 3D NAND Flash memory with a 12MB/s program throughput. The chip achieves a 5.63Gb/mm2 areal density, which is a 41.5% improvement as compared to a 3b/cell NAND Flash memory in the same 3D-NAND technology [4].


Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading


title: Facial Expression Recognition via Deep Learning

abstract: Automated Facial Expression Recognition has remained a challenging and interesting problem in computer vision. The recognition of facial expressions is difficult problem for machine learning techniques, since people can vary significantly in the way they show their expressions. Deep learning is a new area of research within machine learning method which can classify images of human faces into emotion categories using Deep Neural Networks (DNN). Convolutional neural networks (CNN) have been widely used to overcome the difficulties in facial expression classification. In this paper, we present a new architecture network based on CNN for facial expressions recognition. We fine tuned our architecture with Visual Geometry Group model (VGG) to improve results. To evaluate our architecture we tested it with many largely public databases (CK+, MUG, and RAFD). Obtained results show that the CNN approach is very effective in image expression recognition on many public databases which achieve an improvements in facial expression analysis.


title: Shadow removal in depth images morphology-based for Kinect cameras

abstract: In this paper we propose two methods to remove the black regions that appear on Kinect camera depth images. We describe the various reasons why these regions of missing information appear. The proposed algorithms will be applied on only one frame of the captured video stream. Each method is based on applying a morphological filter designed to remove the black regions from the depth image. We present the results and then we conclude the paper.


title: Low-profile cavity-backed crossed-slot antenna with a single-probe feed designed for 2.34-GHz satellite radio applications

abstract: This paper describes a novel, low-profile antenna for the satellite digital audio radio service. The antenna consists of a thin cavity with a pair of crossed slots having unequal length. Both slots are fed by a single-probe-type feed, resulting in a simple low-cost structure. This antenna is left-hand circularly polarized toward the sky for satellite reception, and vertically polarizated toward the horizon for terrestrial reception. The result is a low-profile antenna that can receive simultaneously from both satellite broadcasters and terrestrial repeaters, and can be built using low-cost printed circuit fabrication methods.


title: The Design and Analysis of Computer Algorithms

abstract: From the Publisher: ::: With this text, you gain an understanding of the fundamental concepts of algorithms, the very heart of computer science. It introduces the basic data structures and programming techniques often used in efficient algorithms. Covers use of lists, push-down stacks, queues, trees, and graphs. Later chapters go into sorting, searching and graphing algorithms, the string-matching algorithms, and the Schonhage-Strassen integer-multiplication algorithm. Provides numerous graded exercises at the end of each chapter. ::: ::: ::: 0201000296B04062001


title: Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1

abstract: The hippocampus has differentiated into an extensively connected recurrent stage (CA3) followed by a feed-forward stage (CA1). We examined the function of this structural differentiation by determining how cell ensembles in rat CA3 and CA1 generate representations of rooms with common spatial elements. In CA3, distinct subsets of pyramidal cells were activated in each room, regardless of the similarity of the testing enclosure. In CA1, the activated populations overlapped, and the overlap increased in similar enclosures. After exposure to a novel room, ensemble activity developed slower in CA3 than CA1, suggesting that the representations emerged independently.


Deciphering Severely Degraded License Plates


title: Using Lexical Chains For Text Summarization

abstract: We investigate one technique to produce a summary of an original text without requiring its full semantic interpretation, but instead relying on a model of the topic progression in the text derived from lexical chains. We present a new algorithm to compute lexical chains in a text, merging several robust knowledge sources: the WordNet thesaurus, a part-of-speech tagger, shallow parser for the identification of nominal groups, and a segmentation algorithm. Summarization proceeds in four steps: the original text is segmented, lexical chains are constructed, strong chains are identified and significant sentences are extracted. We present in this paper empirical results on the identification of strong chains and of significant sentences. Preliminary results indicate that quality indicative summaries are produced. Pending problems are identified. Plans to address these short-comings are briefly presented.


title: Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions

abstract: We introduce a novel playlist generation algorithm that focuses on the quality of transitions using a recurrent neural network (RNN). The proposed model assumes that optimal transitions between tracks can be modelled and predicted by internal transitions within music tracks. We introduce modelling sequences of high-level music descriptors using RNNs and discuss an experiment involving different similarity functions, where the sequences are provided by a musical structural analysis algorithm. Qualitative observations show that the proposed approach can effectively model transitions of music tracks in playlists.


title: Overview on DeepMind and Its AlphaGo Zero AI

abstract: The goal of this paper is to give insight into what the company known as DeepMind is and what accomplishments it is making in the fields of Machine Learning and Artificial Intelligence. Among their accomplishments, particular focus will be placed upon the recent success of AlphaGo Zero which made waves in the machine learning and artificial intelligence communities. The various parts of AlphaGo Zero's implementation such as reinforcement learning, neural networks, and Monte Carlo Tree Searches will be explained with brevity to give better understanding of the process as a whole.


What Is the Optimal Way to Deliver a Positive Activity Intervention? The Case of Writing About One’s Best Possible Selves


title: Advanced illumination techniques for GPU-based volume raycasting

abstract: Volume raycasting techniques are important for both visual arts and visualization. They allow efficient generation of visual effects and visualization of scientific data obtained by tomography or numerical simulation. Volume-rendering techniques are also effective for direct rendering of implicit surfaces used for soft-body animation and constructive solid geometry. The focus of this course is on volumetric illumination techniques that approximate physically based light transport in participating media. Such techniques include interactive implementation of soft and hard shadows, ambient occlusion, and simple Monte Carlo-based approaches to global illumination, including translucency and scattering.


title: A Survey of Methods for Explaining Black Box Models

abstract: In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.


title: Effects on the radiation characteristics of using a corrugated reflector with a helical antenna and an electromagnetic band-gap reflector with a spiral antenna

abstract: An axial-mode helical antenna backed by a perfect electric conductor (PEC reflector) is optimized to radiate a circularly polarized (CP) wave, using the finite-difference time-domain method (FDTDM). After the optimization, the PEC reflector is replaced with a corrugated reflector. The effects of the corrugated reflector on the current distribution along the helical arm and the radiation pattern are investigated. A reduction in the backward radiation is attributed to the reduction in the current flowing over the rear surface of the corrugated reflector. A spiral antenna backed by a PEC reflector of finite extent is also analyzed using the FDTDM. As the antenna height decreases, the reverse current toward the feed point increases, resulting in deterioration of the axial ratio. To overcome this deterioration, the PEC reflector is replaced with an electromagnetic band-gap (EBG) reflector composed of mushroom-like elements. Analysis reveals that the spiral radiates a CP wave even when the spiral is located close to the reflector (0.06 wavelength above the EBG surface). The input impedance for the EBG reflector is more stable over a wide frequency band than that for the PEC reflector.


title: Porous titanium scaffolds fabricated using a rapid prototyping and powder metallurgy technique

abstract: One of the main issues in orthopaedic implant design is the fabrication of scaffolds that closely mimic the biomechanical properties of the surrounding bone. This research reports on a multi-stage rapid prototyping technique that was successfully developed to produce porous titanium scaffolds with fully interconnected pore networks and reproducible porosity and pore size. The scaffolds' porous characteristics were governed by a sacrificial wax template, fabricated using a commercial 3D-printer. Powder metallurgy processes were employed to generate the titanium scaffolds by filling around the wax template with titanium slurry. In the attempt to optimise the powder metallurgy technique, variations in slurry concentration, compaction pressure and sintering temperature were investigated. By altering the wax design template, pore sizes ranging from 200 to 400 μm were achieved. Scaffolds with porosities of 66.8 ± 3.6% revealed compression strengths of 104.4 ± 22.5 MPa in the axial direction and 23.5 ± 9.6 MPa in the transverse direction demonstrating their anisotropic nature. Scaffold topography was characterised using scanning electron microscopy and microcomputed tomography. Three-dimensional reconstruction enabled the main architectural parameters such as pore size, interconnecting porosity, level of anisotropy and level of structural disorder to be determined. The titanium scaffolds were compared to their intended designs, as governed by their sacrificial wax templates. Although discrepancies in architectural parameters existed between the intended and the actual scaffolds, overall the results indicate that the porous titanium scaffolds have the properties to be potentially employed in orthopaedic applications.


Management of an Osteogenic Sarcoma of the Maxilla


title: Queue scheduling and advance reservations with COSY

abstract: Summary form only given. Most of current job scheduling systems for supercomputers and clusters provide batch queuing support. With the development of metacomputing and grid computing, users require resources managed by multiple local job schedulers. Advance reservations are becoming essential for job scheduling systems to be utilized within a large-scale computing environment with geographically distributed resources. COSY is a lightweight implementation of such a local job scheduler with support for both queue scheduling and advance reservations. COSY queue scheduling utilizes the FCFS algorithm with backfilling mechanisms and priority management. Advance reservations with COSY can provide effective QoS support for exact start time and latest completion time. Scheduling polices are defined to reject reservations with too short notice time so that there is no start time advantage to making a reservation over submitting to a queue. Further experimental results show that as a larger percentage of reservation requests are involved, a longer mandatory shortest notice time for advance reservations must be applied in order not to sacrifice queue scheduling efficiency.


title: Ka-Band Linear to Circular Polarization Converter Based on Multilayer Slab With Broadband Performance

abstract: In this paper, a Ka-band polarization converter is presented, which is based on multilayer slab. In order to improve impedance matching, metallic circular traces are printed periodically on each dielectric multilayer slab. Simulated results of the polarizer show that it can transform linearly polarized (LP) to circularly polarized (CP) fields over a frequency band from 23 to 35GHz (42%) with an insertion loss less than 0.5 dB. The transmitted CP wave by the polarizer is approximately robust under oblique illuminations. The polarizer is fabricated and measured by a wideband horn antenna satisfying the simulated results. Next, in order to design a high-gain CP structure around 30 GHz, an 8-element LP array antenna with Chebyshev tapered distribution is designed and integrated with the polarizer. Obviously, the antenna limits the overall bandwidth (nearly 28 to 31.5 GHz) due to the narrowband nature of the LP antenna array. When the polarizer is illuminated by an incident LP wave, the two linear components of the transmitted wave with approximately equal amplitudes and 90° phase difference on the frequency band of interest are produced. Experimental results of the proposed structure show a pure CP with a gain of 13 dBi at 30 GHz, which can be suitable for millimeter wave communication.


Filtering of Video using Guided Filter


title: Material capture and representation with applications in virtual reality

abstract: Currently, rendering systems are able to produce photorealistic simulations of the appearance of many real-world materials. In the real world, viewer perception of objects depends on the lighting and object/material/surface characteristics, the way a surface interacts with the light and on how the light is reflected, scattered, absorbed by the surface and the impact these characteristics have on material appearance. In order to re-produce this, it is necessary to understand how materials interact with light. Thus the representation and acquisition of material models has become such an active research area. However, no material model nor acquisition setup is without limitations in terms of the variety of existing materials and they range considerably in terms of portability and ease of use. In this Course the problem of material capture and representation for Virtual Reality is introduced, with the scope ranging from BRDF models for opaque surfaces to volumetric models for fabric. Characteristics of current BRDF representation and more complex functions limiting their use in VR are discussed. A case study high-lighting the importance of being able to render materials in high quality in a VR pipeline is presented, together with the software and hardware challenges. A selection of high quality material models are shown in real VREs for commercial interior design applications. The Course provides an introduction to the fundamental concepts and gradually moves to most advanced and up to date concepts on both the representation and acquisition side. Since fabric plays a very important role in VREs for commercial interior design applications, we decided to extend the scope to volumetric and collection of fibers representation.


title: Sleep-Disordered Breathing and School Performance in Children

abstract: Objective. To assess the impact of sleep-associated gas exchange abnormalities (SAGEA) on school academic performance in children. Design. Prospective study. Setting. Urban public elementary schools. Participants. Two hundred ninety-seven first-grade children whose school performance was in the lowest 10th percentile of their class ranking. Methods. Children were screened for obstructive sleep apnea syndrome at home using a detailed parental questionnaire and a single night recording of pulse oximetry and transcutaneous partial pressure of carbon dioxide. If SAGEA was diagnosed, parents were encouraged to seek medical intervention for SAGEA. School grades of all participating children for the school year preceding and after the overnight study were obtained. Results. SAGEA was identified in 54 children (18.1%). Of these, 24 underwent surgical tonsillectomy and adenoidectomy (TR), whereas in the remaining 30 children, parents elected not to seek any therapeutic intervention (NT). Overall mean grades during the second grade increased from 2.43 ± 0.17 (SEM) to 2.87 ± 0.19 in TR, although no significant changes occurred in NT (2.44 ± 0.13 to 2.46 ± 0.15). Similarly, no academic improvements occurred in children without SAGEA. Conclusions. SAGEA is frequently present in poorly performing first-grade students in whom it adversely affects learning performance. The data suggest that a subset of children with behavioral and learning disabilities could have SAGEA and may benefit from prospective medical evaluation and treatment.


title: Ensemble Learning for Named Entity Recognition

abstract: A considerable portion of the information on the Web is still only available in unstructured form. Implementing the vision of the Semantic Web thus requires transforming this unstructured data into structured data. One key step during this process is the recognition of named entities. Previous works suggest that ensemble learning can be used to improve the performance of named entity recognition tools. However, no comparison of the performance of existing supervised machine learning approaches on this task has been presented so far. We address this research gap by presenting a thorough evaluation of named entity recognition based on ensemble learning. To this end, we combine four different state-of-the approaches by using 15 different algorithms for ensemble learning and evaluate their performace on five different datasets. Our results suggest that ensemble learning can reduce the error rate of state-of-the-art named entity recognition systems by 40%, thereby leading to over 95% f-score in our best run.


title: Smart Agents in Industrial Cyber – Physical Systems This paper surveys and analyzes the current state of the industrial application of agent technology in CPSs , and provides a vision on the way agents can effectively enable emerging CPS challenges

abstract: Future industrial systems can be realized using the cyber–physical systems (CPSs) that advocate the coexistence of cyber and physical counterparts in a network structure to perform the system’s functions in a collaborative manner. Multiagent systems share common ground with CPSs and can empower them with a multitude of capabilities in their efforts to achieve complexity management, decentralization, intelligence, modularity, flexibility, robustness, adaptation, and responsiveness. This work surveys and analyzes the current state of the industrial application of agent technology in CPSs, and provides a vision on the way agents can effectively enable emerging CPS challenges.


title: An Efficient Lock-Free Logarithmic Search Data Structure Based on Multi-dimensional List

abstract: Logarithmic search data structures, such as search trees and skiplists, are fundamental building blocks of many applications. Although the self-balancing binary search trees are among the most ubiquitous sequential search data structures, designing non-blocking rebalancing algorithms is challenging due to the required structural alternation, which may stall other concurrent operations. Skiplists, which probabilistically create multiple levels of shortcuts in an ordered list, provide practical alternatives to balanced search trees. The use of skiplists eliminates the need of rebalancing and ensures amortized logarithmic sequential search time, but concurrency is limited under write-dominated workload because the linkage between multiple distant nodes must be updated. In this paper, we present a linearizable lock-free dictionary design based on a multi-dimensional list (MDList). A node in an MDList arranges its child nodes by their dimensionality and order them by coordinate prefixes. The search operation works by first generating a one-to-one mapping from the scalar keys to a high-dimensional vectors space, then uniquely locating the target position by using the vector as coordinates. Our algorithm guarantees worst-case search time of O(log N) where N is the size of key space. Moreover, the ordering property of the data structure is readily maintained during mutations without rebalancing nor randomization. In our experimental evaluation using a micro-benchmark, our dictionary outperforms the state of the art approaches by as much as 100% when the key universe is large and an average of 30% across all scenarios.


title: Using dialogue to build organization–public relationships, engage publics, and positively affect organizational outcomes

abstract: Abstract The study and practice of public relations continues to explore the many benefits accrued when organizations adopt a relational approach to public relations management. One area that is relatively unexplored in the relationship management literature is the role of dialogue in organization–public relationships. Historically, scholars have focused dialogic research on either interaction – where an organization and a public exchange information – or a debate – where organizations and public engage in a process of statement counterstatement. The current investigation explores the notion of dialogue and examines the ways in which relationship attitudes and dialogue may positively affect key public member evaluations and behavioral intent. The results suggest that both relationship attitudes and dialogue positively affect respondent evaluations of and intended behaviors toward an organization. The implications of the findings are presented and suggestions for managing organization–public relationships are considered.


title: To Beam or Not to Beam: A Study of Remote Telepresence Attendance at an Academic Conference

abstract: Attending and presenting at conferences is a core part of academic and industrial research. Yet it can sometimes be challenging to attend conferences due to travel restrictions, time limitations, or accessibility challenges. To understand how people may be able to attend conferences remotely, we explored the use of commercial telepresence robots called Beams at Ubicomp/ISWC 2014. Seven people attended the conference remotely and used Beams to attend presentations, ask questions, and participate in break mingling activities. We collected data of their activities and conducted an online survey with the broader set of conference attendees. Results show that telepresence robots supported attendance at a basic level and even empowered those with accessibility challenges. However, issues related to identity, interaction, navigation, and privacy emerged. We present recommendations for future telepresence attendance at conferences focused on balancing these concerns with the benefits participants received.


title: The Effects of Task Interruption on Human Performance: A Study of the Systematic Classification of Human Behavior and Interruption Frequency

abstract: Interruptions are prevalent phenomena in modern working environments; yet, few interruption studies have been conducted on different types of human tasks. A study using computer-based human behavior tasks was carried out to investigate the effects of interruptions with 4 different primary-interrupting task sets. The tasks used in this study were determined by cognitive and motor skill processes based on human behavior classification theory. The results showed that interruption effects were most negative in cognitive/cognitive task sets, and skill/cognitive task sets showed the least amount of effects in task completion time, F3, 332 = 77.88, p < .001. In error rate performance, skill/skill task sets showed the highest rate, and error rates for cognitive primary task sets cognitive/cognitive and cognitive/skill task sets were not significantly different, F1, 42 = 41.18, p < .001; F1, 42 = 3.56, p = 0.0661. Interruption frequency also negatively affected task performance, F1, 412 = 89.88, p < .001, but skill tasks' quantitative performance did not show significant effects at different interruption frequencies, F1, 187 = 3.78, p = .0534. The results showed that interruptions increase more time to complete in cognitive tasks and produce more errors in skill tasks. Also, similar types of primary-interrupting tasks were more susceptible from interruptions. Thus, based on task composition of work process, we can estimate different effects from the interruptions and memory load, and task similarity in primary-interrupting task relationship were considered a main factor.


title: Recognizing clothes patterns and colours for blind people using neural network

abstract: In this project, the system of automatically recognizing clothes pattern and colors are proposed. The image is captured by the camera and get processed to identify the pattern of the clothes that is chosen. This can be classified using the support vector machine algorithm. For this the features of the image have to be obtained. These features can be extracted using three descriptors. The Radon Signature descriptor is to extract statistical properties, the wavelet sub-bands are used to extract global features of clothing patterns. This gets combined with local features that are obtained from scale invariance feature transform to recognize complex clothing patterns. After identifying the features the support vector machine classify the images in order to their categories. The system uses the CCNY Clothing Pattern dataset. This system can be an effective method for all the visually impaired people that they can identify the pattern and respective colors without any help.


title: Predicting breast cancer recurrence using effective classification and feature selection technique

abstract: Breast cancer is a major threat for middle aged women throughout the world and currently this is the second most threatening cause of cancer death in women. But early detection and prevention can significantly reduce the chances of death. An important fact regarding breast cancer prognosis is to optimize the probability of cancer recurrence. This paper aims at finding breast cancer recurrence probability using different data mining techniques. We also provide a noble approach in order to improve the accuracy of those models. Cancer patient's data were collected from Wisconsin dataset of UCI machine learning Repository. This dataset contained total 35 attributes in which we applied Naive Bayes, C4.5 Decision Tree and Support Vector Machine (SVM) classification algorithms and calculated their prediction accuracy. An efficient feature selection algorithm helped us to improve the accuracy of each model by reducing some lower ranked attributes. Not only the contributions of these attributes are very less, but their addition also misguides the classification algorithms. After a careful selection of upper ranked attributes we found a much improved accuracy rate for all three algorithms.


title: Online global loop closure detection for large-scale multi-session graph-based SLAM

abstract: For large-scale and long-term simultaneous localization and mapping (SLAM), a robot has to deal with unknown initial positioning caused by either the kidnapped robot problem or multi-session mapping. This paper addresses these problems by tying the SLAM system with a global loop closure detection approach, which intrinsically handles these situations. However, online processing for global loop closure detection approaches is generally influenced by the size of the environment. The proposed graph-based SLAM system uses a memory management approach that only consider portions of the map to satisfy online processing requirements. The approach is tested and demonstrated using five indoor mapping sessions of a building using a robot equipped with a laser rangefinder and a Kinect.


title: Mini-Bucket Heuristics for Improved Search

abstract: The paper is a second in a series of two papers evaluating the power of a new scheme that generates search heuristics mechanically. The heuristics are extracted from an approximation scheme called mini-bucket elimination that was recently introduced. The first paper introduced the idea and evaluated it within Branch-and-Bound search. In the current paper the idea is further extended and evaluated within Best-First search. The resulting algorithms are compared on coding and medical diagnosis problems, using varying strength of the mini-bucket heuristics. Our results demonstrate an effective search scheme that permits controlled tradeoff between preprocessing (for heuristic generation) and search. Best-first search is shown to outperform Branch-and-Bound, when sup plied with good heuristics, and sufficient memory space.


Evaluation of a Migration to Open Source Software


title: Content-Based Image Retrieval at the End of the Early Years

abstract: Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.


title: Walking in Virtual Reality: Effects of Manipulated Visual Self-Motion on Walking Biomechanics

abstract: Walking constitutes the predominant form of self-propelled movement from one geographic location to another in our real world. Likewise, walking in virtual environments (VEs) is an essential part of a users experience in many application domains requiring a high degree of interactivity. However, researchers and practitioners often observe that basic implementations of virtual walking, in which head-tracked movements are mapped isometrically to a VE are not estimated as entirely natural. Instead, users estimate a virtual walking velocity as more natural when it is slightly increased compared to the users physical body movement. ::: In this article, we investigate the effects of such nonisometric mappings between physical movements and virtual motions in the VE on walking velocity and biomechanics of the gait cycle. Therefore, we performed an experiment in which we measured and analyzed parameters of the biomechanics of walking under conditions with isometric as well as nonisometric mappings. Our results show significant differences in most gait parameters when walking in the VE in the isometric mapping condition compared to the corresponding parameters in the real world. For nonisometric mappings we found an increased divergence of gait parameters depending on the velocity of visual self-motion feedback. The results revealed a symmetrical effect of gait detriments for up- or down-scaled virtual velocities, which we discuss in the scope of the previous findings.


title: Optimal rectangle packing

abstract: The rectangle packing problem considered here consists of finding an enclosing rectangle of smallest area that can contain a given set of rectangles without overlap. Although we focus here on optimal solutions, we also provide an anytime algorithm that immediately returns a solution, and continues to improve on that solution as long as it continues to run, until an optimal solution is found and verified. ::: Our algorithm picks the x-coordinates of all the rectangles before picking any of the y-coordinates. For the x-coordinates, we present a dynamic variable ordering heuristic and an adaptation of a pruning algorithm used in previous solvers. We then transform the rectangle packing problem into a perfect packing problem that has no empty space, and present inference rules to reduce the instance size. For the y-coordinates we search a space that models empty positions as variables and rectangles as values. Our solver is over 19 times faster than the previous state-of-the-art on the largest problem previously solved, allowing us to extend the known solutions for a consecutive-square packing benchmark from N=27 to N=32. ::: We propose two new benchmarks, one where the orientation of the rectangles is fixed and one where it is free, that include rectangles of various aspect ratios. The new benchmarks avoid certain properties of easy instances, which we identify as instances where rectangles have dimensions in common or where a few rectangles occupy most of the area. Our benchmarks are much more difficult for solvers developed only on the consecutive-square packing benchmark, requiring orders of magnitude more time, when comparing similar-sized instances. On the new benchmarks, we improve upon the previous strategy used to handle dominance conditions, we define a variable order over non-square rectangles that generalizes previous strategies, and we present a way to adjust the sizes of intervals of values for each rectangle's x-coordinates. Using these techniques together, we can solve the new oriented benchmark about 500 times faster, and the new unoriented benchmark about 40 times faster than a solver without these techniques. ::: Next, to test the limits of our rectangle packing techniques, we introduce another new benchmark where the precision of the dimensions of the rectangles increases with successively larger instance sizes. We present various adaptations of our low-precision techniques to this new high-precision domain, as well as several new ones, which include limiting the x- and y-coordinate values to the subset sums of the widths and heights of rectangles, and a method to learn from the search effort spent on infeasible subtrees. The data reveals that we are able to pack up to 15 rectangles in our high-precision benchmark, already far exceeding the representational ability of a 32-bit integer. Furthermore, we compare our techniques against a solver using an alternative search space not susceptible to problems of high-precision, and demonstrate that our solver remains competitive. ::: We then turn our attention to the old and interesting question of packing an infinite series of rectangles into the unit square. We describe the techniques we use to provide computational results supporting the conjecture that the problem is feasible, and illustrate a packing solution for the first 50,000 such rectangles. ::: Finally, in our appendix, our contributions also include providing all of the optimal solutions to the various benchmarks that we have used.